Search Results (373)

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Wireless sensor networks (WSNs) play a major role in various applications, but the main challenge is to maintain security and balanced energy efficiency. Classical routing protocols struggle to achieve both energy efficiency and security because they are more vulnerable to security risks and resource limitations. This paper introduces QSEER, a novel approach that uses quantum technologies to overcome these limitations. QSEER employs quantum-inspired optimization algorithms that leverage superposition and entanglement principles to efficiently explore multiple routing possibilities, thereby identifying energy-efficient paths and reducing redundant transmissions. The proposed protocol enhances the security of data transmission against eavesdropping and tampering by using the principles of quantum mechanics, thus mitigating potential security vulnerabilities. Through extensive simulations, we demonstrated the effectiveness of QSEER in achieving both security and energy efficiency objectives, which achieves 15.1% lower energy consumption compared to state-of-the-art protocols while maintaining 99.8% data integrity under various attack scenarios, extending network lifetime by an average of 42%. These results position QSEER as a significant advancement for next-generation WSN deployments in critical applications such as environmental monitoring, smart infrastructure, and healthcare systems. Full article

WSNs consist of numerous energy-constrained Sensor Nodes (SNs), making energy efficiency a critical challenge. This paper presents a novel multipath routing model designed to enhance network lifetime by simultaneously optimizing energy consumption, node connectivity, and transmission distance. The model employs an Improved Particle Swarm Optimization (IPSO) algorithm to dynamically determine the optimal weight coefficients of a cost function that integrates three parameters: residual energy, link reliability, and buffer capacity. A compressed Bloom filter is incorporated to improve packet transmission efficiency and reduce error rates. Simulation experiments conducted in the NS2 environment show that the proposed approach significantly outperforms existing protocols, including Reinforcement Learning Q-Routing Protocol (RL-QRP), Low Energy Adaptive Clustering Hierarchical (LEACH), On-Demand Distance Vector (AODV), Secure and Energy-Efficient Multipath (SEEM), and Energy Density On-demand Cluster Routing (EDOCR), achieving a 7.45% reduction in energy consumption and maintaining a higher number of active nodes over time. Notably, the model sustains 19 live nodes at round 800, whereas LEACH and APTEEN experience complete node depletion by that point. This adaptive, energy-aware routing strategy improves reliability, prolongs operational lifespan, and enhances load balancing, making it a promising solution for real-world WSN applications. Full article

Wireless Sensor Network (WSN) technology has constrained computing resources that require efficient and low-cost cryptographic hardware to provide security services, particularly when dealing with large modular polynomial multiplication in cryptography. In this paper, a cost-efficient reconfigurable Karatsuba modular polynomial multiplier is proposed for general modulus polynomials. The modulus polynomial can be changed easily depending on the application. The proposed modular polynomial multiplier is synthesized and simulated by the AMD Vivado Design Tool. The design’s performance on ADP (Area Delay Product) has been improved compared to previous designs. It can be applied in ECC encryption to speed up the security services in WSN. Full article

The Internet of Things (IoT) has become an integral part of today’s smart and digitally connected world. IoT devices and technologies now connect almost every aspect of daily life, generating, storing, and analysing vast amounts of data. One important use of IoT is in utility management, where essential services such as water are supplied through IoT-enabled infrastructure to ensure fair, efficient, and sustainable delivery. The large volumes of data produced by water distribution networks must be safeguarded against manipulation, theft, and other malicious activities. Incidents such as the Queensland user data breach (2020–21), the Oldsmar water treatment plant attack (2021), and the Texas water system overflow (2024) show that attacks on water treatment plants, distribution networks, and supply infrastructure are common in Australia and worldwide, often due to inadequate security measures and limited technical resources. Lightweight cryptographic algorithms are particularly valuable in this context, as they are well-suited for resource-constrained hardware commonly used in IoT systems. This study focuses on the in-house developed ChaosFortress lightweight cryptographic algorithm, comparing its performance with other widely used lightweight cryptographic algorithms. The evaluation and comparative testing used an Arduino and a LoRa-based transmitter/receiver pair, along with the NIST Statistical Test Suite (STS). These tests assessed the performance of ChaosFortress against popular lightweight cryptographic algorithms, including ACORN, Ascon, ChaChaPoly, Speck, tinyAES, and tinyECC. ChaosFortress was equal in performance to the other algorithms in overall memory management but outperformed five of the six in execution speed. ChaosFortress achieved the quickest transmission time and topped the NIST STS results, highlighting its strong suitability for IoT applications. Full article

