k-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks

Abstract

In the process of the wireless sensor network research, the issue on the energy consumption and coverage is an essential and critical one. According to the characteristic of the sensor nodes, it is homogeneous, and we proposed the kdegree coverage algorithm based on optimization nodes deployment. First, the algorithm gives the solving procedure of the maximum seamless coverage ratio, when the three-node joint coverage has been provided. Second, when the sensor nodes are covering the monitoring area, the algorithm gives the solving procedure of the expected coverage quality and the judgment methods of the coverage ratio, when the nodes are compared with the nearby ones. And when there is redundancy coverage in the given monitoring area, we have given the solving procedure of any sensor nodes that exist in the redundant nodes coverage. Finally, using the simulation experiment, the results of the coverage algorithm based on optimization nodes deployment are compared with other algorithms in terms of the coverage quality and the network lifetime, the performance indexes have enhanced to 13.36% and 12.92% on average. Thus, the effectiveness and viability of the coverage algorithm based on optimization nodes deployment have been proved.

Job Performance Optimization Method Based on Data Balance in the Wireless Sensor Networks

“…i3 and ! i4 , the time spent by Reducer r i in processing the loads in the four parts is T i1 , T i2 , T i3 and T i4 , respectively [10][11]. It can be seen from Fig.1 that the completion time of job J depends on the completion time of Reducer r 3 .…”

Grid quorum‐based spatial coverage in mobile wireless sensor networks using nature‐inspired firefly algorithm

“…Each sensor s i has a spatial coordinate ( x i , y i ) and a sensing zone z i ( i = 1, … , n ). Furthermore, all sensors have the following characteristics (Sun et al, ): All the sensing zones of sensors are the same, which equal to the energy. All the sensing zones of sensors comply with the normal distribution. Thus, it is fewer than the length of IR, and the boundary effect is neglected. The surveillance range is equal to or greater than the double value of the sensing zone. All the sensors are independent and homogeneous. …”

“…Aiming to find promising coverage, this paper proposes a new enhanced algorithm, firefly algorithm with crossover and detection (CDFA) phases, for solving area coverage problem by finding the best spatial distribution of MWSN. FA (Yang, , ) is a hybrid with generalized opposition‐based learning (GOBL) method (Sun et al, ), which is based on chaotic behaviour for editing spatial locations of sensors. In addition, the paper is aided by a detailed statistical analysis and comparisons for testing the validity of the proposed algorithm.…”

An attack-defense game based reliability analysis approach for wireless sensor networks

“…[12][13][14][15] But limited energy and other factors limit the normal operation of WSNs. 16 A challenge in WSNs is to reduce communication and computational costs and ensure a high level of coverage 17 and connectivity. 18 In order to solve this problem and make the network work longer, 19,20 many different methods have been studied.…”