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NHBBWO: A novel hybrid butterfly-beluga whale optimization algorithm with the dynamic strategy for WSN coverage optimization

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第2期18卷 1-25页

作者： Chen, Xinyi Zhang, Mengjian Yang, Ming Wang, Deguang Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Wireless sensor networks (WSNs) play a crucial role in the development of emerging technologies, but they face significant challenges in practical applications, particularly concerning the coverage problem. Effective coverage of sensor nodes is essential for ensuring high-quality service in WSNs. However, existing swarm intelligence algorithms for coverage optimization in WSNs often exhibit poor performance and suffer from simplistic experimental setups and low coverage rates. To address these challenges, this study proposes the hybrid butterfly-beluga optimization algorithm with a dynamic quadratic parameter adaptation strategy (NHBBWO) for optimizing WSNs coverage. NHBBWO combines the strengths of beluga whale optimization (BWO), known for rapid convergence, and butterfly optimization (BOA), which excels in global search but struggles with slow convergence and local optima. By hybridizing BWO and BOA, the hybrid butterfly-beluga whale optimization (HBBWO) algorithm improves both convergence accuracy and speed, as well as the ability to avoid local optima. Additionally, a dynamic parameter adaptation strategy using a quadratic function further enhances the exploration capability of HBBWO. The performance of NHBBWO is rigorously evaluated through statistical analyses, including the Friedman rank test and Wilcoxon rank-sum test, applied to twenty-three benchmark functions. The results confirm the superiority of NHBBWO. Finally, simulations in 2D coverage scenarios demonstrate that NHBBWO significantly improves node coverage and reduces redundancy compared to four state-of-the-art swarm intelligence algorithms. This advancement addresses critical limitations in current methodologies and contributes to the broader field of WSN optimization by providing a robust solution to the coverage challenge.

关键词： Wireless sensor network coverage optimization Hybrid algorithm Beluga whale optimization Butterfly optimization algorithm Dynamic parameter adaptation strategy

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Enhanced Deep Learning-Based optimization Model for the coverage optimization in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-25页

作者： Kumar, S. Praveen Nagendranath, M. V. S. S. Alsamri, Jamal Ebad, Shouki A. EGS Pillay Engn Coll Dept Comp Sci & Engn Nagapattinam 611002 Tamil Nadu India Sasi Inst Technol & Engn Dept Comp Sci & Engn Tadepalligudem Andhra Pradesh India Princess Nourah Bint Abdulrahman Univ Coll Engn Dept Biomed Engn Riyadh Saudi Arabia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 91431 Saudi Arabia

A key component of wireless sensor networks (WSN) is optimal coverage. For WSN to have network lifetime and optimal resource utilization, maximum coverage must be guaranteed. The unequal distribution of sensor nodes in densely populated regions contributes to the build-up of network coverage. This research suggests a revolutionary intelligent deep learning-based optimization approach for WSN coverage optimization to tackle this problem. Here, the optimal network coverage of WSN is performed by the novel enhanced deep Q-network (EDQN) algorithm, where the parameters of DQN are tuned by nature inspired optimization algorithm called hippopotamus optimization (HO) with the intention of attaining the fitness function. In order to maximize coverage, the node position is changed in the developed methodology to represent the spatial properties of the network. Along with lowering latency, the increased coverage also increases throughput and network longevity. The outcome of several network coverage optimization trials is computed, and the EDQN-HO's impact on network coverage optimization is also determined by adjusting the parameters. The network coverage optimization studies' simulated findings demonstrate that the suggested EDQN-HO may be effectively utilized in a variety of settings. The proposed EDQN-HO for the WSN coverage model returns superior outcomes with 18.31%, 78.95%, 10.65%, 87.5%, 83.33%, and 38.20% than the existing methods in terms of coverage rate, energy consumption, computing time, positioning error, network lifetime, and average moving distance respectively.

关键词： Wireless sensor networks coverage optimization Enhanced deep Q-learning Hippopotamus optimization Network lifetime

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Wireless Sensor Network coverage optimization Using a Modified Marine Predator Algorithm

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SENSORS 2025年第1期25卷 69页

作者： Wang, Guohao Li, Xun Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network.

