Redefining resilience: A hybrid quantum-fuzzy Deep Q-Network paradigm for perpetual wireless rechargeable sensor networks

HZBSO: A Hybrid Zebra–Butterfly–Snake Metaheuristic for Joint Energy-aware Clustering and Routing in Wireless Sensor Networks

A Hybrid Machine Learning and Deep Learning Framework for Accurate Indoor Cooperative Positioning in Wireless Sensor Networks

Abstract Time synchronization performance of sensors determines the overall efficiency of Wireless Sensor Networks (WSNs). A scientific topology can provide solid support for time synchronization mechanism. Traditional WSNs face the challenges of insufficient accuracy and excessive energy consumption. This paper proposes a time synchronization optimization algorithm based on constructing the nearest $K$-neighboring (NKN) network topology. First, the $\mathrm{k}$-means clustering method is employed to group all the nodes. Each node communicates with its $K$ nearest neighbors in one group. Then, a node identification mechanism is used to distinguish among reference nodes, neighborhood nodes and receiving nodes. Within each group, local time synchronization is achieved between receiving nodes and the reference node via two-way information exchange. Additionally, neighborhood nodes act as information gateways, forwarding synchronization data from reference nodes via the cross-domain interaction mechanism to achieve multi-region time synchronization. Experimental results show that the proposed topology requires less synchronization convergence time than some typical topologies. Compared with the classical time synchronization algorithm, the algorithm has higher synchronization accuracy and lower energy consumption.

There has been huge positive changes in smart infrastructure management due to the creation of systems that perform real time environmental tracking, and process cyber-physical data. These changes are apparent due to the combination of Wireless Sensor Networks (WSNs) with Internet of Things (IoT). Large WSN deployments face obstacles in scheduling, due to restricted energy supplies and operational environments being too hostile or inaccessible. Genetic Algorithms like Simulated Annealing and Artificial Bee Colony tends to perform in suboptimal standards as these algorithms fail to adapt well to environments that have energy depletion along with changing topologies. Hence, a new method for WSN scheduling known as RL-HAPSO, that utilizes Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) algorithms along with the adaptive capability of Q-learning Reinforcement Learning has been addressed in this paper. An energy-efficient node selection by ACO operates during the first phase, followed by PSO optimization, which improves coverage and minimizes redundancy before execution of real-time reinforcement learning algorithm that selects activation schedules based on network states. The model runs multiple simulations, and does performance validation by assessing its execution time and convergence cost along with energy utilization, which is compared to each algorithm independently and the also the hybrid model without RL implementation. Results indicate execution in microseconds interval by each algorithm, yet RL-HAPSO stands out, as it achieves better optimization costs through enhanced fault tolerance, coverage and minimal energy usage. During performance alterations the system automatically adjusts its operations leading to consistent robust behaviour, even in case of node failure and environmental variations. The obtained results indicate that this proposed methodology functions as a viable approach for future-generation IoT applications that support resource-aware and smart WSN scheduling.

Multi-hop networks’ performance strongly depends on relay node placement, which affects delay, throughput, and coverage. This work introduces a dual-layer protocol combining Slotted ALOHA for node-to-relay communication and TDMA for relay-to-gateway transmission. Using a Java-based simulator, we evaluate three relay placement strategies—random, square grid, and hexagonal grid—considering metrics such as delay, throughput, packet collisions, and coverage. Results show that the hexagonal grid offers superior performance, reducing collisions, minimizing delay, and expanding coverage. A fallback mechanism for out-of-range nodes and sensitivity analysis of different backoff values are also included. The study quantifies the benefits of structured relay placement for LoRaWAN and wireless sensor networks, while also identifying challenges for realistic deployments. These findings provide guidelines for designing scalable and reliable IoT networks and highlight directions for future work involving irregular placements and dynamic routing. The simulation results are intended to provide comparative, trend-based insights under conservative modeling assumptions, rather than absolute performance predictions.

