Improving the performance of trickle-based data dissemination in low-power networks

Trickle is a polite gossip algorithm for managing communication traffic. It is of particular interest in low-power wireless networks for reducing the amount of control traffic, as in routing protocols (RPL), or reducing network congestion, as in multicast protocols (MPL). Trickle is used at the network or application level, and relies on up-to-date information on the activity of neighbors. This makes it vulnerable to interference from the media access control layer, which we explore in this paper. We present several scenarios how the MAC layer in low-power radios violates Trickle timing. As a case study, we analyze the impact of CSMA/CA with ContikiMAC on Trickle's performance. Additionally, we propose a solution called Cleansing that resolves these issues

The random disc thrower problem

We describe a number of approaches to a question posed by Philips Research, described as the "random disc thrower" problem. Given a square grid of points in the plane, we cover the points by equal-sized planar discs according to the following random process. At each step, a random point of the grid is chosen from the set of uncovered points as the centre of a new disc. This is an abstract model of spatial reuse in wireless networks. A question of Philips Research asks what, as a function of the grid length, is the expected number of discs chosen before the process can no longer continue? Our main results concern the one-dimensional variant of this problem, which can be solved reasonably well, though we also provide a number of approaches towards an approximate solution of the original two-dimensional problem. The two-dimensional problem is related to an old, unresolved conjecture ([6]) that has been the object of close study in both probability theory and statistical physics. Keywords: generating functions, Markov random fields, random sequential adsorption, Rényi’s parking problem, wireless network

An analytic evaluation of the Trickle algorithm : towards efficient, fair, fast and reliable data dissemination

Wireless sensor networks require communication protocols for efficiently propagating and maintaining data in a distributed fashion. Ideally, a communication protocol is able to disseminate data quickly to all nodes in the network using as few transmissions as possible, while distributing transmission load fairly. In the context of wireless sensor networks the Trickle algorithm is a popular protocol serving as the basis for many of the current communication protocols. In this study we analyze the performance of Trickle with respect to efficiency, fairness, reliability and propagation speed. Additionally, we show how small extensions to the algorithm can improve its performance

A cooperative sequential adsorption model for wireless gossiping

Wireless sensor networks require communication protocols for efficiently maintaining data in a distributed fashion. The Trickle algorithm is a popular protocol serving as the basis for many of the current standard communication protocols. In this paper we develop a mathematical model describing how Trickle maintains data, establish a relationship with a class of spatial stochastic models known as Cooperative Sequential Adsorption (CSA). We derive asymptotic results for the coverage ratio for a specific class of CSA models and investigate the scalability of the Trickle algorithm

Adaptive broadcast suppression for Trickle-based protocols

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

An analytic evaluation of the Trickle algorithm : towards efficient, fair, fast and reliable data dissemination

Wireless sensor networks require communication protocols for efficiently propagating and maintaining data in a distributed fashion. Ideally, a communication protocol is able to disseminate data quickly to all nodes in the network using as few transmissions as possible, while distributing transmission load fairly. In the context of wireless sensor networks the Trickle algorithm is a popular protocol serving as the basis for many of the current communication protocols. In this study we analyze the performance of Trickle with respect to efficiency, fairness, reliability and propagation speed. Additionally, we show how small extensions to the algorithm can improve its performance

Performance of large-scale polling systems with branching-type and limited service

Motivated by emerging Internet-of-Things (IoT) applications and smart building environments, we analyze the performance of large-scale symmetric polling systems where the number of queues grows large. We consider a scenario in which the total arrival rate is kept fixed and the individual switch-over time and service time distributions remain the same. This asymptotic regime leads to cycles of infinite length and queue lengths with non-trivial distributions. We show that for most traditional service policies the scaled cycle times converge to a deterministic value in the limit, which in turn implies that the queue lengths at the various nodes become asymptotically independent. Using these insights, we find that the behavior of individual queues simplifies to that of a discrete-time bulk service queue in the limit, so that the marginal queue length and waiting-time distributions become considerably easier to analyze. Additionally, we propose a new flexible k-limited service discipline aimed at striking a good balance between short mean queue lengths and predictable cycle times for deadline-critical applications

A data propagation model for wireless gossiping

Wireless sensor networks require communication protocols for efficiently propagating data in a distributed fashion. The Trickle algorithm is a popular protocol serving as the basis for many of the current standard communication protocols. In this paper we develop a mathematical model describing how Trickle propagates new data across a network consisting of nodes placed on a line. The model is analyzed and asymptotic results on the hop count and end-to-end delay distributions in terms of the Trickle parameters and network density are given. Additionally, we show that by only a small extension of the Trickle algorithm the expected end-to-end delay can be greatly decreased. Lastly, we demonstrate how one can derive the exact hop count and end-to-end delay distributions for small network sizes. Keywords: Analytical model; Markov renewal process; Wireless communication; Gossip protocol; End-to-end delay; Trickle algorith