Pub Date : 2026-02-10DOI: 10.1016/j.jnca.2026.104439
Marlon Etheredge, Juan Aznar Poveda, Stefan Pedratscher, Abolfazl Younesi, Thomas Fahringer
{"title":"Pulse: Multi-objective scheduling of service-based applications in multi-cluster cloud-edge-IoT infrastructures","authors":"Marlon Etheredge, Juan Aznar Poveda, Stefan Pedratscher, Abolfazl Younesi, Thomas Fahringer","doi":"10.1016/j.jnca.2026.104439","DOIUrl":"https://doi.org/10.1016/j.jnca.2026.104439","url":null,"abstract":"","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"60 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1016/j.jnca.2026.104438
Zainab Alwaisi, Tanesh Kumar, Simone Soderi
Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.
{"title":"Robust and energy-aware detection of Mirai botnet for future 6G-enabled IoT networks","authors":"Zainab Alwaisi, Tanesh Kumar, Simone Soderi","doi":"10.1016/j.jnca.2026.104438","DOIUrl":"https://doi.org/10.1016/j.jnca.2026.104438","url":null,"abstract":"Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"1 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1016/j.jnca.2026.104441
Razvan Bocu, Maksim Iavich
{"title":"Generalized detection of DDoS attack patterns using machine learning models","authors":"Razvan Bocu, Maksim Iavich","doi":"10.1016/j.jnca.2026.104441","DOIUrl":"https://doi.org/10.1016/j.jnca.2026.104441","url":null,"abstract":"","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"48 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.jnca.2026.104437
Anutusha Dogra, Rakesh Kumar Jha
{"title":"Color Based Allocation (CBA) approach for managing high user density in 6G Networks","authors":"Anutusha Dogra, Rakesh Kumar Jha","doi":"10.1016/j.jnca.2026.104437","DOIUrl":"https://doi.org/10.1016/j.jnca.2026.104437","url":null,"abstract":"","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"29 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.jnca.2026.104436
Umar Sa’ad , Woongsoo Na , Nhu-Ngoc Dao , Sungrae Cho
Critical infrastructure systems characterized by complex interdependencies face significant challenges in vulnerability management due to cascading risk propagation through interconnected components. Traditional approaches that individually prioritize vulnerabilities inefficiently manage these dependency structures, leading to suboptimal security outcomes. This paper introduces an adaptive dependency-aware patching technique (ADAPT), a dynamic vulnerability and patch management framework that integrates formal dependency modeling with reinforcement learning to optimize patching strategies for critical interconnected systems. The proposed approach employs a mathematical formulation to capture direct and transitive dependencies via reachability matrices, enabling precise quantification of cascading risk propagation. The framework dynamically adapts patching decisions under resource constraints using proximal policy optimization within a constrained Markov decision process formulation. Comprehensive evaluation across 954 system configurations and six baseline strategies demonstrates consistent performance improvements, with 5.5% advantage over state-of-the-art NSGA-II multi-objective optimization while achieving 1513× computational speedup. Optimality gap analysis reveals 4.33% average deviation from theoretical bounds, validating the framework’s near-optimal solution quality. A critical infrastructure case study confirms practical applicability, with ADAPT achieving 89.7% risk reduction compared to 86.4% for sophisticated baseline methods, while enabling real-time decision-making through sub-second computation times. The results demonstrate superior performance under high dependency density and resource constraints, highlighting the framework’s suitability for environments where cascading failures pose operational threats.
