AI for Cyber-Physical Systems

Context: The fundamental advantage of AI methods is their ability to handle high-dimensional state-space and learn decision procedures/control algorithms from data rather than models. This is critical as real-world state spaces are complex, dynamic, and hard to model. Our goal is to bridge this gap and learn an abstract representation of these state spaces and develop decision procedures for use in the different research verticals we investigate - proactive emergency response systems, transit management systems, and electric power grids. However, the challenge is that AI components learn from training data, which may not necessarily cover the real-world distribution. Further, testing and verifying these components is complex and sometimes not possible. Our work in this area, focuses on the development of the decision procedures to be used in the system along with runtime monitors, and assurance cases to show that the system at runtime will be safe if there is a fault in the component of the system (software, hardware, or AI). We deploy these procedures in the research domains we work in.

Innovation and Research Products:

ReSonAte - We have designed a dynamic assurance approach called ReSonAte that computes the likelihood of unsafe conditions or system failures considering the safety requirements, assumptions made at design time, past failures in a given operating context, and the likelihood of system component failures. The system has been demonstrated in simulations using two separate autonomous system simulations: CARLA and an unmanned underwater vehicle. The system was evaluated across 600 separate simulation scenes where we tested scenes with distribution shifts, component failures, and a high likelihood of collisions (based on past observations). Through the tests, we were able to show that our methodology has a precision of 73% and a recall of 79%. We are currently working on methods to dynamically estimate and learn the conditional probabilities and improve the precision values. On average, the framework takes 0.3 milliseconds for the computation of risk scores.
Assurance Monitors - While ReSonAte monitors the safety assurance at the system-level, we need monitors at component levels to detect its anomalous behavior. Monitors to detect anomalies like data validity, pre-condition, and post-condition failures, user-code failure have been designed for conventional CPS, but a LEC based CPS would require complex monitors or assurance monitors to detect OOD. For this, we have designed an assurance monitor using a 𝛃-Variational Autoencoder, which can detect if an input (e.g. image) to a LEC is OOD or not. Besides, it can also diagnose the precise changes (e.g. brightness, blurriness, occlusion, weather change, etc.) in the input that caused the input to be OOD. Conceptually, we generate an interpretable latent representation for inputs using the 𝛃-VAE and then perform a correspondence between the latent units and generative factors to perform a latent space-based OOD detection and diagnosis. The disentanglement based diagnosis capability of the 𝛃-VAE monitor is the key innovation of this work, and our analysis shows it can be utilized as a multi-class classifier for multi-label datasets.
Runtime Recovery Procedures - We have also developed runtime recovery procedures that manage the health of the system by using the system design information including the system information flow, requirement models and function decomposition models, temporal failure propagation graphs at runtime to identify the problems and recover the system by solving a dynamic constraint problem at runtime. The goal of the runtime problem is to identify the optimal component configuration that can provide the lowest risk of subsequent failures given the currently available resources and environment information.
DeepNNCar - to test the efficacy of our methods on embedded systems, we have developed a low-cost research testbed that was designed in our lab. It is built upon the chassis of Traxxas Slash 2WD 1/10 Scale RC car, and is mounted with a USB forward-looking camera, IR- optocoupler, and a 2D LIDAR. The speed and steer for the robot are controlled using pulse-width modulation (PWM), by varying the duty cycle. Further, for autonomous driving, the robot uses a modified NVIDIA DAVE-II CNN model that takes in the front camera image and speed to predict the steering. More information about the car is available in this Medium Article. Videos of DeepNNCar with different controllers is available here.
Finally, one of the interesting aspects of this work is the focus on the design of the decision procedures and generative predictive models that help us design the decision procedures. We have been applying this across the three research verticals of public transit, emergency response and power systems

Learn more about assured autonomy work at vanderbilt at https://assured-autonomy.isis.vanderbilt.edu/

Follow ups : Further information is available at following links.

Publications in this area

Y. Kim, D. Edirimanna, M. Wilbur, P. Pugliese, A. Laszka, A. Dubey, and S. Samaranayake, Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows, in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), 2023.
```
@inproceedings{youngseo2023,
  title = {Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows},
  author = {Kim, Youngseo and Edirimanna, Danushka and Wilbur, Michael and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek and Samaranayake, Samitha},
  booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23)},
  tag = {ai4cps,transit},
  year = {2023}
}
```
The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a largescale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and highquality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. Due to the lack of benchmark solvers similar to ours (i.e., temporal decomposition with an online solver), we compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances (with an average of 129 requests per instance), the baseline approach is as competitive as our framework. However, in larger problem instances (approximately 2,500 requests per instance), our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.
S. Ramakrishna, B. Luo, Y. Barve, G. Karsai, and A. Dubey, Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems, in 2022 IEEE International Conference on Assured Autonomy (ICAA) (ICAA’22), virtual, Puerto Rico, 2022.
```
@inproceedings{ICAA2022,
  author = {Ramakrishna, Shreyas and Luo, Baiting and Barve, Yogesh and Karsai, Gabor and Dubey, Abhishek},
  title = {{Risk-Aware} Scene Sampling for Dynamic Assurance of Autonomous Systems},
  booktitle = {2022 IEEE International Conference on Assured Autonomy (ICAA) (ICAA'22)},
  address = {virtual, Puerto Rico},
  days = {22},
  month = mar,
  tag = {ai4cps},
  year = {2022},
  keywords = {Cyber-Physical Systems; Dynamic Assurance; Dynamic Risk; High-Risk Scenes;
                 Bow-Tie Diagram; Hazards}
}
```
Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and distribution shifts in the operating environment, thus increasing operational risk. Dynamic Assurance of these systems requires augmenting runtime safety components like out-of-distribution detectors and risk estimators. Designing these safety components requires labeled data from failure conditions and risky corner cases that fail the system. However, collecting real-world data of these high-risk scenes can be expensive and sometimes not possible. To address this, there are several scenario description languages with sampling capability for generating synthetic data from simulators to replicate the scenes that are not possible in the real world. Most often, simple search-based techniques like random search and grid search are used as samplers. But we point out three limitations in using these techniques. First, they are passive samplers, which do not use the feedback of previous results in the sampling process. Second, the variables to be sampled may have constraints that need to be applied. Third, they do not balance the tradeoff between exploration and exploitation, which we hypothesize is needed for better coverage of the search space. We present a scene generation workflow with two samplers called Random Neighborhood Search (RNS) and Guided Bayesian Optimization (GBO). These samplers extend the conventional random search and Bayesian Optimization search with the limitation points. We demonstrate our approach using an Autonomous Vehicle case study in CARLA simulation. To evaluate our samplers, we compared them against the baselines of random search, grid search, and Halton sequence search.
Y. Senarath, A. Mukhopadhyay, S. Vazirizade, hemant Purohit, S. Nannapaneni, and A. Dubey, Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services, in 21st IEEE International Conference on Data Mining (ICDM 2021), 2021.