Wireless Sensor Networks (WSN) are widely used across various fields. WSN is composed of many low-cost, high-performance, plug-and-play sensor nodes. WSN is used across a wide range of applications. Distributed Denial-of-Service (DDoS) attacks that overwhelm targeted resources, denying access to legitimate users. It prevents web servers from serving resources to clients. One type of DDoS is an HTTP flood attack, in which an attacker targets network resources, such as bandwidth (the amount of data a network can carry) and CPU processing (a central processing unit's ability to compute). The attacker sends multiple HTTP POST requests to the server to transmit data. In addition, the attacker sends multiple HTTP GET requests to retrieve data from the server. Previous work identified HTTP TRACE flood attacks that misuse the HTTP TRACE method. The HTTP TRACE method returns the received HTTP request, thereby exposing sensitive data. These attacks employ static URLs, which degrade the overall performance of the WSN. To address these issues, introduce the proposed method, the Enhanced Deep Spectral Multi-Layer Convolutional Neural Network (EDSMCNN), a deep learning model designed to improve CPU performance, handle multiple URL requests, and predict TRACE attack traffic based on the maximum-weighted features. First, input the HTTP flood attack dataset, which is available online. The initial step is preprocessing: analyzing and preparing data. The datasets are preprocessed to reduce the dimensionality (number of input features) of non-redundant data. Average weightage scaling feature to filter using the spider algorithm (an optimization technique based on social spider behavior) selects relevant features based on Lattice Service Rate Access Values (LSRAV, a metric evaluating service rate in the system) and observes Trace flood Traffic, considering parameters such as URL, protocol, and IP address (unique network identifier). Social spiders compare feature selection patterns with rank results. Next, SoftMax (a mathematical function that converts numbers to probabilities) generates logistic neurons using the Logistic Activation Function (SLAF, an activation mechanism for neural networks) for HTTP POST and GET requests to prevent trace attacks. Compared to standard convolutional methods, the system achieves high efficiency in detecting HTTP-TRACE flooding attacks. Experimental results show the proposed system improves CPU performance and reduces computation time, helping avoid traffic in one or more HTTP requests. In WSN, security is of paramount importance, and the emergence of novel attack vectors poses significant challenges. This abstract highlight a concerning scenario in which sensor nodes are targeted by a TRACE attacker via the injection of a backdoor entry file. Once compromised, the attacker gains unauthorized access to the WSN web server, potentially exposing sensitive data or gaining control over the network. This sophisticated attack underscores the need for robust security measures in WSNs, including intrusion detection systems, encryption, and authentication protocols, to safeguard against such threats and ensure the integrity and confidentiality of data transmitted and collected within these networks. Developing effective countermeasures to address these emerging attack vectors is crucial for the continued deployment and reliability of WSN in various critical applications.

Wireless sensor network (WSN) coverage optimization is a critical factor in improving network service quality, yet it faces challenges such as deployment uniformity, high-dimensional optimization, and the balance between exploration and exploitation under limited node resources. To address the shortcomings of the cultural historical optimization algorithm (CHOA), including insufficient global exploration, lack of dynamic regulation, and limited local exploitation accuracy, this paper proposes a film and television strategy-based multi-strategy cultural historical optimization algorithm (FTSCHOA). The proposed algorithm enhances performance through three synergistic mechanisms: a DE-style evolutionary operator that strengthens global exploration and population diversity; a film-and-television strategy that balances exploration and exploitation via random perturbations and adaptive parameter regulation; and a memory-based neighborhood local search that performs refined exploitation around high-quality solution sets to improve local optimization accuracy. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark suites with dimensions of 10, 20, 30, and 50 demonstrate that FTSCHOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability. The Friedman mean rank test indicates that FTSCHOA consistently achieves the best average ranking, while the Wilcoxon rank-sum test confirms that its performance differences with respect to competing algorithms are statistically significant (p<0.05). When applied to WSN coverage optimization in a 100 m×100 m monitoring region, FTSCHOA achieves coverage rates of 0.9351 and 0.9738 with 25 and 30 sensor nodes, respectively, which are significantly higher than those obtained by PSO, GWO, CHOA, and other algorithms. Moreover, the resulting node deployments exhibit greater uniformity, fewer coverage holes, and lower redundancy. The experimental results demonstrate that FTSCHOA effectively overcomes the limitations of traditional algorithms and provides an efficient and practical solution for WSN node deployment optimization, with strong potential for application in real-world scenarios such as environmental monitoring and smart agriculture.

