LDCA: Lightweight Dynamic Clustering Algorithm for IoT-Connected Wide-Area WSN and Mobile Data Sink Using LoRa

Abstract

Wide-area monitoring applications of the Internet-of-Things (IoT) connected wireless sensor network (WSN) consists of sensor nodes (SNs) with limited hardware and energy sources. The distributed nature of such a network and the difficulty of remote access make it more demanding to design an energy-efficient WSN. Moreover, long-range and low-power wireless connectivity is a challenge in IoT-connected applications. Present WSN topologies deal mainly with fixed SNs, SN distribution, and fixed data sink (DS). The majority of the control layer is implemented in the lower hierarchical layer or in a virtual middle layer, which reduces the network lifetime due to excessive processing and data transmission activities. This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a WSN that supports the following two scenarios with limited processing resources: 1) with mobile DSs and static SNs (such as DS and SNs mounted on unmanned aerial vehicles, autonomous vehicles) and 2) with mobile SNs and static DSs (such as livestock monitoring or autonomous robots in smart farming, and urban monitoring). The proposed algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy. Mathematical models were derived for real-time clustering using LoRa. Memory requirement and clustering efficiency of the constrained SN and DS for various mobility scenario were evaluated. The proposed LDCA reduces the energy requirement to 33% compared to static clustering, by reducing the number of concurrent clusters and hops. In addition, a hardware-based approach was used to validate the LDCA algorithm, and evaluate its performance in terms of energy efficiency, packet delivery rate, and network lifetime.