```
@inproceedings{ICDM_2021,
  author = {Senarath, Yasas and Mukhopadhyay, Ayan and Vazirizade, Sayyed and hemant Purohit and Nannapaneni, Saideep and Dubey, Abhishek},
  booktitle = {21st IEEE International Conference on Data Mining (ICDM 2021)},
  tag = {ai4cps,incident},
  title = {Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services},
  year = {2021}
}
```
Emergency response is highly dependent on the time of incident reporting. Unfortunately, the traditional approach to receiving incident reports (e.g., calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze provide an opportunity for early identification of incidents. However, detecting incidents from crowdsourced data streams is difficult due to the challenges of noise and uncertainty associated with such data. Further, simply optimizing over detection accuracy can compromise spatial-temporal localization of the inference, thereby making such approaches infeasible for real-world deployment. This paper presents a novel problem formulation and solution approach for practitioner-centered incident detection using crowdsourced data by using emergency response management as a case-study. The proposed approach CROME (Crowdsourced Multi-objective Event Detection) quantifies the relationship between the performance metrics of incident classification (e.g., F1 score) and the requirements of model practitioners (e.g., 1 km. radius for incident detection). First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents. Then, we use a Pareto optimization-based approach to optimize the output of the CNN in tandem with practitioner-centric parameters to balance detection accuracy and spatial-temporal localization. Finally, we demonstrate the applicability of this approach using crowdsourced data from Waze and traffic accident reports from Nashville, TN, USA. Our experiments demonstrate that the proposed approach outperforms existing approaches in incident detection while simultaneously optimizing the needs for realworld deployment and usability.
M. Wilbur, A. Mukhopadhyay, S. Vazirizade, P. Pugliese, A. Laszka, and A. Dubey, Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2021.
```
@inproceedings{ecml2021,
  author = {Wilbur, Michael and Mukhopadhyay, Ayan and Vazirizade, Sayyed and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek},
  title = {Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  year = {2021},
  tag = {ai4cps,transit}
}
```
Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their eets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for xed-line transit energy prediction. We find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.
S. Singla, A. Mukhopadhyay, M. Wilbur, T. Diao, V. Gajjewar, A. Eldawy, M. Kochenderfer, R. Shachter, and A. Dubey, WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants, in 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021.
```
@inproceedings{wildfiredb2021,
  author = {Singla, Samriddhi and Mukhopadhyay, Ayan and Wilbur, Michael and Diao, Tina and Gajjewar, Vinayak and Eldawy, Ahmed and Kochenderfer, Mykel and Shachter, Ross and Dubey, Abhishek},
  booktitle = {35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks},
  title = {WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants},
  tag = {ai4cps,incident},
  year = {2021}
}
```
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography. Our dataset, named \textitWildfireDB, contains over 17 million data points that capture how fires spread in the continental USA in the last decade. In this paper, we describe the algorithmic approach used to create and integrate the data, describe the dataset, and present benchmark results regarding data-driven models that can be learned to forecast the spread of wildfires.
G. Pettet, A. Mukhopadhyay, M. Kochenderfer, and A. Dubey, Hierarchical Planning for Resource Allocation in Emergency Response Systems, in Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, Nashville, TN, USA, 2021.
```
@inproceedings{iccps2021,
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Dubey, Abhishek},
  title = {Hierarchical Planning for Resource Allocation in Emergency Response Systems},
  booktitle = {Proceedings of the 12th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2021, Nashville, TN, USA},
  year = {2021},
  tag = {ai4cps,decentralization,incident},
  keywords = {emergency},
  project = {smart-cities,smart-emergency-response}
}
```
A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Spatial-temporal allocation of resources is optimized to allocate electric scooters across urban areas, place charging stations for vehicles, and design efficient on-demand transit. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized methodologies have been used to tackle such problems, none of the approaches scale well for large-scale decision problems. We create a general approach to hierarchical planning that leverages structure in city-level CPS problems to tackle resource allocation under uncertainty. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then create a principled framework for solving the smaller problems and tackling the interaction between them. Finally, we use real-world data from a major metropolitan area in the United States to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.
S. Basak, S. Sengupta, S.-J. Wen, and A. Dubey, Spatio-temporal AI inference engine for estimating hard disk reliability, Pervasive and Mobile Computing, vol. 70, p. 101283, 2021.
```
@article{BASAK2021101283,
  title = {Spatio-temporal AI inference engine for estimating hard disk reliability},
  journal = {Pervasive and Mobile Computing},
  volume = {70},
  pages = {101283},
  year = {2021},
  issn = {1574-1192},
  tag = {ai4cps, platform},
  doi = {https://doi.org/10.1016/j.pmcj.2020.101283},
  url = {http://www.sciencedirect.com/science/article/pii/S1574119220301231},
  author = {Basak, Sanchita and Sengupta, Saptarshi and Wen, Shi-Jie and Dubey, Abhishek},
  keywords = {Remaining useful life, Long short term memory, Prognostics, Predictive health maintenance, Hierarchical clustering}
}
```
This paper focuses on building a spatio-temporal AI inference engine for estimating hard disk reliability. Most electronic systems such as hard disks routinely collect such reliability parameters in the field to monitor the health of the system. Changes in parameters as a function of time are monitored and any observed changes are compared with the known failure signatures. If the trajectory of the measured data matches that of a failure signature, operators are alerted to take corrective action. However, the interest of the operators lies in being able to identify the failures before they occur. The state of the art methodology including our prior work is to train machine learning models on temporal sequence data capturing the variations across multiple features and using it to predict the remaining useful life of the devices. However, as we show in this paper temporal prediction capability alone is not sufficient and can lead to low precision and the uncertainty around the prediction is very large. This is primarily due to the non-uniform progression of feature patterns over time. Our hypothesis is that the accuracy can be improved if we combine the temporal prediction methods with a spatial analysis that compares the value of key SMART features of the devices across similar model in a fixed time window (unlike the temporal method which uses the data from a single device and a much larger historical window). In this paper, we first describe both temporal and spatial approaches, describe the methods to select various hyperparameters, and then show a workflow to combine these two methodologies and provide comparative results. Our results illustrate that the average precision of temporal methods using long-short temporal memory networks to predict impending failures in the next ten days was 84 percent. To improve precision, we use the set of disks identified as potential failures and start applying spatial anomaly detection methods on those disks. This helps us remove the false positives from the temporal prediction results and provide a tighter bound on the set of disks with impending failure.
A. Sivagnanam, A. Ayman, M. Wilbur, P. Pugliese, A. Dubey, and A. Laszka, Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service, in Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
```
@inproceedings{aaai21,
  title = {Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service},
  author = {Sivagnanam, Amutheezan and Ayman, Afiya and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
  tag = {ai4cps,transit},
  year = {2021}
}
```
Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO2 emission annually.
S. M. Vazirizade, A. Mukhopadhyay, G. Pettet, S. E. Said, H. Baroud, and A. Dubey, Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems, 2021.
```
@inproceedings{vazirizade2021learning,
  title = {Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems},
  author = {Vazirizade, Sayyed Mohsen and Mukhopadhyay, Ayan and Pettet, Geoffrey and Said, Said El and Baroud, Hiba and Dubey, Abhishek},
  year = {2021},
  eprint = {2106.08307},
  archiveprefix = {arXiv},
  tag = {ai4cps,incident},
  preprint = {https://arxiv.org/abs/2106.08307},
  primaryclass = {cs.LG}
}
```
Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.
