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Meta Heuristic Technique with Reinforcement Learning for node deployment in Wireless sensor Networks

SN Computer Science 2024年第5期5卷 1-11页

作者： Kusuma, S.M. Veena, K.N. Kumar, B. P. Vijaya Naresh, E. Marianne, Lobo Athena School of ECE Reva University Bangalore India Department of ETE MS Ramaiah Institute of Technology Bangalore India Department of ISE MS Ramaiah Institute of Technology Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Wireless sensor Networks (WSNs) are vital in applications like environmental monitoring, smart homes, and battlefield surveillance. Comprising small devices with limited resources, WSNs require efficient node deployment for power optimization and prolonged network lifetime, ensuring sufficient coverage and connectivity. This study introduces an Intelligent Satin Bower Bird Optimizer augmented with reinforcement learning (ISBO-RL), enhancing coverage and connectivity. ISBO-RL focuses on optimal sensor placement for improved coverage and connectivity, using an Optimum Position Finding (OPF) method to identify key sensor node locations. Reinforcement learning is integrated into the ISBO algorithm, allowing nodes to adapt based on performance and changing conditions. Experimental results on diverse platforms highlight ISBO-RL’s efficacy and its superior coverage and connectivity performance as compared to other algorithms. ISBO-RL represents a significant advancement in the field of Wireless sensor Networks, offering a promising solution to address the challenges of efficient node deployment and network optimization in various critical applications. © The Author(s) 2024.

关键词： Intelligent satin bower bird Meta heuristic Reinforcement learning sensor node deployment Wireless sensor networks

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Optimal node deployment and Coverage in Next Generation Wireless sensor Networks Applications 15

Optimal Node Deployment and Coverage in Next Generation Wire...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mandli, Rami Reddy Chandra, M. L. Ravi Dilli, Ravilla Jawaharlal Nehru Technological University Electronics and Communication Engineering Ananthapuramu India Srinivasa Ramanujan Institute of Technology Electronics and Communication Engineering Andhra Pradesh Ananthapuramu India Manipal Institute of Technology Manipal Academy of Higher Education Electronics and Communication Engineering Udupi Karnataka Manipal576104 India

ISBN: (纸本)9798350370249

sensor nodes deployment is very critical in wireless sensor networks (WSNs) to cover a set of predetermined locations called 'targets' in the region of interest (ROI), and to enhance the coverage area, optimal position of sensor nodes in ROI is essential. The aim of sensor nodes deployment and coverage in WSNs is to compute sensor node's locations in the given network terrain such that they effectively detect the events as well as maximize the network lifetime. Battery depletion at sensor nodes creates coverage holes in ROI and it mitigates the network coverage. The objective of this paper is to define optimal locations of sensor nodes prior to their deployment and enhance the coverage area in the given geographical location using an improved particle swarm optimization (IPSO) algorithm. The experimental results showed superior coverage and faster convergence rate with the IPSO algorithm-than random deployments and grid-based deployments of sensor nodes. © 2024 IEEE.

关键词： Network lifetime node coverage particle swarm optimization sensor node deployment wireless sensor networks

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An Enhanced Particle Swarm Optimization-Based node deployment and Coverage in sensor Networks

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sensorS 2024年第19期24卷 6238页

作者： Bhargavi, Kondisetty Venkata Naga Aruna Varma, Gottumukkala Partha Saradhi Hemalatha, Indukuri Dilli, Ravilla Koneru Lakshmaiah Educ Fdn Comp Sci & Engn Hyderabad 500075 Telangana India Koneru Lakshmaiah Educ Fdn Comp Sci Engn Vijayawada 520002 Andhra Pradesh India SRKR Engn Coll Informat Technol Bhimavaram 534204 Andhra Pradesh India Manipal Inst Technol Manipal Acad Higher Educ Elect & Commun Engn Udupi 576104 Karnataka India

Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6-120 m with the existing works.

