A Location-less Energy Efficient Algorithm for Load Balanced Clustering in Wireless Sensor Networks

Madhu, Santanoo; Prasad, Ravi Kumar; Ramotra, Prashant; Edla, Damodar Reddy; Lipare, Amruta

doi:10.1007/s11277-021-08976-1

Cited by 10 publications

(1 citation statement)

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“…The clustering approach has been used for an efficient energy data process. Some cluster heads (CHs) were chosen from the normal nodes such as in LEACH [25] and HEED [26] and somewhere chosen from the advanced node sometimes known as gateways such as in [27][28][29], and selected CH is responsible for sending the data back to the base station itself after data aggregation, filtering, or compression. When individual sensor nodes send their data to the base station, energy is not efficiently used.…”

Section: Introductionmentioning

confidence: 99%

Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Srivastava

Mishra

2023

Journal of Sensors

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In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.

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