Energy-Aware Placement of Device-to-Device Mediation Services in IoT System

Elhabbash, A. and Elkhatib, Y. (2021) Energy-Aware Placement of Device-to-Device Mediation Services in IoT System. In: 19th International Conference on Service-Oriented Computing (ICSOC 2021), Virtual Event, 22–25 November 2021, pp. 335-350. ISBN 9783030914301 (doi: 10.1007/978-3-030-91431-8_21)

Abstract

Internet-of-Things (IoT) systems are becoming increasingly complex, heterogeneous and pervasive, integrating a variety of physical devices, virtual services, and communication protocols. Such heterogeneity presents an obstacle especially for interactions between devices of different systems that encounter each other at run time. Mediation services have been proposed to facilitate such direct communication by translating between messaging protocols, interfacing different middlewares, etc. However, the decision of where to place a mediation service within an IoT topology has repercussions and is in some cases critical for satisfying system objectives. In this paper, we propose an integer linear programming solution to optimize the placement decision specifically in terms of energy consumption. Our solution takes into account the energy consumed by each interaction at each device along the data transfer paths. Through simulations that use topologies of real-world IoT systems, we show the effect of our approach on energy consumption, messaging delay, and placement decision time. Our algorithm outperforms a state-of-the-art solution in terms of reducing energy consumption by almost a third in large-scale typologies. We also demonstrate the feasibility of our approach in terms of overhead.

• Item Type: Conference Proceedings
• Additional Information: This work was supported by the Adaptive Brokerage for the Cloud (ABC) project, UK EPSRC grant EP/R010889/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Elkhatib, Dr Yehia
• Authors: Elhabbash, A., and Elkhatib, Y.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9783030914301
• Copyright Holders: Copyright © Springer Nature Switzerland AG 2021
• First Published: First published in ICSOC 2021, LNCS 13121:335–350
• Publisher Policy: Reproduced in accordance with the publisher copyright policy