M. Wilbur, P. Pugliese, A. Laszka, and A. Dubey, Efficient Data Management for Intelligent Urban Mobility Systems, in Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
```
@inproceedings{wilbur21,
  title = {Efficient Data Management for Intelligent Urban Mobility Systems},
  tag = {ai4cps,transit},
  author = {Wilbur, Michael and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek},
  booktitle = {Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
  year = {2021}
}
```
Modern intelligent urban mobility applications are underpinned by large-scale, multivariate, spatiotemporal data streams. Working with this data presents unique challenges of data management, processing and presentation that is often overlooked by researchers. Therefore, in this work we present an integrated data management and processing framework for intelligent urban mobility systems currently in use by our partner transit agencies. We discuss the available data sources and outline our cloud-centric data management and stream processing architecture built upon open-source publish-subscribe and NoSQL data stores. We then describe our data-integrity monitoring methods. We then present a set of visualization dashboards designed for our transit agency partners. Lastly, we discuss how these tools are currently being used for AI-driven urban mobility applications that use these tools.
C. Hartsell, S. Ramakrishna, A. Dubey, D. Stojcsics, N. Mahadevan, and G. Karsai, ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems, in 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021, 2021.
```
@inproceedings{resonate2021,
  author = {Hartsell, Charles and Ramakrishna, Shreyas and Dubey, Abhishek and Stojcsics, Daniel and Mahadevan, Nag and Karsai, Gabor},
  title = {ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems},
  booktitle = {16th {International} Symposium on Software Engineering for Adaptive and Self-Managing Systems, {SEAMS} 2021},
  year = {2021},
  tag = {ai4cps},
  category = {selectiveconference},
  project = {cps-middleware,cps-reliability}
}
```
Autonomous Cyber-Physical Systems (CPSs) are often required to handle uncertainties and self-manage the system operation in response to problems and increasing risk in the operating paradigm. This risk may arise due to distribution shifts, environmental context, or failure of software or hardware components. Traditional techniques for risk assessment focus on design-time techniques such as hazard analysis, risk reduction, and assurance cases among others. However, these static, design time techniques do not consider the dynamic contexts and failures the systems face at runtime. We hypothesize that this requires a dynamic assurance approach that computes the likelihood of unsafe conditions or system failures considering the safety requirements, assumptions made at design time, past failures in a given operating context, and the likelihood of system component failures. We introduce the ReSonAte dynamic risk estimation framework for autonomous systems. ReSonAte reasons over Bow-Tie Diagrams (BTDs), which capture information about hazard propagation paths and control strategies. Our innovation is the extension of the BTD formalism with attributes for modeling the conditional relationships with the state of the system and environment. We also describe a technique for estimating these conditional relationships and equations for estimating risk-based on the state of the system and environment. To help with this process, we provide a scenario modeling procedure that can use the prior distributions of the scenes and threat conditions to generate the data required for estimating the conditional relationships. To improve scalability and reduce the amount of data required, this process considers each control strategy in isolation and composes several single-variate distributions into one complete multi-variate distribution for the control strategy in question. Lastly, we describe the effectiveness of our approach using two separate autonomous system simulations: CARLA and an unmanned underwater vehicle.
S. Ramakrishna, C. Hartsell, A. Dubey, P. Pal, and G. Karsai, A Methodology for Automating Assurance Case Generation, in Thirteenth International Tools and Methods of Competitive Engineering Symposium (TMCE 2020), 2020.
```
@inproceedings{ramakrishna2020methodology,
  author = {Ramakrishna, Shreyas and Hartsell, Charles and Dubey, Abhishek and Pal, Partha and Karsai, Gabor},
  title = {A Methodology for Automating Assurance Case Generation},
  booktitle = {Thirteenth International Tools and Methods of Competitive Engineering Symposium (TMCE 2020)},
  year = {2020},
  tag = {ai4cps},
  archiveprefix = {arXiv},
  eprint = {2003.05388},
  preprint = {https://arxiv.org/abs/2003.05388},
  primaryclass = {cs.RO}
}
```
Safety Case has become an integral component for safety-certification in various Cyber Physical System domains including automotive, aviation, medical devices, and military. The certification processes for these systems are stringent and require robust safety assurance arguments and substantial evidence backing. Despite the strict requirements, current practices still rely on manual methods that are brittle, do not have a systematic approach or thorough consideration of sound arguments. In addition, stringent certification requirements and ever-increasing system complexity make ad-hoc, manual assurance case generation (ACG) inefficient, time consuming, and expensive. To improve the current state of practice, we introduce a structured ACG tool which uses system design artifacts, accumulated evidence, and developer expertise to construct a safety case and evaluate it in an automated manner. We also illustrate the applicability of the ACG tool on a remote-control car testbed case study.
A. Ayman, M. Wilbur, A. Sivagnanam, P. Pugliese, A. Dubey, and A. Laszka, Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets, in 2020 IEEE International Conference on Smart Computing (SMARTCOMP) (SMARTCOMP 2020), Bologna, Italy, 2020.
```
@inproceedings{Lasz2006Data,
  author = {Ayman, Afiya and Wilbur, Michael and Sivagnanam, Amutheezan and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  title = {{Data-Driven} Prediction of {Route-Level} Energy Use for {Mixed-Vehicle}
                 Transit Fleets},
  booktitle = {2020 IEEE International Conference on Smart Computing (SMARTCOMP)
                 (SMARTCOMP 2020)},
  address = {Bologna, Italy},
  tag = {ai4cps,transit},
  days = {21},
  month = jun,
  year = {2020},
  keywords = {data-driven prediction; electric vehicle; public transit; on-board
                 diagnostics data; deep learning; traffic data}
}
```
Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. Making the best use of these mixed fleets presents a challenge for agencies since optimizing the assignment of vehicles to transit routes, scheduling charging, etc. require accurate predictions of electricity and fuel use. Recent advances in sensor-based technologies, data analytics, and machine learning enable remedying this situation; however, to the best of our knowledge, there exists no framework that would integrate all relevant data into a route-level prediction model for public transit. In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN. We present a data collection and storage framework, which we use to capture system-level data, including traffic and weather conditions, and high-frequency vehicle-level data, including location traces, fuel or electricity use, etc. We present domain-specific methods and algorithms for integrating and cleansing data from various sources, including street and elevation maps. Finally, we train and evaluate machine learning models, including deep neural networks, decision trees, and linear regression, on our integrated dataset. Our results show that neural networks provide accurate estimates, while other models can help us discover relations between energy use and factors such as road and weather conditions.
C. Hartsell, N. Mahadevan, H. Nine, T. Bapty, A. Dubey, and G. Karsai, Workflow Automation for Cyber Physical System Development Processes, in 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2020.
```
@inproceedings{Hartsell_2020,
  author = {Hartsell, Charles and Mahadevan, Nagabhushan and Nine, Harmon and Bapty, Ted and Dubey, Abhishek and Karsai, Gabor},
  title = {Workflow Automation for Cyber Physical System Development Processes},
  booktitle = {2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION)},
  year = {2020},
  tag = {ai4cps},
  month = apr,
  publisher = {IEEE},
  doi = {http://dx.doi.org/10.1109/DESTION50928.2020.00007},
  isbn = {9781728199948},
  journal = {2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION)}
}
```
Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains to achieve ever-increasing demands for improved performance, reduced cost, and more system autonomy. Each engineering discipline commonly relies on domain-specific modeling languages, and analysis and execution of these models is often automated with appropriate tooling. However, integration between these heterogeneous models and tools is often lacking, and most of the burden for inter-operation of these tools is placed on system developers. To address this problem, we introduce a workflow modeling language for the automation of complex CPS development processes and implement a platform for execution of these models in the Assurance-based Learning-enabled CPS (ALC) Toolchain. Several illustrative examples are provided which show how these workflow models are able to automate many time-consuming integration tasks previously performed manually by system developers.
