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Adjusted and Robust Routing Update Algorithms for Internet of Things

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In this paper, two enhanced Trickle-based route update and maintenance algorithms for Low-Power and Lossy Networks (LLNs), called Adaptive and Robust Trickle and Adjusted-Trickle algorithms (AR-Trickle and Trickle-A) are proposed. The purpose is to tackle the main limitations of the standardized route update Trickle algorithm in this type of networks, in addition to the load-balancing problem of the IPv6 Routing Protocol for LLNs (RPL). In the proposed algorithms, new approaches for dynamically choosing the values of the redundancy coefficient (k) and the minimum interval size (Imin) are introduced. In addition, new mechanisms for skipping intervals to double the current interval size (I) are introduced. Moreover, new probabilistic approaches for transmitting and suppressing scheduled transmissions are introduced. In addition, new techniques for selecting the listen-only period and transmission window are introduced. Moreover, in order to evaluate the proposed algorithms, extensive simulation experiments have been conducted under different conditions and scenarios. The experiments results have showed that Trickle-A outperforms the standard Trickle and achieves better efficiency, with high stability, reliability, and scalability for different applications. Thanks to the stepwise approach, this algorithm follows in dealing with network variations by gradually increasing and decreasing parameters and variables values. On the other hand, AR-Trickle outperforms both Trickle-A and the standard Trickle and has achieved strong performance, by effectively reducing control-plane overhead, convergence time, and power consumption by up to 68%, 47%, and 18% respectively, while maintaining nearly the same level of reliability in term of Packet Delivery Ratio (PDR).
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IoT; Low-Power and Lossy Networks; RPL; Trickle Algorithm; Load Balancing

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