ACTOR: Adaptive Control of Transmission Power for Routing in Low-power
and Lossy Networks
Abstract
Routing Protocol for Low-power and lossy networks (RPL), as the de-facto
routing protocol for IoT networks, neglects to exploit IoT devices’ full
capacity to tune their transmission power. One of the reasons is that
optimizing the transmission power in parallel with the routing strategy
is challenging, given the dynamic nature of wireless links and the
constrained resources in IoT devices. Optimizing the transmission power
requires evaluating the probability of packet collisions, energy
consumption, the number of hops, and interference. We propose Adaptive
Control of Transmission pOwer for RPL (ACTOR) for dynamic optimization
of transmission power. ACTOR aims at improving throughput in dense
networks by passively exploring different transmission power levels. The
extent of resources used for this exploration significantly affects the
network throughput. Thus, the exploration needs to adapt to dynamism in
the environment. We formulate this exploration strategy using the
Multi-Armed Bandit framework. The classic solutions of bandit theory
including Upper Confidence Bound and Discounted Upper Confidence Bound
accelerate the convergence of the exploration and guarantee its
optimality. We also enhance ACTOR by mechanisms from RPL to blacklist
undesirable transmission power levels and stabilize the topology.
Results of the experiments on our 40-node testbed and simulations show
that ACTOR achieves higher throughput (increasing the packet delivery
ratio by 20%), energy consumption, end-to-end delay, and the number of
retransmissions are significantly improved against the standard RPL and
the selected benchmark.