The Internet of Vehicles (IoV) presents significant opportunities for enhancing traffic management and vehicle coordination, but it also faces challenges related to traffic congestion, data privacy, and efficient computational resource allocation. Traffic congestion remains a critical problem, impacting travel time, fuel consumption, and emissions. Additionally, task offloading in the edge-cloud environment demands efficient strategies to balance latency, resource usage, and computational load. Our proposed system, Joint Federated Learning and Blockchain-Enabled Traffic Rerouting with Efficient Task Offloading of Consumer IoV in the Edge-Cloud Environment, addresses these issues by integrating federated learning and blockchain technologies. Federated learning allows vehicles to collaboratively train a global model without sharing raw data, preserving privacy and reducing bandwidth usage. Blockchain ensures the security and integrity of the model updates, fostering trust among participants. Efficient task offloading strategies optimize the use of edge and cloud resources, minimizing latency and energy consumption. Our approach is validated using a comprehensive dataset, and the results demonstrate significant improvements in traffic prediction accuracy, security, and overall system performance, highlighting the effectiveness of the integrated solution in addressing the challenges of Consumer Internet of Vehicles (CIoV).

The distributed network utilizing the integrity-based resilient machine learning (DRML) approach offers a robust methodology to address challenges associated with complex Internet of Things (IoT) applications, computational resource limitations, and environmental sustainability. In heterogeneous cloud computing environments, where edge and central clouds collaborate to meet diverse demands in various sectors, significant challenges arise in task offloading. This research proposes an IoT and cloud computing framework integrated with a distributed and resilient deep learning (DLRTO) model to optimize system utility and bandwidth allocation for local controller devices (LCDs). The DLRTO model is designed to generate near-optimal offloading decisions for LCDs, edge cloud servers, and central cloud servers, thus maximizing both system utility and bandwidth allocation. Additionally, we introduce the RL-NN integrated round robin-based dynamic task scheduling (RNRRDTS) algorithm on edge and cloud servers to address job scheduling issues, with a focus on renewable energy generation and job migration. Extensive simulations were conducted using real-world IoT application tasks, renewable energy data, and grid electricity pricing data to evaluate the proposed model. The experimental results demonstrate the superior performance of the DLRTO model compared with existing methods. This research provides valuable insights and advances toward the creation of sustainable, resilient, and optimized IoT systems applicable across various domains.

Smart grids (SGs) are technology powered electricity networks that support bidirectional power and data flows. This allows real-time monitoring of demand and enhances the grid’s capability to dynamically adjust the generation and reduce the gap between supply and demand. However, implementing a smart grid in the power network comes with its own set of security challenges, such as cyber-security, distrust in participants and lack of customer engagement due to various cyber-attacks. Such cyber-attacks which will create a distrust among consumers/prosumers to adapt smart grid and distributed energy resources (DER) framework. To circumvent this, a blockchain supported hybrid authentication and handshake algorithm (BSHAHA) for smart grids is proposed in this work, which authenticates data communication between peer-to-peer, aggregators, virtual power plants, and the grid. The algorithm was developed incorporating elliptic-curve cryptography (ECC) and advanced encryption standards (AES) to enhance privacy and session security. The proposed algorithms are verified and tested using formal cyber-security tools, such as Random oracle model and AVISPA as well as by informal security analysis. Furthermore, to simulate a real time test environment, this paper utilized ns-3 network simulator to simulate different smart meter scenario and proposed algorithm are tested for power consumption and scalability, and result are presented. Moreover, blockchain simulation first done in local blockchain using Ganache and truffle IDE and later using the Holesky Ethereum test-network and remix-IDE. Lastly, this paper presented a comparison analysis of power consumption for different consensus mechanisms.

Imaging technology is a recent advancement in developing smart vehicular environments in the consumer industry. Due to the increase in smart vehicles, road safety and other privacy issues become more common. This paper proposes a deep learning-based hybrid fatigue detection system where extracted features from ECG signals, the standard deviation of lane position, facial images, and detecting driver’s drowsiness were combined using the Deep CNN network model. A sensor-based ResNet model is proposed for vehicle dynamics and road accident detection, which detects vehicle dynamics, road dynamics, and accidents and generates alarm signals for rescue operations. For traffic congestion detection and rerouting, we proposed a Blockchain-enabled Deep CNN network that detects traffic congestion using Deep CNN and gives optimal path suggestions using the Adaptive Cluster Head Selection (ACHS) approach. Furthermore, we introduced integrated blockchain technology with traffic congestion and routing models for enhancing security without depending on trusted third parties to run services. The proposed models performance is analyzed under different dynamics and compared with other state-of-the-art models.