H. Tu, S. Lukic, A. Dubey, and G. Karsai, An LSTM-Based Online Prediction Method for Building Electric Load During COVID-19, in Annual Conference of the PHM Society, 2020.
```
@inproceedings{haophm2020,
  author = {Tu, Hao and Lukic, Srdjan and Dubey, Abhishek and Karsai, Gabor},
  title = {An LSTM-Based Online Prediction Method for Building Electric Load During COVID-19},
  booktitle = {Annual Conference of the PHM Society},
  year = {2020},
  tag = {ai4cps,power}
}
```
Accurate prediction of electric load is critical to optimally controlling and operating buildings. It provides the opportunities to reduce building energy consumption and to implement advanced functionalities such as demand response in the context of smart grid. However, buildings are nonstationary and it is important to consider the underlying concept changes that will affect the load pattern. In this paper we present an online learning method for predicting building electric load during concept changes such as COVID-19. The proposed methods is based on online Long Short-Term Memory (LSTM) recurrent neural network. To speed up the learning process during concept changes and improve prediction accuracy, an ensemble of multiple models with different learning rates is used. The learning rates are updated in realtime to best adapt to the new concept while maintaining the learned information for the prediction.

V. Sundar, S. Ramakrishna, Z. Rahiminasab, A. Easwaran, and A. Dubey, Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE, in 2020 IEEE Security and Privacy Workshops (SPW), Los Alamitos, CA, USA, 2020, pp. 250–255.

@inproceedings{sundar2020detecting,
  author = {Sundar, V. and Ramakrishna, S. and Rahiminasab, Z. and Easwaran, A. and Dubey, A.},
  booktitle = {2020 IEEE Security and Privacy Workshops (SPW)},
  title = {Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of {\beta}-VAE},
  year = {2020},
  volume = {},
  issn = {},
  pages = {250-255},
  keywords = {training;support vector machines;object detection;security;task analysis;testing;meteorology},
  doi = {10.1109/SPW50608.2020.00057},
  url = {https://doi.ieeecomputersociety.org/10.1109/SPW50608.2020.00057},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  tag = {ai4cps},
  month = may
}

G. Pettet, M. Ghosal, S. Mahserejian, S. Davis, S. Sridhar, A. Dubey, and M. Meyer, A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling, in 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020.
```
@inproceedings{pettetisgt2020,
  author = {Pettet, Geoffrey and Ghosal, Malini and Mahserejian, Shant and Davis, Sarah and Sridhar, Siddharth and Dubey, Abhishek and Meyer, Michael},
  title = {A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling},
  booktitle = {2020 IEEE Power \& Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
  year = {2020},
  organization = {IEEE},
  tag = {ai4cps,power}
}
```
While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid’s distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price and grid load. The framework co-simulates models for traffic (Simulation of Urban Mobility) and the electric grid (GridLAB-D), which are used by a decision-theoretic planner to evaluate charging decisions with regard to their long-term effect on grid reliability and cost. We evaluated the framework on a simulation of Richland, WA’s bus and grid network, and found that it could save over $100k per year on operating costs for the city compared to greedy methods.
A. Ayman, A. Sivagnanam, M. Wilbur, P. Pugliese, A. Dubey, and A. Laszka, Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles, ACM Transations of Internet Technology, 2020.
```
@article{aymantoit2020,
  author = {Ayman, Afiya and Sivagnanam, Amutheezan and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  title = {Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles},
  journal = {ACM Transations of Internet Technology},
  year = {2020},
  tag = {ai4cps,transit}
}
```
Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal-combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.
M. Wilbur, A. Ayman, A. Ouyang, V. Poon, R. Kabir, A. Vadali, P. Pugliese, D. Freudberg, A. Laszka, and A. Dubey, Impact of COVID-19 on Public Transit Accessibility and Ridership, in Preprint at Arxiv, 2020.
```
@inproceedings{wilbur2020impact,
  author = {Wilbur, Michael and Ayman, Afiya and Ouyang, Anna and Poon, Vincent and Kabir, Riyan and Vadali, Abhiram and Pugliese, Philip and Freudberg, Daniel and Laszka, Aron and Dubey, Abhishek},
  title = {Impact of COVID-19 on Public Transit Accessibility and Ridership},
  booktitle = {Preprint at Arxiv},
  year = {2020},
  tag = {ai4cps,transit},
  archiveprefix = {arXiv},
  eprint = {2008.02413},
  preprint = {https://arxiv.org/abs/2008.02413},
  primaryclass = {physics.soc-ph}
}
```
Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus disease COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19’s affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9% and 65.1% from 2019 baselines before stabilizing at 48.4% and 42.8% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs 58%) in Nashville.
G. Pettet, A. Mukhopadhyay, M. Kochenderfer, Y. Vorobeychik, and A. Dubey, On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities, in Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2020, Auckland, New Zealand, 2020.
```
@inproceedings{Pettet2020,
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Vorobeychik, Yevgeniy and Dubey, Abhishek},
  title = {On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities},
  booktitle = {Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2020, Auckland, New Zealand},
  year = {2020},
  tag = {ai4cps, decentralization,incident},
  category = {selectiveconference},
  keywords = {emergency, performance},
  project = {smart-emergency-response,smart-cities},
  timestamp = {Wed, 17 Jan 2020 07:24:00 +0200}
}
```
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite its importance, it is common for ERM systems to follow myopic and straight-forward decision policies in the real world. Principled approaches to aid decision-making under uncertainty have been explored in this context but have failed to be accepted into real systems. We identify a key issue impeding their adoption — algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. However, this is not a trivial planning problem - a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is to plan under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both the problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.
A. Mukhopadhyay, G. Pettet, C. Samal, A. Dubey, and Y. Vorobeychik, An online decision-theoretic pipeline for responder dispatch, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 185–196.
```
@inproceedings{Mukhopadhyay2019,
  author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy},
  title = {An online decision-theoretic pipeline for responder dispatch},
  booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
  year = {2019},
  tag = {ai4cps,incident},
  pages = {185--196},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/iccps/MukhopadhyayPSD19},
  category = {selectiveconference},
  doi = {10.1145/3302509.3311055},
  file = {:Mukhopadhyay2019-An_Online_Decision_Theoretic_Pipeline_for_Responder_Dispatch.pdf:PDF},
  keywords = {emergency},
  project = {smart-cities,smart-emergency-response},
  timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
  url = {https://doi.org/10.1145/3302509.3311055}
}
```
The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.
S. Basak, F. Sun, S. Sengupta, and A. Dubey, Data-Driven Optimization of Public Transit Schedule, in Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad, India, 2019, pp. 265–284.
```
@inproceedings{Basak2019,
  author = {Basak, Sanchita and Sun, Fangzhou and Sengupta, Saptarshi and Dubey, Abhishek},
  title = {Data-Driven Optimization of Public Transit Schedule},
  booktitle = {Big Data Analytics - 7th International Conference, {BDA} 2019, Ahmedabad, India},
  year = {2019},
  tag = {ai4cps,transit},
  pages = {265--284},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/bigda/BasakSSD19},
  category = {selectiveconference},
  doi = {10.1007/978-3-030-37188-3\_16},
  file = {:Basak2019-Data_Driven_Optimization_of_Public_Transit_Schedule.pdf:PDF},
  keywords = {transit},
  project = {smart-cities,smart-transit},
  timestamp = {Fri, 13 Dec 2019 12:44:00 +0100},
  url = {https://doi.org/10.1007/978-3-030-37188-3\_16}
}
```
Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.
