University of Oulu

Teemu Leppänen, & Jukka Riekki. (2019). Energy efficient opportunistic edge computing for the Internet of Things. Web Intelligence, 17(3), 209–227. https://doi.org/10.3233/WEB-190414

Energy efficient opportunistic edge computing for the Internet of Things

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Author: Leppänen, Teemu1; Riekki, Jukka1
Organizations: 1Center for Ubiquitous Computing, University of Oulu, P.O. Box 4500, FI-90014 University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019082225157
Language: English
Published: IOS Press, 2019
Publish Date: 2019-08-22
Description:

Abstract

Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from these devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate, as a multi-agent system, on the opportunistic network of heterogeneous end devices. The benefits include autonomous, asynchronous and adaptive execution and relocation of application-specific computational tasks, while taking into account the local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of information between these devices. Use cases are presented where mobile agents address challenges in current edge computing platforms. An edge application is evaluated where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their tasks results in the system. The mobile agents operate atop a REST-compliant software agent framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly, up to 60%, in the edge application execution.

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Series: Web Intelligence
ISSN: 2405-6456
ISSN-E: 2405-6464
ISSN-L: 2405-6456
Volume: 17
Issue: 3
Pages: 209 - 227
DOI: 10.3233/WEB-190414
OADOI: https://oadoi.org/10.3233/WEB-190414
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: Copyright © 2019 IOS Press All rights reserved. This is the accepted version of the work. The final publication is available at IOS Press through https://www.iospress.nl/journal/web-intelligence-and-agent-systems/