Owing to the increased worldwide awareness regarding pollution caused by the consumption of fossil fuels, Battery-powered vehicles are bound to take over the conventional Internal Combustion Engine. Keeping the difficulties faced by the economy of the city and the populace of adapting to an entirely grid-run charging infrastructure in mind, a framework incorporating electric vehicles to everything (EV2X) communication and Charge Slot booking based on data got from a survey conducted has been developed in this literature. The conclusions drawn from the survey develop key insights into developing statisticorating the use of LTE to support the conventional OCPP and promote user control models that are further explored in this context. Algorithms and strategies to implement next-generation efficient EV2X communications have been implemented and developed for the city. Further, we have established a priority order for slot booking and in corp over charge-cycles. Introducing IPMUs using an LTE connection to act as a supplement to the conventional OCPP is explored in this context. Besides that, we have built the M/M/m queuing model of EVs in the charging station and its optimization.We have done the exhaustive evaluation of the robustness of the proposed system in a fairly large-scale network in a discrete-time event simulator. The proposed system's results (simulation, analytical, and comparison) show the reduction of waiting time, good accuracy, and saving of charging time and costs. These performances measures improve shows the real-time applicability of the proposed system.

Advancement of information and communication technologies (ICTs) there is high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services and many extensive benefits to the commuters’ and transportation authorities. In this paper, we propose a novel edge-based AI-IoT integrated energy efficient intelligent transport system for smart cities by using distributed multi-agent system. An urban area is divided into multiple numberof regions and each region is sub-divided into finite number of zones. At each zone optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects the huge volume of data from the various sensors, devices and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain the smooth and steady movement which result into reduction in the GHG emissions and energy consumption, and finally improves the freight vehicles mileage by reducing the traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

The subsequent establishment towards 5G technology has hastened the development of the diverse set of V2X communication possibilities. The Internet of Vehicles (IoV) uses core 5G network infrastructure to allow vehicles to communicate to other entities on the road very efficiently.   As a result, V2X will inevitably require 5G. In our proposed work, the design and development of various core network configurations with Standalone – (5G Core) / (4G Core) and Non-Standalone – (4G and 5G core) architectures combined with realistic traffic conditions in dense urban settings are formulated. The design of channel path loss, fading and shadowing models is also fused in our deployment scenario to make the scenarios truly adaptive in the urban environment. The performance analysis such as throughput, latency and jitter are evaluated for various V2V applications in different design scenario setups.

The continuous urbanization with extensive dynamic situations on evolving cities, urban, and suburban areas, it is not feasible to categorize the navigation as fastest route, toll-free, and other variants. Metropolitan areas are more prone to traffic congestion, lane blocking, accidents, etc., due to the overcrowding and dynamic change of commuters’ arrival rates. In the metropolitan areas, most of the commuters’ use Google map to reach their desired destinations. It is quite often that route specified by navigation will not be reliable because sometimes due to the inability to update the sudden occurrence of incidents on the routes. Currently, Google map and GPS provide the time required to cover the distance and shortest route to reach the destination. The main issues with the existing Google map are it does not considers the impact of sudden occurrence of incidents, does not show the type of incidents that occurred, clearance time, and optimal routes. These issues are solved by designing an efficient context-aware vehicle incidents route service management for an intelligent transport system. The proposed system takes the context information of incidents, vehicles, weather conditions, roadside units, roads, and so on. This context information will be collected and shared with the nearby vehicles and roadside units using both mobile agents and dedicated short-range communication protocols. The proposed system suggests alternative routes with minimal delay and traffic clearance time and severity of incidents to the commuters. Also, it provides the incident information to the neighborhood vehicles, roadside units, nearby hospitals, ambulance, and members of the victims. The proposed system is exhaustively simulated in objective modular network test-bed in C++, simulation of urban mobility, and Veins with different simulation parameters. The proposed system’s simulation results reduce the travel time (7 min) compared to the without the context information system (25 min), least collision rate (0.785%) compared to the existing system, minimizes the traffic clearance time in the incident zone, and uniform distribution of vehicle traffic on the estimated routes.

This work will integrate Multi-Agent System (MAS) based EVs traffic pattern analysis and prediction, an interaction model among EVs, charging stations, power grid, and aggregators with the traffic simulation to bridge the gap in metropolitan cities. Metropolitan cities (especially in India) are divided into the finite number of regions and each region is again subdivided into the multiple numbers of zones depending upon the population, demands, type of area, etc. An Emergent Intelligence Technique (EIT) is used for EVs traffic pattern analysis, prediction, identifying the potential traffic zones and locations for installing the charging stations. The Emergent Intelligence technique is a group of multi-agent systems (MASs) intelligence. These MASs are deployed at each charging stations, power grid, aggregator and EVs which are also integrated with the ITS technologies, Communication technologies and transportation simulation framework to visualize the realistic communication framework and its implementation in the metropolitan cities. Through this work, we propose an EVs traffic pattern analysis model and MAS-based communication framework between EVs and Smart Power Grid.

The migration towards 5G technology has expedited the development of a host of new applications and use case driven methodologies. As we know, the KPI requirements for futuristic use cases like Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC) and Ultra Reliable and Low Latency Communications (uRLLC) requires 1 ms latency and almost 99.99% network reliability, which puts the traditional networks like 4G to obsolete. As a result, fully transitioning 5G network is becoming mandatory in near future. So, in this study, the design and development of various core network configurations with Standalone (SA) – (5G Core)/ (4G Core) and Non-Standalone (NSA) – (4G and 5G core) architectures combined with MIMO configurations in dense urban settings are formulated using 3GPP standard. The design of channel path loss, fading and shadowing models is also fused in the deployment scenario to make the scenarios truly adaptive in the urban environment. The performance analysis such as throughput and latency are evaluated for various applications in different design scenario setups for both static and mobility user equipment (UE).

Designed an on-demand space-efficient multi-level EV charging station infrastructure for metropolitan cities. We have integrated the novel structure of the infrastructure with the 33-bus power grid for power transaction, load management, and power grid network balance. A novel methodology, that is, a multi-agent system, is deployed on each bus and charging station to collect, share and analyze various power system parameters. The analyzed parameters are used to control and maintain the stability of the power grid. The multi-level charging infrastructure’s stress and displacement analysis has been done, and the cost analysis of the proposed infrastructure has been discussed. Developed the analytical model of the proposed method, the optimization problem is formulated and solved using the Genetic algorithm, and simulation has been conducted with varying parameters such as arrival rates, service rates, and the number of charging points available at the charging station.