Remote inference energy model for Internet of Things devices

Large wireless sensor networks (WSNs) with thousands of motes are expected to be a significant part of the future connected world. As battery life of such motes is still an issue, it is important to provide scalable methods to estimate energy usage without introducing significant overhead. We foresee that in the future significant amount of data will be collected and inferred on the behavior of motes. Such collection and inference of data can be considered as a virtual representation of the physical mote, with a potentially longer life-cycle. The virtual mote is a valuable resource for application development, diagnostics and behavior prediction. In this paper, we focus on the energy consumption aspect of such motes. To that end, we develop a packet-based real-time energy model, which works by analyzing network traffic traces collected at the backend to estimate energy consumption of the mote. Such approach scales well to the number of motes and does not require modification to the mote or its network stack. Experimental results from extensive measurements conducted on two platforms, i.e., OpenMote and Zolertia Z1, demonstrate promising potential. The energy model can achieve an estimation accuracy over 90% for platform power, and over 85% accuracy for radio power consumption, on various scenarios.