eHint: An Efficient Protocol for Uploading Small-Size IoT Data

Chan, Tsung-Yen, Ren, Yi ORCID: https://orcid.org/0000-0001-7423-6719, Tseng, Yu-Chee and Chen, Jyh-Cheng (2017) eHint: An Efficient Protocol for Uploading Small-Size IoT Data. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC). The Institute of Electrical and Electronics Engineers (IEEE). ISBN 978-1-5090-4184-8

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Abstract

IoT (Internet of Things) has attracted a lot of attention recently. IoT devices need to report their data or status to base stations at various frequencies. The IoT communications observed by a base station normally exhibit the following characteristics: (1) massively connected, (2) lightly loaded per packet, and (3) periodical or at least mostly predictable. The current design principals of communication networks, when applied to IoT scenarios, however, do not fit well to these requirements. When a large number of devices contend to send small packets, the signaling overhead is not cost-effective. To address this problem, our previous work [1] proposes the Hint protocol, which is slot-based and schedule- oriented for uploading IoT devices' data. In this work, we extend [1] to support data transmissions for multiple resource blocks. We assume that the uplink payloads from IoT devices are small, each taking very few slots (or resource blocks), but devices are massive. The main idea is to "encode" information in a tiny broadcast that allows each device to "decode" its transmission slots, thus significantly reducing transmission overheads and contention overheads. Our simulation results verify that the protocol can significantly increase channel utilization compared with traditional schemes.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://ieeexplore.ieee.org/document/7925...
Depositing User:	Pure Connector
Date Deposited:	26 Jan 2018 15:30
Last Modified:	10 Dec 2024 01:11
URI:	https://ueaeprints.uea.ac.uk/id/eprint/66106
DOI:	10.1109/WCNC.2017.7925509

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