MAPLE: Mobility support using asymmetric transmit power in low-power and lossy networks

• Published in: Journal of communications and networks Vol. 20; no. 4; pp. 414 - 424
• Main Authors: Jeong, Seungbeom, Park, Eunjeong, Woo, Dongyeon, Kim, Hyung-Sin, Paek, Jeongyeup, Bahk, Saewoong
• Format: Journal Article
• Language: English
• Published: Seoul Editorial Department of Journal of Communications and Networks 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); 한국통신학회
• Subjects: Asymmetric transmit power; Electronic devices; Energy management; IEEE 802.15.4; Internet of Things; Logic gates; lowpowerlossy network (LLN); Mobile nodes; mobility; Performance enhancement; Power management; Protocol (computers); Reliability; Routing; Routing protocols; Signal strength; Uplink; 전자/정보통신공학
• ISSN: 1229-2370; 1976-5541
• DOI: 10.1109/JCN.2018.000057

With the proliferation of emerging Internet of Things (IoT) devices and applications, mobility is becoming an integral part of low-power and lossy networks (LLNs). However, most LLN protocols have not yet focused on the support for mobility with an excuse of resource constraints. Some work that do provide mobility support fail to consider radio duty-cycling, control overhead, or memory usage, which are critical on resource-limited low-power devices. In this paper, we introduce MAPLE, an asymmetric transmit power-based routing architecture that leverages a single resource-rich LLN border router. MAPLE supports mobility in duty-cycled LLNs using received signal strength indicator (RSSI) gradient field-based routing. High-power transmission of the gateway not only allows LLN endpoints to be synchronized for low duty-cycle operation, but also establishes an RSSI gradient field which can be exploited for opportunistic routing without a need for any neighbor or routing table. This eliminates the scalability problem due to memory limitation, and provides a responsive routing metric without control overhead. MAPLE also addresses the RSSI local maximum problem through local adaptation. We implement MAPLE on a low-power embedded platform, and evaluate through experimental measurements on a real multihop LLN testbed consisting of 31 low-power ZigBee nodes and 1 high-power gateway. We show that MAPLE improves the performance of mobile devices in LLN by 27.2%/55.7% and 17.9% in terms of both uplink/downlink reliability and energy efficiency, respectively.