Area Coverage Strategy in IoT Networks Based on Redeployment, Descriptive Statistics, Correlation and Regression Parameters

Boualem, Adda; Runz, Cyril de; Ayaida, Marwane

doi:10.1007/s42979-022-01235-5

Cited by 3 publications

References 38 publications

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A fuzzy/possibility approach for area coverage in wireless sensor networks

et al. 2023

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Deterministic methods used to address the coverage problem in an uncertain deployment environment have not proven to be very successful. For this purpose, the original idea of this paper is to deal with the coverage problem in an uncertain environment with uncertain theories. We consider the imperfection in the deployment environment and in the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in WSN is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to schedule the Active/ Passive state of sensor nodes. This model helps to plan the scheduling of node states (Active / Passive) based on the possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated the proposed model with (a) a running example, (b) a statistical evaluation (calculation of the confidence interface), and (c) a comparison with maximum sensing coverage region problem (MSCR), Coverage Maximization with Sleep Scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP) and PEAS with location information protocols. The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem.

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A fuzzy/possibility approach for area coverage in wireless sensor networks

et al. 2023

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Sm-vsn-3c: a new Starlings model-based virtual sensor networks for coverage, connectivity, and data ccommunication

Boualem,

Ayaida,

de Runz

et al. 2024

Cluster Comput

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Coverage-based cooperative searching method for UAV swarm under multisensor errors

Leng,

Song,

Tao

et al. 2024

First Aerospace Frontiers Conference (AFC 2024)

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In recent years, the threat of UAV swarm weapons to air security is increasing, the full coverage detection of the incoming target swarm and the acquisition of the global movement information of the target is the premise and key to achieve efficient interception. Therefore, this paper proposes a coverage-based cooperative searching method for UAV swarm under multi-sensor errors. First, according to the typical spatial distribution characteristics of the incoming target swarm, the studied problem is simplified to a two-dimensional plane. Then, the detection probability distribution function of the ground-based rader and UAV airborne seeker are established, respectively. Based on these functions , a cooperative searching assignment model for maximizing detection probability of target swarm is proposed, where the flight safety distance constraint of the prusuer UAVs is also considered. Subsequently, the grey Wolf algorithm is applied to solve the multi-constraint multi-decision variable optimization probelm and generate the expected formation and line of sight direction of the UAV. Numerical simulation demonstrates that the proposed algorithm can efficiently detect the target swarm with a field coverage probability of no less than 99%, and the allocation time does not exceed 1.2 seconds.

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Area Coverage Strategy in IoT Networks Based on Redeployment, Descriptive Statistics, Correlation and Regression Parameters

Cited by 3 publications

References 38 publications

A fuzzy/possibility approach for area coverage in wireless sensor networks

A fuzzy/possibility approach for area coverage in wireless sensor networks

Sm-vsn-3c: a new Starlings model-based virtual sensor networks for coverage, connectivity, and data ccommunication

Coverage-based cooperative searching method for UAV swarm under multisensor errors

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