{"title":"Dynamic dependency-aware vulnerability and patch management for critical interconnected systems","authors":"Umar Sa’ad , Woongsoo Na , Nhu-Ngoc Dao , Sungrae Cho","doi":"10.1016/j.jnca.2026.104436","DOIUrl":"10.1016/j.jnca.2026.104436","url":null,"abstract":"<div><div>Critical infrastructure systems characterized by complex interdependencies face significant challenges in vulnerability management due to cascading risk propagation through interconnected components. Traditional approaches that individually prioritize vulnerabilities inefficiently manage these dependency structures, leading to suboptimal security outcomes. This paper introduces an adaptive dependency-aware patching technique (ADAPT), a dynamic vulnerability and patch management framework that integrates formal dependency modeling with reinforcement learning to optimize patching strategies for critical interconnected systems. The proposed approach employs a mathematical formulation to capture direct and transitive dependencies via reachability matrices, enabling precise quantification of cascading risk propagation. The framework dynamically adapts patching decisions under resource constraints using proximal policy optimization within a constrained Markov decision process formulation. Comprehensive evaluation across 954 system configurations and six baseline strategies demonstrates consistent performance improvements, with 5.5% advantage over state-of-the-art NSGA-II multi-objective optimization while achieving 1513× computational speedup. Optimality gap analysis reveals 4.33% average deviation from theoretical bounds, validating the framework’s near-optimal solution quality. A critical infrastructure case study confirms practical applicability, with ADAPT achieving 89.7% risk reduction compared to 86.4% for sophisticated baseline methods, while enabling real-time decision-making through sub-second computation times. The results demonstrate superior performance under high dependency density and resource constraints, highlighting the framework’s suitability for environments where cascading failures pose operational threats.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"248 ","pages":"Article 104436"},"PeriodicalIF":8.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.jnca.2026.104428
Jiali Tang, Lang Li, Xingqi Yue
The rapid expansion of Vehicular Ad Hoc Networks (VANETs) has intensified the need for secure and efficient communication protocols, particularly in resource-constrained environments. Conventional encryption schemes offer strong security but frequently underperform in terms of computational efficiency and resource utilization due to their high complexity and resource demands. Ascon, a widely adopted lightweight encryption algorithm, provides robust security but poses challenges in hardware optimization. This paper introduces ILAD, a hardware-efficient authenticated encryption algorithm specifically designed for VANET applications. ILAD improves security and reduces resource overhead by optimizing key components within the round function and restructuring the sponge construction to enhance module reusability and hardware efficiency. The S-box is constructed using a Tent map-based chaotic system and refined through iterative optimization to ensure strong cryptographic properties and minimal resource usage, implemented with area-saving MOAI1 logic gates. Experimental results demonstrate that ILAD achieves a 36.1% reduction in S-box area and a 15.2% reduction in total area under UMC process technology. The algorithm has been successfully deployed on the i.MX6ULL_PRO development board, a Cortex-A7-based low-power processor platform, where it achieved stable performance with low latency and energy consumption. Comprehensive security evaluations confirm ILAD’s robustness against various cryptanalytic attacks, making it a strong candidate for secure and lightweight VANET deployments.
{"title":"ILAD: A hardware-efficient authenticated encryption scheme for VANET applications based on Ascon","authors":"Jiali Tang, Lang Li, Xingqi Yue","doi":"10.1016/j.jnca.2026.104428","DOIUrl":"10.1016/j.jnca.2026.104428","url":null,"abstract":"<div><div>The rapid expansion of Vehicular Ad Hoc Networks (VANETs) has intensified the need for secure and efficient communication protocols, particularly in resource-constrained environments. Conventional encryption schemes offer strong security but frequently underperform in terms of computational efficiency and resource utilization due to their high complexity and resource demands. Ascon, a widely adopted lightweight encryption algorithm, provides robust security but poses challenges in hardware optimization. This paper introduces ILAD, a hardware-efficient authenticated encryption algorithm specifically designed for VANET applications. ILAD improves security and reduces resource overhead by optimizing key components within the round function and restructuring the sponge construction to enhance module reusability and hardware efficiency. The S-box is constructed using a Tent map-based chaotic system and refined through iterative optimization to ensure strong cryptographic properties and minimal resource usage, implemented with area-saving MOAI1 logic gates. Experimental results demonstrate that ILAD achieves a 36.1% reduction in S-box area and a 15.2% reduction in total area under UMC <span><math><mrow><mn>0</mn><mo>.</mo><mn>18</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> process technology. The algorithm has been successfully deployed on the i.MX6ULL_PRO development board, a Cortex-A7-based low-power processor platform, where it achieved stable performance with low latency and energy consumption. Comprehensive security evaluations confirm ILAD’s robustness against various cryptanalytic attacks, making it a strong candidate for secure and lightweight VANET deployments.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"248 ","pages":"Article 104428"},"PeriodicalIF":8.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.jnca.2026.104427
