Multi-Objective WSN Deployment: Quality of Monitoring, Connectivity and Lifetime

Aitsaadi, Nadjib; Achir, Nadjib; Boussetta, Khaled; Pujolle, Guy

doi:10.1109/icc.2010.5502276

Cited by 27 publications

(14 citation statements)

References 14 publications

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“…This implies that the number of nodes has to be kept at a minimum in order to reduce the probability of any of them being discovered. [98]- [103] are dedicated to optimal node deployment by considering the accomplishment of the specified goals at a minimum cost.…”

Section: ) Number Of Nodesmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

356

202

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Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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Section: ) Number Of Nodesmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

356

202

View full text Add to dashboard Cite

show abstract

“…To do this, all the routes of the network must be discovered and then the PRR for each hop of the route will be used to calculate APRR. The solutions based on evolutionary algorithms [10][11][12][13] can use this metric to evaluate the resulting deployment in each step of their algorithms. The solutions may also be modified by using this metric to improve the evolution of the result set in the iterations of the algorithms.…”

Section: Non-regular Deploymentmentioning

confidence: 99%

Analysing wireless sensor network deployment performance using connectivity

Aval¹,

Razak²,

Ismail³

2013

ScienceAsia

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In contrast to the random deployment method for wireless sensor networks, there are various applications that require manual deployment. While the deployment is costly and usually there is no change in the position of the nodes, it is better to evaluate the deployment scheme before doing it. There are different existing metrics to evaluate a deployment including coverage, connectivity, network lifetime, and cost. With emerging real applications of WSNs, it has become a must to evaluate the deployments with more realistic metrics and model. In this paper we have proposed an accumulative path reliability rate metric, regarding the hop-based nature of routing in these networks, to measure the connectivity between two nodes that are not adjacent. We have applied the metric to existing deployment methods and analysed the results.

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“…Lin et al [12] researched on the decision fusion rules for a WSN, but did not consider the deployment strategy of sensors. Aitsaadi et al [13] derived http://jwcn.eurasipjournals.com/content/2013/1/55 a new WSN deployment strategy named multi-objective deployment algorithm based on evolutionary and neighborhood search algorithms. Kapnadak and Coyle [14] and Gogu et al [15] determined the optimal spatial node densities for target detection.…”

Section: Introductionmentioning

confidence: 99%

Graphical deployment strategies in radar sensor networks (RSN) for target detection

Yang

Liang

Liu

2013

J Wireless Com Network

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Nodes deployment is a major challenge to a successful implementation of radar sensor network (RSN). The goal of the deployment is to ensure that the target can achieve expected detection performance with high-energy efficiency. In this article, two deployment strategies, named Hexagonal Deployment Strategy (HDS) and Diamond Deployment Strategy (DDS), are proposed to solve this problem. Each Radar Sensor (RS), separately, obtains probability of target detection depending on the position of RS according to the deployment strategies. To appraise these two deployment strategies, two decision fusion rules are derived over pass-loss fading channel in multi-hop RSN. We combine these two decision fusion rules with unfixed local detection performance. Simulations results show that given a finite number of RSs, our proposed strategies are far superior to Random Deployment Strategy in terms of detection probability and energy consumption to satisfy detection and false alarm requirements. The DDS achieves higher probability of detection and consumption fewer energy than HDS, no matter in decision fusion rules with Binary Transmission (BT) or without Binary Transmission (NBT). The BT fusion rules performs better than NBT and the number of RS needed for expected detection performance is not the more the better.

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Multi-Objective WSN Deployment: Quality of Monitoring, Connectivity and Lifetime

Cited by 27 publications

References 14 publications

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Analysing wireless sensor network deployment performance using connectivity

Graphical deployment strategies in radar sensor networks (RSN) for target detection

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