Abstract
Wireless Sensor Networks (WSNs) are a fundamental enabling technology for the Medical Internet of Things (MIoT), yet their deployment is severely constrained by the limited energy capacity of battery-powered sensor nodes. Compressive Data Gathering (CDG) has emerged as a promising technique to reduce communication overhead by exploiting the sparsity of sensed signals. However, existing CDG-based clustering models often rely on complex adaptive mechanisms that are computationally expensive for resource-constrained MIoT devices. This paper presents a simple, practical CDG-based clustered routing model specifically designed for WSN-based MIoT applications. The model employs fixed virtual circular clusters with random Cluster Head (CH) selection and a two-candidate next-hop routing strategy, significantly reducing computational overhead. We derive a closed-form energy consumption model that separately quantifies intra-cluster and inter-cluster energy expenditures. Simulation results demonstrate that the proposed model extends network lifetime by up to 38% compared to the state-of-the-art adaptive clustering approach, with a packet delivery ratio exceeding 97% and reconstruction error below 0.025. The model’s simplicity and energy efficiency make it well-suited for real-world MIoT deployments.