Learning, computing, and trustworthiness in intelligent IoT environments : performance-energy tradeoffs
Soret, Beatriz; Nguyen, Lam D.; Seeger, Jan; Bröring, Arne; Issaid, Chaouki Ben; Samarakoon, Sumudu; Gabli, Anis El; Kulkarni, Vivek; Bennis, Mehdi; Popovski, Petar (2021-12-28)
B. Soret et al., "Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs," in IEEE Transactions on Green Communications and Networking, vol. 6, no. 1, pp. 629-644, March 2022, doi: 10.1109/TGCN.2021.3138792
© 2021 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022020417761
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Abstract
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semiautonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multiple iIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in energyefficient iIoTe and a roadmap to address the open research challenges.
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