Particle swarm optimization–assisted ivy algorithm for cluster head selection in wireless sensor networks

Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3-13% improvement in last node death (LND) compared with the standard SBOA.

Retraction Note: Energy Efficient Data Gathering Technique Based on Optimal Mobile Sink Node Selection for Improved Network Life Time in Wireless Sensor Network (WSN)

Insect pests pose significant threats to agricultural production, causing substantial yield losses and economic damage worldwide. Conventional insect monitoring methods, such as manual scouting and trap-based sampling are labour-intensive, time-consuming and often fail to provide timely and actionable information for effective pest management. Recent advancements in sensor technologies have paved the way for real-time, in-situ monitoring of insect pests, enabling precision targeting and site-specific control interventions. This review article explores the various types of sensors, including acoustic, optical and chemical sensors and their applications in detecting, identifying and quantifying insect pests. The review also discussed the design and implementation of wireless sensor networks for scalable and robust insect monitoring, along with data analytics approaches for automated pest detection and spatio-temporal modelling of pest population dynamics. Furthermore, the study highlights the integration of sensor data into precision pest management systems, such as sensor-guided pesticide application technologies and intelligent pest control strategies. Real-world case studies demonstrating the efficacy of sensor-based pest management in diverse agricultural settings are presented.

Retraction Note: Void Hole Avoidance Based on Sink Mobility and Adaptive Two Hop Vector-Based Forwarding in Underwater Wireless Sensor Networks

Retraction Note: An Intrusion Detection and Prevention Protocol for Internet of Things Based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are emerging as a vital technology in future network utilization due to their broad application range and cost-effectiveness. The Exclusion Basis System (EBS) is a combinatorial technique of group key management that offers long-term and flexible security mechanism in WSNs while allowing the efficient eviction of compromised nodes and secure updates to the key system through its re-keying strategy. Combinatorial design theory is an innovative technique to developing and analysing cryptographic algorithms for secure communication in WSNs. This article generalised the study of N. Karst and S. B. Wicker to [Formula: see text]-design with [Formula: see text], triangular Partially Balanced Incomplete Block Designs (PBIBD) with [Formula: see text]=1 and [Formula: see text]=0, transversal design (TD[Formula: see text]), and [Formula: see text]-net. We observed that [Formula: see text]-design ([Formula: see text]) with [Formula: see text] provides less re-keying value than N. Karst and S. B. Wicker result [34]. Furthermore, when [Formula: see text], TD[Formula: see text] and triangular PBIBD yield the same re-keying values, whereas when [Formula: see text], TD[Formula: see text] provides a lower re-keying value than triangular PBIBD, except at [Formula: see text], where they are equal. Additionally, when [Formula: see text] and [Formula: see text] triangular PBIBD shows lower re-keying values than TD[Formula: see text] if [Formula: see text] otherwise equal and when [Formula: see text], the re-keying value of triangular PBIBD is consistently lower than TD[Formula: see text]. Finally, when [Formula: see text], TD[Formula: see text] and [Formula: see text]-net provide the same re-keying value.

The construction industry is under mounting pressure to enhance its sustainability performance. Increasing project complexity and risk require real-time data collection, monitoring, and assistance in decision making via the Internet of Things (IoT). IoT has emerged as a critical enabling technology to overcome these hurdles. This study provides a bibliometric and thematic overview of IoT applications in sustainable construction project management to identify research trends, key themes, and practical implications for project managers. We used a structured screening process to analyze peer-reviewed journal papers, conference articles, and book chapters listed in the Scopus database. We identified 77 publications published between 2019 and 2025. Using VOSviewer_1.6.20_exe, we analyzed publication trends, source influences, geographical dispersion, and keyword co-occurrence patterns. Since 2023, research output and citation impact have increased dramatically, with sustainability, project management, and IoT serving as the main conceptual foundations recorded. Real-time monitoring, wireless sensor networks, safety improvement, BIM and digital twin integration, and resource and energy optimization are the five main application domains recognized using thematic synthesis. This shows a marked transition from standalone sensing applications to integrated, intelligent, and predictive systems that enable data-driven decision making throughout the construction lifecycle. This review highlights the ongoing difficulties associated with data quality, sensor dependability, system interoperability, and energy limitations. IoT is progressing from a support technology to a core operational and managerial infrastructure for sustainable construction, with major consequences for project management and future research.

