Low-power wireless networks play an important role in the Internet of Things. Typically, these networks consist of a very large number of lossy and low-capacity devices, challenging the current state of the art in protocol design. In this context the Trickle algorithm plays an important role, serving as the basic mechanism for message dissemination in notable protocols such as RPL and MPL. While Trickle's broadcast suppression mechanism has been proven to be efficient, recent work has shown that it is intrinsically unfair in terms of load distribution and that its performance relies strongly on network topology. This can lead to increased end-to-end delays (MPL), or creation of sub-optimal routes (RPL). Furthermore, as highlighted in this work, there is no clear consensus within the research community about what the proper parameter settings of the suppression mechanism should be. We propose an extension to the Trickle algorithm, called adaptive-k, which allows nodes to individually adapt their suppression mechanism to local node density. Supported by analysis and a case study with RPL, we show that this extension allows for an easier configuration of Trickle, making it more robust to network topology.
However, little is known about the influence of @math on other QoS measures such as hop-count, end-to-end delay and load distribution. The authors of @cite_28 study the performance of Trickle as a flooding mechanism compared to classic flooding and multipoint relaying. They conclude that while Trickle can outperform both protocols, its performance is highly sensitive to the choice of parameters, stating: .
@cite_28
1966381945
“The Trickle Algorithm” is conceived as an adaptive mechanism for allowing efficient and reliable information sharing among nodes, communicating across a lossy and shared medium. Its basic principle is, for each node, to monitor transmissions from its neighbours, compare what it receives with its current state, and schedule future transmissions accordingly: if an inconsistency of information is detected, or if few or no neighbours have transmitted consistent information “recently”, the next transmission is scheduled “soon” - and, in case consistent information from a sufficient number of neighbours is received, the next transmission is scheduled to be “later”. Developed originally as a means of distributing firmware updates among sensor devices, this algorithm has found use also for distribution of routing information in the routing protocol RPL, standardised within the IETF for maintaining a routing topology for low-power and lossy networks (LLNs). Its use is also proposed in a protocol for multicast in LLNs, denoted “Multicast Forwarding Using Trickle”. This paper studies the performance of the Trickle algorithm, as it is used in that multicast protocol.