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

TL;DR

This paper reviews the technologies enabling intelligent IoT environments, focusing on performance-energy tradeoffs, and discusses how to optimize resource use, latency, privacy, and energy consumption in edge computing scenarios.

Contribution

It provides a comprehensive overview of current technologies in intelligent IoT environments and proposes a roadmap for integrating energy-efficient solutions.

Findings

Analysis of energy consumption sources in iIoTe

Tradeoff insights among resources, latency, privacy, and energy

Future research directions for energy-efficient iIoTe

Abstract

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous 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 multipleiIoTe 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 energy-efficient iIoTe and a roadmap to address the open research challenges