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

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

On the scalability and message count of Trickle-based broadcasting schemes

As the use of wireless sensor networks increases, the need for efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper, we analyze the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. We show that the broadcasting process of a network using Trickle can be modeled by a Markov chain and that this chain falls under a class of Markov chains, closely related to residual lifetime distributions. It is then shown that this class of Markov chains admits a stationary distribution of a special form. These results are used to analyze the Trickle algorithm and its message count. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period. Keywords: Analytical model Message count Trickle Wireless network