Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review (original) (raw)
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International Journal on Recent and Innovation Trends in Computing and Communication, 2023
A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering.
2021
Article History: Received: 11 January 2021; Accepted: 27 February 2021; Published online: 5 April 2021 Abstract: A wireless sensor network (WSN) is a state-of-the-art technology for radio communication. A WSN includes several sensors that are arbitrarily distributed in a particular region to detect and track physical characteristics that are hard for humans to observe, like temperature, humidity, and pressure. Because of the nature of WSNs, many issues may arise, including information routing, power consumption, clustering, and cluster head (CH) selection. Although there are still some difficulties in the WSN, owing to its versatility and robustness, it has gained considerable attention among scientists and technologists despite the shortcomings. Various protocols were designed to solve these problems. Low energy adaptive clustering hierarchy (LEACH) is one of the significant hierarchical protocols used to reduce energy consumption in WSNs. This article provides an extensive analysi...
Survey on Recent Clustering Algorithms in Wireless Sensor Networks
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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.
A Survey on Clustering Algorithms for Wireless Sensor Networks
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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.
Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms
International Journal of Computer Applications, 2013
WSN has been directed from military applications to various civil applications. However, many applications are not ready for real world deployment. Most challenging issues are still unresolved. The main challenge facing the operation of WSN is saving energy to prolong the network lifetime. Clustering is an efficient technique used for managing energy consumption. However, clustering is an NP hard optimization problem that can't be solved effectively by traditional methods. Computational Intelligence (CI) paradigms are suitable to adapt for WSN dynamic nature. This paper explores the advantages of CI techniques and how they may be used to solve varies problems associated to WSN. Finally, a short conclusion and future recommendation is being provided.
A Comprehensive Survey of Clustering Approaches in Wireless Sensor Networks
Presently, Wireless Sensor Networks (WSNs) are not only limited to military application but also used by general public for their number of applications areas like healthcare applications, home automation, habitat monitoring, medicine health monitoring, engineering applications etc. Since sensor nodes are battery operated and energy is the biggest constraint for wireless sensor capabilities. In this paper, we survey different approaches based on energy efficiency, security, network lifetime and formulate the problems of WSNs with the help of routing based protocols, game theory, genetic algorithm, swarm intelligence and security based approaches. Then, we present a comprehensive taxonomy of energy efficient clustering approaches, which are discussed in the depth. The basic challenges, open research issues and research gaps are briefly explored in this paper. Finally, we conclude our work insight for future research direction about energy conservation in WSNs
Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
Applied Sciences, 2021
Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.
Applying hierarchical agglomerative clustering to wireless sensor network
… and Algorithmic Aspects of Sensor and …, 2007
Wireless Sensor Networks (WSNs) have a wide range of applications that base on the collaborative effort of a number of sensor nodes. Cluster-based network architecture can enhance network self-control capability and resource efficiency, and prolong the whole network lifetime. Thus, finding an effective and efficient way to generate clusters is an important topic in WSNs. Existing clustering approaches may not be flexible enough to cope with various factors or have higher communication overhead. To achieve the goal, we tailor the HAC (Hierarchical Agglomerative Clustering) algorithm for WSNs. HAC is a well-known approach and has been successfully applied to many disciplines. HAC uses simple numerical methods to make clustering decisions. In addition, HAC provides flexibility with respect to input data type (e.g., location data or connectivity information) and weight assignment to different factors (e.g., connections or power strength). This paper demonstrates our preliminary work in applying several well-understood HAC methods to WSNs. Initial results look promising. We are investigating other specific factors of WSNs, such as degree of connectivity, power level, and reliability, and are incorporating them into the HAC approaches. Many clustering approaches have been proposed for WSNs. The existing approaches typically first select a set of CHs among the nodes in the network by considering one or multiple factors, and then gather the rest of the nodes under these CHs. LEACH [7, 8] is an important clustering protocol for WSNs as there are many approaches that are based on it. LEACH is fully distributed through randomly selecting CHs and rotating the CH task among nodes. Thus, the approach can uniformly distribute the energy consumption in the whole network. PEGASIS [9, 10] is based on LEACH and uses the greedy algorithm to organize all sensor nodes into a chain and then periodically promote the first node on the chain to be the CH. HEED [13] extends LEACH by initializing a probability for each node to be a tentative CH depending on its residual energy and making the decision according to the cost based on the connectivity degree of the node. These approaches have two main disadvantages. The first one is the random selection of the CHs, which may cause higher communication overhead for: (i) the ordinary member nodes in communicating with their corresponding CH, (ii) CHs in establishing the communication among them, or (iii) between a CH and a base station (BS) or other sinks. Another issue is the periodic CH rotation or election which needs extra energy to rebuild clusters. To avoid the problem of random CH selection, there are many other approaches focusing on how to select appropriate CHs to achieve efficient communications. Stojmenovic, et al. [11] proposed a dominating set algorithm which focuses on the efficiency of broadcasting to all the nodes. The approach divides all the nodes into four types: Gateway, Inter-Gateway, Intermediate and Member. The selected Gateway nodes which form a View publication stats View publication stats
Energy Efficient Clustering Techniques for Wireless Sensor Networks-A Review
International Journal of Scientific Research and Management, 2017
The applications of Wireless Sensor Networks (WSNs) are growing at rapid pace and providing pervasive computing environments. Energy constraints is the most critical issue in sensor applications and that needs be optimized to prolong the life of resource constrained sensor network. Clustering is an efficient technique to group the sensor nodes of entire network into number of clusters to support high scalability and provide better data aggregation by efficient utilization of limited resources of sensor nodes and that prolongs network lifetime. In this paper, some widely explored clustering algorithms in WSNs are discussed on several aspects and characteristics such as clustering timings, clustering attributes, convergence rate etc. The advantages and disadvantages of corresponding clustering algorithms are also explained with suitable examples. The paper finally concludes with discussion on the challenges of clustering in WSNs with mentioning the future research topics.