Improving coverage and connectivity in mobile sensor networks using harmony search

Mohamed, Shaimaa M.; Hamza, Haitham S.; Saroit, Imane Aly

doi:10.1109/wiopt.2014.6850285

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…The proposed algorithm tried to improve initial coverage and to achieve the required K -coverage degree efficiently. Also, the same author in [13] proposed HSA based on dynamic deployment to maximize the network coverage and connectivity. Authors in [43] have proposed the combination of the Markov chain model and HSA to solve the coverage hole problem by redistribution mobile nodes after the initial deployment of the static nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where R s i and U e are calculated according to Equations (1) and (2), whereas d (s i , t) is the calculated distance between the position of the sensor node s i and the position of target point t in the AoI. This distance is considered the real Euclidean distance between s i and t in AoI as shown in Equation (13) [2]: R s .…”

Section: Categorization Of Coverage Probability Based On Psmmentioning

confidence: 99%

“…The second type of WSN deployment is called random sensor deployment, which is the most popular deployment strategy and can be considered in this paper. Dynamic deployment was used to enhance coverage in WSNs by redistribution sensor nodes after initial random deployment [13]. In the same view, for the sensor localization, a novel localization methods using dynamic deployment, collection of a novel local search process, and HS algorithm was used in [14].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

et al. 2020

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Due to the increase of Wireless Sensor Network (WSN) technologies demand, the optimal sensor node deployment is considered as one of the most important factors that directly affect the network coverage. Most researches in WSNs that solved the problem of coverage in homogeneous and heterogeneous cases are suffering from many drawbacks such as consumed energy and high cost. In this paper, we propose an efficient deployment model based on probabilistic sensing model (PSM) and harmony search algorithm (HSA) to achieve the balance between the network coverage performance and the network cost in a heterogeneous wireless sensor network (HEWSN). The HSA is used for deployment optimization of HEWSN nodes which makes a balance between the coverage and financial cost. The PSM is used to solve the overlapping problem among the sensors. The performance of the proposed model is analyzed in terms of coverage ratio and cost evaluations. The simulation results showed the capability of the proposed heterogeneous deployment model to achieve maximum coverage and a minimum number of sensors compared to homogeneous deployment. Furthermore, a comparative study with a meta-heuristic genetic-based algorithm in HEWSN has also been conducted, and its results confirm the superiority of the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Categorization Of Coverage Probability Based On Psmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

et al. 2020

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“…If the nodes are sparsely deployed, the sensing accuracy would be compromised, while a dense deployment would result in a high power requirement and cost of the system. This has led to the research into techniques to improve deployment [10], which could be either planned [9], where all the nodes are organized in a grid, random [11], which is more suitable for large-scale AoI, or dynamic [12], where the nodes can be redeployed after the initial deployment.…”

Section: Introductionmentioning

confidence: 99%

Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network

2021

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Wireless sensor networks have attracted worldwide attention in recent years. The failure of the nodes is caused by unequal energy dissipation. The reasons that cause unequal energy dissipation are, first and foremost, the distance between the nodes and the base station, and secondly, the distance between the nodes themselves. In wireless sensor networks, the location of the base station has a substantial impact on the network’s lifetime effectiveness. An improved genetic algorithm based on the crossover elitist conservation genetic algorithm (CECGA) is proposed to optimize the base station location, while for clustering, the K-medoids clustering (KMC) algorithm is used to determine optimal medoids among sensor nodes for choosing the appropriate cluster head. The idea is to decrease the communication distance between nodes and the cluster heads as well as the distance among nodes. For data routing, a multi-hop technique is used to transmit data from the nodes to the cluster head. Implementing an evolutionary algorithm for this optimization problem simplifies the problem with improved computational efficiency. The simulation results prove that the proposed algorithm performed better than compared algorithms by reducing the energy use of the network, which results in increasing the lifetime of the nodes, thereby improving the whole network.

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Review on target coverage and network connectivity in Mobile Sensor Networks

Karegaonkar

Raut

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Target Coverage and network connectivity are two main challenging and practically important issues of Mobile Sensor Networks (MSNs). Target coverage covers a set of specified points of interest in the deployment region of a WSN. Target coverage guarantees that every location in the sensing field is covered by at least one sensor and Network Connectivity guarantees that there are sufficient routing paths between sensors. Connectivity is necessary for sensors to collect data and report data to the sink node. Target Coverage and Network Connectivity may also affect the quality of network. Many studies have been introduced to improve the quality of coverage and network connectivity. In this paper, the study of different approaches which improves quality of both Target coverage and Network connectivity, are presented with their features and drawbacks. Improving both Target coverage and Network Connectivity by minimizing the number of sensors used and sensor's movement distance is the important issue in mobile sensor network.

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Improving coverage and connectivity in mobile sensor networks using harmony search

Cited by 4 publications

References 37 publications

An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network

Review on target coverage and network connectivity in Mobile Sensor Networks

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