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

TL;DR

This paper introduces a context-aware energy management policy for IoT sensor nodes that balances information value and energy use, extending operational lifetime without sacrificing timely data collection.

Contribution

It formulates a novel Model Predictive Control approach that optimizes sensor sampling and transmission based on information value and energy state, incorporating a new VoI mathematical model.

Findings

Effective balancing of data timeliness and energy conservation demonstrated.

Framework adapts to varying energy availability in real-world scenarios.

Improved sensor network longevity without compromising critical data collection.

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.