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CLRPL: Context-Aware and Load Balancing RPL for Iot Networks Under Heavy and Highly Dynamic Load - 2018

ClRPL: Context-Aware And Load Balancing RPL For IoT Networks Under Heavy And Highly Dynamic Load

Research Area:  Internet of Things

Abstract:

The IPv6 routing protocol for low power and lossy networks (RPL) was accepted as the standard routing protocol for Internet of Things (IoT) by Internet engineering task force on March 2012. Since then, it has been used in different IoT applications. Though RPL considerably deals with IoT network requirements, there are still some open problems to solve, since it was not initially designed for IoT applications. In this paper, we address the problem of packet loss and power depletion in an RPL-based network under heavy and highly dynamic load. We address this problem in three steps: First, we present a context-aware objective function (CAOF), which computes the rank, considering the context of the node. CAOF also avoids thundering herd phenomenon by gradual shifting from a high rank value toward the real rank value. Second, we present a new routing metric, known as context-aware routing metric (CARF) which considers the status of remaining power and queue utilization of parent chain toward the root in a recursive manner while lessening the effect of upstream parents as it gets farther down the path. Through comprehensive evaluations, we show that this metric leads to a better decision about the proper parent in a network with high traffic dynamicity, rather than deciding merely based on the parent rank. Third, we present a new parent selection mechanism, which selects the best parent based on CARF and some other metrics while avoiding routing loops by a simple yet effective countermeasure. Evaluation results reveal improvements in network lifetime while decreasing packet loss in comparison with standard specification of RPL.

Keywords:  

Author(s) Name:  Seyedreza Taghizadeh; Hossein Bobarshad; Halima Elbiaze

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/ACCESS.2018.2817128

Volume Information:  Volume: 6, Page(s): 23277 - 23291