With the rapid advancement of the Internet of Things (IoT), artificial intelligence (AI), and blockchain technologies, educational research has increasingly explored smart and personalized learning systems. However, current approaches often suffer from fragmented integration of health monitoring and instructional adaptation, insufficient prediction accuracy of physiological states, and unresolved concerns regarding data privacy and security. To address these challenges, this study introduces SHARP, a novel blockchain-enhanced wireless sensor networks (WSNs) framework designed for real-time student health monitoring and personalized learning in smart educational environments. Wearable sensors enable continuous collection of physiological data, including heart rate variability, body temperature, and stress indicators. A deep neural network (DNN) processes these inputs to detect students’ physical and affective states, while a reinforcement learning (RL) algorithm dynamically generates individualised educational recommendations. A Proof-of-Authority (PoA) blockchain ensures secure, immutable, and transparent data management. Preliminary evaluations in simulated smart classrooms demonstrate significant improvements: the DNN achieves a 94.2% F1-score in state recognition, the RL module reduces critical event response latency, and energy efficiency improves by 23.5% compared to conventional baselines. Notably, intervention groups exhibit a 156% improvement in quiz scores over control groups. Compared to existing solutions, SHARP uniquely integrates multi-sensor physiological monitoring, real-time AI-based personalization, and blockchain-secured data governance in a unified framework. This results in superior accuracy, higher energy efficiency, and enhanced data integrity compared to prior IoT-based educational platforms. By combining intelligent sensing, adaptive analytics, and secure storage, SHARP offers a scalable and privacy-preserving solution for next-generation smart education. Full article

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments. Full article

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objective of this research is to design and evaluate a deep learning-based Intrusion Detection System (IDS) that is both accurate and efficient for real-time threat detection in WSNs. This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. A Conv1D extracts spatial features from network traffic, GRU captures temporal dependencies, and Self-Attention emphasizes critical sequence components, collectively enhancing detection of subtle and complex intrusion patterns. The model was evaluated using the WSN-DS dataset and demonstrated superior performance compared to traditional machine learning and simpler deep learning models. It achieved an accuracy of 98.6%, precision of 98.63%, recall of 98.6%, F1-score of 98.6%, and an ROC-AUC of 0.9994, indicating strong predictive capability even with imbalanced data. In addition to centralized training, the model was tested under cooperative, node-based learning conditions, where each node independently detects anomalies and contributes to a collective decision-making framework. This distributed approach improves detection efficiency and robustness. The proposed IDS offers a scalable and resilient solution tailored to the unique challenges of WSN security. Full article

As the low-altitude economy undergoes rapid growth, unmanned aerial vehicles (UAVs) have served as mobile sink nodes in wireless sensor networks (WSNs), significantly enhancing data collection efficiency. However, the open nature of wireless channels and spectrum scarcity pose severe challenges to data security, particularly when legitimate UAVs (UAV-L) receive confidential information from ground sensor nodes (SNs), which is vulnerable to interception by eavesdropping UAVs (UAV-E). In response to this challenge, this study presents a cooperative jamming (CJ) scheme for Reconfigurable Intelligent Surfaces (RIS)-assisted UAV-WSN to combat aerial malicious eavesdropping. The multi-dimensional optimization problem (MDOP) of system security under quality of service (QoS) constraints is addressed by collaboratively optimizing the transmit power (TP) of SNs, the flight trajectories (FT) of the UAV-L, the frame length (FL) of time slots, and the phase shift matrix (PSM) of the RIS. To address the challenge, we put forward a Cooperative Jamming Joint Optimization Algorithm (CJJOA) scheme. Specifically, we first apply the block coordinate descent (BCD) to decompose the original MDOP into several subproblems. Then, each subproblem is convexified by successive convex approximation (SCA). The numerical results demonstrate that the designed algorithm demonstrates extremely strong stability and reliability during the convergence process. At the same time, it shows remarkable advantages compared with traditional benchmark testing methods, effectively and practically enhancing security. Full article

