Coverage Hole Patching of Hybrid Wireless Sensor Network in Marine Environment

2019 ◽  
Vol 94 (sp1) ◽  
pp. 296
Author(s):  
Xinmiao Lu ◽  
Qiong Wu
2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jing ◽  
Lingfu Kong ◽  
Liang Kong

The existing coverage hole boundary detection methods cannot detect large-scale coverage hole boundary in wireless sensor network quickly and efficiently. Aiming at this problem, a boundary detection method for large-scale coverage holes in wireless sensor network based on minimum critical threshold constraint is proposed. Firstly, the optimization problem of minimum critical threshold is highlighted, and its formulaic description is constructed according to probabilistic sensing model. On the basis of this, the distributed gradient information is used to approximately solve the optimization problem. After that, local-scale rough boundary detection algorithm incorporating the minimum critical threshold and its iterative thinning algorithm are proposed according to blocking flow theory. The experimental results show that the proposed method has low computational complexity and network overhead when detecting large-scale coverage hole boundary in wireless sensor network.


2020 ◽  
Vol 17 (6) ◽  
pp. 2488-2495
Author(s):  
Shalu ◽  
Amita Malik

Nodes in the wireless sensor network have a minimal power source and they exhaust very quickly in communicating with each other. If any of the nodes die, a coverage hole creates in that region. This coverage hole leads to fast energy depletion of other nodes along with the security issues due to intruder node’s placement at that location. The solution to detection of coverage hole is discussed in our paper and it is experimentally validated. We propose an unsupervised machine learning clustering algorithm to cluster the network graph metrics. An undirected network graph of nodes is created and five graph metrics are extracted. The vector of features is clustered by Ant colony optimized expectation.maximization Gaussian mixture model (ACO-EM GMM) clustering algorithm. Our algorithm is compared with the state of art works based on false detection parameter.


2014 ◽  
Vol 10 (1) ◽  
pp. 950973 ◽  
Author(s):  
César Ortega-Corral ◽  
Luis E. Palafox ◽  
J. Antonio García-Macías ◽  
Jaime Sánchez-García ◽  
Leocundo Aguilar

2019 ◽  
Author(s):  
Mohamed A Bayoumi ◽  
Tarek M Salem ◽  
Samir M Koriem

Abstract Area detection and measuring is one of the most important problems in wireless sensor network because it mainly relates to the continuity and functionality of most routing protocols applied to the region of interest (ROI). Electronics failure, random deployment of nodes, software errors or some phenomena such as fire spreading or water flood could lead to wide death of sensor nodes. The damage on ROI can be controlled by detecting and calculating the area of the holes, resulting from the damaged sensor networks. In this paper, a new mathematical algorithm, wireless sensor hole detection algorithm (WHD), is developed to detect and calculate the holes area in ROI where the sensor nodes are spread randomly. WHD is developed for achieving quality of service in terms of power consumption and average hole detection time. The dynamic behavior of the proposed WHD depends on executing the following steps. Firstly, WHD algorithm divides down the ROI into many cells using the advantage of the grid construction to physically partition the ROI into many small individual cells. Secondly, WHD algorithm works on each cell individually by allocating the nearest three sensor nodes to each of the cell’s coordinates by comparing their positions, WHD connects each cell’s coordinate points with the selected sensor nodes by lines that construct a group of triangles, then WHD calculates the area of upcoming triangles. Repeating the previous step on all the cells, WHD can calculate and locate each hole in the ROI. The performance evaluation depends on the NS-2 simulator as a simulation technique to study and analyze the performance of WHD algorithm. Results show that WHD outperforms, in terms of average energy consumption and average hole discovery time, path density algorithm, novel coverage hole discovery algorithm and distriputed coverage hole Detection.


Sign in / Sign up

Export Citation Format

Share Document