The convergence of intelligent transportation, digital twin (DT) ecosystems, and vehicular edge intelligence is reshaping smart cities. With the rise of autonomous and context-aware vehicles, ultra-reliable, low-latency, and energy-efficient task orchestration frameworks are crucial in Internet of Vehicles (IoV) networks. In mission-critical situations, such as emergency response routing, disaster relief, and high-priority health transport, semantic task offloading must account for trust, adapt to high vehicle mobility, edge node reliability, and energy limitations. This paper proposes a quantum-inspired meta Proximal Policy Optimization (Qi-mPPO) algorithm for trust-aware semantic offloading in DT-enabled IoV systems with wireless energy harvesting. The approach leverages variational quantum circuits integrated with meta-learning to address non-stationary environments. A multi-objective reward function is formulated to jointly optimize latency, energy efficiency, semantic accuracy, and trust preservation. To ensure semantic relevance under vehicular mobility and energy constraints, quantum-informed policy regularization is applied. Simulation results demonstrate that Qi-mPPO outperforms classical and meta-reinforcement learning baselines in convergence rate, task latency, trust robustness, and energy consumption under realistic vehicular conditions.

Blockchain technology has found applications across diverse domains owing to its ability to establish trust in a decentralized manner. Nevertheless, the integration of blockchain into critical infrastructure domains encounters significant challenges posed by the computational demands and storage requirements associated with the proof-of-work puzzle during the mining process. This scenario becomes particularly complex in the context of applications within the Industrial Internet of Things (IIoT), where stringent timeliness constraints are inherent, notably in functions such as intrusion detection and control. This paper presents a novel solution that takes into account the time-sensitive nature of application constraints within the IIoT. Specifically, we focus on online functions involving intrusion detection and control. By doing so, we address the imperative need for timely and secure data delivery, crucial in maintaining the integrity of hard-to-tamper ledger blocks. These blocks encapsulate measurements that are seamlessly utilized by various system functions and components. The proposed approach optimizes the utilization of heterogeneous resources governing blockchain computations. This optimization ensures that the desired properties for logging within the blockchain are met, enabling the prompt delivery of measurements. The novel collaborative mining technique entails the sharing of nonce ranges among miners, which effectively reduces the overall mining time and enhances the efficiency of the process.

With the rapid growth of the Internet of Things (IoT) applications in Maritime Transportation Systems (MTS), cyber-attacks and challenges in data safety have also increased extensively. Meanwhile, the IoT devices are resource-constrained and cannot implement the existing security systems, making them susceptible to various types of debilitating cyber-attacks. The dynamics in the attack processes in IoT-enabled MTS networks keep changing, which makes a traditional offline or batch ML-based attack detection systems intractable to apply. This paper provides a novel approach of using an adaptive incremental passive-aggressive machine learning (AI-PAML) method to create a network attack detection system (NADS) to protect the IoT devices in an MTS environment. In this paper, we propose an NADS that utilizes a multi-access edge computing (MEC) platform to provide computational resources to execute the proposed model at a network end. Since online learning models face data saturation problems, we present an improved approximate linear dependence and a modified hybrid forgetting mechanism to filter the inefficient data and keep the detection model up-to-date. The proposed data filtering ensures that the model does not experience a rapid increase in unwarranted data, which affects the model's attack detection rate. A Markov transition probability is applied to control the MEC selection and data offloading process by the IoT devices. The performance of the NADS is verified using selected benchmark datasets and a realistic IoT environment. Experimental results demonstrate that AI-PAML achieves remarkable performance in the NADS design for an MTS environment.

With the growth of the Internet of Vehicles (IoV) in Intelligent Autonomous Transport Systems (IATS), a huge volume of data is exchanged between vehicles in these newly developed infrastructures. As a result, the requirements of securing data exchange between vehicles, autonomous or otherwise, have also increased tremendously. Securing data transfer and keeping a record of each transaction becomes a necessity in IoV/IATS. In this paper, we propose some optimized security algorithms using symmetric encryption for secure multimedia data transfer between vehicles. The main feature of these optimized algorithms is that they use a lower amount of data to generate fingerprints. The algorithms convert approximately$3.7 \times 10^{5}$samples of data into 3600 samples to generate the fingerprint. Fast Fourier Transform (FFT) is used to fetch the highest three peak values of the signal in the frequency domain. A centralized server authenticates the data transfer by comparing the$HASH$of the fingerprints and also keeps the transaction record. Through experimental analysis, the performance of proposed algorithms is confirmed by achieving reduced size samples to generate fingerprints and their authentication at the server-side.

With the advent of Connected and Autonomous Vehicles (CAVs) comes the very real risk that these vehicles will be exposed to cyber-attacks by exploiting various vulnerabilities. This paper gives a technical overview of the H2020 CARAMEL project (currently in the intermediate stage) in which Artificial Intelligent (AI)-based cybersecurity for CAVs is the main goal. Most of the possible scenarios are considered, by which an adversary can generate attacks on CAVs, such as attacks on camera sensors, GPS location, Vehicle to Everything (V2X) message transmission, the vehicle's On-Board Unit (OBU), etc. The counter-measures to these attacks and vulnerabilities are presented via the current results in the CARAMEL project achieved by implementing the designed security algorithms.

