Coverage maximization in mobile Wireless Sensor Networks utilizing immune node deployment algorithm

Abo‐Zahhad, Mohammed; Ahmed, Sabah M.; Sabor, Nabil; Sasaki, Shigenobu

doi:10.1109/ccece.2014.6901069

Cited by 26 publications

(8 citation statements)

References 10 publications

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“…As for the coverage gain, we adopt the evaluation method of Abo-Zahhad et al in [10]. We treat the Area of Interest (AoI) as a matrix C of dimension w × h. C(i, j) presents the cell at position (i, j) in the matrix C where i ≤ w is a row in C, and j ≤ h is a column C. To exemplify, Figure 7 shows the distribution of two devices in the matrix D A (the red circle) and D B (the black circle).…”

Section: Coverage Gain and Displacementmentioning

confidence: 99%

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MCCM: An Approach for Connectivity and Coverage Maximization

Ghaddar¹,

Hatoum²,

Fadlallah³

et al. 2020

Future Internet

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The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called M C C M . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art.

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Section: Coverage Gain and Displacementmentioning

confidence: 99%

“…The basic requirement after deployment is to have sufficient coverage and connectivity [9]. In many cases, the mobility feature of devices is used to maximize the coverage [10]. Wireless sensor devices can sense, process, and in some applications move and organize themselves for coverage missions.…”

Section: Introductionmentioning

confidence: 99%

MCCM: An Approach for Connectivity and Coverage Maximization

Ghaddar¹,

Hatoum²,

Fadlallah³

et al. 2020

Future Internet

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show abstract

“…However, mobile nodes can be also employed to elevate sensing coverage or yet network connectivity. In [2,3,12] authors propose strategies to spread sensor nodes aiming to improve sensing coverage. As the former, this work also proposes a strategy to dynamically maximize coverage considering mobile sensor nodes.…”

Section: Mobile Nodesmentioning

confidence: 99%

“…As, in these cases, an initial random deployment is obtained, a dynamic reorganization strategy can be helpful to spread nodes into the desired area, moving them according to a given strategy. The mobility of nodes can be explored for deploying nodes in the area of interest maximizing coverage [1][2][3]. Besides, the movement can be also adopted to reorganize nodes in such a way that a working node can replace a dead node in order to keep or restore coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Different strategies can be used to dynamically manage WSN's resources, aiming to optimize their usage. Researchers have proposed adaptations that can on-the-fly decide which kind of services a node will provide based on its battery level, or balance the load among nodes [4,5], aiming to increase lifetime, or yet move nodes to improve coverage 2 Wireless Communications and Mobile Computing [2]. Since several management strategies can be adopted, designers should decide which strategy applies to a given WSN configuration.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency Evaluation of Strategies for Dynamic Management of Wireless Sensor Networks

Gonzalez

Brisolara

Ferreira

2017

Wireless Communications and Mobile Computing

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This paper presents and evaluates dynamic management strategies to improve efficiency in event-triggered wireless sensor networks. We are considering mobility, where nodes move themselves to maximize the coverage, and load balancing state-of-the-art techniques, by which the number of nodes sensing the same area is reduced. To explore mobility, we present a simple method by which nodes can dynamically reorganize themselves based on the force fields approach of mobile robotics. Firstly, the strategies are evaluated separately through experiments with different network configurations and, afterwards, a joint evaluation has been conducted to observe the impact of mobility on the efficiency of load balancing techniques. We show that mobile nodes significantly contribute to keeping the coverage as nodes die in mesh and powerfully improving it in random deployments. Load balancing techniques achieve important results, increasing lifetime and the number of sensed events. However, in random deployments, these techniques lose efficiency and become unsuitable strategies. Combining these strategies with mobility, we observe that PS-based technique keeps its contribution in mesh and random deployments, as well as improving its performance for not so dense networks. Ant-based technique when combined with mobile nodes loses performance significantly in mesh and keeps its good performance in random deployed and less dense networks.

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