The lack of resources to meet the exponentially growing number of users is one of the major problems, along with shortages of computing resources, security issues, vulnerability, reliability, concerns, and complexity. This paper aims to achieve maximum efficiency in resource allocation. The resources will be allocated to users based on their intent and needs, with the assistance of an ML model and edge computing. Intelligent resource allocation by dynamically changing the scheduler algorithm yields much greater efficiency than traditional methods. The dynamic scenario simulation implemented in NS3, the dynamic allocation of resources through different algorithms for various scenarios, and the verification of the results show that the proposed methodology will increase the efficiency in resource allocation for each user according to the environment, without compromising on parameters such as QoS, SINR, delay, throughput, etc.

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment.

Road traffic crashes are one of the prime issues in the world. Every year 1.35 million people die, 20–50 million fatal injuries, and many incur a disability due to road traffic crashes. 50% of the total death, injuries, and disabilities are among vulnerable road users (VRUs), such as motorcyclists, cyclists, and pedestrians. Among these VRUs, highly vulnerable is pedestrians. In this article, we are dealing with providing safety and alert system to pedestrians and vehicles to reduce and/or avoid the causes of road traffic crashes in metropolitan areas. In this article, we develop a cooperative communication framework between pedestrians and nearby vehicles as well as traffic light systems using mobile agent systems and apps. The proposed cooperative communication framework controls the mobility of vehicles as well as pedestrians to avoid accidents. We have developed an application that will notify the pedestrian if there is any automobile within the range of 200 m of the pedestrian. If there are any vehicles in this range, using direct Wi-Fi, connect the vehicles and pedestrians to notify them about their presence of them. The proposed system is implemented and tested in real time as well as simulated in the SUMO, Veins, OMNeT++, and MiXiM simulator. The proposed system’s results (real time, simulation, and comparison) show real-time deployability, accuracy, and reliability.

As the high-mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in network simulator version 3, and integrated them with the simulation urban mobility traffic control interface API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.

With the advancement of information and communication technologies (ICTs), there has been 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 provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a 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 smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

Public transport systems in a metropolitan area experiences several complex issues, like resource scarcity, resource allocation, congestion, resource reliability and so on, due to the dynamic arrivals of heterogeneous commuter and exceptional occurrence of unforeseen events. The progress of these issues may lead to economic losses, under-utilization of transport resources, and commuters’ queuing delay. In this paper, we propose a novel dynamic public transport vehicle allocation scheme based on Emergent Intelligence (EI) technique in a metropolitan area. In addition, we demonstrate the EI technique’s capability for solving public transport system problems. To do so, the EI technique maintains historical information, commuters’ arrival rates, resource avaialability, deficit resources and surplus resources of neighbor depots’s agent. In the proposed scheme, the EI technique is utilized to collect, analyze, share and optimally allocate transport resources effectively. The proposed EI technique provides reliable services (allocation and scheduling) by coordinating with a reliable neighborhood depot’s agent. We have build mathematical models for estimation of resources, utilization and reliability parameters. The proposed scheme is exhaustively tested by simulation and analyzed with varying commuters’ arrival rates, number of vehicles, number of requests, and different values of reliability parameters. The proposed scheme’s results (analytical, simulation and comparison) show the reliabiltiy, accuracy and real time deployability.

The public transportation system (PTS) in a metropolitan area is a nonlinear, dynamic, and complex system. Managing and providing suitable public transportation services are difficult. In this article, we propose an Internet of Things-based intelligent PTS (IoT-IPTS) in a metropolitan area. An IoT is used to interconnect transportation entities, such as vehicles, commuters (mobile phones), routes (sensors), roadside units (RSUs), etc., in a metropolitan area. The IoT provides the seamless connectivity between different networking technologies whenever the commuters or vehicles move from one location to another location. Hence, IoT provides the suitable seamless public transportation services in the metropolitan area. In addition, we have used context information of transportation entities, such as routes condition, traffic density, number of routes available, traffic congestion, vehicles' movement, and their mobility, which are stored in the cloud. The stored context information in cloud along with the IoTs are used to find the relevant routes, alternative modes, departure times, and many more for providing public transportation services in a metropolitan area. The proposed IoT-IPTS makes use of static and mobile agents with the emergent intelligence technique (EIT) for collecting, analyzing, and sharing context information. The analyzed context information is used to form the policies to provide the best available public transportation services to the commuters in a metropolitan area. The software-defined network is used to enable the cloud computing and EI network to manage the public transportation services to the commuters.