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Radio Resource Management Framework for Energy-Efficient Communications in the Internet-of-Things

G C, D, Bouhafs, F, Raschella, A, MacKay, M and Shi, Q Radio Resource Management Framework for Energy-Efficient Communications in the Internet-of-Things. Transactions on Emerging Telecommunications Technologies. ISSN 2161-3915 (Accepted)

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The Internet-of-Things (IoT) is the vision of a global network that connects various physical world objects to the IT infrastructure through a wireless medium. Despite the availability of a number of mature Radio Access Technologies (RATs) such as GSM, LTE,Wi-Fi and due to the current progress made in developing 5G technology, more and more IoT operators are opting to use Low PowerWide Area (LPWA) technologies due to their low cost and easy deployment. However, recent studies show that the radio resource allocation used in these technologies is not scalable. This limitation often results in packet collisions, retransmission and unnecessary waste of scarce energy resources. In this paper, we propose a Radio Resource Management (RRM) framework, based on Software-Defined Networking (SDN), to overcome the inefficient radio resource allocation of LPWA technologies. This is possible through the centralized nature of SDN, which allows collecting network monitoring information in order to analyze and calculate the optimal channel assignment configuration across the IoT network. We perform software-defined radio based spectrum monitoring within the real IoT network platform in 868 MHz bands in which the latestIoT technologies, i.e., LoRa and SigFox, operate.We demonstrate, through extensive simulations, that the proposed approach provides a better radio resource allocation for LPWA, reduces the number of packet collisions, and significantly improves the energy efficiency of the IoT communications.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: Wiley
Date Deposited: 05 Sep 2019 08:33
Last Modified: 05 Sep 2019 08:45
URI: http://researchonline.ljmu.ac.uk/id/eprint/11301

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