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Published on: November 26, 2019
Improved energy efficient load balanced mobility management RPL protocol for mobile internet of things networks
V C Diniesh1, Basker Palaniswamy2, L Murali3
1Department of Electrical and Electronics Engineering , Amrita School of Engineering , Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India.
None:
The Internet of Things (IoT) offers new global challenges in the digital revolution. Many real-time applications currently generate various heterogeneous traffic patterns induced by a range of sensors, including both static and mobile ones. In heterogeneous traffic networks, sensors equipped with mobility demand a significant role in future mobile IoT applications. To enhance the network lifetime, the routing protocol for low-power lossy networks (RPL) is a standardised and default routing protocol for IoT networks. Although there have been prior Proposals for MIoT applications, an open research issue remains. Initially, the RPL routing protocol was designed for static environments, making it challenging to support mobility. In this paper, we present a mobility-aware queue and workload-based protocol called MQW-RPL, which significantly improves the overall performance. Our proposed work first computes the reliable preferred parent rank based on the current buffer and workload traffic of one-hop neighbors. In the next stage, to support mobility and smooth handoff, we adopt a multi-metric approach, namely, Expected Transmission Count (ETX), energy, and Received Signal Strength Indicator (RSSI). Additionally, the proposed MQW-RPL routing protocol supports backward compatibility, which works better in both static and mobile environments by incorporating an additional flag bit without affecting the existing standard. Finally, the proposed protocol is compared with existing routing protocols, namely ERPL, mRPL, and standard RPL (S-RPL). Our evaluation results reveal that MQW-RPL significantly outperforms ERPL, mRPL, and S-RPL in terms of PDR, minimum delay, energy, packet overhead, and fast convergence time, with an average improvement of 13.5% over ERPL, 15.7% over mRPL, and 21.5% over S-RPL in heterogeneous networks.
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