Physiological signals are of great importance for clinical analysis but are prone to diverse interferences. To enable practical applications, biosignal quality issues, especially contaminants, need to be dealt with automated processes. For example, after processing surface electromyography (sEMG), fatigue analysis can be done by looking into muscle contraction and expansion for clinical diagnosis. Contaminants can make this diagnosis difficult for the clinician. In real scenarios, there is a possibility of the presence of multiple contaminants in a biosignal. However, most of the work done until now focuses on the presence of a single contaminant at a time. This paper proposes a new method for the identification and classification of contaminants in sEMG signals where multiple contaminants are present simultaneously. We train a 1D convolutional neural network (1D-CNN) to classify different contaminant types in sEMG signals without prior feature extraction. The network is trained on simulated and real sEMG signals to identify five types of contaminants. Additionally, we train and test 1D-CNN to identify multiple contaminants when present simultaneously. Furthermore, to securely and accurately transfer the data to the clinician, we also present experimental results to securely route the data in a proposed Internet of health things (IoHT) by using received signal strength indicators (RSSI) to generate link fingerprints (LFs). The results show higher accuracy of the classification system at low signal-to-noise ratios (SNR) and witness lightweight security of the IoHT.

Attacks on the GPS receiver of Connected and Autonomous Vehicles (CAV) and specifically GPS location spoofing is of great concern for the automotive industry as the attacker can compromise the security of CAVs leading to serious repercussions for the drivers and pedestrians. Attack detection solutions based on specialized hardware (e.g., antenna arrays) and satellite signal processing techniques are accurate, yet bulky and expensive to mount on CAVs. Thus, lightweight and cost-effective solutions for detecting location spoofing attacks are highly desirable. This work presents an in-vehicle attack detection solution that fuses multi-source data readily available from the CAV's onboard sensors. It can be implemented in software running on cheap embedded computing platforms integrated into the CAV. The proposed solution is validated using the real-time CARLA simulator, while extensive experimental results demonstrate its effectiveness under different attack scenarios.

Vehicle to vehicle (V2V) communication has gained importance in recent times because of the increasing number of traffic accidents and advancements in information sharing. A secure and reliable data transfer has become important to ensure the safety and trust of vehicular network users. Because of scalability of V2V communication, proposed solutions must have low computational complexities and free from latency issues. In this paper, we utilize the channel characteristics of wireless networks in V2V communication, which are used to generate link fingerprints. By using blockchain technology, data authentication among vehicles can be achieved in real time. The proposed algorithms are used to address the time complexity and delay issues in the Internet of Vehicles (IoV), which are lightweight and provide real time adversary detection within the network. Blockchain technology is used to generate blocks in which each hash is generated and shared with corresponding vehicles. The hash itself is not generated if an adversary affects the communication among vehicles. The Pearson Correlation Coefficient is calculated for each link and it is calculated as 0.9749 when there is no adversary and 0.1282 when an adversary is introduced into the network. The time complexity is computed as low asO(1) for the network.

Internet of Things (IoT) is an emerging networking paradigm that enhances smart device communications through Internet-enabled systems. Due to massive IoT devices connectivity with economic and greenhouse emission effects, the energy-efficiency poses critical concerns. Under imperfect channel state information (CSI), this article investigates joint optimization of user selection, power allocation, and the number of activated base station (BS) antennas of multiple IoT devices considering the transmit power and different Quality-of-Service (QoS) requirements in combinatorial mode to maximize energy-efficiency. The optimization problem formulated is a nonconvex mixed-integer nonlinear programming, which is NP-hard with no practical solution. The primal optimization problem is transformed into a tractable convex optimization problem and separated into inner and outer loop subproblems. This article proposes a joint energy-efficient iterative algorithm, which utilizes a successive convex approximation technique and the Lagrangian dual decomposition method to achieve near-optimal solutions with guaranteed convergence. The simulation results are provided to evaluate the proposed algorithm and its significant performance gain over the baseline algorithms in terms of energy-efficiency maximization.

Smart meters (SMs) in Advanced Metering Infrastructure (AMI) are physically accessible due to which the protection against eavesdropping of AMI and energy theft control have gained utmost importance. A light-weight security solution is the requirement for AMI networks because of the small size and less computational capabilities of SMs. To address this problem, a light-weight security solution is proposed in this paper to detect any adversarial node in between two SMs. Through the proposed algorithms, adversarial node can be detected in AMI. Received Signal Strength Indicator (RSSI) is used to generate link fingerprints after every 60 seconds, which are sent to the Data Concentrator Unit (DCU). The DCU applies these algorithms on the received link fingerprints in order to detect any unwanted activity. MICAz motes are used as communication module of SM and adversary to generate RSSI values. These RSSI values are simulated in MATLAB in which it detects adversarial node or meter tempering with 100% accuracy by getting values other than 0 and 1 as the average of consecutive RSSI and distance between the RSSI of connected SMs.