Frugal Geofencing via Energy-aware Sensing and Reporting

TL;DR

This paper presents an energy-aware geofencing framework for low-power IoT devices with energy harvesting, optimizing device placement and activity to enhance detection timeliness and reliability under strict energy constraints.

Contribution

It introduces a novel energy-aware sensing and reporting framework that integrates a directional sensing model with reinforcement learning for optimal IoTD placement.

Findings

Fewer devices are needed for effective geofencing.

Detection timeliness is improved compared to uniform deployments.

The framework achieves reliable detection under tight energy constraints.

Abstract

Timely and accurate monitoring in geofencing scenarios is challenging when relying on ultra-low power Internet of Things devices (IoTDs) powered by energy harvesting (EH). This is mainly because frequent wake-ups for data acquisition and data uploading may quickly deplete their limited energy buffer. Conventional grid-like IoT deployments overlook these limitations and merely rely on continuously powered sensing. Herein, we propose an energy-aware geofencing framework for camera-equipped EH IoTDs deployed around a protected area and its surrounding perimeter zone. The framework integrates a directional sensing power model with an operational representation of EH, sensing, sleeping, and reporting, accounting for the limited field-of-view (FoV) and distance-dependent detection confidence of the IoTDs. Device activity is controlled by the coverage-providing access point, which hosts a mobile edge host and a facility geocencing system to ensure timely and reliable detection under tight energy constraints. Reinforcement learning is used to determine IoTD placement, enabling earlier intruder detection than uniform grid-based deployments. Numerical results show that the proposed coordinated sensing and reporting configuration achieves frugal geofencing with fewer devices, while concurrently improving detection timeliness and dependability.