Vehicular edge computing (VEC) brings computational resources at the edge of vehicular networks (VANETs). In VEC, the roadside unit (RSU) across the road segment acts as an edge server. The vehicle having less computational capability offloads high computation tasks to its nearby RSU for processing. There is a significant energy consumption occurs at the RSU in computing each high computation task. To minimize the energy consumption, a caching technique is used at RSUs. The greatest challenge of caching in VEC is the mobility of vehicles. In this poster, we propose a Mobility-Aware Caching technique (MobiCache) in VEC. MobiCache uses an actor-critic deep reinforcement learning framework to find the best routes for migrating the popular cache contents of RSUs according to the mobility pattern of vehicles. Simulation results show that our proposed caching strategy reduces the energy consumption by an average of 39.54% as compared to other existing caching techniques.

COVID-19 is spreading at an exponential rate and declared as ‘Pandemic’ by the World Health Organization (WHO). The only way of controlling its spread is ‘social distancing’ among the human beings. Apart from human beings, it can also spread through other objects such as metals and plastics which are used in public passenger buses, cars and trains. Studies reveal that the COVID-19 virus can stay on metallic things for 2-5 days. This makes traveling more difficult for regular employees to their workplace. Also, due to increasing number of COVID-19 patients, it is very difficult for a patient to find the required resources at hospitals. In this paper, we propose a VANET-IoT framework for detection of COVID-19 exposure in various entities such as vehicles. A vehicle is said to be virus-exposed if it carries any COVID-19 positive patient. We develop an Android application ‘Corona Assistant’ for travelers to find all virus-exposed vehicles and chose virus-free vehicle for traveling. In addition, a hospital recommendation system (HRS) is developed inside the ‘Corona Assistant’ application. HRS helps the COVID-19 positive patient to select the nearby hospital based on available resources.

Cyber-physical systems (CPSs) comprise physical systems or objects incorporated with computing functionalities and data storage systems. Different sensor nodes in a CPS communicate with each other and devices like actuators and microcontrollers regulate the physical systems through smart algorithms. Efficient communication and networking algorithms play a critical role in supporting smooth interaction between the cyber and physical worlds. Internet of Things (IoT) belongs to the physical part of CPS. In the physical part, the sensor nodes are connected with each other and each node sends data to the root node through their respective parent node in a multi-hop manner. The physical devices in a CPS may stop sensing the environment due to logical and physical failures. To solve this, we propose a real time defective nodes detection scheme (R2D) in a cyber-physical system. The root node of the CPS gathers sensed data, detects the defective nodes and performs data analysis in the cyber part of the CPS. The results show that our proposed R2D protocol achieves a 10.2% higher packet delivery ratio and a 12.5% increase in throughput as compared to the flooding approach. Moreover, an improvement by almost 50% in throughput is observed when compared to RPL with an increase in packet loss rate of 20%.

Road-side unit (RSU) plays an important role in providing connectivity among the vehicles on the road. In rural areas, RSUs are powered using renewable energy, such as solar or wind energy. Hence, the energy consumption across such RSUs should be efficient i.e., energy consumption at RSUs should be minimized and no RSU gets over-utilized while others are under-utilized. The amount of energy consumption depends upon the scheduling of different kinds of data requests at RSU. In this paper, we propose a scheduling architecture for minimizing energy consumption at RSU and attaining uniform energy consumption across neighboring RSUs. This, in turn, increases the request fulfillment percentage at RSUs. The proposed architecture categorizes the incoming request as a Traditional (less computation) or a Smart request (high computation). Two approaches- Hard-deadline Less Computation requirement Approach (HLCA) and Soft-deadline High Computation requirement Approach (SHCA) are proposed for addressing Traditional and Smart data requests, respectively. In HLCA approach, the receiving RSU uses the scheduling metric to select the servicing RSU for request fulfillment. We prove by analysis, how scheduling metric helps in minimizing and achieving uniform energy consumption across the RSUs. In SHCA approach, Fog computing is used for handling high computation requests. Energy consumption at RSUs is further optimized by using Auction game-based relay vehicle selection mechanism. Simulation results demonstrate that our proposed approaches achieve uniform energy consumption across multiple RSUs and 10% more efficient than scheduling algorithms for single RSU model such as Nearest Fastest Set Scheduler (NFS).

Air pollution is a major cause of health problem in urban areas. Vehicles are the major sources of the current air pollution in urban cities. In this work, we proposed a pollution monitoring system for vehicles using IoTs. This system measures the real-time pollution generated by vehicles on road. This paper describes the design of the system for sensing the pollution using sensor, arduino, smart phone and mobile applications for displaying the personalize air pollution information for individual vehicle. We evaluate the proposed approach on real-data experiment and it shows some preliminary results.