Published Online:August 2026
Product Name:The IUP Journal of Telecommunications
Product Type:Article
Product Code:IJTC020826
DOI:10.71329/IUPJTC/2026.18.3.27-47
Author Name:T Maheshwari and J M Dhayashankar
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:27-47
Wireless sensor networks (WSNs) have emerged as the key technological tool for environmental monitoring, agricultural applications, industrial automation, and Internet of things (IoT) applications. However, low capacity of batteries in sensor nodes, nonuniform energy consumption, suboptimal cluster head (CH) selection strategy, and high level of communication cost are among the main factors that affect the network lifetime and decrease the performance of the routing algorithm. The most popular clustering and routing protocols use a small amount of decision criteria, which cause nonuniform energy dissipation, continuous reorganization of clusters, and increase in packet sending costs. In order to solve the abovementioned problems, the paper proposes an Energy-Harvesting Adaptive Fuzzy Routing (EHAFR) system. This framework combines intelligent selection of CHs, energyaware routing, and data compression strategy. First, random deployment of sensor nodes takes place and then energy aware routing discovery creates reliable communication paths among cluster members (CMs), CHs, and sink node. Mamdani fuzzy inference system (FIS) is used in order to choose the optimal CHs according to residual energy, energy harvest, node degree, betweenness centrality, and sink distance criteria. Additionally, adaptive UDQ arithmetic coding method is used in order to compress the sensed data before its transmission. The simulation results show that the proposed EHAFR technique performs better than other existing techniques such as CPO, DBO, and ESBOA and is a viable and energy-efficient routing method that improves communication efficiency and extends the lifetime of WSNs.
Wireless sensor networks (WSNs) have emerged as one of the most important enabling technologies of intelligent sensing and ubiquitous computing owing to their capability of providing information about their environment through an enormous number of low-cost and autonomous sensors (Etman et al., 2025; Mehra, et al., 2020). These sensors are provided with the capability of sensing, computing, wireless communication and energy, which enables them to work together in order to collect and transfer data about their environment to a sink node. Due to the selfconfiguration and infrastructure-less nature of WSNs, they have been used in a wide range of applications such as environmental and habitat monitoring, precision agriculture, and Internet of things (IoT)-based monitoring systems.