Towards Improved Clustering and Routing Protocol for Wireless Sensor Networks

Abstract

Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multihop communication and a shorter transmission range. Researchers propose different but isolated clustering and routing solutions that are inefficient in terms of energy efficiency and network connectivity in IoT-based WSNs. In this work, we emphasize the importance of considering the context of IoT applications that have further requirements for dedicated data collection per node. We address two interlinked issues, clustering and routing, in a large-scale IoT-based WSN. We propose an improved clustering and routing (ICR) protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. This clustering also develops a strong network backbone that provides fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols, Joint Clustering and Routing (JCR), Low Energy Adaptive Hierarchical Clustering (LEACH) and other recent protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

New Directions in Sensor Research: A Bibliometric Analysis for Detecting Emerging Research Fields and New Technological Trajectories

“…The equation [2] also has a' = constant; b' = coefficient of regression (a' and b' are the parameters to be estimated); t=time; εt = error term in equation.…”

“…The relationships under study here for scientific and technology analysis are investigated using the Ordinary Least Squares (OLS) method for estimating the unknown parameters in regression models [1] and [2]. Statistical analyses are performed with the IBM SPSS Statistics 26 ®.…”

“…The methodology here has the purpose to investigate the structure of emerging research fields in sensor technology, detected with previous statistical analysis by the highest coefficients of regression of estimated relationships based on publication and patent data (equations [1] and [2]); in particular, the magnitude of coefficients of regression is a proxy of high evolutionary growth of sensor research over time. Emerging research fields under study here, having the highest coefficients of regression, are given by:…”

“…The parametric estimates of models [1][2], based on scientific production, are presented in Table 1. In a majority of cases, the significance of the coefficients of regression and the explanatory power of the equations has p-value<.001.…”

“…The R 2 values are high and in general the models explain more than 80% variance in the data. Table 2 shows the parametric estimates of models [1][2] based on patents. Table 2 also reveals that in a majority of cases, the significance of the coefficients of regression and the explanatory power of the equations has p-value<.001, except model [2] for remote sensing.…”