Vehicular Ad hoc Networks (VANETs) enable vehicles and roadside units (RSUs) to exchange safety-related information over public wireless channels, thereby enhancing transportation system security and efficiency. However, malicious adversaries may impersonate RSUs to disseminate false information or masquerade as legitimate vehicles to gain unauthorized services. To counter such threats, mutual authentication between vehicles and RSUs is crucial. This task is particularly challenging due to the high mobility of vehicles and the resource constraints of both vehicles and RSUs. In this paper, we propose an Efficient and Anonymous Authentication Scheme with Session Key Agreement (EA2S2KA), which leverages Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs) to achieve lightweight, fast, and secure authentication. We conduct an informal security analysis, a formal security proof under the real-or-random (RoR) model, and formal security verification using AVISPA, all of which confirm that EA2S2KA resists a broad range of security threats in VANETs. Performance comparisons with recently proposed schemes show that EA2S2KA provides stronger security guarantees while achieving the lowest total computation cost and ranking among the top three in communication efficiency. Furthermore, NS-3 simulations confirm its practicality in large-scale and dynamic environments through evaluations of the authentication success rate, average authentication delay, and authentication message throughput.

In vehicular Ad hoc networks (VANETs), vehicles and roadside units (RSUs) utilize open wireless channels to exchange safety-critical data, facilitating real-time decision-making for enhanced road safety and traffic management efficiency in intelligent transportation systems (ITS). However, the openness of these channels exposes them to various security threats. Malicious adversaries may impersonate RSUs to forge and distribute harmful commands, manipulating vehicular behavior, or masquerade as legitimate vehicles to bypass authentication protocols and gain unauthorized access. Such attacks jeopardize the security and functionality of the VANETs, underscoring the necessity of robust mutual authentication between vehicles and RSUs. Existing centralized trust authority (TA)-dependent schemes for vehicle-to-RSU (V2R) authentication incur high computational overhead, introduce authentication latency, and cause a single point of failure, particularly in dense traffic scenarios. To address these challenges, we propose ERAS2KN, an efficient and revocable authentication scheme with session key negotiation. By integrating Physical Unclonable Functions (PUFs) with lightweight cryptography, such as one-way hash functions, bitwise XOR, and symmetric encryption, ERAS2KN enables rapid mutual authentication and secure session key establishment. Comprehensive security analysis, including informal evaluation, formal security proof based on the Real-or-Random (RoR) model, and automated validation using AVISPA, confirms ERAS2KN’s resilience against vehicle impersonation, eavesdropping, vehicle/RSU compromise, man-in-the-middle, and other advance attacks. Performance evaluations demonstrate that ERAS2KN surpasses existing schemes by delivering enhanced security features while achieving the lowest computational overhead, communication overhead, and energy consumption cost, making it ideal for high-density VANETs environments.

Range-based localization technology achieves high accuracy under clear signal paths (Line-of-Sight, LOS). However, its performance deteriorates significantly due to errors in distance estimation when signals encounter obstructions, resulting in Non-Line-Of-Sight (NLOS) propagation. In light of these challenges, we investigate the combined effects of measurement noise and NLOS errors on target localization performance and propose a novel approach using Rotated Quadratic Cone Programming (RotQCP) for target localization in Wireless Sensor Networks (WSNs). By formulating the localization problem as a Maximum Likelihood (ML) estimation and employing relaxation techniques, we demonstrate that RotQCP can effectively address it even in the worst-case scenario. Compared to existing methods, the proposed approach eliminates the requirement for specific NLOS error statistics and delivers robust performance in sparsely and heavily congested NLOS environments. The simulation results demonstrate the efficacy of the proposed method in mitigating NLOS errors and attaining accurate localization. Moreover, the experimental outcomes based on open datasets substantiate the effectiveness of the proposed algorithm and indicate its superiority over existing algorithms. Notably, this research offers a robust and efficient solution for target localization in WSNs, particularly in a real harsh environment characterized by mixed LOS and NLOS propagation conditions.

This article presents a robust 3-D Received Signal Strength Difference (RSSD) localization algorithm under mixed Gaussian noise in Underwater Wireless Sensor Networks (UWSNs) with Non-Line-Of-Sight (NLOS) paths. To mitigate the adverse effects, concurrent to absorption and path losses on accurate underwater localization, an Efficient RSSD-based Iterative Estimator (ERIE) in mixed Gaussian noise and NLOS environments is proposed. First, the corresponding non-convex problem in such environments is formulated, and the direct solution to this problem is not tractable unfortunately. Considering underwater acoustic signal attenuation, an RSSD-based min-max strategy is designed to transform it into a problem minimizing the worst-case loss, combined with the Huber cost function, constitutes a Huber function-based equivalent problem (H-ADMM) solved by Alternating Direction Method of Multipliers (ADMM). A compensation matrix is designed based on the H-ADMM solution to compensate for the bias introduced by the transformation, and the corresponding Cramér-Rao Lower Bound (CRLB) is derived to provide a performance benchmark. Numerical results indicate that the proposed approach achieves a higher localization accuracy than state-of-the-art methods.

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques.