IoT-Enhanced Vehicular Networks: Simulation Frameworks for Energy Efficiency and Cyber-Security in Smart Cities

Abstract

The Internet of Things (IoT) has rapidly evolved over the past two decades, transforming the way we interact with the environment through a network of interconnected devices. The purpose of this thesis is to explore the integration of IoT with Vehicular Ad-Hoc Net works (VANETs) in order to enhance intelligent transportation systems (ITS) and smart city infrastructure through the use of IoT. VANETs, characterized by high mobility and dynamic topology, play a crucial role in enhancing traffic safety, efficiency, and vehicu lar services. They improve traffic safety by enabling real-time communication between vehicles and roadside infrastructure, allowing the sharing of critical information such as accident warnings and road conditions to prevent collisions and enhance emergency re sponse times. VANETs boost traffic efficiency through intelligent traffic management, optimizing signal timings and route planning based on real-time data to reduce con gestion and travel times. Additionally, they provide enhanced vehicular services such as infotainment, navigation assistance, and maintenance alerts, thereby improving the overall driving experience and vehicle performance monitoring. This research addresses the significant challenges of simulating VANET environments, particularly the high mobility of vehicles and the need for realistic traffic scenarios. Ex isting VANET simulators, while advanced, often lack support for new technologies and comprehensive security systems, highlighting the necessity for more comprehensive sim ulation frameworks. The primary aim of this PhD thesis is to integrate IoT and traffic simulations to accurately evaluate vehicular energy efficiency and overall network perfor mance. Therefore, this thesis presents multilateral research towards optimization, mod eling, and simulation of VANET and IoT environments. Several tools and algorithms have been proposed, implemented, and evaluated, considering various environments and applications. The main contributions of this thesis are as follows: • Conducting a review of current IoT simulators highlights their strengths and lim itations, particularly their inability to address energy depletion security concerns. The survey identified a lack of support for renewable energy sources or VANET integration, which are essential for modern IoT applications. The absence of a ver-- i satile, generic IoT simulator is noted, as existing tools often specialize in specific applications and lack flexibility. • Conducting an in-depth performance evaluation of emerging VANET technologies, this survey addresses the urgent need for updated reviews considering electric vehi cles, self-driving cars, SDN, edge computing, and 5G. The survey identifies critical gaps, including the lack of support for renewable energy, dynamic battery recharg ing, and encryption impact. • Conducting a feasibility study on coupling IoT simulators with traffic simulators to enhance VANET simulations, this toward study introduces the novel SUMO toOsmosis framework. Investigating the integration of IoTSim-Osmosis for IoT simulations with SUMO for traffic simulations, SUMOtoOsmosis marks a first in the literature. The proposed system, tested with the Hamburg dataset, focuses on communication time, this framework enables the simulation of traffic environments based on IoT infrastructure. • Proposing and modeling a new holistic framework that simulates real-world traffic scenarios for electric vehicles, SimulatorBridger. Its flexible architecture allows for integration with any traffic simulator. Preliminary results validate its accuracy in simulating vehicular battery consumption and network performance, highlighting the need for efficient communication policies. This platform supports policymakers in optimizing VANET performance and developing energy-efficient transportation networks. • Introducing SimulatorBridgerDfT, a novel simulator platform extending Simulator Bridger to support different formats of real traffic data, enhances the flexibility and applicability of urban traffic simulations by integrating IoTSim-OsmosisRES with DfT traffic data. Evaluating the impact of SUMO car traces versus static DfT data on communication delays in IoT simulations provides valuable insights for designing efficient and effective traffic simulation tools, aiding researchers and practitioners in traffic management and urban planning. • Introducing IoTSimSecure, a novel simulation framework designed to detect the security attacks, particularly battery draining attacks. IoTSimSecure supports a- ii range of detection algorithms, including threshold-based detection and Exponential Weighted Moving Average (EWMA) techniques. This flexibility allows for com prehensive analysis and testing of various security strategies, thus enhancing the simulator’s ability to develop effective countermeasures against battery-draining attacks. As a result of addressing the key challenges in IoT and VANET simulation, the results of this thesis will contribute to the development of flexible, efficient, and secure intelligent transportation systems and smart city infrastructures.