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.
Anomaly detection plays a critical role in mitigating cybersecurity threats, particularly Distributed Denial of Service (DDoS) attacks. This study evaluates the performance of tree-based and ensemble learning models, including Decision Tree, Random Forest, and XGBoost, for classifying Snort log data, alongside the application of Isolation Forest for time-series anomaly detection. The experiments were conducted using ICMP-based Ping Flood attacks in a controlled network environment, with data collected from Snort intrusion detection system logs. The classification results indicate that XGBoost achieved the highest performance, with 99.81% accuracy, 99.93% precision, 99.65% recall, and 99.79% F1-score under a 70-30 train-test split. Random Forest and Decision Tree also demonstrated strong performance, while Logistic Regression showed lower effectiveness due to its limitations in modeling nonlinear patterns. For anomaly detection, Isolation Forest was applied to time-series data collected over a 19-day period. The model detected 93 anomaly points, of which 41 overlapped with Wireshark-confirmed events. However, a false positive rate of 41.67% was observed, indicating the need for parameter tuning to balance detection sensitivity and operational efficiency. Overall, the findings demonstrate that ensemble-based learning approaches, particularly XGBoost, are effective for detecting DDoS-related patterns within the experimental setting. However, the results are limited to ICMP-based attack scenarios in a controlled environment. Further validation, including cross-validation, multi-attack evaluation, and deployment-level performance analysis, is required to assess generalizability and practical applicability.
An improved Raft consensus algorithm called AAR-Raft (auction and reputation-replicated and fault tolerant) is proposed in this study to meet the dual requirements of timely synchronization and secure uplink of rescue information in UAV-swarm rescue models. By introducing a reputation-based evaluation mechanism and a leader authentication scheme, AAR-Raft achieves an effective balance between security and efficiency in leader election. Additionally, a reputation-driven reward-and-penalty strategy combined with joint authentication among leader nodes further lowers the long-term risk posed by persistent Byzantine nodes. Meanwhile, a secret-sharing scheme established on RSA encryption provides the necessary privacy protection for uploading rescue information. Compared with other consensus algorithms, AAR-Raft achieves superior leader-election efficiency while yielding significant reductions in consensus latency and notable improvements in overall system throughput. Furthermore, AAR-Raft still maintains consistently low task-completion latency under adversarial scenarios such as collusive and adaptive attacks, thus validating its robustness under complex network conditions.
The rapid expansion of the Industrial Internet of Things (IIoT) presents pressing challenges for secure, efficient, and privacy-preserving communication among users, sensors, and cloud infrastructures. Existing lightweight authentication schemes, primarily based on classical cryptographic assumptions, are increasingly vulnerable to the emerging threat of quantum computing. To address these challenges, we propose a lightweight, quantum-resilient, and privacy-preserving mutual authentication scheme tailored to the IIoT ecosystem. The proposed scheme integrates the learning with errors (LWE) assumption to achieve postquantum secure authentication and identity protection, hash functions for message integrity, and ephemeral elliptic curve Diffie-Hellman (ECDH) to provide classical forward secrecy within a hybrid security model. A novel use of ephemeral pseudonyms further enhances unlinkability and resilience against traceability attacks. The security of the scheme is established through informal analysis, covering resistance to forgery, impersonation, replay, man-in-the-middle, and key compromise impersonation attacks, and through formal analysis in both the random oracle model (ROM) and BAN logic, proving mutual authentication and secrecy properties. Performance evaluation demonstrates that the scheme achieves low computational cost for resource-constrained sensors (approximate to 4 ms) and practical communication overhead while maintaining comprehensive security features superior to existing solutions. These results highlight that the proposed scheme provides a robust, efficient, and deployable framework for postquantum secure authentication in IIoT ecosystems.
With the large-scale popularity of wireless terminals, user equipment (UE) puts forward higher requirements for wireless network service quality. By using device-to-device (D2D) communication technology for cooperative relay transmission, any UE can be used as a potential relay UE (RUE). Making RUEs relay data for other UEs can improve the total throughput of the network. Considering the energy consumption of data relay transmission, this paper proposes a novel relay selection and resource allocation method for an energy harvesting (EH) cognitive D2D network based on a coalition game. This method first analyzes the resource allocation scheme for RUEs, either in underlay or interweave cognitive mode. Secondly, the energy harvesting incentive mechanism is used to encourage the RUE to obtain additional time to collect energy as a reward during the relay process, which can improve the willingness of the RUE to establish D2D relay links for other UEs. Finally, according to the EH incentive mechanism, the coalition comparison rules are determined. The cooperative behavior between UE and RUE is analyzed based on the coalition game, and then the relay selection problem of cognitive D2D network communication is solved. The result of the coalition game divides the user set into several subcoalition partitions, and the coalition is equivalent to the relay selection result. Simulation results show that compared with the traditional method, the proposed relay selection and resource allocation method based on a coalition game can improve the throughput of the whole network.
