A coverage hole detection and repair algorithm in wireless sensor networks

Feng, Xia‐Ting; Zhang, Xin; Zhang, Jing; Muhdhar, Ali Ahmed

doi:10.1007/s10586-017-1665-y

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…In [18], the authors dynamically analyze the network and determine the coverage holes. Firstly, the network topology is analyzed based on a simple subnet.…”

Section: Related Workmentioning

confidence: 99%

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

2021

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Coverage is an important factor for the effective transmission of data in the wireless sensor networks. Normally, the formation of coverage holes in the network deprives its performance and reduces the lifetime of the network. In this paper, a multi-intelligent agent enabled reinforcement learning-based coverage hole detection and recovery (MiA-CODER) is proposed in order to overcome the existing challenges related to coverage of the network. Initially, the formation of coverage holes is prevented by optimizing the energy consumption in the network. This is performed by constructing the unequal Sierpinski cluster-tree topology (USCT) and the cluster head is selected by implementing multi-objective black widow optimization (MoBWo) to facilitate the effective transmission of data. Further, the energy consumption of the nodes is minimized by performing dynamic sleep scheduling in which Tsallis entropy enabled Bayesian probability (TE2BP) is implemented to switch the nodes between active and sleep mode. Then, the coverage hole detection and repair are carried out in which the detection of coverage holes if any, both inside the cluster and between the clusters, is completed by using the virtual sector-based hole detection (ViSHD) protocol. Once the detection is over, the BS starts the hole repair process by using a multi-agent SARSA algorithm which selects the optimal mobile node and replaces it to cover the hole. By doing so, the coverage of the network is enhanced and better QoSensing is achieved. The proposed approach is simulated in NS 3.26 and evaluated in terms of coverage rate, number of dead nodes, average energy consumption and throughput.

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“…In [18], the authors dynamically analyze the network and determine the coverage holes. Firstly, the network topology is analyzed based on a simple subnet.…”

Section: Related Workmentioning

confidence: 99%

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

2021

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“…A coverage hole detection and repair algorithm is proposed for solving the network disconnection problem [15]. The algorithm dynamically analyses the network topology based on maximum simple subnet and determines the boundary node of the empty area.…”

Section: Related Workmentioning

confidence: 99%

Distributed energy efficient algorithm for ensuring coverage of wireless sensor networks

Dezfuli

Barati

2019

IET Communications

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“…In general, algorithms to recognize different coverage holes within the sensor networks can be divided into two main classes, depending on the system domains and the degree of data constraints such probabilistic approaches and computational geometry based approaches. Compared to the probabilistic approaches, the computational geometry based approaches improve the precision of identification by finding the accurate size and shape of the coverage holes by coordinate information on the nodes and by providing better information for restoring of coverage holes [6,7].…”

Section: Introductionmentioning

confidence: 99%

DPLHD: A computational geometry based provision for coverage hole detection in critical Wireless Sensor Network Applications

Angelin

Silas

2022

Preprint

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Wireless Sensor Networks (WSNs) have quite an exceptionally significant effect in diverse fields correlated to surveillance in which coverage plays an essential role. In specific, the coverage holes are actually instigated by both randomized distribution of the wireless sensor nodes and the failure of nodes. Recharging or repairing the battery is difficult, and thus the collaborative identification and estimation of coverage holes and the reclamation of these holes has been considered significant. When organizing the sensor nodes in a large scale WSNs, it is indeed complicated to cover the target or the region of interest (ROI). Coverage quality is compromised by the occurrence of holes in the monitored area of surveillance. This paper proposes a collaborative distributed point location based coverage hole detection methodology to spot coverage holes in an energy efficient manner. Firstly, construct a polygon using a visibility estimation approach on the basis of neighborhood information. Then a point location based hole detection algorithm is used to diagnose whether coverage hole is existing or not in the ROI. Also, the extent of the coverage hole is estimated based on computational geometry based modified partitioning approaches which not only determines the exact region of the hole but also accurately detects the point where the node fails. The accuracy of the algorithm is evaluated here based on statistical proofs. Comparing current coverage hole detection approaches, the output results of the proposed framework surpasses existing algorithms in standings of coverage rate, energy efficiency and network lifetime.

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A coverage hole detection and repair algorithm in wireless sensor networks

Cited by 13 publications

References 7 publications

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

Distributed energy efficient algorithm for ensuring coverage of wireless sensor networks

DPLHD: A computational geometry based provision for coverage hole detection in critical Wireless Sensor Network Applications

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