A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Abstract

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

“…The research in the area of WSN has become more active over the years, and a wide range of works has been executed to enhance WSN. To address the challenges of WSN, different surveys have been conducted based on different factors ( e.g ., Osamy et al (2022a) , Majid et al (2022) , Sridhar et al (2022) and Osamy et al (2022b) ). A survey for solving various WSN challenges using computational intelligence (CI) techniques is proposed by Kulkarni, Forster & Venayagamoorthy (2010) .…”

An Efficient Cooperative Routing with ML based Energy Efficiency Model for Distributed Underwater WSN Electricity Meter Warning System

“…The performance of the proposed model is evaluated and compared with existing routing approaches such as Cooperative energy efficient routing (CEER) [1], Distributed adaptive Cooperative routing with RL (DACR-RL) [15] and softmax regression with Tanimoto-Reweight-Boost-Classification (SRTBC) [12] routing schemes.…”

“…Based on the knowledge of the relay node and reliability, the quality of service and energy consumption of the network is improved. Sridhar et al, [12] Softmax Regressed Tanimoto Reweight Boost Ullah et al, [14] developed single and multiple route path selection using minimum BER, distance to the sink node with increased residual energy. The authors obtained improved energy usage and reliable transfer of data.…”