OpenFlow, a key data plane protocol in software-defined networking (SDN), relies heavily on ternary content addressable memory (TCAM) to achieve high-speed packet matching. However, as OpenFlow evolves to support a broader set of match fields, the demand for TCAM storage grows significantly due to its support for arbitrary wildcards. To address this challenge, this paper introduces DECTCAM, a mathematical model designed to decrease TCAM space consumption without compromising OpenFlow’s flexibility or functionality. DECTCAM identifies and classifies redundancies across match fields into three distinct types and applies tailored compression strategies interfield merging, field mapping, and intrafield compression to optimize flow entry representations. These techniques produce more compact flow entries, which are then deployed to TCAM for efficient matching. Simulation results demonstrate that DECTCAM can decrease TCAM usage by nearly 78%, with consistent performance even as the flow table scales, all while preserving the original behavior of the OpenFlow rules.
Altitude chambers are used to train crews of the military and civil aviation by creating a high-altitude environment in which the vital signs of the people in the chamber are monitored to assess their health and identify symptoms that indicate there may be a physical condition that needs to be managed. Human observers monitor the process and react when required to avoid health damage to the people in the altitude chamber. However, considering that human observers may miss some effects and are slower to react than an automated device, this work proposes designing and implementing a cyberphysical system to automate the monitoring process while protecting the collected data. The system includes an Internet of Things (IoT)-based glove with a sensor that measures pulse rate and oxygen saturation and sends data, via MQTT, to a gateway that activates an alert if a crew member inside the chamber experiences hypoxia symptoms. The system also includes servers to store and process the collected data. Since the system collects sensitive health-related data, its design must follow architectural principles for building secure IoT-based healthcare applications. Most papers on IoT-based healthcare application security only identify design principles without applying them to specific implementations, or work with isolated components of a larger end-to-end system. This work, in contrast, addresses the design and implementation of a system that considers the complete end-to-end architecture of an IoT-based healthcare application. Furthermore, considering computational and storage limitations of IoT devices, our prototype runs ASCON, a lightweight encryption algorithm, to provide encryption and hashing functions that secure data exchange between IoT devices and processing servers. Our design meets the functional and security requirements and can be used as a use case to enhance developers’ understanding of the design and implementation principles to build secure IoT-based healthcare applications.
Industrial control systems (ICS) are increasingly vulnerable to cybersecurity threats due to the integration of IoT devices and operational technology networks. Traditional centralized anomaly detection methods face significant challenges, including privacy concerns, bandwidth limitations, and single points of failure. Federated learning (FL) offers a promising alternative by enabling collaborative model training across distributed devices without sharing raw data. However, FL in industrial settings introduces challenges such as communication overhead, resource heterogeneity, and the risk of Byzantine failures. This study proposes a comprehensive framework for efficient and secure FL-based anomaly detection in ICS. We introduce two novel compression techniques, weight clustering with adaptive bit allocation (WC-ABA) and energy-aware knowledge distillation (EA-KD), to reduce communication overhead while maintaining detection accuracy. Additionally, we implement differential delta compression with adaptive clustering and entropy coding, which further minimizes data transmission by sending only the differences between model updates. Our robust aggregation mechanism, SparseVariance Aggregator, filters out suspicious updates and ensures model integrity. The framework also integrates differential privacy techniques to protect against inference attacks. Evaluated on the Secure Water Treatment (SWaT) and Water Distribution Attack Dataset (WADI) datasets, our approach demonstrates superior anomaly detection performance, reduced communication costs, and lower computational demand compared to centralized alternatives. The results highlight the effectiveness of our framework in enhancing the security and efficiency of ICS environments.
The sixth generation (6G) wireless networks are used to provide a large coverage, and also this generation needed to support different emerging applications in harsh areas with higher user equipment mobility. For the above case, a new modulation technique called orthogonal time-frequency space (OTFS) modulation has emerged as a promising technique to mitigate the challenges in higher-Doppler channels. It is a two-dimensional modulation scheme in which information symbols are encoded in the delay-Doppler domain. For this case, the channel is also implemented in the delay-Doppler domain. In this study, we investigate channel estimation and signal detection of OTFS for the case of fractional Doppler channels with lower computational complexity using pilot-based iterative path estimation and maximum ratio combining (MRC) signal detection. We simulate the error rate performance of the investigated channel estimation and signal detection in a fractional Doppler shift. And also, we simulate the average iteration performance, indicating the complexity in terms of the number of iterations needed to detect the signal.
