Survey on energy efficient load-balanced clustering algorithm based on variable convergence time for wireless sensor networks

Krishnakumar, A. S.; Anuratha, V.

doi:10.1109/icaccs.2016.7586361

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…So, optimising energy consumption has been the subject of numerous research and innovative approaches aimed at effectively harnessing the benefits of Internet-of-Things applications, including smart homes [6,7], smart cities [8,9], and health care [10]. In this context, clustering techniques and other data routing methods constitute an important asset in order to optimise the collection of information, thereby minimising the energy consumption generated mainly by redundant data transfers by nearby IoT terminals [11,12].…”

Section: Introductionmentioning

confidence: 99%

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

2023

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The Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small‐distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog‐IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness.

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Section: Introductionmentioning

confidence: 99%

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

2023

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“…However, this is not true and there is various research works where researchers have chosen non-energy parameters too in the selection process. At present, there are various studies being carried out towards enhancing clustering process [5], routing process [6], load balancing process [7], and traffic management process. Unfortunately, very less number of works has been found to be attained a height of standards.…”

Section: Introductionmentioning

confidence: 99%

MOMEE: Manifold Optimized Modeling of Energy Efficiency in Wireless Sensor Network

Rajalakshmi¹,

Prakash²

2017

ijacsa

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Abstract-Although adoption pace of wireless sensor network has increased in recent times in many advance technologies of ubiquitous-ness, but still there are various open-end challenges associated with energy efficiencies among the sensor nodes till now. We reviewed the existing research approaches towards energy optimization techniques to explore significant problems. This paper introduces MOMEE i.e. Manifold Optimized Modeling of Energy Efficiency that offers novel clustering as well as novel energy optimized routing strategy. The proposed system uses analytical modeling methodology and is found to offer better resiliency against traffic bottleneck condition. The study outcome of MOMEE exhibits higher number of alive nodes, lower number of dead nodes, good residual energy, and better throughput as compared to existing energy efficient routing approaches in wireless sensor network.

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