关键词： wireless sensor network marine predator algorithm coverage optimization

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Digital Twin Enhanced Multi-Agent Reinforcement Learning for Large-Scale Mobile Network coverage optimization

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2025年第1期19卷 1-23页

作者： Liu, Haoqiang Su, Weikang Li, Tong Huang, Wenzhen Li, Yong Tsinghua Univ Dept Elect Engn Beijing Peoples R China

With the rapid advancement of communication technology and the exponential growth of mobile users, improving network coverage quality and throughput has become increasingly important. In particular, largescale Base Station (BS) cooperative optimization has become a highly significant topic. BSs can adjust various parameters for high-quality communication, but automating this optimization remains challenging due to environmental sensitivity and interdependencies. Traditional methods for network optimization are constrained by the intricate nature of real-world environments. Further, Reinforcement Learning (RL) techniques, which are effective for configuration policies, encounter difficulties in intricate, high-dimensional wireless communication networks, especially in multi-agent cooperative optimization. To overcome these challenges, this article proposes the Enhanced Multi-Agent Proximal Policy optimization (EMAPPO), which utilizes the capabilities of the UNet network to extract multi-spatial relationships among a massive number of network elements and employs the DiffPool network to efficiently depict the impact of large-scale action coordination among massive agents on coverage performance. To facilitate evaluation in communication optimization, we further introduce a high-fidelity digital twin-driven mobile network. Extensive experiments validate the effectiveness and superior performance of EMAPPO by utilizing the network digital twin. The results demonstrate significant improvements in signal coverage rate and network throughput compared to the competing methods.

关键词： Digital twin Mobile networks Network optimization Multi-agent reinforcement learning coverage optimization

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Enhancing Immune Plasma Algorithm for coverage optimization Problem in Wireless Sensor Networks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-22页

作者： Tasdemir, Adnan Demirci, Sercan Aslan, Selcuk Ondokuz Mayis Univ Dept Comp Engn TR-55139 Samsun Turkiye Erciyes Univ Dept Aeronaut Engn TR-38280 Kayseri Turkiye

Wireless sensor networks (WSNs) have gained paramount importance in diverse applications, resulting in extensive research efforts. Among the pivotal challenges facing WSNs is the strategic deployment of nodes, which are critical for efficient data processing and dissemination. Maximizing the coverage area of the sensor nodes emerges as a key determinant of optimal performance in various application domains. Leveraging advanced node deployment algorithms holds the promise of significantly enhancing sensor node coverage within monitoring regions, thereby yielding benefits such as reduced energy consumption, prolonged network lifespan, and streamlined sensor operations. This study endeavors to address the coverage area challenge by employing two variants of the immune plasma algorithm (IP), augmented by sophisticated modeling techniques and tools. Inspired by the biological transfer of plasma or antibodies between patients, the IP algorithm offers a robust framework to optimize WSN deployment. Rigorous experimentation showcases the efficacy of the proposed algorithm in effectively addressing the multifaceted challenges inherent in WSN deployment, thereby presenting compelling avenues for future research and implementation.

关键词： Wireless sensor networks Node deployment coverage optimization Immune plasma algorithm Meta-heuristic algorithms Sensor coverage models Binary detection model Probabilistic detection model optimization techniques

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A Mobile Wireless Sensor coverage optimization Method for Bridge Monitoring

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SENSORS 2025年第9期25卷 2772页

作者： Mu, Cong Yang, Jiguang Huo, Jiuyuan Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Natl Cryosphere Desert Data Ctr Lanzhou 730000 Peoples R China

Aiming at the problem of resource allocation optimization of heterogeneous mobile wireless sensor (HMWS) networks in bridge structural health monitoring, this study proposes an enhanced coverage strategy for heterogeneous sensors based on an improved Hiking optimization Algorithm (HOA). This paper integrates the Good Point Set theory with a heterogeneous degree-of-freedom t-distribution perturbation mechanism to improve the basic HOA, developing the GPTHOA with global optimization characteristics. Based on this, a virtual force-guided HMWS coverage enhancement strategy (the VF-GPTHOA) is proposed. After determining the optimal deployment scheme, the GPTHOA is further employed to optimize node movement trajectories, minimizing the movement distance. Simulation results show that when deploying 60 heterogeneous sensors of three types in a 40 m x 200 m bridge model (BM), the coverage rate (CR) of the VF-GPTHOA reaches 97.07%, which represents improvements of 13.81%, 4.18%, 15.81%, 2.52%, and 12.44% over DE, MA, GWO, SSA, and HOA, respectively. In dynamic node scale scenarios, the VF-GPTHOA maintains optimal coverage performance, demonstrating its robustness and applicability in engineering practice.

关键词： heterogeneous mobile wireless sensor hiking optimization algorithm coverage optimization bridge monitoring

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coverage optimization for Defect-Tolerance Logic Mapping on Nanoelectronic Crossbar Architectures

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Journal of Computer Science & Technology 2012年第5期27卷 979-988页

作者：袁博李斌 Natural Inspired Computation and Applications Lab Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications University of Science and Technology of China

Emerging nano-devices with the corresponding nano-architectures are expected to supplement or even replace conventional lithography-based CMOS integrated circuits, while, they are also facing the serious challenge of high defect rates. In this paper, a new weighted coverage is defined as one of the most important evaluation criteria of various defecttolerance logic mapping algorithms for nanoelectronic crossbar architectures functional design. This new criterion is proved by experiments that it can calculate the number of crossbar modules required by the given logic function more accurately than the previous one presented by Yellambalase et al. Based on the new criterion, a new effective mapping algorithm based on genetic algorithm （GA） is proposed. Compared with the state-of-the-art greedy mapping algorithm, the proposed algorithm shows pretty good effectiveness and robustness in experiments on testing problems of various scales and defect rates, and superior performances are observed on problems of large scales and high defect rates.

