Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey

Abstract

The next wave in smart transportation is directed towards the design of renewable energy sources that can fuel automobile sector to shift towards the autonomous electric vehicles (AEVs). AEVs are sensor-driven and driverless that uses artificial intelligence (AI)-based interactions in Internet-of-vehicles (IoV) ecosystems. AEVs can reduce carbon footprints and trade energy with peer AEVs, smart grids (SG), and roadside units (RSUs). It supports green transportation vision. However, the sensor information, energy units, and user data are exchanged through open channels, and thus, are susceptible to various security and privacy attacks. Thus, AEVs can be remotely operated and directed by malicious entities that can propagate false updates to the peer nodes in IoV environment. This can cause the failure of components, congestion, as well as the entire disruption of IoV network. Globally researchers and security analysts have addressed solutions that pertain to specific security requirements, but still, the detection and classification of malicious AEVs is a widely studied topic. Malicious AEVs exhibit an anomaly behavior that differentiates them from normal AEVs, and thereby, the detection of anomalous AEVs and classification of anomaly type is required. Motivated from the aforementioned facts, the survey presents a systematic outlook of AI techniques in anomaly detection of AEVs.A solution taxonomy is proposed based on research gaps in the existing surveys, and the evaluation metrics for AI-based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique. Thus, the proposed survey is designed to guide the manufacturing industry, AI practitioners, and researchers worldwide to formulate and design accurate and precise mechanisms to detect anomalies.

Secure and Smart: Enhancing Energy Systems in Core Electrical Networks

“…Secure communication methods and cryptographic techniques can help keep important data, like control orders and data on how much energy is used, private, and accessible. By taking these security steps, utilities can protect themselves from cyberattacks and make sure that energy supply is always reliable.A number of studies have also looked into how advanced analytics and machine learning techniques can be used to make smart grid systems more reliable [9], [10]. These technologies can look at a lot of data that smart devices send, which lets utilities see problems coming and stop them before they happen.…”

Introduction to the special issue on big data analytics with internet of things‐oriented infrastructures for future smart cities

“…This paper (Dixit et al, 2022) presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in existing surveys, and the evaluation metrics for AI‐based anomaly detection are discussed.…”

ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

“…An equivalent gasoline or diesel vehicle has substantially higher operating costs than an EV. Moreover, EVs have low maintenance costs since they do not have as many moving parts as internal combustion vehicles [5]. Additionally, EVs produce zero emissions, making them a more environmentally friendly option than traditional vehicles [6].…”