S. Basak, A. Dubey, and B. P. Leao, Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks, in IEEE Big Data, Los Angeles, Ca, 2019.
```
@inproceedings{basak2019bigdata,
  author = {Basak, Sanchita and Dubey, Abhishek and Leao, Bruno P.},
  title = {Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks},
  booktitle = {IEEE Big Data},
  year = {2019},
  tag = {ai4cps,incident,transit},
  address = {Los Angeles, Ca},
  category = {selectiveconference},
  keywords = {reliability, transit}
}
```
This paper presents a data-driven approach for predicting the propagation of traffic congestion at road seg-ments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events.
C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, Model-based design for CPS with learning-enabled components, in Proceedings of the Workshop on Design Automation for CPS and IoT, DESTION@CPSIoTWeek 2019, Montreal, QC, Canada, 2019, pp. 1–9.
```
@inproceedings{Hartsell2019,
  author = {Hartsell, Charles and Mahadevan, Nagabhushan and Ramakrishna, Shreyas and Dubey, Abhishek and Bapty, Theodore and Johnson, Taylor T. and Koutsoukos, Xenofon D. and Sztipanovits, Janos and Karsai, Gabor},
  title = {Model-based design for {CPS} with learning-enabled components},
  booktitle = {Proceedings of the Workshop on Design Automation for {CPS} and IoT, DESTION@CPSIoTWeek 2019, Montreal, QC, Canada},
  year = {2019},
  tag = {ai4cps},
  pages = {1--9},
  month = apr,
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/cpsweek/HartsellMRDBJKS19},
  category = {workshop},
  doi = {10.1145/3313151.3313166},
  file = {:Hartsell2019-Model-based_design_for_CPS_with_learning-enabled_components.pdf:PDF},
  keywords = {assurance},
  project = {cps-autonomy},
  timestamp = {Wed, 20 Nov 2019 00:00:00 +0100},
  url = {https://doi.org/10.1145/3313151.3313166}
}
```
Recent advances in machine learning led to the appearance of Learning-Enabled Components (LECs) in Cyber-Physical Systems. LECs are being evaluated and used for various, complex functions including perception and control. However, very little tool support is available for design automation in such systems. This paper introduces an integrated toolchain that supports the architectural modeling of CPS with LECs, but also has extensive support for the engineering and integration of LECs, including support for training data collection, LEC training, LEC evaluation and verification, and system software deployment. Additionally, the toolsuite supports the modeling and analysis of safety cases - a critical part of the engineering process for mission and safety critical systems.
C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, and G. Karsai, A CPS toolchain for learning-based systems: demo abstract, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 342–343.
```
@inproceedings{Hartsell2019a,
  author = {Hartsell, Charles and Mahadevan, Nagabhushan and Ramakrishna, Shreyas and Dubey, Abhishek and Bapty, Theodore and Karsai, Gabor},
  title = {A {CPS} toolchain for learning-based systems: demo abstract},
  booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
  year = {2019},
  pages = {342--343},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/iccps/HartsellMRDBK19},
  category = {poster},
  doi = {10.1145/3302509.3313332},
  file = {:Hartsell2019a-A_CPS_Toolchain_for_Learning_Based_Systems_Demo_Abstract.pdf:PDF},
  keywords = {assurance},
  tag = {ai4cps},
  project = {cps-autonomy},
  timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
  url = {https://doi.org/10.1145/3302509.3313332}
}
```
Cyber-Physical Systems (CPS) are expected to perform tasks with ever-increasing levels of autonomy, often in highly uncertain environments. Traditional design techniques based on domain knowledge and analytical models are often unable to cope with epistemic uncertainties present in these systems. This challenge, combined with recent advances in machine learning, has led to the emergence of Learning-Enabled Components (LECs) in CPS. However, very little tool support is available for design automation of these systems. In this demonstration, we introduce an integrated toolchain for the development of CPS with LECs with support for architectural modeling, data collection, system software deployment, and LEC training, evaluation, and verification. Additionally, the toolchain supports the modeling and analysis of safety cases - a critical part of the engineering process for mission and safety critical systems.
M. P. Burruss, S. Ramakrishna, G. Karsai, and A. Dubey, DeepNNCar: A Testbed for Deploying and Testing Middleware Frameworks for Autonomous Robots, in IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Valencia, Spain, May 7-9, 2019, 2019, pp. 87–88.
```
@inproceedings{Burruss2019,
  author = {Burruss, Matthew P. and Ramakrishna, Shreyas and Karsai, Gabor and Dubey, Abhishek},
  title = {DeepNNCar: {A} Testbed for Deploying and Testing Middleware Frameworks for Autonomous Robots},
  booktitle = {{IEEE} 22nd International Symposium on Real-Time Distributed Computing, {ISORC} 2019, Valencia, Spain, May 7-9, 2019},
  year = {2019},
  tag = {ai4cps},
  pages = {87--88},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/isorc/BurrussRKD19},
  category = {poster},
  doi = {10.1109/ISORC.2019.00025},
  file = {:Burruss2019-DeepNNCar_Testbed_for_Deploying_and_Testing_Middleware_Frameworks_for_Autonomous_Robots.pdf:PDF},
  keywords = {assurance},
  project = {cps-autonomy},
  timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
  url = {https://doi.org/10.1109/ISORC.2019.00025}
}
```
This demo showcases the features of an adaptive middleware framework for resource constrained autonomous robots like DeepNNCar (Figure 1). These robots use Learning Enabled Components (LECs), trained with deep learning models to perform control actions. However, these LECs do not provide any safety guarantees and testing them is challenging. To overcome these challenges, we have developed an adaptive middleware framework that (1) augments the LEC with safety controllers that can use different weighted simplex strategies to improve the systems safety guarantees, and (2) includes a resource manager to monitor the resource parameters (temperature, CPU Utilization), and offload tasks at runtime. Using DeepNNCar we will demonstrate the framework and its capability to adaptively switch between the controllers and strategies based on its safety and speed performance.
C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, CPS Design with Learning-Enabled Components: A Case Study, in Proceedings of the 30th International Workshop on Rapid System Prototyping, RSP 2019, New York, NY, USA, October 17-18, 2019, 2019, pp. 57–63.
```
@inproceedings{Hartsell2019b,
  author = {Hartsell, Charles and Mahadevan, Nagabhushan and Ramakrishna, Shreyas and Dubey, Abhishek and Bapty, Theodore and Johnson, Taylor T. and Koutsoukos, Xenofon D. and Sztipanovits, Janos and Karsai, Gabor},
  title = {{CPS} Design with Learning-Enabled Components: {A} Case Study},
  booktitle = {Proceedings of the 30th International Workshop on Rapid System Prototyping, {RSP} 2019, New York, NY, USA, October 17-18, 2019},
  year = {2019},
  pages = {57--63},
  tag = {ai4cps},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/rsp/HartsellMRDBJKS19},
  category = {selectiveconference},
  doi = {10.1145/3339985.3358491},
  file = {:Hartsell2019b-CPS_Design_with_Learning-Enabled_Components_A_Case_Study.pdf:PDF},
  keywords = {assurance},
  project = {cps-autonomy},
  timestamp = {Thu, 28 Nov 2019 12:43:50 +0100},
  url = {https://doi.org/10.1145/3339985.3358491}
}
```
Cyber-Physical Systems (CPS) are used in many applications where they must perform complex tasks with a high degree of autonomy in uncertain environments. Traditional design flows based on domain knowledge and analytical models are often impractical for tasks such as perception, planning in uncertain environments, control with ill-defined objectives, etc. Machine learning based techniques have demonstrated good performance for such difficult tasks, leading to the introduction of Learning-Enabled Components (LEC) in CPS. Model based design techniques have been successful in the development of traditional CPS, and toolchains which apply these techniques to CPS with LECs are being actively developed. As LECs are critically dependent on training and data, one of the key challenges is to build design automation for them. In this paper, we examine the development of an autonomous Unmanned Underwater Vehicle (UUV) using the Assurance-based Learning-enabled Cyber-physical systems (ALC) Toolchain. Each stage of the development cycle is described including architectural modeling, data collection, LEC training, LEC evaluation and verification, and system-level assurance.
