Multi-objective Optimization (MOO) approach for sensor node placement in WSN

Abidin, Husna Zainol; Din, Norashidah Md; Jalil, Yanti Erana

doi:10.1109/icspcs.2013.6723994

Cited by 20 publications

(9 citation statements)

References 19 publications

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“…The size of the region is 100 *100 m 2 . The predefined values of sensing and the transmission ranges are (5,10,15,20) and (10,20,30,40) respectively. The genetic parameters are considered as follow:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this algorithm, periodic sending of broadcast messages, and listening for the reply messages are required, so increases the number of message exchanges between the nodes and the network lifetime will be decreased. Author of [15] proposed a multi-objective optimization approach for optimizing the energy consumption and coverage in WSNs. In [16], the sensing ranges of sensor nodes are adjustable and the impact of this adjustment on coverage is studied.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficient Approach Based on Evolutionary Algorithm for Coverage Control in Heterogeneous Wireless Sensor Networks

Nikravan¹,

Jameii²

2014

IJCSEIT

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Coverage and connectivity are two important requirements in Wireless Sensor Networks (WSNs). In this paper, we address the problem of network coverage and connectivity and propose an energy efficient approach based on genetic evolutionary algorithm for maintaining coverage and connectivity where the sensor nodes can have different sensing ranges and transmission ranges. The proposed algorithm is simulated and it' efficiency is demonstrated via different experiments.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy Efficient Approach Based on Evolutionary Algorithm for Coverage Control in Heterogeneous Wireless Sensor Networks

Nikravan¹,

Jameii²

2014

IJCSEIT

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“…Coverage and lifetime of the sensor network have been jointly optimized in [ 86 ] by using a multi-objective optimization algorithm based on memetic algorithm. The sensor node deployment problem has been considered in [ 145 ] to jointly optimize the two objectives namely, maximum coverage and minimum energy consumption. Coverage, delay and energy consumption are optimized by using multi-objective optimization algorithm in [ 151 ].…”

Section: Relationship Between Different Desirable Objectivesmentioning

confidence: 99%

“…In this subsection, we review some of the work using bio-inspired algorithms to solve the multi-objective optimization formulations in order to address different issues relating to wireless sensor networks. The sensor node placement problem has been modeled as a multi-objective optimization problem in [ 145 ], where authors have used a bio-inspired algorithm to maximize the coverage and minimize the energy consumption. A bio-inspired based algorithm has been used to solve a multi-objective optimization problem in [ 93 ] by finding the optimal transmission range in order to avoid energy hole problem in wireless sensor networks and to maximize the life time of the network.…”

Section: Solution Types/algorithmsmentioning

confidence: 99%

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Iqbal

Naeem

Anpalagan

et al. 2015

Sensors

118

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Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

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“…A flexible algorithm presented in [25] uses an evolutionary computational approach to optimize connectivity and energy cost of WSNs in addition to coverage. The problem of positioning sensor nodes in WSN considering coverage with minimum energy consumption is studied in [26] and a multiobjective optimization approach is proposed for optimizing the energy consumption and coverage. The research presented in [27] used the characteristics of Voronoi diagram and direction-adjustable directional sensors and proposed a distributed greedy algorithm, which can improve the effective field coverage of directional sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks

Jameii

Faez

Dehghan

2015

International Journal of Distributed Sensor Networks

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Coverage, connectivity, and network lifetime are important issues in wireless sensor networks (WSNs). Balancing the energy consumption in the network, reducing the transmission range of nodes, and density control of active nodes are approaches to extend the network lifetime. However, transmission range reduction and smaller number of active nodes can affect the network topology and may cause the network to be disconnected. So, there exist conflicts among lifetime, coverage, and connectivity. In this paper, these conflicting issues are considered and an evolutionary multiobjective optimization approach based on nondominated sorting genetic algorithm-II (NSGA-II) is proposed to optimize them. Simulation results demonstrate that the proposed algorithm can improve the network lifetime and coverage while maintaining the network connectivity.

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Multi-objective Optimization (MOO) approach for sensor node placement in WSN

Cited by 20 publications

References 19 publications

Energy Efficient Approach Based on Evolutionary Algorithm for Coverage Control in Heterogeneous Wireless Sensor Networks

Energy Efficient Approach Based on Evolutionary Algorithm for Coverage Control in Heterogeneous Wireless Sensor Networks

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks

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