The convergence of the Internet of Things (IoT) with cloud, edge, and fog computing has catalyzed a transformative shift in data processing and real-time analytics across multiple sectors. This review explores the emerging IoT and cloud continuum, emphasizing its role in enhancing system scalability, reducing latency, and enabling intelligent, distributed decision-making. A central contribution of this work is the design and implementation of a real-time greenhouse monitoring and control system based on an FPGA platform, specifically the DE2-115 development board with a Cyclone IV EP4CE115F29C7 device. The system integrates real-time environmental sensing based on temperature, humidity, soil moisture, and light status with dynamic control of actuators such as fans, water pumps, LEDs, and humidifiers. Operating in both manual and autonomous modes, the system demonstrates high efficiency, utilizing only 2% of logic elements and < 3% of logic array blocks, while providing robust data visualization via onboard displays and a mobile interface. This practical implementation is contextualized within the broader IoT-cloud continuum, illustrating how edge processing with FPGA complements cloud-based analytics for precision agriculture. Moreover, the review investigates the architectures and protocols that underpin the continuum, addressing challenges such as security, privacy, and interoperability. Special attention is given to the role of AI, machine learning, and predictive analytics in enhancing decision-making. The paper concludes with future research directions, highlighting the potential of emerging technologies including 5G, edge AI, and blockchain to overcome current limitations, enhance data processing, and drive innovation. By integrating real-world application with theoretical advancement, this work offers a scalable, efficient, and intelligent model for future smart systems in agriculture, healthcare, smart cities, and beyond.
Data privacy is a major concern in the present data-driven era when sensitive information is being increasingly shared and analyzed across distributed systems. The existing mechanisms for privacy preservation, such as differential privacy (DP), local differential privacy (LDP), homomorphic encryption, and secure multiparty computing (SMPC), often face challenges in maintaining a balance between utility and privacy, especially in dynamic and heterogeneous environments. This paper introduces partial DP as a flexible framework for addressing the above challenges across a variety of data settings, such as federated learning, decentralized systems (blockchain and IoT), graph data, and streaming analytics. Dynamic sensitivity differential privacy (DSDP) employs adaptive noise mechanism and dynamically adjusts the data sensitivity by ensuring the robust privacy without compromising data utility. The experimental evaluations on real-world datasets prove the superiority of DSDP over traditional approaches with minimal utility loss and high privacy guarantees at efficiency. DSDP is a promising solution in the evolving computer paradigms for data privacy protection. The proposed methods are carried out to evaluate the parameters such as execution time, utility loss, and privacy level. From the experimental results, DSDP achieves up to 15% higher utility than DP in federated learning, 30% reduced latency in decentralized systems, and 25% better structural integrity for graph data for privacy-preserving guarantees.
Channel models are the basis of system design, theoretical analysis, performance evaluation, optimization, and deployment of communication systems in wireless fading environments. LTE, a global standard for high-speed data transmission, relies on channel modeling techniques due to its multiple-input multiple-output (MIMO) architecture. In MIMO systems, the correlation coefficient is a key metric for evaluating antenna performance, particularly in terms of how effectively the system can exploit spatial diversity and multiplexing gains. This manuscript proposes a three-dimensional geometry channel modeling method based on the correlation of received signals in a multipath channel, aiming to enhance the performance of MIMO communication systems. We address the fading problem by employing diversity transmission with state-transparent modulation codes, thereby enhancing link robustness and performance in noisy environments. This method uses geometric diffusion models where poly-ellipsoid curves or surfaces define scattering locations. The model includes geometrical scattering for indoor environments. We establish an elliptical area and uniformly distribute a specified number of objects within it, taking into account the maximum excess delay and the distance between transmitters and receivers. The transmitter emits data as spherical waves, which scatter upon encountering objects before reaching the antennas. The statistical properties of the received data are derived from the calculated wave parameters, utilizing the common probability distribution function (PDF) of wave amplitudes. Furthermore, essential receiver parameters can be determined, such as signal-to-noise ratio, bit error rate for any antenna array, and correlation coefficients between antenna pairs. By utilizing correlation coefficients, we can more accurately represent the interdependencies between various parameters within the LTE framework. The proposed 2-GHz indoor 1 x 2 MIMO model demonstrates significant enhancements, including 53%-135% reduced SNR fluctuations, 27%-59% improved antenna decorrelation, and 25%-31% increased channel capacity compared to industry-standard models such as WINNER II, COST 2100, and QuaDRiGa. Furthermore, it achieves a near-ideal diversity order (1.82-2.0). These advantages stem from its physics-accurate modeling of spherical wave propagation and poly-ellipsoid scattering, making it suitable for reliable 5 G/6 G indoor deployments utilizing compact antenna arrays. The proposed model is generated using a ray-tracing simulation, with numerical results confirming its effectiveness.