Riya Goyal, Abhinav Tomar
Wireless Rechargeable Sensor Networks (WRSNs) offer a transformative solution to energy constraints in remote and mission-critical Internet of Things (IoT) environments by leveraging Wireless Energy Transfer (WET). However, current state-of-the-art approaches often suffer from limited scalability, inefficient energy scheduling, and inadequate adaptability to dynamic network states. Key challenges such as optimal mobile charger (MC) deployment, cooperative multi-agent scheduling, and intelligent threshold determination for on-demand charging remain insufficiently addressed—particularly in large-scale, real-time WRSNs. This paper proposes a novel hybrid framework that integrates Deep Q-Networks (DQNs) with a Quantum-Inspired Fuzzy Logic (QIFL) model for resilient and perpetual energy replenishment. To overcome spatial and load imbalances, an Enhanced Black Hole Optimization (EBHO) technique is used for partitioning the network and deploying MCs optimally. Unlike prior work, the proposed approach dynamically adapts charging thresholds using QIFL, capturing nonlinear energy consumption patterns and spatial heterogeneity. A multi-agent DQN model is deployed to handle high-dimensional state–action spaces, facilitating decentralized decision-making under uncertainty. Further, a proximity-aware charging mechanism, empowered by an improved Adaptive Genetic Algorithm (AGA), ensures real-time task redistribution among MCs, maintaining network longevity and zero sensor node failure. Experimental results demonstrate a 19.59% improvement in energy utilization and complete elimination of dead nodes compared to leading benchmarks, establishing the superiority of the proposed scheme for large-scale, adaptive, and sustainable WRSN operations.
{"title":"Redefining resilience: A hybrid quantum-fuzzy Deep Q-Network paradigm for perpetual wireless rechargeable sensor networks","authors":"Riya Goyal, Abhinav Tomar","doi":"10.1016/j.jnca.2026.104427","DOIUrl":"10.1016/j.jnca.2026.104427","url":null,"abstract":"<div><div>Wireless Rechargeable Sensor Networks (WRSNs) offer a transformative solution to energy constraints in remote and mission-critical Internet of Things (IoT) environments by leveraging Wireless Energy Transfer (WET). However, current state-of-the-art approaches often suffer from limited scalability, inefficient energy scheduling, and inadequate adaptability to dynamic network states. Key challenges such as optimal mobile charger (MC) deployment, cooperative multi-agent scheduling, and intelligent threshold determination for on-demand charging remain insufficiently addressed—particularly in large-scale, real-time WRSNs. This paper proposes a novel hybrid framework that integrates Deep Q-Networks (DQNs) with a Quantum-Inspired Fuzzy Logic (QIFL) model for resilient and perpetual energy replenishment. To overcome spatial and load imbalances, an Enhanced Black Hole Optimization (EBHO) technique is used for partitioning the network and deploying MCs optimally. Unlike prior work, the proposed approach dynamically adapts charging thresholds using QIFL, capturing nonlinear energy consumption patterns and spatial heterogeneity. A multi-agent DQN model is deployed to handle high-dimensional state–action spaces, facilitating decentralized decision-making under uncertainty. Further, a proximity-aware charging mechanism, empowered by an improved Adaptive Genetic Algorithm (AGA), ensures real-time task redistribution among MCs, maintaining network longevity and zero sensor node failure. Experimental results demonstrate a 19.59% improvement in energy utilization and complete elimination of dead nodes compared to leading benchmarks, establishing the superiority of the proposed scheme for large-scale, adaptive, and sustainable WRSN operations.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"248 ","pages":"Article 104427"},"PeriodicalIF":8.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.jnca.2025.104411
Yilun Ma, Yuanming Wu
In an event-driven wireless sensor network (EWSN), events occur randomly, prompting sensor nodes within the event area to detect and transmit data packets to a sink via router nodes (RNs) through multi-hop communication. Some RNs, referred to as malicious nodes, may launch selective forwarding attacks by selectively dropping part or all of the received packets. Additionally, harsh environmental conditions can degrade channel quality, sometimes forcing RNs to abandon forwarding data packets. Under these conditions, it becomes challenging to distinguish malicious nodes from normal nodes solely based on their packet forwarding rates. To address this issue, we propose the DPC-LSTM-MD scheme to detect selective forwarding attacks. This approach utilizes the time series of nodes’ packet forwarding behaviors as a dataset. The Density Peaks Clustering (DPC) method is employed to extract features representative of normal node behavior. Subsequently, a Long Short-Term Memory (LSTM) network predicts the single round forwarding rate (SFR) of nodes in the next time series interval. Based on the prediction error, we apply the minimum density (MD) method combined with the 3-sigma rule to identify and isolate malicious nodes. Our results demonstrate that the DPC-LSTM-MD scheme achieves a low false detection rate (FDR) of 2% and a low missed detection rate (MDR) of 3%, significantly improving network throughput.