S. Ramakrishna, A. Dubey, M. P. Burruss, C. Hartsell, N. Mahadevan, S. Nannapaneni, A. Laszka, and G. Karsai, Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots, in IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Valencia, Spain, May 7-9, 2019, 2019, pp. 108–117.
```
@inproceedings{Ramakrishna2019,
  author = {Ramakrishna, Shreyas and Dubey, Abhishek and Burruss, Matthew P. and Hartsell, Charles and Mahadevan, Nagabhushan and Nannapaneni, Saideep and Laszka, Aron and Karsai, Gabor},
  title = {Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots},
  booktitle = {{IEEE} 22nd International Symposium on Real-Time Distributed Computing, {ISORC} 2019, Valencia, Spain, May 7-9, 2019},
  year = {2019},
  tag = {ai4cps},
  pages = {108--117},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/isorc/RamakrishnaDBHM19},
  category = {selectiveconference},
  doi = {10.1109/ISORC.2019.00032},
  file = {:Ramakrishna2019-Augmenting_Learning_Components_for_Safety_in_Resource_Constrained_Autonomous_Robots.pdf:PDF},
  keywords = {assurance},
  project = {cps-autonomy},
  timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
  url = {https://doi.org/10.1109/ISORC.2019.00032}
}
```
Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, and testing them is challenging. In this paper, we introduce a framework that performs weighted simplex strategy based supervised safety control, resource management and confidence estimation of autonomous robots. Specifically, we describe two weighted simplex strategies: (a) simple weighted simplex strategy (SW-Simplex) that computes a weighted controller output by comparing the decisions between a safety supervisor and an LEC, and (b) a context-sensitive weighted simplex strategy (CSW-Simplex) that computes a context-aware weighted controller output. We use reinforcement learning to learn the contextual weights. We also introduce a system monitor that uses the current state information and a Bayesian network model learned from past data to estimate the probability of the robotic system staying in the safe working region. To aid resource constrained robots in performing complex computations of these weighted simplex strategies, we describe a resource manager that offloads tasks to an available fog nodes. The paper also describes a hardware testbed called DeepNNCar, which is a low cost resource-constrained RC car, built to perform autonomous driving. Using the hardware, we show that both SW-Simplex and CSW-Simplex have 40% and 60% fewer safety violations, while demonstrating higher optimized speed during indoor driving around 0.40m/s than the original system (using only LECs).

M. Wilbur, A. Dubey, B. Leão, and S. Bhattacharjee, A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 274–282.

@inproceedings{Wilbur2019,
  author = {Wilbur, Michael and Dubey, Abhishek and Le{\~{a}}o, Bruno and Bhattacharjee, Shameek},
  title = {A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
  year = {2019},
  pages = {274--282},
  month = jun,
  tag = {ai4cps,platform,decentralization,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/WilburDLB19},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2019.00063},
  file = {:Wilbur2019-A_Decentralized_Approach_for_Real_Time_Anomaly_Detection_in_Transportation_Networks.pdf:PDF},
  keywords = {transit, reliability},
  project = {cps-reliability,smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00063}
}

S. Basak, S. Sengupta, and A. Dubey, Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 208–216.
```
@inproceedings{Basak2019a,
  author = {Basak, Sanchita and Sengupta, Saptarshi and Dubey, Abhishek},
  title = {Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
  year = {2019},
  pages = {208--216},
  month = jun,
  tag = {ai4cps},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/BasakSD19},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2019.00055},
  file = {:Basak2019a-Mechanisms_for_Integrated_Feature_Normalization_and_Remaining_Useful_Life_Estimation_Using_LSTMs_Applied_to_Hard-Disks.pdf:PDF},
  keywords = {reliability},
  project = {cps-reliability},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00055}
}
```
In this paper we focus on application of data-driven methods for remaining useful life estimation in components where past failure data is not uniform across devices, i.e. there is a high variance in the minimum and maximum value of the key parameters. The system under study is the hard disks used in computing cluster. The data used for analysis is provided by Backblaze as discussed later. In the article, we discuss the architecture of of the long short term neural network used and describe the mechanisms to choose the various hyper-parameters. Further, we describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through online simulation sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure. With the proposed approach we are able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435. We also show that the architecture trained on a particular model is generalizable and transferable as it can be used to predict RUL for devices in other models from same manufacturer.
S. Basak, A. Aman, A. Laszka, A. Dubey, and B. Leao, Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks, in Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM), 2019.
```
@inproceedings{Basak2019b,
  author = {Basak, Sanchita and Aman, Afiya and Laszka, Aron and Dubey, Abhishek and Leao, Bruno},
  title = {Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks},
  booktitle = {Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM)},
  year = {2019},
  month = oct,
  tag = {ai4cps,transit},
  attachments = {https://www.isis.vanderbilt.edu/sites/default/files/PHM_traffic_cascades_paper.pdf},
  category = {conference},
  doi = {https://doi.org/10.36001/phmconf.2019.v11i1.861},
  file = {:Basak2019b-Data_Driven_Detection_of_Anomalies_and_Cascading_Failures_in_Traffic_Networks.pdf:PDF},
  keywords = {transit, reliability},
  project = {smart-transit,smart-cities,cps-reliability}
}
```
Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. By failure, we mean in this domain the hindrance of the normal operation of a traffic network due to cyber anomalies or physical incidents that cause cascaded congestion throughout the network. We are specifically interested in analyzing the cascade effects of traffic congestion caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. Our results show that our LSTM-based traffic-speed predictors attain an average mean squared error of 6.55\times10^-4 on predicting normalized traffic speed, while Gaussian Process Regression based predictors attain a much higher average mean squared error of 1.78\times10^-2. We are also able to detect anomalies with high precision and recall, resulting in an AUC (Area Under Curve) of 0.8507 for the precision-recall curve. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a traffic simulator. Finally, we analyzed the cascading effect of the congestion propagation by formulating the problem as a Timed Failure Propagation Graph, which led us in identifying the source of a failure/congestion accurately.
F. Sun, A. Dubey, C. Samal, H. Baroud, and C. Kulkarni, Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks, in 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018, Taormina, Sicily, Italy, June 18-20, 2018, 2018, pp. 155–162.
