To enhance the reliability of industrial wireless sensor networks (IWSNs), this study develops a state-based continuous-time Markov model for duplex and triplex architectures equipped with warm and cold standby backup sensors. The proposed model explicitly defines the state space, transition structure, and failure–repair dynamics for one, two, and three simultaneous sensor failures. Failure rates β are assumed identical across nodes, while repair and replacement rates r are modeled as state-dependent, allowing the system to accelerate maintenance when multiple failures occur. Warm and cold standby units follow different failure intensities, and numerical stability conditions (e.g., β < r) are ensured for steady-state availability. In the triplex configuration, two sensors operate in parallel, while the third remains in cold standby. Upon failure of any active sensor, the standby node is activated, and the faulty unit transitions to the repair state. This reversible mechanism enables continuous operation without service interruption. The model incorporates sleep–wake scheduling, parallel activation, and priority-based repair, which collectively improve the repair and replacement rates. Analytical results demonstrate enhanced reliability, reduced downtime, and improved fault tolerance compared with classical Markov redundancy models. Sensitivity analysis highlights the impact of repair acceleration and standby activation on system availability. The proposed framework provides a practical basis for maintenance planning and reliability optimization in duplex and triplex IWSN architectures.
This study investigates the outage probability of a multiple reflective intelligent surface (M-RIS)–assisted bidirectional full-duplex (FD) wireless communication system operating over a Nakagami-m channel fading environment. The proposed system facilitates information exchange between source nodes through a single, optimally selected RIS from the available M-RIS within a single time slot. The optimal RIS selection strategy aims to maintain service quality and improve user experience. Self-interference is a significant challenge in FD systems. However, this issue can be effectively addressed by increasing the number of RIS panels M, and the number of reflective elements Nm per panel. Initially, closed-form analytical expressions are derived for the statistical characteristics of the RIS channel model, assuming independent and non-identically distributed (i.ni.d.) elements within the Nakagami-m fading environment. Subsequently, a closed-form analytical expression for the outage probability is established. Furthermore, the asymptotic outage probability is derived in the high-signal-to-noise ratio (SNR) regime, providing insights into coding gain and diversity order. The numerical results demonstrate that augmenting the number of reflective elements and employing multiple intelligent reflective surfaces significantly enhances system performance, yielding an improved SNR compared to No-RIS, single RIS-FD/HD, and greedy RIS positions. The analytical expressions for system parameters were validated for accuracy through comparison with Monte Carlo simulations.
Cluster-based routing in wireless sensor networks (WSNs) remains constrained by rapid energy depletion of cluster heads (CHs), unstable multihop links, and unbalanced traffic forwarding toward the sink. This article proposes a fuzzy multipath routing (FMR) protocol that jointly improves CH election and intercluster forwarding reliability. In the setup stage, CHs are selected using a fuzzy decision process that integrates residual energy, node degree, and distance to the sink to promote energy-balanced and well-distributed clustering. In the routing stage, each CH discovers multiple feasible paths to the sink under a maximum-hop constraint and then ranks candidate paths using a fuzzy scoring function that considers route energy stability and sink proximity. To enhance reliability and mitigate early path exhaustion, FMR splits each data packet into equal-sized segments and forwards them concurrently over the top-ranked stable paths, with any remainder assigned to the final path. MATLAB-based simulation compares FMR with M-LEACH and HEED over 100 rounds, evaluating residual energy, dead nodes, throughput, and delay. Results demonstrate that FMR achieves up to 40% higher throughput and 50% lower average delay compared to conventional protocols, while maintaining a more balanced energy distribution and prolonging network lifetime. These findings highlight the potential of fuzzy logic and multipath routing in enhancing WSN performance under static deployment scenarios.
Collaborative cyber threat intelligence systems require secure information sharing across distributed organizations while preserving data privacy and preventing unauthorized access. However, conventional centralized cybersecurity architectures introduce significant privacy risks, limited trust management, and increased vulnerability to data leakage and adversarial manipulation. Although federated learning (FL) enables decentralized collaborative model training without direct data sharing, federated environments remain vulnerable to poisoning attacks, inference attacks, unauthorized participation, and heterogeneous non-IID data distributions. This paper proposes an integrated federated learning and zero-trust architecture (FL–ZTA) framework for privacy-preserving collaborative cyber threat intelligence sharing. The proposed framework combines decentralized federated learning, continuous zero-trust verification, trust-aware aggregation, adaptive policy enforcement, and secure collaborative coordination mechanisms to improve both privacy preservation and distributed cybersecurity resilience. The proposed framework was evaluated using the CIC-IDS2017 cybersecurity dataset under both IID and non-IID federated environments. Experimental analysis compared FedAvg, FedProx, and differentially private federated averaging (DP-FedAvg) within the proposed FL–ZTA architecture. The results demonstrate improved resilience against poisoning and inference-based attacks while maintaining stable collaborative learning performance under heterogeneous federated conditions. Furthermore, the proposed framework improves secure organizational collaboration by integrating continuous trust verification and adaptive federated coordination without requiring centralized raw data sharing. The findings demonstrate the potential of FL–ZTA architectures for privacy-preserving collaborative cybersecurity systems operating across distributed and security-sensitive infrastructures.
Fog Radio Access Networks (RANs) impose stringent latency requirements in heterogeneous networks. Addressing latency at the MAC layer based on traffic priority stands out as a crucial strategy in the Internet of Things (IoT) environment. Common techniques include data compression, analyzing the correlation between data values, node distance considerations, and data fragmentation based on the priority assignments. This paper presents a comparative analysis of urgMAC and FROG-MAC for their latency performance for fog-to-cloud interaction (F2C); both protocols have been developed to improve the delay associated with heterogeneous traffic, which is a core challenge in the fog RANs. Through our investigation, we reveal that FROG-MAC, owing to its innovative design, exhibits superior performance compared to urgMAC in fog environments. Furthermore, our study also evaluates the fragmentation scheme introduced by FROG-MAC by conducting a comprehensive evaluation to identify the optimal fragment size.
