A survey on clustering algorithms for wireless sensor networks (original) (raw)
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Survey on Recent Clustering Algorithms in Wireless Sensor Networks
2013
The use of wireless sensor networks (WSNs) has grown enormously in the last decade, pointing out the crucial need for scalable and energy-efficient routing and data gathering and aggregation protocols in corresponding large-scale environments. To maximize network lifetime in Wireless Sensor Networks (WSNs) the paths for data transfer are selected in such a way that the total energy consumed along the path is minimized. To support high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes and thus extends network lifetime. The objective of this paper is to present a survey on clustering algorithms reported in the literature of WSNs. This paper presents taxonomy of energy efficient clustering algorithms in WSNs.
Review on Basic Clustering Techniques for Heterogeneous Wireless Sensor Networks
Clustering technique is the basic technique used in the wireless sensor network to enhance the lifetime of a sensor network by mitigating energy consumption and provide efficiency, scalability, and security. Formerly sensor nodes are considered to be homogeneous in which each node has the same processing capacity, energy and functionality, but to prolong network lifetime researches has been evolved to infuse heterogeneity in wireless sensor network such as to provide different energy level to some nodes. In this paper, we present a survey on basic clustering techniques for Heterogeneous wireless sensor networks.
Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network orientation, WSN can be of two types: flat network and hierarchical or cluster-based network. Various advantages of cluster-based WSN are energy efficiency, better network communication, efficient topology management, minimized delay, and so forth. Consequently, clustering has become a key research area in WSN. Different approaches for WSN, using cluster concepts, have been proposed. The objective of this paper is to review and analyze the latest prominent cluster-based WSN algorithms using various measurement parameters. In this paper, unique performance metrics are designed which efficiently evaluate prominent clustering schemes. Moreover, we also develop taxonomy for the classification of the clustering schemes. Based on performance metrics, quantitative and qualitative analyses are performed to compare the advantages and disadvantages of the algorithms. Finally, we also put forward open research issues in the development of low cost, scalable, robust clustering schemes.
Survey on clustering in heterogeneous and homogeneous wireless sensor networks
The Journal of Supercomputing, 2017
In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged. In order to tackle this problem, clustering methods are employed to optimize energy consumption, gather data and also enhance the effective lifetime of the network. In spite of the clustering methods advantages, there are still some important challenges such as choosing a sensor as a cluster head (CH), which has a significant effect in energy efficiency. In clustering phase, nodes are divided into some clusters and then some nodes, named CH, are selected to be the head of each cluster. In typical clustered WSNs, nodes sense the field and send the sensed data to the CH, then, after gathering and aggregating data, CH transmits them to the Base Station. Node clustering in WSNs has many advantages, such as scalability, energy efficiency, and reducing routing delay. In this paper, several clustering methods are studied to 123 278 A. S. Rostami et al. demonstrate advantages and disadvantages of them. Among them, some methods deal with homogenous network, whereas some deals with heterogeneous. In this paper, homogenous and heterogeneous methods of clustering are specifically investigated and compared to each other.