Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs

Beatriz Soret, Lam Duc Nguyen, Jan Seeger, Arne Bröring, Chaouki Ben Issaid, Sumudu Samarakoon, Anis El Gabli, Vivek Kulkarni, Mehdi Bennis, Petar Popovski

Research output: Contribution to journalJournal articleResearchpeer-review

7 Citations (Scopus)
48 Downloads (Pure)

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.

Original languageEnglish
JournalIEEE Transactions on Green Communications and Networking
Volume6
Issue number1
Pages (from-to)629-644
Number of pages16
ISSN2473-2400
DOIs
Publication statusPublished - 1 Mar 2022

Keywords

  • Autonomous IoT
  • Distributed Ledger Technology
  • Distributed learning
  • Edge IoT
  • Edge computing
  • Energy consumption
  • Internet of Things
  • Manufacturing
  • Robots
  • Task analysis
  • Trustworthiness.
  • Wireless communication
  • wireless AI

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