关键词： coverage problem Delaunay triangulation particle swarm optimization sensor node deployment wireless sensor network

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An Adaptive ACO-Based node deployment Algorithm in Wireless sensor Networks

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JOURNAL OF INTERNET TECHNOLOGY 2017年第5期18卷 1193-1202页

作者： Zhang, Shun-Miao Huang, Degen Chu, Shu-Chuan Sung, Tien-Wen Wu, Tsu-Yang Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Fujian Peoples R China Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Fujian Peoples R China Flinders Univ South Australia Sch Comp Sci Engn & Math Adelaide SA Australia

sensor node deployment is one of the critical problems in wireless sensor networks. Although several methods were proposed to solve the deployment problem in the past years, there still exists some weaknesses. In this paper, we propose an adaptive node deployment algorithm, named ACO-GD, based on the concept of ant colony optimization (ACO) algorithm and an idea of gradient diffusion (GD). A preliminary algorithm was designed and proposed in this paper before ACO-GD. It is a non-uniform gradient diffusion approach and can be used to solve the energy hole problems and extend the network lifetime. The preliminary algorithm was then integrated with the ACO algorithm. The simulation results show that our ACO-GD algorithm performs better efficiency for the cost of coverage, network lifetime, and the number of hops.

关键词： WSNs ACO-GD sensor node deployment Non-uniform

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An Adaptive ACO-Based node deployment Algorithm in Wireless sensor Networks

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网际网路技术学刊 2017年第5期18卷 1193-1202页

作者： Shun-Miao Zhang Degen Huang Shu-Chuan Chu Tien-Wen Sung Tsu-Yang Wu

关键词： WSNs ACO-GD sensor node deployment Non-uniform

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Layering of edge node for jamming attack detection and elimination in wireless sensor network

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第22期35卷 e7737-e7737页

作者： Murugaveni, S. Priyalakshmi, B. SRM Inst Sci & Technol Fac Engn & Technol Dept Elect & Commun Kattankulathur 603203 India

Wireless sensor network (WSN) is an environmental monitoring system exposing sensor nodes to various attacks. Jamming is an attack that disrupts the entire sensor network and affects the network's life span. The jammer broadcasts the signal at a frequency similar to the broadcast signal to jam the corresponding sensor node. There are many jamming detection schemes that only detect the jamming node, but they do not focus on eliminating the node that is not jamming. In this article, a novel strategy is used along with optimization to eliminate the disturbance in the sensor network and save the power consumed by nodes. The system uses the edge node, the sub-edge node, and the cluster head for detection of jamming attack. Hyperbolic optimization is implemented in the cluster to eliminate data transmission failures. The proposed model considers the node's round trip time to detect jamming attacks. The optimization method considers the cluster head as the cluster member based on the probability of jamming in the network. The design is implemented and analyzed in MATLAB platform. As a result, the proposed method has improved the detection accuracy to 11.1% compared with extant techniques.

关键词： Wireless sensor network sensor network topology sensor node deployment jamming attack cluster head edge node and spider monkey optimization

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deployment of sensors in WSN: An Efficient Approach Based on Dynamic Programming

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Chinese Journal of Electronics 2015年第1期24卷 33-37页

作者： LI Yongyan GAO Wen WU Chunming WANG Yansong University of Shaoxing at Shang Yu Institute of Computer System Architecture Zhejiang University ZTE Corporation

Efficient sensor node deployment is extremely important in wireless sensor networks. It earns great practical meanings through using fewer sensor nodes as far as possible to satisfy different requirements such as the requirement on coverage and overcoming the potential sensor node failures and the adverse influence from the environment. We propose an efficient approach for the deployment of sensor nodes in wireless networks, termed as EDSNDA, which is excellent in taking both the requirements of sensor coverage and network connectivity into consideration when minimizing the number of necessary sensor nodes to the best of its ability. We proposed a new coverage model of sensor node. Based on the sensor coverage model, we establish four dynamic programming models in four different practical situations, respectively. The algorithms are then proposed which are used for solving the corresponding dynamic programming models. The validity of the method is justified by simulation studies in which the method is compared with the current representative methods. The simulation results show that our method performs better than the other ones with fewer sensor nodes, better coverage and network connectivity result in the same circumstance.

关键词： Wireless sensor network sensor node deployment Dynamic programming sensor coverage model

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An Ant Colony Optimization Approach for the deployment of Reliable Wireless sensor Networks

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IEEE ACCESS 2017年 5卷 10744-10756页

