Context-Aware Management of IoT Nodes: Balancing Informational Value with Energy Usage

Abstract

The operational lifetime of energy-harvesting wireless sensor nodes is limited by availability of the energy source and the capacity of the installed energy buffer. When a sensor node depletes its energy reserves, manual intervention is often required to resume node operation. While lowering the duty cycle would help extend the network lifetime, this is often undesirable, especially in time-critical applications, where rapid collection and dissemination of information is vital. In this paper, we propose a context-aware energy management policy that helps balance the two opposing objectives of timely data collection and dissemination with energy conservation. We capture these objectives through the Value of Information (VoI) of observations made by a sensor node and the State of Energy (SoE) of the energy buffer. We formulate the energy management policy as a Model Predictive Control (MPC) problem which computes device sampling and transmission frequencies to maximize a defined utility criterion over a finite, receding, time-horizon. In the process, we also develop a unique mathematical representation for VoI, that adequately captures aspects related to continuity in monitoring, urgency of dissemination, and representation of the phenomena being observed. In the end, we use data collected from a real-world flash flood event, to evaluate our decision framework across multiple scenarios of energy availability.