The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilities related to data breaches, privacy concerns, cyber threats, and trust management issues. Addressing these risks requires advanced security mechanisms, with machine learning (ML) emerging as a powerful tool for anomaly detection, intrusion detection, and threat mitigation. This survey provides a comprehensive review of ML-driven IoT security solutions from 2020 to 2024, examining the effectiveness of supervised, unsupervised, and reinforcement learning approaches, as well as advanced techniques such as deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL). A systematic classification of ML techniques is presented based on their IoT security applications, along with a taxonomy of security threats and a critical evaluation of existing solutions in terms of scalability, computational efficiency, and privacy preservation. Additionally, this study identifies key limitations of current ML approaches, including high computational costs, adversarial vulnerabilities, and interpretability challenges, while outlining future research opportunities such as privacy-preserving ML, explainable AI, and edge-based security frameworks. By synthesizing insights from recent advancements, this paper provides a structured framework for developing robust, intelligent, and adaptive IoT security solutions. The findings aim to guide researchers and practitioners in designing next-generation cybersecurity models capable of effectively countering emerging threats in IoT ecosystems.

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services.

Vehicular ad hoc networks (VANETs) are envisioned as the future of intelligent transportation systems, which enable various kinds of applications aiming at improving road safety and transportation efficiency. Uni-cast routing is required for many of these applications. As VANET is expected to be massive in terms of number of nodes and amount of generated information, geographic routing protocols are considered the most suitable for such network owing to their scalability. Due to VANETs' extremely dynamic topology and variable channel condition, multiple metrics related to vehicles' mobility, link quality, and bandwidth availability need to be considered to make more informed and reliable routing decisions. However, some of these metrics might oppose each other. While the main forwarding strategy in geographic routing selects nodes closer to the destination to maximize distance progress, these nodes are most probably located at the border of the communication range where the probability of link breakage increases. Furthermore, the continuous selection of these nodes without considering their available bandwidth might result in higher packet delays and losses. This paper proposes a novel routing protocol based on fuzzy logic systems, which can help in coordinating and analyzing contradicting metrics. The proposed routing protocol combines multiple metrics considering vehicles' position, direction, link quality, and achievable throughput to select the most suitable next-hop for packet forwarding. Results from our simulation experiments of relatively dense urban environments show remarkable performance improvements in terms of packet delivery ratio, end-to-end delay, and total network throughput.

Vehicular networking is an emerging technology that holds the great promise of supporting innovative safety and non-safety applications. Vehicular ad hoc networks (VANETs) share many of the same challenges that are encountered within more general mobile ad hoc networks (MANETs). However, by the very nature of the vehicular environment, each of these challenges may be of greater or lesser significance. Communication channel loading and changing network topology tend to be of greater concern. Battery, computing, and memory resource utilization tend to be of lesser concern. Cooperative caching is an approach that has been used extensively within MANETs to address various performance and resource challenges. Applied properly, cooperative caching can significantly reduce overall bandwidth utilization. However, within the vehicular environment, care must be taken to not overly rely on topographical knowledge during cache discovery. In this paper, we develop a unique taxonomy for cache discovery, survey a representative set of MANET-based cooperative caching schemes, and classify the associated cache discovery techniques within the taxonomy. Using this classification, we then highlight the various cache discovery techniques that have been utilized, analyze their potential in addressing the specific challenges that occur when deploying non-safety applications within VANETs, and identify general pitfalls that should be avoided. Future research can utilize the results of this analysis to develop novel cooperative caching schemes that will offer better performance within VANETs when compared to existing MANET-based cooperative caching schemes.

There is a rapid increase in the utilization of renewable sources such as solar and wind to provide power and electricity. The reason for this trend is to reduce costs and preserve the environment. However, the challenge is to efficiently use and store the energy from these sources. One approach is to optimize the decision when to charge or discharge a battery. The objective is to generate the greatest monetary gain. More charge/discharge cycles will reduce the life of a battery, thereby increasing the cost. A strategy to reduce the number of charge cycles while maintaining the effectiveness of electricity distribution from battery storage will improve battery life. With the inevitable proliferation of electric vehicles (EVs) in the market, strategies specific to electric vehicle battery profitability will be explored. An additional concern which threatens the financial feasibility of battery energy storage systems (BESSs) is the requirement of secure operation. There is currently little research study on strategies to detect cyberattacks on such systems. In this paper, we have presented a novel taxonomy for battery optimization, survey representative BESS utilization strategies, and classify these schemes within the taxonomy. Within our classification, we outline the battery optimization methods that have been discussed, analyze their ability to address issues that arise when implementing a BESS, and describe alternative research that could be explored. Future research could refer to this information to create unique battery optimization schemes to provide more efficiency and optimal revenue for a BESS when compared to current strategies.