Optimization and Anomaly Detection for Smart City-based Applications

Smart cities are envisioned to include million of sensors and devices tied together through the Internet. The sensors generate huge amount of data that can be potentially used to develop several applications. Connected vehicles can be considered as a significant realm of the smart city. Furthermore, among connected vehicles, Plug-in Electric Vehicles (PEVs) are becoming an integral component of smart city. Plug-in Electric Vehicles (PEVs) can play a major role in reducing carbon footprints in transportation sector. However, with the growing increase in the usage of PEVs, their charging infrastructure need to be comprehensively developed.This thesis develops optimization strategies for parked and en route PEV charging. First, the thesis develops a charging strategy for parked vehicles. For parked vehicle charging, electricity demand must have less variation at different times in a day. This helps in avoiding any detrimental effect on power electronic equipment, reduces electricity cost, and simplifies the electric energy demand prediction from the smart grid. This thesis delineates an intelligent aggregator architecture to automate parked Plug-in Electric Vehicle (PEV) charging in large parking places to reduce dynamic load variation. The thesis presents a set of novel rectangles placement-based algorithms to schedule the PEV charging based on their arrival, departure time at parking place, and their charging requirement.En route PEVs have different requirements as compared to parked PEVs. To facilitate en route PEV charging, this research investigates an integrated COP architecture. The COP constitutes a Communication unit that provides network management solution for PEV charging, an Optimization unit for allocating charging station to PEVs, and a Prediction unit to determine traffic flow information in advance. The thesis shows how three units can interact with each other to provide efficient charging infrastructure to PEVs.A related problem regarding application service development in smart city is the anomaly detection. The detection of any variation in environmental parameters (anomalies/outliers) is important problem for efficient functioning of automated application services. However, the false anomalies by malicious manipulation of sensor data may have adverse effect on such services. Thus, for connected communities, this research explores these two interrelated problems:1) detection of anomalies and 2) classification between real anomalies from manipulated anomalies.For solving anomaly detection problem, this thesis presents a scalable anomaly detector that uses Hierarchical clustering in conjunction with Long Short-Term Memory (LSTM) neural network. Hierarchical clustering provides scalability to the anomaly detector by finding correlated sensors. The LSTM neural network is coupled with the robust statistics, M-estimator, to accurately detect outliers in time-series data. The thesis proceeds to discuss the distinction between anomalies occurring due to external environmental variations or intentional manipulation/faults in sensors. Towards this direction, our research has focused on using the idea that heterogeneous sensors may not directly affect a certain measured quantity. However, if measured values from individual sensors are combined then that may have correlation with a certain value. Specifically, this research uses air quality, wind-speed, and temperature to predict vehicular traffic. Based on the predicted quantity, we have employed rule-based statistical approach to detect malicious anomalies