A New Dynamic Deployment Approach Based on Whale Optimization Algorithm in the Optimization of Coverage Rates of Wireless Sensor Networks

Ozdag, Recep O.; Canayaz, Murat

doi:10.23884/ejt.2017.7.2.06

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…The linear weighted sum method is adopted to derive a single-objective function that optimizes the coverage and energy consumption of nodes. In [20], a dynamic deployment algorithm for optimizing area coverage in WSNs is suggested. The authors used the whale optimization algorithm to update the positions of sensors after initial random deployment.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Algorithm for Node Deployment with the Guarantee of Connectivity in Wireless Sensor Networks

Deghbouch

Debbat

2022

IJCAI

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In this paper, the problem of deployment in wireless sensor networks is investigated. The authors propose a Hybrid Modified Crow Search Bee Algorithm (HMCSBA) for coverage maximization with the guarantee of connectivity between the deployed sensors. Firstly, a Modified Crow Search Algorithm (MCSA) is proposed based on the basic CSA algorithm to form a connected network after initial random deployment. The position equation of the original CSA was updated by introducing a linear flight length that increases throughout iterations to force the sensors to join the network. Then, the Bees Algorithm (BA) is applied to optimize the network coverage without losing connectivity between the deployed sensors. Simulations and comparative studies were carried out to prove the relevance of the proposed algorithm. Results demonstrate that the proposed algorithm can optimize the coverage and guarantee network connectivity.Povzetek: Razvit je bil hibridni algoritem HMCSBA za zagotovljeno povezljivost brezžičnih senzorjev v omrežju.

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Section: Related Workmentioning

confidence: 99%

“…For this purpose, a set of experiments are conducted using HM-CSBA for different sensor densities. The obtained results are compared with the results of two recent deployment algorithms that are also based on metaheuristics namely WOA [20] and MCHSA [16]. The comparison is performed essentially on network coverage.…”

Section: Comparative Studymentioning

confidence: 99%

Hybrid Algorithm for Node Deployment with the Guarantee of Connectivity in Wireless Sensor Networks

Deghbouch

Debbat

2022

IJCAI

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“…A dynamic deployment technique for optimizing coverage ratio for WSNs using whale optimization technique (MADA-WOA) is addressed in [45]. A random deployment problem with mobile sensor nodes is considered.…”

Section: B Coverage Based On Meta-heuristic Techniquesmentioning

confidence: 99%

Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey

et al. 2019

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Wireless sensor networks (WSNs) have gained wide attention from researchers in the last few years because it has a vital role in countless applications. The main function of WSN is to process extracted data and to transmit it to remote locations. A large number of sensor nodes are deployed in the monitoring area. Therefore, deploying the minimum number of nodes that maintain full coverage and connectivity is of immense importance for research. Hence, coverage and connectivity issues, besides maximizing the network lifetime, represented the main concern to be considered in this paper. The key point of this paper is to classify different coverage techniques in WSNs into three main parts: coverage based on classical deployment techniques, coverage based on meta-heuristic techniques, and coverage based on self-scheduling techniques. Moreover, multiple comparisons among these techniques are provided considering their advantages and disadvantages. Additionally, performance metrics that must be considered in WSNs and comparison among different WSNs simulators are provided. Finally, open research issues, as well as recommendations for researchers, are discussed. INDEX TERMS Coverage, connectivity, deployment techniques, power consumption, wireless sensor network (WSN).

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“…The HS algorithm [23] uses a harmony search to maximize network coverage with minimal cost. The MADA-WOA algorithm [24] employs the whale optimization technique to optimize the dynamic deployment of sensor nodes. The Firefly algorithm [25] uses a firefly optimization technique to improve the coverage of mobile IoT.…”

Section: B Related Workmentioning

confidence: 99%

Parallel Genetic Algorithm to Extend the Lifespan of Internet of Things in 5G Networks

Zhang

Chen

et al. 2020

IEEE Access

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With the development and popularization of 5G networks, the coverage problem of the Internet of Things (IoT) will encounter the massive-node problem. In this paper, we design a parallel genetic algorithm that divides the coverage problem of IoTs with massive nodes into many small problems and then solves these problems using Hadoop in parallel. First, the algorithm uses partitioning and grouping operations to degrade the scale of a large IoT and makes the coverage problem solvable. The algorithm then adopts the multi-objective programming-based genetic algorithm (MPGA) to solve the coverage problem. MPGA uses the fast non-dominated sorting to optimize the IoT coverage and node redundancy; it implements the preferential selection of non-critical nodes to maximize the length of the configuration sequence of working nodes. Finally, the parallel genetic algorithm uses uniform mutation and individual pruning to optimize the genetic algorithm internally and force its solving process to quickly converge toward feasible solutions. Experimental results confirm that the MPGA outperforms the existing algorithm on small IoTs in terms of coverage, the number of nodes, computing time, and the IoT lifespan. They also demonstrate that the parallel genetic algorithm successfully solves the coverage problem of IoTs with massive nodes and significantly extends the IoT lifespan.INDEX TERMS 5G networks, Internet of Things, parallel computing, genetic algorithm. II. BACKGROUND, RELATED WORKS AND MOTIVATION OF THIS WORK

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A New Dynamic Deployment Approach Based on Whale Optimization Algorithm in the Optimization of Coverage Rates of Wireless Sensor Networks

Abstract: The dynamic deployments of Wireless Sensor Networks refer to the process of determining the location of the networking sensors in the region

Cited by 11 publications

References 19 publications

Hybrid Algorithm for Node Deployment with the Guarantee of Connectivity in Wireless Sensor Networks

Hybrid Algorithm for Node Deployment with the Guarantee of Connectivity in Wireless Sensor Networks

Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey

Parallel Genetic Algorithm to Extend the Lifespan of Internet of Things in 5G Networks

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