在事件驱动的无线传感器网络（EWSN）中，事件是随机发生的，事件区域内的传感器节点通过多跳通信的方式检测数据包，并通过路由器节点（RNs）将数据包发送到sink。有些rn被称为恶意节点，可能会选择性地丢弃部分或全部接收到的报文，从而发起选择性转发攻击。此外，恶劣的环境条件会降低信道质量，有时会迫使RNs放弃转发数据包。在这种情况下，仅根据报文转发速率来区分恶意节点和正常节点变得很有挑战性。为了解决这个问题，我们提出了DPC-LSTM-MD方案来检测选择性转发攻击。该方法利用节点数据包转发行为的时间序列作为数据集。采用密度峰聚类（DPC）方法提取正常节点行为的特征。随后，LSTM （Long - short - Memory）网络预测下一个时间序列间隔内节点的单轮转发速率（SFR）。基于预测误差，我们采用最小密度（MD）方法结合3-sigma规则来识别和隔离恶意节点。我们的研究结果表明，DPC-LSTM-MD方案实现了2%的低误检率（FDR）和3%的低漏检率（MDR），显著提高了网络吞吐量。
{"title":"The DPC-LSTM-MD scheme for detecting selective forwarding attack under variable environment in event-driven wireless sensor networks","authors":"Yilun Ma, Yuanming Wu","doi":"10.1016/j.jnca.2025.104411","DOIUrl":"10.1016/j.jnca.2025.104411","url":null,"abstract":"<div><div>In an event-driven wireless sensor network (EWSN), events occur randomly, prompting sensor nodes within the event area to detect and transmit data packets to a sink via router nodes (RNs) through multi-hop communication. Some RNs, referred to as malicious nodes, may launch selective forwarding attacks by selectively dropping part or all of the received packets. Additionally, harsh environmental conditions can degrade channel quality, sometimes forcing RNs to abandon forwarding data packets. Under these conditions, it becomes challenging to distinguish malicious nodes from normal nodes solely based on their packet forwarding rates. To address this issue, we propose the DPC-LSTM-MD scheme to detect selective forwarding attacks. This approach utilizes the time series of nodes’ packet forwarding behaviors as a dataset. The Density Peaks Clustering (DPC) method is employed to extract features representative of normal node behavior. Subsequently, a Long Short-Term Memory (LSTM) network predicts the single round forwarding rate (SFR) of nodes in the next time series interval. Based on the prediction error, we apply the minimum density (MD) method combined with the 3-sigma rule to identify and isolate malicious nodes. Our results demonstrate that the DPC-LSTM-MD scheme achieves a low false detection rate (FDR) of 2% and a low missed detection rate (MDR) of 3%, significantly improving network throughput.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"247 ","pages":"Article 104411"},"PeriodicalIF":8.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-25DOI: 10.1016/j.jnca.2025.104410
Muhammad Suhail Shaikh , Shuwei Qiu , Xiaoqing Dong , Chang Wang , Wulfran Fendzi Mbasso
Enhancing coverage and reducing energy consumption are fundamental challenges in wireless sensor networks (WSNs) for high-volume and data-intensive deployment. WSNs play an important role in emerging technologies and face practical limitations, particularly related to coverage and energy consumption. Strategical placement of these sensor nodes is important to ensure service quality; however, many existing optimization algorithms for sensor node placement struggle with low coverage rate and high energy consumption. A significant issue lies in determining the optimal sensor node locations, as these significantly influence the network's coverage and energy consumption. This work presented a Piecewise Chaotic Starfish Optimization Algorithm (CSFOA) for addressing the challenge of optimizing the sensor node placement to maximize coverage and minimize energy consumption in WSNs. The integration of the piecewise chaotic map enhances the convergence and exploration capacity of the algorithm in identifying better solutions. The effectiveness of CSFOA is confirmed by a range of diverse