The study concentrates on improving the dependability and operational efficacy of LoRa-based Wireless Sensor Networks (WSNs), which are extensively utilized in IoT applications, especially for long-range private networks. It seeks to deal with the problems that arise when a single node or communication line fails, which can have a big effect on network performance. The research utilizes a Markovian matrix theoretical framework to examine and simulate the behavior of LoRa-based Wireless Sensor Networks (WSNs), incorporating states such as Sleep (S), Idle (I), Transmit (T), and Receive (R) mode. A Python software program was created to put this model into action, allowing for testing and simulation with 50 fake data sets. The method stresses that the network should always be running, that sensor nodes should be replaced quickly, and that the network should be able to handle failures of individual nodes. The simulations indicate that using the Markov chain model in conjunction with detailed step-by-step math computation may yield a more accurate analysis of the data sets. The methodology also helps you evaluate protocols, change control, look at scalability, and make informed choices about how to build a network. This work offers practical benefits for the design, deployment, and maintenance of LoRa-based WSNs in real-world IoT scenarios. It supports network administrators and engineers in predicting power consumption, designing resilient protocols, scaling networks efficiently, and implementing adaptive control measures to ensure continuous and dependable operation. The integration of Markov chain mathematical modeling with Python-based simulation provides a robust solution for ensuring reliable operation of LoRa-based WSNs. The approach mitigates the impact of node failures, supports rapid recovery, and maintains network integrity.

ABSTRACT Mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs) face significant challenges due to dynamic topology, limited energy resources, and security threats. This work presents a hybrid Genghis Khan shark–lotus effect optimizer (GKS‐LEO) for trust‐aware, energy‐efficient, and secure routing. Soft k ‐mean clustering and a crayfish optimization algorithm enable stable cluster formation and adaptive cluster head selection, while a deep squeeze multichannel attention network evaluates hop quality using trust, energy, and link features. Simulation results demonstrate that GKS‐LEO achieves a 99% packet delivery ratio (PDR) and reduces energy consumption to 0.5 mJ, outperforming traditional approaches by up to 30% in network longevity. These results validate the proposed model's capability to balance security, trust, and efficiency, making it suitable for deployment in mission‐critical and resource‐constrained environments. Future work will extend this framework with blockchain‐enabled trust management and SDN‐based control for enhanced scalability.

Deep learning-based detection and recovery mechanism for mitigating selective forwarding attacks in event-driven wireless sensor network

In wireless sensor networks (WSNs), energy limitations remain a primary challenge to the reliability and longevity of deployed systems, particularly in remote or inaccessible environments. Traditional approaches often treat signal detection and data routing as separate optimization problems, leading to inefficiencies in resource utilization. This paper presents a unified framework that integrates statistical signal detection theory with optimal routing strategies under strict energy constraints. By formulating the problem as a joint statistical optimization task, the model captures the trade-offs between detection accuracy, routing cost, and energy consumption. The proposed approach uses Bayesian decision theory for signal detection and a convex optimization scheme for routing paths that minimize overall energy expenditure while preserving detection performance. Theoretical analyses demonstrate that the joint framework outperforms decoupled strategies in both detection reliability and network lifetime. Comprehensive simulations are conducted on dynamically varying network topologies to evaluate the model’s performance under different node densities, noise levels, and energy budgets. Results demonstrate consistent and substantial improvements in packet delivery ratio, false detection rate, and energy consumption compared to benchmark methods, with energy savings exceeding 35% while maintaining detection probability above 0.93. Furthermore, the algorithm adapts to fluctuating signal-to-noise ratios and network degradation over time, making it robust for real-world deployment scenarios such as disaster response, structural health monitoring, and smart agriculture. This integrated approach offers a promising pathway for future WSN designs that demand both accuracy and sustainability.