Adaptive Clustering for Internet of Battery-Less Things

Enabled by energy harvesting technology, Internet of Battery-less Things (IoBTs) have been attracting increasing attention, as lifetime and battery capacity throttle the performance of Internet of Things (IoTs). Due to the dynamism of ambient energy generation, it is non-trivial for IoBT nodes to work collaboratively and deliver information in high throughput. In this paper, we propose a novel adaptive clustering framework for IoBT systems, combining optimal Cluster Head (CH) selection and Lexicographic Rate Assignment. Using the remaining energy level of each IoBT node as the energy budget, the proposed framework assigns a lexicographic optimal rate vector that enables each node to fully utilize the scavenged energy and achieve high transmission rate. For each possible CH candidate, the proposed clustering framework evaluates the throughput it can provide, and then select the one with the highest rate as the CH node to optimize the performance of the cluster. Real world energy harvesting profile is used to validate the effectiveness of the proposed methodology. The simulation results demonstrate that the proposed framework achieves higher throughput when compared with the existing strategies and is robust to the varying ambient energy.