```
@inproceedings{Sun2018,
  author = {Sun, Fangzhou and Dubey, Abhishek and Samal, Chinmaya and Baroud, Hiba and Kulkarni, Chetan},
  title = {Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks},
  booktitle = {2018 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2018, Taormina, Sicily, Italy, June 18-20, 2018},
  year = {2018},
  pages = {155--162},
  tag = {ai4cps,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunDSBK18},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2018.00086},
  file = {:Sun2018-Short-Term_Transit_Decision_Support_System_Using_Multi-task_Deep_Neural_Networks.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,cps-reliability,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2018.00086}
}
```
Unpredictability is one of the top reasons that prevent people from using public transportation. To improve the on-time performance of transit systems, prior work focuses on updating schedule periodically in the long-term and providing arrival delay prediction in real-time. But when no real-time transit and traffic feed is available (e.g., one day ahead), there is a lack of effective contextual prediction mechanism that can give alerts of possible delay to commuters. In this paper, we propose a generic tool-chain that takes standard General Transit Feed Specification (GTFS) transit feeds and contextual information (recurring delay patterns before and after big events in the city and the contextual information such as scheduled events and forecasted weather conditions) as inputs and provides service alerts as output. Particularly, we utilize shared route segment networks and multi-task deep neural networks to solve the data sparsity and generalization issues. Experimental evaluation shows that the proposed toolchain is effective at predicting severe delay with a relatively high recall of 76% and F1 score of 55%.
S. Pradhan, A. Dubey, S. Khare, S. Nannapaneni, A. S. Gokhale, S. Mahadevan, D. C. Schmidt, and M. Lehofer, CHARIOT: Goal-Driven Orchestration Middleware for Resilient IoT Systems, TCPS, vol. 2, no. 3, pp. 16:1–16:37, 2018.
```
@article{Pradhan2018,
  author = {Pradhan, Subhav and Dubey, Abhishek and Khare, Shweta and Nannapaneni, Saideep and Gokhale, Aniruddha S. and Mahadevan, Sankaran and Schmidt, Douglas C. and Lehofer, Martin},
  title = {{CHARIOT:} Goal-Driven Orchestration Middleware for Resilient IoT Systems},
  journal = {{TCPS}},
  year = {2018},
  volume = {2},
  number = {3},
  pages = {16:1--16:37},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/journals/tcps/PradhanDKNGMSL18},
  doi = {10.1145/3134844},
  tag = {ai4cps,platform},
  file = {:Pradhan2018-CHARIOT_Goal-Driven_Orchestration_Middleware_for_Resilient_IoT_Systems.pdf:PDF},
  keywords = {reliability, middleware},
  project = {cps-middleware,cps-reliability},
  timestamp = {Wed, 21 Nov 2018 00:00:00 +0100},
  url = {https://doi.org/10.1145/3134844}
}
```
An emerging trend in Internet of Things (IoT) applications is to move the computation (cyber) closer to the source of the data (physical). This paradigm is often referred to as edge computing. If edge resources are pooled together they can be used as decentralized shared resources for IoT applications, providing increased capacity to scale up computations and minimize end-to-end latency. Managing applications on these edge resources is hard, however, due to their remote, distributed, and (possibly) dynamic nature, which necessitates autonomous management mechanisms that facilitate application deployment, failure avoidance, failure management, and incremental updates. To address these needs, we present CHARIOT, which is orchestration middleware capable of autonomously managing IoT systems consisting of edge resources and applications. CHARIOT implements a three-layer architecture. The topmost layer comprises a system description language, the middle layer comprises a persistent data storage layer and the corresponding schema to store system information, and the bottom layer comprises a management engine that uses information stored persistently to formulate constraints that encode system properties and requirements, thereby enabling the use of Satisfiability Modulo Theories (SMT) solvers to compute optimal system (re)configurations dynamically at runtime. This paper describes the structure and functionality of CHARIOT and evaluates its efficacy as the basis for a smart parking system case study that uses sensors to manage parking spaces.
S. Basak, S. Sengupta, and A. Dubey, A Data-driven Prognostic Architecture for Online Monitoring of Hard Disks Using Deep LSTM Networks, CoRR, vol. abs/1810.08985, 2018.
```
@article{Basak2018,
  author = {Basak, Sanchita and Sengupta, Saptarshi and Dubey, Abhishek},
  title = {A Data-driven Prognostic Architecture for Online Monitoring of Hard Disks Using Deep {LSTM} Networks},
  journal = {CoRR},
  year = {2018},
  tag = {ai4cps},
  volume = {abs/1810.08985},
  archiveprefix = {arXiv},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/journals/corr/abs-1810-08985},
  eprint = {1810.08985},
  file = {:Basak2018-A_Data-driven_Prognostic_Architecture_for_Online_Monitoring_of_Hard_Disks_Using_Deep_LSTM_Networks.pdf:PDF},
  timestamp = {Wed, 31 Oct 2018 00:00:00 +0100},
  url = {http://arxiv.org/abs/1810.08985}
}
```
With the advent of pervasive cloud computing technologies, service reliability and availability are becoming major concerns,especially as we start to integrate cyber-physical systems with the cloud networks. A number of smart and connected community systems such as emergency response systems utilize cloud networks to analyze real-time data streams and provide context-sensitive decision support.Improving overall system reliability requires us to study all the aspects of the end-to-end of this distributed system,including the backend data servers. In this paper, we describe a bi-layered prognostic architecture for predicting the Remaining Useful Life (RUL) of components of backend servers,especially those that are subjected to degradation. We show that our architecture is especially good at predicting the remaining useful life of hard disks. A Deep LSTM Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. We describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through test sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure.The proposed architecture is able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435.In future, we will extend this architecture to learn and predict the RUL of the edge devices in the end-to-end distributed systems of smart communities, taking into consideration context-sensitive external features such as weather.
F. Sun, A. Dubey, and J. White, DxNAT - Deep neural networks for explaining non-recurring traffic congestion, in 2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017, 2017, pp. 2141–2150.
```
@inproceedings{Sun2017,
  author = {Sun, Fangzhou and Dubey, Abhishek and White, Jules},
  title = {DxNAT - Deep neural networks for explaining non-recurring traffic congestion},
  booktitle = {2017 {IEEE} International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017},
  year = {2017},
  pages = {2141--2150},
  tag = {ai4cps,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/bigdataconf/SunDW17},
  category = {selectiveconference},
  doi = {10.1109/BigData.2017.8258162},
  file = {:Sun2017-DxNAT-Deep_neural_networks_for_explaining_non-recurring_traffic_congestion.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities,cps-reliability},
  timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
  url = {https://doi.org/10.1109/BigData.2017.8258162}
}
```
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately.
A. Ghafouri, A. Laszka, A. Dubey, and X. D. Koutsoukos, Optimal detection of faulty traffic sensors used in route planning, in Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017, 2017, pp. 1–6.
```
@inproceedings{Ghafouri2017,
  author = {Ghafouri, Amin and Laszka, Aron and Dubey, Abhishek and Koutsoukos, Xenofon D.},
  title = {Optimal detection of faulty traffic sensors used in route planning},
  booktitle = {Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017},
  year = {2017},
  pages = {1--6},
  tag = {ai4cps,platform,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/cpsweek/GhafouriLDK17},
  category = {workshop},
  doi = {10.1145/3063386.3063767},
  file = {:Ghafouri2017-Optimal_detection_of_faulty_traffic_sensors_used_in_route_planning.pdf:PDF},
  keywords = {transit},
  project = {cps-reliability,smart-transit,smart-cities},
  timestamp = {Tue, 06 Nov 2018 16:59:05 +0100},
  url = {https://doi.org/10.1145/3063386.3063767}
}
```
In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real- world dataset and the route planning platform OpenTripPlanner.
C. Samal, F. Sun, and A. Dubey, SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation, in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, China, May 29-31, 2017, 2017, pp. 1–6.