关键词： coverage optimization defect-tolerance nanoelectronic crossbar logic mapping evolutionary algorithm

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coverage optimization for Large-Scale Mobile Networks With Digital Twin and Multi-Agent Reinforcement Learning

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 18316-18330页

作者： Liu, Haoqiang Li, Tong Jiang, Fenyu Su, Weikang Wang, Zhaocheng Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

With the exponential growth of mobile users, ensuring high-quality network coverage has become paramount. Large-scale mobile networks consist of numerous base stations (BSs), each with adjustable parameters such as angles and beam widths. Automatically optimizing network coverage can be difficult due to environmental factors and the interdependence of the adjustable parameters. Due to the inherent uncertainties and unpredictable nature of large-scale wireless networks, traditional methods such as heuristics and meta-heuristics lack the adaptability and scalability required to cope with their dynamic environment. To address these challenges, we propose utilizing digital twin and reinforcement learning (RL) techniques within mobile networks characterized by multiple collaborating agents. We initially introduce DT-SimNet, a digital twin-enabled mobile network simulator to facilitate optimization evaluation. DT-SimNet can efficiently simulate communication behaviors of network elements within a complex environment while revealing user mobility patterns. Moreover, to address challenges arising from multifaceted relationships among users, BSs, and the parameters across BSs, we introduce an innovative strategy named Optimized Multi-Agent Proximal Policy optimization with Self-supervised Prediction (OMAPPO-SSP). Compared to MAPPO, which leads to limited applicability and inferior performance due to the dynamic characteristics of 5G networks, this approach leverages network structure optimization and a self-supervised prediction mechanism, employing multi-agent reinforcement learning (MARL) principles to enhance efficiency. By harnessing collaborative neural networks, OMAPPO-SSP facilitates the explicit learning of behavioral interactions among all BSs, enabling effective decision-making in environments characterized by intricate spatial relationships, dynamic user behaviors, and diverse interactions. Extensive experiments are conducted to validate the efficiency and effecti

关键词： optimization 5G mobile communication Reinforcement learning Heuristic algorithms Digital twins Collaboration Wireless networks Training Signal to noise ratio Complexity theory coverage optimization wireless network multi-agent reinforcement learning self-supervised prediction digital twin

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coverage optimization for Directional Sensor Networks: A Novel Sensor Redeployment Scheme

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IEEE INTERNET OF THINGS JOURNAL 2023年第2期10卷 1461-1475页

作者： Cai, Xuelian Wang, Luqiao Hui, Yilong Chen, Yue Yue, Wenwei Wang, Hui Zhang, Yao Cheng, Nan Li, Changle Xidian Univ Res Inst Smart Transportat State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Northwestern Polytech Univ Dept Comp Sci Xian 710072 Peoples R China

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

关键词： coverage optimization directional sensor networks (DSNs) Internet of Things (IoT) minimum exposure path (MEP)

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coverage optimization of Robotic Sensor Networks in Nonconvex Environments via Rotary Pointer Partitions

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2024年第3期32卷 1066-1073页

作者： Zhai, Chao Fan, Pengyang Zhang, Hehong China Univ Geosci Wuhan Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Fujian Prov Univ Key Lab Intelligent Metro Fuzhou 350108 Peoples R China

Efficiently handling random events in the uncertain environment requires the farsighted deployment of robotic sensor networks to minimize the service cost. Nevertheless, environmental nonconvexity makes an intangible barrier for spatially distributed design of coverage control algorithms. To solve this dilemma, a novel coverage formulation is proposed for robotic sensor networks with the assistance of rotary pointer partitions. As a result, the coverage region is partitioned into multiple subregions by rotary pointers, and workload equalization can be fulfilled by adjusting phase angles of rotary pointers. Thus, a distributed coverage control algorithm is developed to deploy robotic sensor networks by driving each robot toward the centroid of its own subregion with load balancing. In addition, a simple criterion on communication topology of robotic sensor networks is presented to determine the exponential convergence of partition dynamics. Moreover, a self-perturbation strategy is developed to reduce the service cost without impairing existing workload partition, which improves the coverage quality of robotic sensor networks in a short time. Theoretical analysis is conducted to guarantee the coverage performance of robotic sensor networks, as compared to the classic Voronoi partitions. Finally, numerical simulations are carried out to substantiate the efficacy of the proposed coverage control approach.

关键词： coverage optimization nonconvex environment self-perturbation workload equalization

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