Adaptive traffic lights based on traffic flow prediction using machine learning models

Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections

Real-time GPS Tracking System for IoT-Enabled Connected Vehicles

This paper presents a real-time GPS tracking solution for connected vehicle networks, leveraging IoT, V2X communication, and VANET technologies. The system uses Arduino Uno R3, SIM800L, NEO6M GPS, Node.js, socket, and Firebase for seamless real-time GPS data collection, storage, and visualization. Users can access and monitor GPS data on a web interface. Integration of Node.js and sockets ensures efficient hardware-software communication, while Firebase enables realtime data storage and synchronization for resource management and tracking. The paper explores the system’s applications in dynamic routing for energy efficiency, eco-driving feedback, smart charging stations, environmental data collection, intelligent traffic management, and fleet emissions reduction. These applications highlight the system’s versatility, promoting energy efficiency and sustainability across industries. By incorporating IoT, VANET, and V2X communication, the system enables seamless connectivity and data exchange among vehicles, infrastructure, and the cloud, enhancing decision-making and system efficiency. Insights into system implementation, including IoT, VANET, and real-time GPS integration, are provided. The paper discusses transportation, logistics, and vehicule tracking as potential application domains, which hold promise for optimizing energy consumption. The presented solution offers an efficient, reliable platform for real-time GPS tracking in connected vehicle networks, harnessing IoT, VANET, and V2X communication for enhanced decisionmaking and sustainable transportation systems

AODV-based Defense Mechanism for Mitigating Blackhole Attacks in MANET

Mobile Ad hoc Networks (MANETs) are decentralized and self-configuring networks composed of mobile devices that communicate without a fixed infrastructure. However, the open nature of MANETs makes them vulnerable to various security threats, including blackhole attacks, where malicious nodes attract and discard network traffic without forwarding it to its intended destination. Mitigating blackhole attacks is crucial to ensure the reliability and security of communication in MANETs. This paper focuses on the development and evaluation of AODV (Ad hoc On-Demand Distance Vector)-based defence mechanisms for effectively mitigating blackhole attacks in MANETs, while simultaneously addressing energy efficiency and environmental sustainability. AODV is a widely used routing protocol in MANETs due to its on-demand nature and low overhead. However, it lacks built-in security mechanisms, making it susceptible to attacks. We incorporate energyaware route selection, solar-powered routing, collaborative energy sharing, energy-efficient intrusion detection, green routing optimization, and energy harvesting from environmental sources. By considering energy consumption and environmental factors in the route selection process, our defense mechanism not only enhances the security of the network but also contributes to energy conservation and reduced environmental impact. To evaluate the effectiveness of the proposed defence mechanisms, extensive simulations and performance analyses are conducted using network simulation tools. Through simulation-based evaluations, we demonstrate the effectiveness of our approach in achieving robust blackhole attack mitigation while extending the network’s lifetime and minimizing its carbon footprint. Our research offers valuable insights into the development of energy-efficient and environmentally sustainable solutions for securing MANETs in the face of evolving security threats

Real-time GPS Tracking System for IoT-Enabled Connected Vehicles

This paper presents a real-time GPS tracking solution for connected vehicle networks, leveraging IoT, V2X communication, and VANET technologies. The system uses Arduino Uno R3, SIM800L, NEO6M GPS, Node.js, socket, and Firebase for seamless real-time GPS data collection, storage, and visualization. Users can access and monitor GPS data on a web interface. Integration of Node.js and sockets ensures efficient hardware-software communication, while Firebase enables realtime data storage and synchronization for resource management and tracking. The paper explores the system’s applications in dynamic routing for energy efficiency, eco-driving feedback, smart charging stations, environmental data collection, intelligent traffic management, and fleet emissions reduction. These applications highlight the system’s versatility, promoting energy efficiency and sustainability across industries. By incorporating IoT, VANET, and V2X communication, the system enables seamless connectivity and data exchange among vehicles, infrastructure, and the cloud, enhancing decision-making and system efficiency. Insights into system implementation, including IoT, VANET, and real-time GPS integration, are provided. The paper discusses transportation, logistics, and vehicule tracking as potential application domains, which hold promise for optimizing energy consumption. The presented solution offers an efficient, reliable platform for real-time GPS tracking in connected vehicle networks, harnessing IoT, VANET, and V2X communication for enhanced decisionmaking and sustainable transportation systems