CCLRSP - A collaborative cross layer route selection protocol for low-power lossy wireless networks

초록

Route selection in low-power lossy networks (LLN) is a challenging task due to the constrained nature of wireless devices. With the LLN providing the underlying network for several IoT applications, which require devices with high network activity, energy efficiency becomes more crucial in comparison to typical wireless sensor networks (WSNs). Also, most of the works in this direction that are based on cross-layer designs only rely on either physical layer or medium access control (MAC) sub-layer optimizations to select routes. The majority of the contributions identified with Cross Layer Design that depend on just physical layer grandstand where by changing the transmitting power, the energy devoured by the nodes can be upgraded. Additionally, the works that depend on just MAC sub-layer can only target on the issues like contention, impacts with least yields towards the collective advantages. In this work, a novel attempt is made to achieve the collaborative benefits of the node and as well as the network. In this paper, we initially show that considering both physical and MAC layer properties can benefit the route selection process by lowering the individual devices' energy and also the overall network lifetime. Then, we propose a collaborative cross-layer route selection protocol - CCLRSP that relies on physical layer and MAC layer properties to choose a low-cost route. At the physical layer, we employ the adaptive and robust topology control mechanism (ART) that is based on determining the optimal transmission power for a transmitter to reach the next-hop neighbor. At the MAC level, the physical layer findings are cross verified to minimize the contention and interference through the expected transmission count. The proposed protocol combines the parameters from these two layers to select an energy efficient route that employs a balanced approach to prevent few network nodes from draining energy quickly by load-balancing. The proposed protocol is evaluated using extensive simulations and is shown to perform better than existing works. © 2021 Little Lion Scientific.

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