Node Clustering for Wireless Sensor Networks (original) (raw)
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The large-scale deployment of wireless sensor networks (WSNs) and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy. In this article, we highlight the challenges in clustering a WSN, discuss the design rationale of the different clustering approaches, and classify the proposed approaches based on their objectives and design principles. We further discuss several key issues that affect the practical deployment of clustering techniques in sensor network applications.
Clustering objectives in wireless sensor networks: A survey and research direction analysis
Computer Networks, 2020
Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of networks given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.
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.
An Introduction to Various Basic Concepts of Clustering Techniques on Wireless Sensor Networks
International Journal of Mobile Network Communications & Telematics, 2013
Wireless sensor networks (WSNs) are new generation of computer networks which have many potential applications and unique challenges. They usually consist of hundreds or thousands small sensor nodes such as MICA2, which operate autonomously; conditions such as cost, invisible deployment and many application domains, lead to small size and limited resources sensors. WSNs are susceptible to energy criterion and most of traditional networks architectures (i.e. Flat) are unusable on WSNs; due to count of existent sensor nodes, large-scale networks and their constraints. Also, WSNs have dynamic topology. One of most important method against to these problems is clustering. Clustering leads to more scalability, energy efficiency and prolong network lifetime in large-scale WSNs. As a result, this paper is focused on clustering in WSNs. It is including of: an overview of WSNs and clustering in WSNs, consist of its functionality, advantages, weaknesses, applications and various classifications. This work enables us to verify the purpose and capabilities of the WSNs and clustering techniques; also, the goal and effects of clustering techniques on WSNs are introduced. This would enable WSNs designers and managers to design and manage WSNs, more significant.
A Survey on Clustering Algorithms for Wireless Sensor Networks
2010
A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.
A Survey on Clustering Techniques for Wireless Sensor Network
International Journal of Research in Computer Science, 2012
Wireless sensor networks have been used in various fields like battle feilds, surveillance, schools, colleges, etc. It has been used in our day-today life. Its growth increases day by day. Sensor node normally senses the physical event from the environment such as temperature, sound, vibration, pressure etc. Sensor nodes are connected with each other through wireless medium such as infrared or radio waves it depends on applications. Each node has its internal memory to store the information regarding the event packets. In this paper we will come to know the various algorithms in clustering techniques for wireless sensor networks and discuss them. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption .It can also increase network scalability. Sensor nodes are considered to be homogeneous since the researches in the feild of WSNs have been evolved but in reality homogeneous sensor networks hardly exist. Here we will discuss some of the impact of heterogeneous sensor networks on WSN and various clustering algorithms used in HWSN.
Some Issues in Clustering Algorithms for Wireless Sensor Networks
Wireless Sensor Networks (WSNs) present new generation of real time embedded systems with limited computation, energy and memory resources that are being used in wide variety of applications where traditional networking infrastructure is practically infeasible. In recent years many approaches and techniques have been proposed for optimization of energy usage in Wireless Sensor Networks. In order to gather information more efficiently, wireless sensor networks are partitioned into clusters. However, these methods are not without problems. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop wireless sensor networks.
Clustering Structure and Deployment of Node in Wireless Sensor Network
International Journal of Information Technology and Computer Science, 2014
Generally, grouping sensor nodes into clusters has been widely adopted by the research community to satisfy the above scalability objective and generally achieve high energy efficiency and prolong network lifetime in large scale WSN environments. The corresponding hierarchical routing and data gathering protocols imply cluster based organization of the sensor nodes in order that data fusion and aggregation are possible, thus leading to significant energy savings. We propose a clustering approach which organizes the whole network into a connected hierarchy and discuss the design rationale of the different clustering approaches and design principles. Further, we propose several key issues that affect the practical deployment of clustering techniques in wireless sensor network applications. Index Terms-WSN (Wireless Sensor Network), Sensor Node (SN), Base Station (BS), Cluster Head (CH), Mobile ad hoc network (MANET).
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.
An Alternative Clustering Scheme in WSN
IEEE Sensors Journal, 2015
Despite significant advancements in wireless sensor networks (WSNs), energy conservation in the networks remains one of the most important research challenges. One approach commonly used to prolong the network lifetime is through aggregating data at the cluster heads (CHs). However, there is possibility that the CHs may fail and function incorrectly due to a number of reasons such as power instability. During the failure, the CHs are unable to collect and transfer data correctly. This affects the performance of the WSN. Early detection of failure of CHs will reduce the data loss and provide possible minimal recovery efforts. This paper proposes a self-configurable clustering (SCCH) mechanism to detect the disordered CHs and replace them with other nodes. Simulation results verify the effectiveness of the proposed approach.