Learnings from Implementation of a BDI Agent-based Battery-less Wireless Sensor

TL;DR

This paper presents the first implementation of a BDI agent on a battery-less, energy-harvesting embedded system, demonstrating how agent programming can address energy uncertainty in wireless sensors.

Contribution

It introduces novel methods for integrating BDI agents with low-power energy-harvesting devices, including belief management and dynamic intention annotation.

Findings

Successful implementation of BDI agent on a battery-less sensor

Enhanced programming simplicity and code readability

Identified integration challenges and proposed solutions

Abstract

Battery-less embedded devices powered by energy harvesting are increasingly being used in wireless sensing applications. However, their limited and often uncertain energy availability challenges designing application programs. To examine if BDI-based agent programming can address this challenge, we used it for a real-life application involving an environmental sensor that works on energy harvested from ambient light. This yielded the first ever implementation of a BDI agent on a low-power battery-less and energy-harvesting embedded system. Furthermore, it uncovered conceptual integration challenges between embedded systems and BDI-based agent programming that, if overcome, will simplify the deployment of more autonomous systems on low-power devices with non-deterministic energy availability. Specifically, we (1) mapped essential device states to default \textit{internal} beliefs, (2) recognized and addressed the need for beliefs in general to be \textit{short-} or \textit{long-term}, and (3) propose dynamic annotation of intentions with their run-time energy impact. We show that incorporating these extensions not only simplified the programming but also improved code readability and understanding of its behavior.