Trickle-D: High Fairness and Low Transmission Load with Dynamic Redundancy

Abstract

International audienceEmbedded devices of the Internet of Things form the so-called low-power and lossy networks. In these networks, nodes are constrained in terms of energy, memory and processing. Links are lossy and exhibit a transient behavior. From the point of view of energy expenditure, governing control overhead emission is crucial and is the role of the Trickle algorithm. We address Trickle's fairness problem to evenly distribute the transmission load across the network, while keeping the total message count low. First, we analytically analyze two underlying causes of unfairness in Trickle networks: desynchronization among nodes and non-uniform topologies. Based on our analysis, we propose a first algorithm whose performance and parameters we study in an emulated environment. From this feedback, we design a second algorithm TrickleD that adapts the redundancy parameter to achieve high fairness while keeping the transmission load low. We validate TrickleD in real-life conditions using a large scale experimental testbed. TrickleD requires minimal changes to Trickle, zero user input, emits 17.7% less messages than state-of-the-art and 37.2% less messages than state-of-practice, while guaranteeing high fairness across the network