Narendra Singh Yadav

Clusterhead selection in clustering algorithms for wireless sensor networks: A survey

Most of the research in energy efficient data gathering in data centric applications of wireless sensor networks is motivated by LEACH scheme which by allowing rotation of clusterhead role among the sensor nodes tries to distribute the energy consumption over the network. Selection of sensor node for such role rotations greatly affects the energy efficiency of the network. Hence many proposals are reported in the literature suggesting different clusterhead selection strategies. However, till now, these strategies have not been talked about for their collective understanding. So, efforts have been made, in this paper, to discuss them in unison and present a global view. Comparison of their costs of clusterhead selection in different rounds and after effects like cluster formation, distribution of clusterheads and creation of clusters shows a need of more energy efficient clusterhead selection strategy.

The Effect of Spreading Factor on Network Lifetime and Energy Consumption Rate of Low Energy Adaptive Clustering Hierarchy (LEACH)

Clustering is a widely discussed as an important architecture for data centric wireless sensor networks. Low energy adaptive clustering hierarchy (LEACH), proposed in past, is used as benchmark scheme by many proposals and its performance is widely studied in the literature, over last few years. The probabilistic and randomized clusterhead selection strategy results in variable number of clusterheads during different rounds of data gathering. To avoid communication interference in different clusters the signals need to be spread which results in increased data size. The variable number of clusterheads requires spreading factor to be set corresponding to the probable maximum number of clusterheads, to avoid collisions. This increases the data size and correspondingly the amount of energy required to transmit it to the base station. This directly affect the network lifetime and energy consumption of the sensor nodes, which are treated as important performance metrics for evaluation of many of the schemes proposed, in the literature, for data gathering in such networks. To study the effect of spreading factor on these performance metrics, LEACH scheme was simulated in NS-2. With the results obtained 8 is observed as an optimum value of the spreading factor for the simulated scenario, to achieve the desired performance in terms of network lifetime and energy consumption rate which demands development of a distributed clustering scheme with exactly equal number of clusterheads, during different rounds of data gathering.

Chain based Conical Clustering (CCC) framework for data gathering in WSNs

Non uniform energy consumption of the nodes is a major area of concern in cluster based data gathering architectures proposed for wireless sensor networks in the literature and is an active area of research over recent past years. The solutions suggested in the literature, for uniform distribution of energy consumption, require rotation of clusterhead role among sensor nodes but every such rotation needs reclustering of the nodes in the network. The overhead associated with such reclustering are not actual part of data gathering activities and consume energy unnecessarily only for creating and maintaining the clusters. To avoid such unnecessary wastage of energy, due to forced reclustering associated with the clusterhead role rotation, a new hybrid clustering framework, chain based conical clustering (CCC) scheme, is proposed in this paper. CCC keeps the cluster stable and rotates the clusterhead role among the cluster members using layered chains of these cluster members. It is proved, theoretically, that CCC is expected to outperform the scheme used as a benchmark, in the clustered data gathering performance comparison, in wireless sensor network literature.

A particle swarm approach for uniform cluster distribution in data centric wireless sensor networks

This paper presents a scheme, in which the collaborative efforts of limited number of sensor nodes provide the social guidance for localization process to be used for uniform distribution of the clusters, over the network area. Utilizing the information provided in the form of gbest value and the direction for position update as social guidance, the sensor nodes localize them iteratively adjusting their position in the swarm, relying on the measurements obtained from RSSI. Utilizing this location information, sensor nodes assign them to different clusters. Results of the simulations carried out in NS-2, show that the proposed algorithm is capable of localizing the sensor nodes and has a great potential of uniform cluster distribution, provided RSS indicators give necessary support for distance estimation.

Clusterhead life extension in cluster based data centric wireless sensor networks using forwarding node

A novel hybrid clustering scheme is proposed in this paper which keeps the clusterhead stable and uses sensor nodes, termed as forwarding nodes, in its cluster to forward the cluster data towards the base station. The role of these forwarding nodes is suggested to be rotated among the sensor nodes during different rounds. It is expected that the proposed framework will extend the clusterhead life, avoid frequent re-clustering and cluster formations. The less number of clusterheads required, energy efficient cluster formation and the use of forwarding nodes are expected to increase the network life.