An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Abstract

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and Internet of Things (IoT) is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, Adaptive Fuzzy C means clustering and Seagull Optimization Algorithm (AFCM-SOA) is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of Fuzzy C Means Clustering and Seagull Optimization Algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The Fuzzy C-Means Clustering is utilized to empower the energy efficient operation and load balancing. In the Fuzzy C-Means Clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as Atom Search Optimization (ASO) and Emperor Penguin Optimization (EPO) respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

Machine learning‐based clustering protocols for Internet of Things networks: An overview

“…-Most of the FCM-based solutions consider energy efficiency. [101][102][103][105][106][107][108][112][113][114]118,123,124110,111,115 In terms of distribution, previous works 102,103,106 are effective. Previous studies 111,112,118,121 provide load-balancing.…”

“…[101][102][103][105][106][107][108][112][113][114]118,123,124110,111,115 In terms of distribution, previous works 102,103,106 are effective. Previous studies 111,112,118,121 provide load-balancing. Works in the literature 105,112 are effective for delay optimization.…”

“…Then, clusters are formed based on node identifiers (IDs), STF of the node (i.e., transitions between active and passive mode), and their neighbors' IDs, using the enhanced FCM. Karunkuzhali et al 111 offered an adaptive scheme involving the FCM clustering algorithm along with the seagull optimization algorithm (SOA) for monitoring environmental conditions in smart agriculture. The FCM clustering algorithm is adopted to enable energy‐efficient operation and load balancing.…”

“…‐ Most of the FCM‐based solutions consider energy efficiency 101‐103,105‐108,112‐114,118,123,124 110,111,115 . In terms of distribution, previous works 102,103,106 are effective.…”