This paper presents an integrated framework for intelligent agricultural monitoring and development by combining Internet of Things (IoT) technology, machine learning algorithms, sensor networks and custom hardware design. A comprehensive system was developed using environmental sensors including DHT11, soil moisture probes, BMP180 pressure modules, MQ-4 gas detectors, rain detection sensors and HC-SR04 ultrasonic modules, interfaced via custom-designed printed circuit boards (PCBs) fabricated using Proteus software. NodeMCU ESP8266, ESP32 DevKit and ESP32-CAM microcontrollers served as the hardware backbone for real-time data acquisition, wireless transmission, and image capture. Collected sensor data were transmitted to cloud platforms through Adafruit IO for remote visualization and analysis. Machine learning models, including Random Forest and XGBoost classifiers, were trained on features extracted from VGG16-based image processing to classify plant health conditions with high accuracy. Intelligent irrigation control was achieved through autonomous decision-making based on real-time sensor feedback and environmental conditions, dynamically activating a water pump system. The integration of low-power hardware, efficient PCB layouts, cloud-based dashboards, and lightweight machine learning models resulted in a scalable, portable, and cost-effective smart farming solution. Experimental results validate the system’s capability for accurate environmental monitoring, efficient resource utilization, and intelligent crop management, offering significant potential for sustainable agriculture in resource-constrained settings.
Advancements in communication technologies have enabled vehicles to be equipped with computing devices, facilitating communication and autonomous operations. This has led to the emergence of a new networking paradigm known as the vehicular ad hoc Network (VANET). A primary objective of VANET is to enhance road safety and traffic efficiency by enabling the exchange of information among vehicles in various intelligent transportation system (ITS) applications. Vehicles regularly transmit safety messages at a fixed rate, typically 10 messages per second. In high-traffic scenarios, such as multilane highways or densely packed areas, a vehicle may receive an overwhelming number of safety messages. However, before these messages can be reliably used, they must undergo rigorous cryptographic verification. A significant challenge arises when the message reception rate exceeds the verification rate, leading to inefficiencies. In existing schemes, the basic safety messages (BSMs) of nearby vehicles often undergo redundant verification due to consecutive broadcasts, while BSMs from more distant vehicles within the communication range may not receive adequate verification time. To address this issue, we propose a trust-based approach to improve the efficiency of message verification in VANET. Our simulation results demonstrate that the proposed method optimizes verification time by selectively skipping the verification of one BSM for trusted vehicles, utilizing the road side unit (RSU). This approach enhances vehicle awareness in compliance with the WAVE standard. The study findings indicate that the proposed method achieves an average awareness quality of 85% for neighboring vehicles, outperforming the existing MLPQ-CA method, which attains only 70% within the same 100-m communication range.
Fluctuations in traffic within the Internet of Things (IoT) can affect the performance of the control plane. It is important to maintain stable control plane performance by load balancing strategies. To address the issue of controller load balancing in software-defined Internet of Things (SD-IoT), and meet the energy consumption requirements of nodes in the IoT during the adjustment process, a load balancing algorithm based on multi-agent deep reinforcement learning (MADRL) is proposed. This approach models two critical factors: load difference and migration cost, and constructs a load balancing optimization problem based on these two factors. Subsequently, considering the dynamic changes in the state of the SD-IoT, the load balancing problem is formulated as a Markov game process, and an algorithm is designed based on MADRL to solve this problem. Finally, the algorithm is validated based on real-world topology, and a comparison is conducted from multiple perspectives including delay, load difference, energy consumption, and migration cost, demonstrating the effectiveness and advantages of the proposed algorithm.
Collecting data from nodes in mobile ad hoc wireless sensor networks is a persistent challenge. Traditional methods rely on specialized routing protocols designed for these environments, with research often aimed at improving efficiency in terms of throughput and energy consumption. However, these improvements are often interconnected, where gains in one area can lead to compromises in another. An alternative approach uses unmanned vehicles (UVs), particularly unmanned aerial vehicles (UAVs), due to their adaptability to various terrains. Unlike traditional methods, UAVs can collect data directly from mobile nodes, eliminating the need for routing. While most existing research focuses on static nodes, this paper introduces a multiple objective evolutionary approach "Strength Pareto Evolutionary Algorithm for Dynamic UAV Paths" (SPEA-DUP) for UAV data collection that predicts the future positions of caching-enabled mobile ad hoc wireless sensor network nodes. SPEA-DUP aims to maximize encounters with nodes and gather the most valuable data in a single trip. The proposed technique is tested across different simulation scenarios, movement models, and parameter configurations and is compared to our genetic algorithm 'Genetic Algorithm-Aerial Paths' (GA-AP) counterpart to evaluate its effectiveness.
