Collaborative In-Network Processing for Internet of Battery-Less Things

Abstract

Internet of Battery-less Things (IoBT) has recently emerged as a promising solution to enable prolonged lifetime for Internet of Things, by utilizing energy harvesting technology. With insufficient computational power reside in and time-varying energy availability for a single IoBT system, collaborative operation is desired to shorten the data processing time for IoBT networks. In this paper, we propose a novel collaborative in-network processing framework, combining feasibility check, latency-aware data partition (LDP), and joint computation and transmission optimization (JCTO). At the beginning of every slot, a capacity planning module provides an upper bound of the feasible input data size for the IoBT networks. LDP reduces the overall latency by providing efficient input data partition and considering the latency and remaining energy constraints. By jointly optimizing the micro control unit frequency and wireless transmit power, the proposed framework further reduces the overall data processing latency. The real world energy harvesting profiles are used to evaluate the performance of the proposed methodology. Compared with the existing strategies, the proposed framework achieves significantly lower overall latency of collaborative in-network processing. The simulation results also show that the overall latency is close to the minimal latency after three rounds of iterative optimizations of LDP and JCTO.