```
@inproceedings{Samal2017,
  author = {Samal, Chinmaya and Sun, Fangzhou and Dubey, Abhishek},
  title = {SpeedPro: {A} Predictive Multi-Model Approach for Urban Traffic Speed Estimation},
  booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
  year = {2017},
  pages = {1--6},
  tag = {ai4cps,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalSD17},
  category = {workshop},
  doi = {10.1109/SMARTCOMP.2017.7947048},
  file = {:Samal2017-SpeedPro_A_Predictive_Multi-Model_Approach_for_Urban_Traffic_Speed_Estimation.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2017.7947048}
}
```
Data generated by GPS-equipped probe vehicles, especially public transit vehicles can be a reliable source for traffic speed estimation. Traditionally, this estimation is done by learning the parameters of a model that describes the relationship between the speed of the probe vehicle and the actual traffic speed. However, such approaches typically suffer from data sparsity issues. Furthermore, most state of the art approaches does not consider the effect of weather and the driver of the probe vehicle on the parameters of the learned model. In this paper, we describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.
F. Sun, C. Samal, J. White, and A. Dubey, Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles, in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, China, May 29-31, 2017, 2017, pp. 1–8.
```
@inproceedings{Sun2017a,
  author = {Sun, Fangzhou and Samal, Chinmaya and White, Jules and Dubey, Abhishek},
  title = {Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles},
  booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
  year = {2017},
  tag = {ai4cps,transit},
  pages = {1--8},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunSWD17},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2017.7947057},
  file = {:Sun2017a-Unsupervised_Mechanisms_for_Optimizing_On-Time_Performance_of_Fixed_Schedule_Transit_Vehicles.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2017.7947057}
}
```
The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at timepoints that improve the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.
G. Pettet, S. Nannapaneni, B. Stadnick, A. Dubey, and G. Biswas, Incident analysis and prediction using clustering and Bayesian network, in 2017 IEEE SmartWorld, 2017, pp. 1–8.
```
@inproceedings{Pettet2017,
  author = {Pettet, Geoffrey and Nannapaneni, Saideep and Stadnick, Benjamin and Dubey, Abhishek and Biswas, Gautam},
  title = {Incident analysis and prediction using clustering and Bayesian network},
  booktitle = {2017 {IEEE} SmartWorld},
  year = {2017},
  tag = {ai4cps,incident},
  pages = {1--8},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/uic/PettetNSDB17},
  category = {selectiveconference},
  doi = {10.1109/UIC-ATC.2017.8397587},
  file = {:Pettet2017-Incident_analysis_and_prediction_using_clustering_and_Bayesian_network.pdf:PDF},
  keywords = {emergency},
  project = {smart-emergency-response,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
  url = {https://doi.org/10.1109/UIC-ATC.2017.8397587}
}
```
Advances in data collection and storage infrastructure offer an unprecedented opportunity to integrate both data and emergency resources in a city into a dynamic learning system that can anticipate and rapidly respond to heterogeneous incidents. In this paper, we describe integration methods for spatio-temporal incident forecasting using previously collected vehicular accident data provided to us by the Nashville Fire Department. The literature provides several techniques that focus on analyzing features and predicting accidents for specific situations (specific intersections in a city, or certain segments of a freeway, for example), but these models break down when applied to a large, general area consisting of many road and intersection types and other factors like weather conditions. We use Similarity Based Agglomerative Clustering (SBAC) analysis to categorize incidents to account for these variables. Thereafter, we use survival analysis to learn the likelihood of incidents per cluster. The mapping of the clusters to the spatial locations is achieved using a Bayesian network. The prediction methods we have developed lay the foundation for future work on an optimal emergency vehicle allocation and dispatch system in Nashville.
A. Mukhopadhyay, Y. Vorobeychik, A. Dubey, and G. Biswas, Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model, in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, May 8-12, 2017, 2017, pp. 168–177.
```
@inproceedings{Mukhopadhyay2017,
  author = {Mukhopadhyay, Ayan and Vorobeychik, Yevgeniy and Dubey, Abhishek and Biswas, Gautam},
  title = {Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model},
  booktitle = {Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2017, S{\~{a}}o Paulo, Brazil, May 8-12, 2017},
  year = {2017},
  pages = {168--177},
  tag = {ai4cps,incident},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/atal/MukhopadhyayVDB17},
  category = {selectiveconference},
  file = {:Mukhopadhyay2017-Prioritized_Allocation_of_Emergency_Responders_based_on_a_Continuous-Time_Incident_Prediction_Model.pdf:PDF},
  keywords = {emergency},
  project = {smart-emergency-response,smart-cities},
  timestamp = {Wed, 27 Sep 2017 07:24:00 +0200},
  url = {http://dl.acm.org/citation.cfm?id=3091154}
}
```
Efficient emergency response is a major concern in densely populated urban areas. Numerous techniques have been proposed to allocate emergency responders to optimize response times, coverage, and incident prevention. Effective response depends, in turn, on effective prediction of incidents occurring in space and time, a problem which has also received considerable prior attention. We formulate a non-linear mathematical program maximizing expected incident coverage, and propose a novel algorithmic framework for solving this problem. In order to aid the optimization problem, we propose a novel incident prediction mechanism. Prior art in incident prediction does not generally consider incident priorities which are crucial in optimal dispatch, and spatial modeling either considers each discretized area independently, or learns a homogeneous model. We bridge these gaps by learning a joint distribution of both incident arrival time and severity, with spatial heterogeneity captured using a hierarchical clustering approach. Moreover, our decomposition of the joint arrival and severity distributions allows us to independently learn the continuous-time arrival model, and subsequently use a multinomial logistic regression to capture severity, conditional on incident time. We use real traffic accident and response data from the urban area around Nashville, USA, to evaluate the proposed approach, showing that it significantly outperforms prior art as well as the real dispatch method currently in use.
G. Biswas, H. Khorasgani, G. Stanje, A. Dubey, S. Deb, and S. Ghoshal, An application of data driven anomaly identification to spacecraft telemetry data, in Prognostics and Health Management Conference, 2016.
```
@inproceedings{Biswas2016,
  author = {Biswas, Gautam and Khorasgani, Hamed and Stanje, Gerald and Dubey, Abhishek and Deb, Somnath and Ghoshal, Sudipto},
  title = {An application of data driven anomaly identification to spacecraft telemetry data},
  booktitle = {Prognostics and Health Management Conference},
  year = {2016},
  tag = {ai4cps},
  category = {conference},
  file = {:Biswas2016-An_application_of_data_driven_anomaly_identification_to_spacecraft_telemetry_data.pdf:PDF},
  keywords = {reliability}
}
```
In this paper, we propose a mixed method for analyzing telemetry data from a robotic space mission. The idea is to first apply unsupervised learning methods to the telemetry data divided into temporal segments. The large clusters that ensue typically represent the nominal operations of the spacecraft and are not of interest from an anomaly detection viewpoint. However, the smaller clusters and outliers that result from this analysis may represent specialized modes of operation, e.g., conduct of a specialized experiment on board the spacecraft, or they may represent true anomalous or unexpected behaviors. To differentiate between specialized modes and anomalies, we employ a supervised method of consulting human mission experts in the approach presented in this paper. Our longer term goal is to develop more automated methods for detecting anomalies in time series data, and once anomalies are identified, use feature selection methods to build online detectors that can be used in future missions, thus contributing to making operations more effective and improving overall safety of the mission.

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