作者： Deif, Dina S. Gadallah, Yasser Amer Univ Cairo Cairo 11835 Egypt

A reliable wireless sensor network (WSN) is defined as a network that functions satisfactorily, in terms of both its coverage and connectivity to the sink(s), throughout its intended mission time. Deploying reliable WSNs is especially important for critical Internet of Things (IoT) applications, such as industrial, structural health-monitoring, and military applications. In such applications, failure of the WSN to carry out its required tasks can have serious effects, and hence, cannot be tolerated. However, the deployment of reliable WSNs is a challenging problem. This is primarily attributed to the fact that sensor nodes are subject to random failures due to different factors, such as hardware failures, battery depletion, harsh environmental conditions, and so on. In this paper, the problem of deploying a WSN with a specified minimum level of reliability at a minimum deployment cost is addressed. This problem is coined the minimum cost reliability constrained sensor node deployment problem (MCRC-SDP). The MCRC-SDP is proved to be an NP-Complete. An ant colony optimization algorithm coupled with a local search heuristic is proposed as a solution. Extensive experimental results demonstrate the effectiveness of the proposed approach in finding high-quality solutions to the problem.

关键词： Wireless sensor networks sensor node deployment network reliability ant colony optimization greedy heuristic local search

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Quality of Service Enhancement in Wireless sensor Network Using Flower Pollination Algorithm

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PROGRAMMING AND COMPUTER SOFTWARE 2018年第6期44卷 398-406页

作者： Muruganantham, R. Ganeshkumar, P. MAM Sch Engn Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India Anna Univ Dept Informat Technol Reg Campus Coimbatore Tamil Nadu India

One of the renowned and drastically rising research works is developing and deploying emerging applications under wireless sensor networks. Surveillance monitoring, military and health care industries are some examples of emerging applications. Generally, sensor nodes are deployed randomly and distributed within the network region. In Wireless sensor network, it is hard to increase the network lifetime and to maximize the coverage for interest points under certain constraints. A sensor node can able to sense only the environmental information within its sensing region;hence the deployment of the node must cover the points of interests by considering energy of the nodes. To do that, we propose a Flower Pollination Algorithm (FPA) to optimize the locations of the sensor nodes and balance the residual energy of the nodes. The FP algorithm adjusts the sensor nodes and schedules the node condition (Sleep/Active) to maintain target coverage for long period of time. The proposed FPA is experimented using a simulation tool and the obtained results are verified. From the results it is noticed that the target coverage time is increased by 30% and also the performance is evaluated by comparing the obtained results with the existing IWD and ACO algorithms. Finally, it is confirmed that the FP algorithm outperforms than the IWD and ACO by increasing the energy efficiency 40% than the existing approaches.

关键词： Wireless sensor Networks target coverage sensor node deployment quality of service optimization Flower Pollination Algorithm Artificial Bee Colony particle swarm optimization

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Wireless sensor Network Coverage Optimization Based on the Novel Enhanced Hunter-Prey Optimization Algorithm

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IEEE sensorS JOURNAL 2024年第19期24卷 31172-31187页

作者： Song, Jie Hu, Yongmao Luo, Yanbi Dali Univ Coll Engn Dali 671003 Peoples R China

Optimizing the coverage of wireless sensor nodes is a critical issue in wireless sensor networks (WSNs). Current coverage optimization techniques often suffer from local optimization problems, resulting in poor optimization effects, ample coverage blind areas, and uneven distribution. Therefore, based on the hunter-prey optimization (HPO) algorithm, we propose a novel enhanced HPO (NEHPO) algorithm to address its drawbacks of low search precision and easy fall into local optima, achieving enhanced coverage performance. First, the initial population is deployed using Sobol sequence sampling to increase diversity. Second, we propose a novel nonlinear convergence factor to balance global exploration and local exploitation. Finally, the improved algorithm employs Levy flight and vertical-horizontal crossover strategies to mitigate the late-stage local optimum problem of the HPO algorithm. The particle swarm optimization (PSO), sine-cosine algorithm (SCA), sparrow search algorithm (SSA), HPO, and NEHPO are numerically tested by a benchmark function, and the experimental results show that the convergence performance and global search ability of the NEHPO algorithm are better than those of the other four algorithms. For the WSN coverage problem, this article tests the above five algorithms using four cases involving variations in node count, node radius, monitoring area size, and deployment in different obstacle regions. Compared to the other four algorithms, simulation results indicate that the NEHPO algorithm achieves the most uniform distribution of network nodes, the highest network coverage and optimization rates, the best convergence performance, and more robust adaptability for optimizing node deployment in obstacle environments.

关键词： sensors Optimization Wireless sensor networks Heuristic algorithms Convergence Statistics Sociology Algorithm improvement network coverage optimization novel enhanced hunter-prey optimization (NEHPO) sensor node deployment wireless sensor networks (WSNs)

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