Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.

Rate splitting multiple access (RSMA) is a powerful multiple access technology that enables communication systems to achieve both reliable and fair data transmission by splitting and encoding user messages into common and private streams. This capability is particularly critical for space-air-ground-sea (SAGS) integrated networks, where heterogeneous nodes (e.g., satellites, UAVs, and underwater sensors) coexist with significant channel quality disparities. The common stream is formed by consolidating the diverse common messages that all users can decode, allowing the system to balance resource allocation and maintain reliable connectivity even for users with weaker channel conditions. Nevertheless, the performance of the common stream is often limited by the user with the weakest channel strength, a prevalent challenge in SAGS integrated networks with mixed near-far field communications and dynamic topology. To address this issue, this study proposes a generalized RSMA strategy to mitigate the rate limitation of common streams in RSMA systems, thereby enhancing system fairness and overall performance. The solution holds potential for crossdomain applications where strong and weak maritime/aerial users share spectrum resources. Furthermore, an algorithm is designed to optimize Max-min fairness (MMF) rate among all users. This is formulated as a non-convex optimization problem, which poses significant challenges for direct solution. To tackle this challenge, we design a low-complexity suboptimal iterative algorithm employing the successive convex approximation (SCA) method. Simulations demonstrate that the proposed generalized RSMA system outperforms traditional one-layer RSMA system and other existing counterparts, particularly in systems with weak users, by effectively enhancing the MMF rate and overall system fairness. This improvement suggests broader applicability for future integrated networks requiring unified management of heterogeneous links.

Wireless sensor networks (WSNs) are vital for addressing vehicle-related challenges information management, congestion, and safety in Intelligent Transportation Systems (ITS). Ensuring reliable communication is critical, particularly in urban environments where real-time data from roadside infrastructure enhances traffic flow efficiency and safety. This paper proposes R2Com, a reliable and resilient communication protocol for duty-cycled Software-Defined Wireless Sensor Networks (SDWSNs), specifically designed for urban traffic monitoring. By integrating reliable routing and adaptive duty cycling, R2Com ensures low-latency, energy-efficient data exchange between vehicle detection units and traffic control centers. The protocol leverages four attributes: direct trust, recommended trust, signal-to-interference noise ratio, and residual energy, considering their probability distributions to ensure reliability and resilience in the data plane communication. Secondly, the SDN controller calculates these attributes alongside the Expected Duty Cycled Wake-ups (EDC), enhancing reliability through flexible management and low latency. It then assigns communication strategies to each node through reliable nodes and limits the number of forwarding nodes per node to reduce packet duplication. Simulation results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of average energy consumption, packet delivery ratio, average latency, network lifetime, communication overhead, packet success ratio, reliable coverage degree, coverage percentage, traffic density estimation accuracy, and intersection congestion levels.

IoT-based wireless sensor networks (WSNs) face significant energy constraints, which can be alleviated by wireless power transfer (WPT) technology. Integrating WPT with WSNs creates wireless rechargeable sensor networks (WRSNs), where optimizing charging efficiency and scheduling is critical. This paper introduces an ISAC-facilitated optimal on-demand mobile charging scheme for IoT-based WRSNs (IOMSN) with three key components. First, it presents an ISAC-assisted prioritized charging queue, incorporating four attributes with probability distributions: residual energy, traffic load, MCV travel time, and direction angle. Second, it provides ISAC-driven estimations of MCV distance, speed, and location to enhance prioritization, thereby optimizing the charging route and potentially reducing travel costs. Third, a time-allocated partial charging model improves charging efficiency. Numerical results show that the proposed protocol outperforms cutting-edge protocols in energy usage efficiency, travel distance, charging delay, and service time.

The internet of things (IoT) and wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The attributes are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, charging coverage, survival rate, travel distance, queue length, and service time.

The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The distributions are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, survival rate, and travel distance.

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This article proposes a hierarchical protocol called territory-bound data delivery (TBDD) that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption (EC) throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last, we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, EC, delay, network lifetime, and success ratio.

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks.

