Increasing the Intelligence of low-power Sensors with Autonomous Agents

Item Type Conference or Workshop Item (Paper)
Abstract Low-power sensors are becoming ever more powerful, increasing both their energy efficiency as well as their processing capabilities. Much work in recent years has focused on optimizing machine learning models to low-power systems, typically to locally process sensor data. Significantly less attention has been paid to other artificial intelligence fields such as knowledge representation and automated reasoning, which may contribute to building autonomous devices. In this work, we present a low-power sensor node with an autonomous belief-desire-intention agent. This kind of agent simplifies the implementation of both proactive and reactive behaviors, promoting autonomy in our target applications. It does so by locally perceiving and reasoning, and then wirelessly broadcasting an intention, which can be forwarded to an actuator. The capabilities of the autonomous agent are demonstrated with a light-control application. Experiments demonstrate the feasibility of running intelligent agents in low-power platforms with little overhead.
Authors William, Jannik; Muller dos Santos, Matuzalém; de Brito, Maiquel; Hübner, Jomi Fred; Vachtsevanou, Danai & Gomez, Andres
Language English
Subjects computer science
HSG Classification contribution to scientific community
Date 2022
Event Title Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT)
Event Location Boston, United States
Event Dates 6 November 2022
Depositing User Danai Vachtsevanou
Date Deposited 21 Nov 2022 14:35
Last Modified 21 Nov 2022 14:35
URI: https://www.alexandria.unisg.ch/publications/268007

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William, Jannik; Muller dos Santos, Matuzalém; de Brito, Maiquel; Hübner, Jomi Fred; Vachtsevanou, Danai & Gomez, Andres: Increasing the Intelligence of low-power Sensors with Autonomous Agents. 2022. - Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT). - Boston, United States.

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https://www.alexandria.unisg.ch/id/eprint/268007
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