Wireless sensor networks (WSNs) have emerged to address unique challenges in urban environments. This survey dives into the challenges faced in urban areas and explores how WSN applications can help overcome these obstacles. The diverse applications of WSNs in urban settings discussed in this paper include gas monitoring, traffic optimization, healthcare, disaster response, and security surveillance. The innovative research is considered in an urban environment, where WSNs such as energy efficiency, throughput, and scalability are deployed. Every application scenario is distinct and examined in details within this paper. In particular, smart cities represent a major domain where WSNs are increasingly integrated to enhance urban living through intelligent infrastructure. This paper emphasizes how WSNs are pivotal in realizing smart cities by enabling real-time data collection, analysis, and communication among interconnected systems. Applications such as smart transportation systems, automated waste management, smart grids, and environmental monitoring are discussed as key components of smart city ecosystems. The synergy between WSNs and smart city technologies highlights the potential to significantly improve the quality of life, resource management, and operational efficiency in modern cities. This survey specifies existing work objectives with results and limitations. The aim is to develop a methodology for evaluating the quality of performance analysis. Various performance metrics are discussed in existing research to determine the influence of real-time applications on energy consumption, network lifetime, end-to-end delay, efficiency, routing overhead, throughput, computation cost, computational overhead, reliability, loss rate, and execution time. The observed outcomes are that the proposed method achieves a higher 16% accuracy, 36% network lifetime, 20% efficiency, and 42% throughput. Additionally, the proposed method obtains 36%, 30%, 46%, 35%, and 32% reduction in energy consumption, computation cost, execution time, error rate, and computational overhead, respectively, compared to conventional methods. Full article

Wireless sensor networks face challenges such as energy consumption, scalability, security vulnerabilities, and communication range limitations, impacting their overall performance and reliability. To resolve these problems, energy-efficient protocols and adaptive sleep modes are implemented in wireless sensor networks (WSNs). Actually, LEACH clustering is widely regarded as one of the primary strategies to extend the lifetime of WSNs. However, clustering does not always guarantee optimal performance. In this paper, we demonstrate that clustering effectiveness is contingent on specific conditions related to several key parameters, including cluster density and the distance of nodes from the base station. Our research presents a mathematically validated analysis, supported by simulation results, that illustrates how clustering can enhance WSN performance, particularly in terms of network lifetime, throughput, and the timing of the first, middle, and last node deaths. Our findings indicate that LEACH is inefficient when nodes are within 80 m of the base station. Furthermore, clusters’ densities are related directly to the distance to the base station. Specifically, for distances less than 80 m, nodes should send their data individually; for distances between 83 and 123 m, a cluster density of two is most effective; and for distances between 123 and 149 m, the optimal density increases to three nodes. Full article