benchmark functions as unimodal, multimodal, fixed, and variable, proving its excellence in optimization performance. CSFOA obtained better results for sensor node deployment in real test cases. For instance, in Test System 1 with 20 nodes, the coverage rate is 97.4757 % and the energy consumption is 0.29967 nJ/bit. In Test System 2 with 30 nodes, the coverage is 99.9713 % and the energy consumption is 3.2193 nJ/bit. Test System 3 with 40 nodes has a 98.8690 % coverage rate and energy consumption of 5.1107 nJ/bit. Compared to CMFO, CSSA, CPSO, SFOA, MFO, SSA, and PSO algorithms, CSFOA realizes an average improvement of 16.41 %, 5.36 %, 3.45 %, 2.371 %, 2.80 %, and 2.18 % on various evaluation metrics. These results underscore the algorithm's capability in balancing coverage and energy efficiency enhancement, and they confirm the algorithm's value as a more effective solution to sensor node deployment issues in different applications.
{"title":"A piecewise chaotic starfish optimization algorithm for energy-efficient coverage in wireless sensor networks","authors":"Muhammad Suhail Shaikh , Shuwei Qiu , Xiaoqing Dong , Chang Wang , Wulfran Fendzi Mbasso","doi":"10.1016/j.jnca.2025.104410","DOIUrl":"10.1016/j.jnca.2025.104410","url":null,"abstract":"<div><div>Enhancing coverage and reducing energy consumption are fundamental challenges in wireless sensor networks (WSNs) for high-volume and data-intensive deployment. WSNs play an important role in emerging technologies and face practical limitations, particularly related to coverage and energy consumption. Strategical placement of these sensor nodes is important to ensure service quality; however, many existing optimization algorithms for sensor node placement struggle with low coverage rate and high energy consumption. A significant issue lies in determining the optimal sensor node locations, as these significantly influence the network's coverage and energy consumption. This work presented a Piecewise Chaotic Starfish Optimization Algorithm (CSFOA) for addressing the challenge of optimizing the sensor node placement to maximize coverage and minimize energy consumption in WSNs. The integration of the piecewise chaotic map enhances the convergence and exploration capacity of the algorithm in identifying better solutions. The effectiveness of CSFOA is confirmed by a range of diverse benchmark functions as unimodal, multimodal, fixed, and variable, proving its excellence in optimization performance. CSFOA obtained better results for sensor node deployment in real test cases. For instance, in Test System 1 with 20 nodes, the coverage rate is 97.4757 % and the energy consumption is 0.29967 nJ/bit. In Test System 2 with 30 nodes, the coverage is 99.9713 % and the energy consumption is 3.2193 nJ/bit. Test System 3 with 40 nodes has a 98.8690 % coverage rate and energy consumption of 5.1107 nJ/bit. Compared to CMFO, CSSA, CPSO, SFOA, MFO, SSA, and PSO algorithms, CSFOA realizes an average improvement of 16.41 %, 5.36 %, 3.45 %, 2.371 %, 2.80 %, and 2.18 % on various evaluation metrics. These results underscore the algorithm's capability in balancing coverage and energy efficiency enhancement, and they confirm the algorithm's value as a more effective solution to sensor node deployment issues in different applications.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"247 ","pages":"Article 104410"},"PeriodicalIF":8.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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