Adaptive broadcast suppression for Trickle-based protocols

Abstract

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 suboptimal 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.

A Survey of Limitations and Enhancements of the IPv6 Routing Protocol for Low-Power and Lossy Networks: A Focus on Core Operations

“…It does so by counting the number of consistent messages that are received within a specific window and, then, when such a number surpasses a preconfigured redundancy constant (k), it suppresses any further propagation of such received messages. However, studies have reported that the optimal setting of the redundancy constant is not a trivial task and relies greatly on the application scenario, in addition to that some issues may emerge if configured incorrectly [118] [120]. For instance, it was shown in [118] that, if the redundancy constant is not configured correctly, the suppression mechanism might result in sub-optimal routes, especially in heterogeneous topologies with regions of different densities.…”

“…The work in [120] highlighted the ambiguity associated with configuring the redundancy parameter, k, in RPL networks. For instance, the Trickle RFC [69] states that typical values for k are 1-5, whereas the RPL RFC [14] sets the value 10 as the default value.…”

“…Finally, it is recommended in the MPL RFC [117] to set the default value of k to one. This shows that the optimal setting of k is not a trivial task and relies greatly on the application scenario as well as the network topology at hand [120]. The authors here suggest setting k for each node individually based on that node's degree, a mechanism they call adaptive-k.…”

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

“…This assumes d is known which is usually the case in the MAC layers of wireless networks. In [20], the authors use a similar approach with k = α * c, where c can be seen as an approximation of the node degree. The value c is dynamically computed from scratch for each interval and adjusted within the interval.…”

“…Each node runs Trickle to reach a consistent state with its neighbors with the same parameters I min and I max . As in [3,4] and [20], we assume that the redundancy constant k is not necessarily the same on all nodes. The network is assumed to be in steady-state, i.e., I = I max , all nodes are consistent, and no node triggers a Trickle reset because of external events.…”