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Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques

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A Correction to this article was published on 21 July 2023

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Abstract

Localization in wireless sensor networks (WSN) is gaining a lot of attention from the researchers due to its usefulness in many real-time applications to identify the source of events. Wireless sensor networks are also utilized to transmit multimedia data such as audio, video and images along with scalar data. This type of WSN is termed as Wireless Multimedia Sensor Network (WMSN). Many applications exist, such as battlefields, environmental monitoring, ecosystem monitoring, and forest fire detection, among others, where the source of an event’s occurrence is critical. As a result, using simulations or simulating real-time scenarios, researchers have developed various techniques for locating sensor nodes in wireless sensor networks. This paper presents the clustering-based localization strategies for WSN and WMSN in detail. In a wireless sensor network and wireless multimedia sensor networks, clustering is utilized to offer distributed computing and parallel processing by grou** sensor nodes into small subgroups that operate independently. To improve the performance of the traditional localization procedure, clustering techniques might be greatly useful. Clustering divides the network into partitions which can independently function thus reducing the communication cost. Clustering also increases the throughput of the network by providing distributed computing. Furthermore, future concerns and issues are highlighted in order to determine the future study direction for new wireless sensor network researchers.

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The authors, thanks to all the anonymous reviewers of Multimedia Tools and Applications Journal for their constructive remarks and fruitful suggestions to improve the manuscript.

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Wajgi, D.W., Tembhurne, J.V. Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques. Multimed Tools Appl 83, 6829–6879 (2024). https://doi.org/10.1007/s11042-023-15956-z

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