A Novel Coverage Holes Discovery Algorithm Based on Voronoi Diagram in Wireless Sensor Networks

Dai, Guoyong; Lv, Hexin; Chen, Luyi; Zhou, Binbin; Xu, Peng

doi:10.14257/ijhit.2016.9.3.25

Cited by 20 publications

(14 citation statements)

References 9 publications

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“…Additionally, the holes were classified by connecting the centre of empty circles of each Delaunay triangle with its neighbor by a line segment. In [7], authors proposed Voronoi diagram based coverage-hole detection algorithm (VCHDA) that was able to identify the coverage-holes and could label the border nodes of the coverage-hole area.…”

Section: Related Workmentioning

confidence: 99%

Node Position Estimation for Efficient Coverage Hole-Detection in Wireless Sensor Network

Das

Debbarma

2019

CyS

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In recent times, Wireless Sensor Networks (WSNs) have active involvement in diverse applications such as: environment monitoring, security & surveillance, health care, precision agriculture, industrial applications and many more. Generally, sensor nodes are deployed randomly in such applications. Therefore, estimation of node location is a legitimate problem in WSNs, due to the fact that uncovered region can result in coverage-holes in the network. One state-of-art solution of this problem is Global Positioning System (GPS) but GPS based solution to localize a node might not be worthy due to the cost of extra hardware and power requirements. Therefore, a low cost solution to this problem might be Computational Geometry based approach to localize a node. In this paper, we first find out distance between Anchor Node (AN) and Unknown Node (UN) based on RSSI Profiling. Subsequently, the node location is estimated using Trilateration. Finally, a Delaunay Triangle is constructed on the basis of node location information. Then the property of empty circle is used to recognize whether coverage hole is present or not in the given ROI. Correctness of the algorithm is checked based on the simulations and theoretical proofs.

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Section: Related Workmentioning

confidence: 99%

Node Position Estimation for Efficient Coverage Hole-Detection in Wireless Sensor Network

Das

Debbarma

2019

CyS

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“…The first step is to construct the Voronoi diagram for the organized wireless sensor network with the composed locations of all the sensor nodes involved in simulation as shown in Fig 1. In this work the distance information from edges and vertices of its associated Voronoi cell is carried by each sensor node in the network [16]. Next is to identify the presence of cover holes.…”

Section: A Coverage Hole Detection Using Voronoi Diagrammentioning

confidence: 99%

Behavioral Based Uncertainty Handling in Energy Depletion Reduction (BUEDR) through Alleviation of Holes in an IoT Enabled Wireless Sensor Networks

P*¹,

A²

2019

IJRTE

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In current era the usage of Internet is gradually improvising from Internet of People to an Internet of Things (IoT). Through the gateway, Wireless Sensors Networks (WSN) are integrating things to the Internet. But there are two major elements which affect the performance of the network, the presence of void hole and energy hole. Due to the unavailability of forwarded nodes, a particular region may suffer from void hole. In addition, if the load of data is not correctly balanced among the sensor nodes, then energy hole occurs. To overcome these issues, the proposed work uses a threefold behavioral based uncertainty handling transmission policy. In the first fold, the area of WSN is divided using voronoi diagram, which resolves the geometric relationships between sensors and to distribute transmission uniformly over the network area. The second fold alleviates energy hole vagueness in an adversarial environment by gathering information about the node denoted by intuitionistic fuzzy domain based cluster head selection to enhance energy dissipation. The third fold is optimal route selection for which fuzzy fish swarm optimization is adopted. From the simulation results, it is observed that the energy consumption achieves its superiority by hole alleviation and thus maximizes the network life time.

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“…Many researchers have studied the discovery and repair of coverage holes. Coverage holes exist, if the required coverage degree is k in a specific application of the WSN and the number of nodes, which covers the region, is less than k during the lifetime of the network [1]. Coverage holes occur often with the random deployment, where the sensor nodes positions are not well controlled, and thus, it is usually uncertain to achieve full coverage, especially when the number of nodes is low [5].…”

Section: Wsn Coverage Holesmentioning

confidence: 99%

“…They are designed for multiple objectives, such as tracking objects (animals, humans, and vehicles), environmental observations (temperature changes, humidity, vibrations, pollution, etc. ), habitat monitoring, intrusion detection, and structural monitoring [1], [2]. The objectives of these applications are to first detect the events occurring within a region of interest (ROI) and then transmit all the collected data to the base station (BS) for further processing.…”

Section: Introductionmentioning

confidence: 99%

A Recovery Algorithm to Detect and Repair Coverage Holes in Wireless Sensor Network System

Khamlichi¹,

Mesmoudi²,

Tahiri³

et al. 2018

jcm

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Nowadays, wireless sensor network (WSN) systems are widely used in various applications designed to monitor real world physical or environmental phenomena, in more than a few disciplines such as healthcare and habitat monitoring. The position of the sensor nodes significantly affects the accuracy of the information collected, which decides the quality of service offered by the application system. Hence, the deployment strategy is one of the key issues to be solved in WSN. Generally, to reach the end for which the WSN system is designed, full coverage and connectivity must be provided, so each and every event can be detected, and then, guarantee that all the collected information is transmitted to the desired base station for processing and analyzing. When the deployment is not optimally achieved, coverage holes and serious coverage overlapping can occur, which creates uncovered areas and undetected application key events, leading to system mission failure. In this paper, we propose a recovery method based on a gradient algorithm combined with a clustering technique in order to detect the redundant sensor nodes in the whole WSN system and relocates them to heal the identified coverage holes.

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A Novel Coverage Holes Discovery Algorithm Based on Voronoi Diagram in Wireless Sensor Networks

Abstract: Coverage is a key and fundamental problem in wireless sensor networks.

Cited by 20 publications

References 9 publications

Node Position Estimation for Efficient Coverage Hole-Detection in Wireless Sensor Network

Node Position Estimation for Efficient Coverage Hole-Detection in Wireless Sensor Network

Behavioral Based Uncertainty Handling in Energy Depletion Reduction (BUEDR) through Alleviation of Holes in an IoT Enabled Wireless Sensor Networks

A Recovery Algorithm to Detect and Repair Coverage Holes in Wireless Sensor Network System

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