These days, wireless sensor networks (WSNs) are expanding fast and are used in many fields such as healthcare, battlefields, etc. Depending upon the type of sensor, they are transmitting a considerable amount of data in a short duration, so security is a significant issue while transferring the data. So, it is essential to solve security concerns while transferring data by secure routing in wireless sensor networks. We address this challenge by proposing Trusted Energy-Aware Hierarchical Routing (TEAHR), a new framework for a multi-level trust assessment that raises the security level in WSNs. TEAHR introduces a variety of trust metrics ranging from energy trust to forwarding trust to consistency trust to behavioral trust to anomaly detection, unlike existing models, enabling it to effectively address the challenges of dynamic network topologies and evolving cyber threats. Trust-based routing mechanisms are usually associated with high computation and storage complexity and susceptibility to collusive attacks such as spoofing. The mechanism in TEAHR overcomes these challenges by placing an adaptive trust assessment mechanism that adapts to the background network conditions and real-time activities of the nodes. We show through empirical analysis in this paper that TEAHR not only uses computational and storage resources efficiently but also enhances network performance and security. Our experimental setup presents the simulation approach to prove our proposed protocol of TEAHR in comparison with typical trust models under different scenarios of node mobility, variable node density, and sophisticated security attacks such as Sybil, wormhole, and replay attacks. TEAHR keeps the network connected, even when the nodes are isolated due to trust misbehavior, and demonstrates that widely it reduces the chances of misjudgment in trust evaluation. Moreover, we explore the scalability of TEAHR across large networks as well as its performance in computationally constrained contexts. We have verified through our detailed investigation that the energy metrics used uniquely in TEAHR extend the life of the network while increasing data routing trust and trustworthiness. The comparisons of TEAHR with conventional techniques show that the proposed algorithm reduces total latency by 15%, enhances energy efficiency by around 20%, and maintains a stable packet forwarding rate, which is highly desirable for accurate operation in adversarial environments, as demonstrated through comparative analysis. Through in-depth theoretical and practical analysis, TEAHR is confirmed as a high-performance framework that outperforms currently existing studies for WSN security, making TEAHR a strong candidate for use in industrial IoT applications and urban sensor networks. Full article

The Internet of Military Things (IoMT) is transforming defense operations by enabling the seamless integration of sensors and actuators for the real-time transmission of critical data in diverse military environments. End devices (EDs) collect essential information, including troop locations, health metrics, equipment status, and environmental conditions, which are processed to enhance situational awareness and operational efficiency. In scenarios involving large-scale deployments across remote or austere regions, wired communication systems are often impractical and cost-prohibitive. Wireless sensor networks (WSNs) provide a cost-effective alternative, with Long-Range Wide Area Network (LoRaWAN) emerging as a leading protocol due to its extensive coverage, low energy consumption, and reliability. Existing LoRaWAN network simulation modules, such as those in ns-3, primarily support uniform periodic data transmissions, limiting their applicability in critical military and healthcare contexts that demand adaptive transmission rates, resource optimization, and prioritized data delivery. These limitations are particularly pronounced in healthcare monitoring, where frequent, high-rate data transmission is vital but can strain the network’s capacity. To address these challenges, we developed an enhanced sensor data sender application capable of simulating priority-based traffic within LoRaWAN, specifically targeting use cases like border security and healthcare monitoring. This study presents a priority-based data flow control protocol designed to optimize network performance under high-rate healthcare data conditions while maintaining overall system reliability. Simulation results demonstrate that the proposed protocol effectively mitigates performance bottlenecks, ensuring robust and energy-efficient communication in critical IoMT applications within austere environments. Full article

This study explores the design and performance of an underwater magnetic sensor network (UMSN) tailored for intrusion detection in complex environments such as riverbeds and areas with dense vegetation. The system utilizes wireless sensor network (WSN) principles and integrates AMR-based magnetic sensors (e.g., LSM303AGR) with MEMS-based accelerometers to provide accurate and high-resolution magnetic field measurements. Extensive calibration techniques were employed to correct hard-iron and soft-iron distortions, ensuring reliable performance in fluctuating environmental conditions. Field tests included both controlled setups and real-world scenarios, such as detecting intrusions across river sections, shorelines, and coordinated land-water activities. The results showed detection rates consistently above 90%, with response times averaging 2.5 s and a maximum detection range of 5 m. The system also performed well under adverse weather conditions, including fog and rain, demonstrating its adaptability. The findings underline the potential of UMSN as a scalable and cost-efficient solution for monitoring sensitive areas. By addressing the limitations of traditional surveillance systems, this research offers a practical framework for enhancing security in critical regions, laying the groundwork for future developments in magnetic sensor technology. Full article