Cross-modal retrieval has attracted great attention due to the increasing demand for tremendous amounts of multimodal data in recent years. These retrievals could either be text-to-image or image-to-text. To address the problem of inappropriate information included between images and texts, we propose two cross-modal recovery techniques established on a dual-branch neural network defined on a common subspace and the hashing learning method. First, a cross-modal recovery technique established on a multilabel information deep ranking model (MIDRM) is provided. In this method, we introduce a triplet-loss function into the dual-branch neural network model. This function takes advantage of the semantic information of the bimodal components, focusing on not only the similarities between similar images and text features but also the distances between dissimilar images and texts. Second, we establish a new cross-modal hashing technique said to be the deep regularized hashing constraint (DRHC). In this method, the regularized function is used to replace the binary constraint, and the discrete value is constrained to a certain numerical range so that the network can achieve end-to-end training. Overall, the time complexity is greatly improved, and the occupied storage space is also greatly reduced. Different experiments on our proposed MIDRM and DRHC models demonstrate their superior performance to those of the state-of-the-art methods on two widely used data sets. The experimental results show that our approach also increases the mean average precision of cross-modal recovery.

Traditional approaches generally focus on the privacy of user’s identity in a smart IoT environment. Privacy of user’s behavior pattern is an important research issue to address smart technology towards improving user’s life. User’s behavior pattern consists of daily living activities in smart IoT environment. Sensor nodes directly interact with activities of user and forward sensing data to service provider server (SPS). While availing the services provided by a server, users may lose privacy since the untrusted devices have information about user’s behavior pattern and it may share data with adversary. In order to resolve this problem, we propose a multilevel privacy controlling scheme (MPCS) which is different from traditional approaches. MPCS is divided into two parts: (i) behavior pattern privacy degree (BehaviorPrivacyDeg), which works as follows: firstly, frequent pattern mining‐based time‐duration algorithm (FPMTA) finds the normal pattern of activity by adopting unsupervised learning. Secondly, patterns compact algorithm (PCA) is proposed to store and compact the mined pattern in each sensor device. Then, abnormal activity detection time‐duration algorithm (AADTA) is used by current triggered sensors, in order to compare the current activity with normal activity by computing similarity among them; (ii) multilevel privacy design model: we have divided privacy of users into four levels in smart IoT environment, and by using these levels, the server can configure privacy level for users according to their concern. Multilevel privacy design model consists of privacy‐level configuration protocol (PLCP) and activity design model. PLCP provides fine privacy controls to users while enabling users to set privacy level. In PLCP, we introduce level concern privacy algorithm (LCPA) and location privacy algorithm (LPA), so that adversary could not damage the data of user’s behavior pattern. Experiments are performed to evaluate the accuracy and feasibility of MPCS in both simulation and real‐case studies. Results show that our proposed scheme can significantly protect the user’s behavior pattern by detecting abnormality in real time.

Opportunistic routing (OR) is gaining popularity in low-duty wireless sensor network (WSN), so the need for efficient and reliable data transmission is becoming more essential. Reliable transmission is only feasible if the routing protocols are secure and efficient. Due to high energy consumption, current cryptographic schemes for WSN are not suitable. Trust-based OR will ensure security and reliability with fewer resources and minimum energy consumption. OR selects the set of potential candidates for each sensor node using a prioritized metric by load balancing among the nodes. This paper introduces a trust-based load-balanced OR for duty-cycled wireless sensor networks. The candidates are prioritized on the basis of a trusted OR metric that is divided into two parts. First, the OR metric is based on the average of four probability distributions: the distance from node to sink distribution, the expected number of hops distribution, the node degree distribution, and the residual energy distribution. Second, the trust metric is based on the average of two probability distributions: the direct trust distribution and the recommended trust distribution. Finally, the trusted OR metric is calculated by multiplying the average of two metrics distributions in order to direct more traffic through the higher priority nodes. The simulation results show that our proposed protocol provides a significant improvement in the performance of the network compared to the benchmarks in terms of energy consumption, end to end delay, throughput, and packet delivery ratio.

The security is a critical issue in wireless sensor networks (WSNs). A complex form of Denial of Service attack type is collaborative black hole attacks which challenge the security of wireless sensor networks. The purpose of this attack is to receive and drop all packets. Wireless sensor network nodes have limited energy and also have limited processing capability. WSNs devices have limited resources and they are particularly susceptible to the destruction and consumption of these limited resources. Collaborative black hole attacks are effective to partition the networks so the important data do not reach to base station. To secure wireless sensor network from collaborative black hole attacks, many security techniques have been proposed, most of them are energy inefficient and complex. In this paper, we discuss various techniques which detect and prevent the collaborative black hole attacks in WSNs and proposed a cluster-based energy preserving detection model of WSNs security against collaborative black hole attacks.