A sensor deployment approach for target coverage problem in wireless sensor networks

Yarinezhad, Ramin; Hashemi, Seyed Naser

doi:10.1007/s12652-020-02195-5

Cited by 36 publications

(20 citation statements)

References 36 publications

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“…Zakariayi and Babaie [12] proposed a clustering algorithm for maximizing region coverage in WSNs. The coverage is performed in a hexagonal structure of connecting nodes by differentiating static and mobile nodes.…”

Section: Related Workmentioning

confidence: 99%

Transformed Data Relaying Method for Coverage Hole Prone Wireless Sensor Network

Rajesh¹,

Tamizhazhagan²

2022

INDJCSE

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Wireless Sensor Network (WSN) is a collection of tiny sensing devices capable of aggregating and transmitting environmental information to common sink nodes. Due to limited battery capacity, the nodes cover a small region wherein multi-hop communications are adapted for long-distance relaying. Early energy drain and node replacement results in the coverage-hole problem, resulting in information loss. This paper introduces a Distance-to-Coverage Transformed Relaying (DCTR) method to lessen information loss due to connectivity issues. The proposed method relies upon a conclusive machine learning paradigm for analyzing the residual energy-to-path existence feature of a neighboring node. Based on the residual energy, the distance or coverage metric is satisfied for non-intervening transmissions. The learning outcome provides definite transmission intervals preventing additional delay due to re-discovery. The transmissions are confined in the provided interval recommended, based on connectivity and coverage. The performance of the proposed method is analyzed using coverage rate, data loss ratio, and energy consumption. The proposed method improves the coverage factor by 7.93%, reduces data loss by 9.64%, and energy consumed by 8.49% for different unconnected nodes.

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Section: Related Workmentioning

confidence: 99%

Transformed Data Relaying Method for Coverage Hole Prone Wireless Sensor Network

Rajesh¹,

Tamizhazhagan²

2022

INDJCSE

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“…Their simulation results demonstrated that the proposed approaches can solve the target coverage problem. Furthermore, their network lifetimes were longer than other existing methods [ 38 ]. In [ 39 ], Arivudainambi and Pavithra developed a sensor deployment scheme based on vertex coloring.…”

Section: Background and Related Workmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

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“…The network sensor nodes' unbalanced usage of energy or high utilization of power isa major factor in the network lifetime's reduction. The reason being that every sensor node's power supply is a battery which, because of the coverage [1] area's conditions, maybe neither replaceable nor rechargeable. Due to this, prolongation of network lifespan can be achieved through the control and balancing of the network's energy usage.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

Senthil

Raaza

Kumar

2021

Wireless Pers Commun

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Specialized transducers in Wireless Sensor Networks (WSNs) that offer sensing services to the Internet of Things (IoT) devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes' batteries. A prominent role in conserving power for energyconstrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the Base Station (BS), is done by the Low Energy Adaptive Clustering Hierarchy (LEACH). However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to be cover the network. The proposed work's primary novel contribution is the O-LEACH protocol that supplies the entire network's coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing Simulated Annealing (SA) with Lightning Search Algorithm (LSA) (SA-LSA), and Particle Swarm Optimization (PSO) with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectivelymanage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique's superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

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A sensor deployment approach for target coverage problem in wireless sensor networks

Cited by 36 publications

References 36 publications

Transformed Data Relaying Method for Coverage Hole Prone Wireless Sensor Network

Transformed Data Relaying Method for Coverage Hole Prone Wireless Sensor Network

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

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