Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks. VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections.

Artificial Intelligence of Medical Things (AIoMT) requires storing, preprocessing, monitoring, and analytics of large-scale sensor data fusion in the cloud. However, migrating to the cloud possesses intrinsic issues of cost, performance constraints, and sustainable computing. This research explores the potential of AIoMT in crafting intelligent models for daily activity patterns and predicting unusual occurrences. It delves into power-efficient and sustainable computing tailored for the IoT sensors, methods, and systems geared toward crafting digitally enhanced smart homes for the elderly. Fusion data is collected from heterogenous sensors to track daily patterns and processed for anomaly detection and alert generation. The AIoMT model has employed the time and energy minimization scheduler (TEMS) algorithm, which considers energy consumption, processing duration, data transmission expenses, and standby device power consumption. This enables local computing in the IoMT systems, mobile edge servers, and cloud controllers, promoting sustainability in healthcare. To optimize execution time and cost-effectiveness, task scheduling options include local Internet of Things devices, cloud infrastructure, and multiaccess edge computing (MEC). This approach could benefit digitally enhanced communities significantly, promoting low-carbon, power-efficient, sustainable computing (LCPESC). The LCPESC AIoMT approach demonstrates precision close to a 95% confidence level. Further, the proposed model is extended beyond individual households to encompass digitally augmented communities.