An efficient clustering algorithm for wireless sensor networks

Abstract

Purpose – This paper aims to propose a new node energy-efficient algorithm with energy threshold to replace cluster heads. The proposed algorithm uses node ranking to elect cluster heads based on energy levels and positions of the nodes in reference to the base station (BS) used as a sink for gathered information. Because the BS calculates the number of rounds a cluster head can remain for as a cluster head in advance, this reduces the amount of energy wasted on replacing cluster heads each round which is the case in most existing algorithms, thus prolonging the network lifetime. In addition, a hybrid redundant nodes duty cycle is used for nodes to take turn in covering the monitored area is shown to improve the performance further. Design/methodology/approach – Authors designed and implemented the proposed algorithm in MATLAB. The performance of the proposed algorithm was compared to other well-known algorithms using different evaluation metrics. The performance of the proposed algorithm was enhanced over existing ones by incorporating different mechanisms such as the use of an energy-based threshold value to replace CHs and the use of a hybrid duty-cycle on nodes. Findings – Through simulation, the authors showed how the proposed algorithm outperformed PEGASIS by 15 per cent and LEACH by almost 70 per cent for the network life-time criterion. They found that using a fixed pre-defined energy threshold to replace CHs improved the network lifetime by almost 15 per cent. They also found that the network lifetime can be further improved by almost 7 per cent when incorporating a variable energy threshold instead of a fixed value. In addition to that, using hybrid-redundant nodes duty-cycle has improved the network lifetime by an additional 8 per cent. Originality/value – The authors proposed an energy-efficient clustering algorithm for WSNs using node ranking in electing CHs and energy threshold to replace CHs instead of being replaced every round.

Data Gathering in Delay Tolerant Wireless Sensor Networks Using a Ferry

“…The proposed algorithm is an extension based on our previously published work NRCA [ 7 , 8 , 9 ] with node ranking being based on the planned path of the ferry rather than the location of the BS. The following steps give a description of the algorithm and cluster heads’ selection process: The BS divides the sensing field into smaller partitions called clusters based on the assumed communication/sensing range of the nodes.…”

“…This operator randomly flips or alters one or more bit values at randomly selected locations in a chromosome. An example is shown below where 8 has been altered to 9: Path 1 = (1 2 3 4 5 6 7 [ 8 ]) Candidate path 1 = (1 2 3 4 5 6 7 [ 9 ]) …”

“…In this paper, we propose a mobile ferry algorithm based on our previous published work, the Node Ranking Clustering Algorithm (NRCA) [ 7 , 8 , 9 ]. Using NRCA, the decision of selecting cluster heads in a WSN is based on their residual energy, their distance from the base station and an energy threshold that is used to replace cluster-heads.…”

Mobile sink for data collection in wireless sensor networks

“…A checkpoint is the cluster head position to be visited by the ferry for data collection purposes. They applied a non-ranking clustering algorithm (NRCA) [89][90][91] to compute the best placement position for each checkpoint.…”