WSN's energy-aware coverage preserving optimization model based on multi-objective bat algorithm

Sharawi, Marwa; Emary, Eid; Saroit, Imane Aly; Elmahdy, Hesham N.

doi:10.1109/cec.2015.7256927

Cited by 15 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the aforementioned reasons, in order to achieve optimal results, coverage maximization should be pursued in combination with the attainment of energy conservation, connectivity preservation, and other performance metrics through the application of proper multiobjective schemes [61][62][63][64][65][66][67]. This is the next challenge for the authors of this article in terms of future research work.…”

Section: Discussionmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

View full text Add to dashboard Cite

The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.

show abstract

Section: Discussionmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Distance between two different node having position (x1, y1) and (x2, y2) has been computed using distance formula that works as follow Distance= ( 2 − 1) 2 + ( 2 − 1) 2 (2) On the basis of these energy and distance computation formulas energy of a single node and distance of a node from all the nodes have been computed and cluster head has been selected in the network so that reliable communication can be achieved.…”

Section: Energy Calculation Of Nodementioning

confidence: 99%

Dynamic Clustering and Cluster Head Selection for Energy Optimization under Wireless Sensor Network

Singh¹,

Boparai²

2018

IJARCSSE

View full text Add to dashboard Cite

Wireless sensor network is a field of networking that has been used for sensing information from environment. In WSN the sensor nodes are attached to a battery for sensing information. Each node utilizes three types of energy during its lifetime over the network. These energies are sensing energy, transmission or receiving energy and idle energy. During the sensing information the nodes consumes energy and transmission energy is used to transmit a data over a distance. Idle energy is that when node is not working but remains in on state. Due to deployment of WSN in unreachable area energy is main constraint for network to be cost effective.The major issue is network lifetime that must be increase so that network performs for long duration of time and provide cost effective for an n organization. To overcome this issue various methods had been proposed, chaining, pegasis, clustering and chain head selection are one of these methods.

show abstract

“… BMAC BMAC [2] is an asynchronous and light weight sensor network MAC Protocol. Like an asynchronous and light weight sensor network MAC protocol that aims at providing versatile medium access while keeping the MAC functionality as simple as possible.…”

Section: Mac Protocolsmentioning

confidence: 99%

Data Aggregation and Life Time Improvement in wireless Sensor Networks using Dynamic Clustering

Daman¹,

Chaudhary²,

Kumar³

2016

IJCA

View full text Add to dashboard Cite

Wireless sensor network is an emerging area of research due to vulnerability of sensing information from not approachable areas. In WSN sensor nodes have been deployed for sensing information and transmit this information to base station so that various decisions can be developed. In the processing of WSN data aggregation and energy consumption are major issues. In this paper a new approach has been purposed for data management and energy consumption reduction using dynamic clustering and avoidance ofredundant information transmission over the network. This approach use check sum approach for data redundancy checking and discard redundant or repeated information. This approach provides better results than previous approaches.

show abstract

WSN's energy-aware coverage preserving optimization model based on multi-objective bat algorithm

Cited by 15 publications

References 7 publications

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

Dynamic Clustering and Cluster Head Selection for Energy Optimization under Wireless Sensor Network

Data Aggregation and Life Time Improvement in wireless Sensor Networks using Dynamic Clustering

Contact Info

Product

Resources

About