An Improved Energy-Efficient Clustering Algorithm Based on MECA and PEGASIS for WSNs

Wang, Jin; Jun, Cao; Cao, Yiquan; Li, Bin; Lee, Sungyoung

doi:10.1109/cbd.2015.49

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The efficiency of the proposed system was assessed by the number of live nodes and the number of data packets transmitted and received. In the case of MECA and PEGASIS, [5] we propose an energy-efficient lifetime improvement Strategy for WSNs. The objective of this algorithm is to minimise and balance the use of energy for all sensor nodes and cluster heads.…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Rawat¹

2021

TURCOMAT

View full text Add to dashboard Cite

Wireless networks data aggregation allows in-network processing, reduces packet transmission and data redundancy, and thus helps extend wireless sensor systems to the full duration of their lives. There have been many ways of dividing the network into clusters, collecting information from nodes and adding it to the base station, to extend wireless sensor network life. Certain cluster algorithms consider the residual energy of the nodes when selecting clusterheads and others regularly rotate the selection head of the cluster. However, we seldom investigate the network density or local distance. In this report we present an energy-efficient clustering algorithm that selects the best cluster heads of the system after dividing the network into clusters. The cluster head selection depends on the distance between the base station nodes and the remaining power of this approach.Each node's residual energy is compared to the node count. Our results show that the solution proposed more efficiently extends the life of the wireless sensor network. The algorithm prolongs the life and effectiveness of the Wireless Sensor Network.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Rawat¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…Wang et al [12] proposed an energy efficiency strategy to increase lifetime at the WSN based on PEGASIS and MECA. This algorithm intends to limit and adjust energy utilization for all cluster heads and sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

Enhanced PEGASIS using Dynamic Programming for Data Gathering in Wireless Sensor Network

Mufid

Rasyid

Syarif

2019

emitter

View full text Add to dashboard Cite

A number of routing protocol algorithms such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) have been proposed to overcome the problem of energy consumption in Wireless Sensor Network (WSN) technology. PEGASIS is a development of the LEACH protocol, where within PEGASIS all nodes are active during data transfer rounds thus limiting the lifetime of the WSN. This study aims to propose improvements from the previous PEGASIS version by giving the name Enhanced PEGASIS using Dynamic Programming (EPDP). EPDP uses the Dominating Set (DS) concept in selecting a subset of nodes to be activated and using dynamic programming based optimization in forming chains from each node. There are 2 topology nodes that we use, namely random and static. Then for the Base Station (BS), it will also be divided into several scenarios, namely the BS is placed outside the network, in the corner of the network, and in the middle of the network. Whereas to determine the performance between EPDP, PEGASIS and LEACH, an analysis of the number of die nodes, number of alive nodes, and remaining of energy were analyzed. From the experiment result, it was found that the EPDP protocol had better performance compared to the LEACH and PEGASIS protocols in terms of number of die nodes, number of alive nodes, and remaining of energy. Whereas the best BS placement is in the middle of the network and uses static node distribution topologies to save more energy.

show abstract

“…Marta and Cardei [10] proposed a multiple sink relocation scheme with multiple pre-determined hexagon trajectories, and each trajectory has a mobile sink constantly relocating itself along the hexagon path. Wang et al [11] proposed an energy efficient strategy based on MECA and PEGASIS. In this strategy, the sink move along a circular trajectory, the network is divided into different clusters through two routing algorithms MECA and PEGASIS, then the cluster header nearest the sink is chosen as the leader node, which is responsible for transmitting the data to the sink.…”

Section: The Background and Related Workmentioning

confidence: 99%

Virtual Force Based Sink Relocation Strategy in Wireless Sensor Networks

Wang¹,

Yang²,

Feng³

et al. 2018

IJCCE

View full text Add to dashboard Cite

Sink relocation has been proven as an effective solution to relieve the network hot point problem, and to balance nodes' energy consumption and thus prolong the network lifetime for wireless sensor networks. In this paper, we propose a Virtual Force based Sink Relocation strategy, called VFSR, which utilizes the resultant force derived from two kinds of virtual force, gravitational one and border repulsive one, to impel the sink node to relocate to the desired position. In VFSR, the gravitational force exists between each sensor and the sink, and is proportional to the node's residual energy and inversely proportional to the distance from the node to the sink. The border repulsive force is adapted to avoid the sink node wandering nearby the network border. Under the action of the gravitational and the border repulsive force, the sink node approaches the nodes with high residual energy and makes them relay data from other nodes, which reduces data transmission overload of the nodes with low energy. Through simulation, we validate the effectiveness of VFSR.

show abstract

An Improved Energy-Efficient Clustering Algorithm Based on MECA and PEGASIS for WSNs

Cited by 5 publications

References 12 publications

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Enhanced PEGASIS using Dynamic Programming for Data Gathering in Wireless Sensor Network

Virtual Force Based Sink Relocation Strategy in Wireless Sensor Networks

Contact Info

Product

Resources

About