Activities scheduling algorithms based on probabilistic coverage models for wireless sensor networks

Boudali, Mohammed; Senouci, Mustapha Réda; Aissani, Mohamed; Hidouci, Walid-Khaled

doi:10.1007/s12243-017-0564-9

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…Boudali et al 24 proposed three efficient activities scheduling algorithms. The first algorithm (C1L‐PBC) is centralized all calculations in one sink, and it is based on a coverage graph.…”

Section: Related Workmentioning

confidence: 99%

HDRM: A hole detection and recovery method in wireless sensor network

Papi

Barati

2022

Int J Communication

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Summary Wireless sensor networks (WSNs) consist of many sensor nodes, gathering data from the environment and sending it to base station (BS). One of the major challenges in WSNs is coverage maintenance. Since batteries provide sensor nodes' energy, battery depletion leads to the death of sensor nodes; when several nodes are dead, connectivity is lost, and network coverage fails. This paper proposed a method consisting of two phases to provide and maintain coverage in WSNs. In the first phase, hole detection is performed through a simple distributed method with low overhead. Nodes explore whether there are holes around them or not. In the second phase, coverage holes are covered by mobile nodes. When the number of holes exceeds mobile nodes, the holes with the highest priority and the shortest distance to mobile nodes will be selected. The performance of the proposed method (HDRM) was compared with Dezfuli et al. method, PEAS‐LI, CACA, Dezfuli et al. method, and Verma et al. method in terms of average coverage percentage, the average number of alive nodes, energy consumption, and network lifetime. The results of the simulation reveal that the proposed method leads to a decrease in the energy consumption of network, an increase in network lifetime, and improved coverage in the network.

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Section: Related Workmentioning

confidence: 99%

HDRM: A hole detection and recovery method in wireless sensor network

Papi

Barati

2022

Int J Communication

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“…The second one is located and it ensures 1-barrier coverage. On the other hand, the third one is distributed and it can ensure 2-barrier coverage [9].…”

Section: Related Workmentioning

confidence: 99%

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Chen¹,

Xiao-guang²,

Wang³

2019

DCDS-S

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As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.

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“…Some works report probabilistic models that factor environmental conditions [3–5, 10, 11, 31, 32, 34, 36, 37]. Other probabilistic models reported are truncated attenuated disk model [21], staircase model [15, 34], cubic probabilistic model [38], irregular sensing model [20, 33] and directional sensing models [7, 14]. However, all these models consider a subset of factors; either the sensing device characteristics or the environmental parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Various probabilistic sensing models have been reported in the literature. The most commonly used probabilistic models involves the sensing capability to be an exponentially decreasing function whose constants depict the sensing device characteristics [3–5, 10, 12, 13, 15–17, 19, 21, 26, 28–30, 38]. Some works report probabilistic models that factor environmental conditions [3–5, 10, 11, 31, 32, 34, 36, 37].…”

Section: Introductionmentioning

confidence: 99%

Node density optimisation using composite probabilistic sensing model in wireless sensor networks

Rai¹,

Daruwala²

2019

IET wirel. sens. syst.

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Network coverage is a measure of efficiency that signifies the extent to which the deployed nodes collectively cover the network area. It is a fundamental and critical quality of service (QoS) parameter for designing wireless sensor networks (WSNs). Various sensing models are reported which can be used to predict the coverage fraction for a given number of nodes in a predetermined network area. However, each of these reported models consider a subset of parameters. In this study, a novel formulation and hence a new model, composite probabilistic sensing model (CPSM) is proposed which combines the cumulative effects of all the possible factors, thus resulting in a realistic study. Further, the model is revisited to estimate the optimal density of randomly deployed nodes required to attain the desired network area coverage. An exhaustive parametric study is carried out and the results obtained are used to empirically derive a formula based on regression analysis using least square polynomial curve fitting technique. The formulation can be readily and accurately used to design any practical WSN system.

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Activities scheduling algorithms based on probabilistic coverage models for wireless sensor networks

Cited by 4 publications

References 25 publications

HDRM: A hole detection and recovery method in wireless sensor network

HDRM: A hole detection and recovery method in wireless sensor network

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Node density optimisation using composite probabilistic sensing model in wireless sensor networks

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