Mean-field analysis of data flows in wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) are often used for environment monitoring, an application which requires reliable routing of messages from source to sink nodes via multihop networks. Prior to installing such WSNs, engineers commonly analyse the network using discrete event simulation (DES). Whilst sophisticated simulators such as Castalia and TOSSIM take into account many low-level features of WSNs, their biggest drawback is the lack of scalability. This inhibits design-time system optimisation for large or complex networks. In this paper, we discuss how Population CTMC (PCTMC) models, used in conjunction with meanfield analysis, can be used to mitigate this problem. To illustrate the potential of PCTMC models in the WSN domain, we present a PCTMC model for a failsafe, dynamic routing protocol, which we implemented in Castalia. We show that the mean-field solution for the model yields good qualitative agreement with corresponding low-level simulations, but at a fraction of the computational cost. In particular we see good agreement for average metrics describing buffer occupancy and data flow behaviour. Moreover, our PCTMC model produces good results when packets are lost due to channel interference, an important consideration for WSNs.

Challenges for Quantitative Analysis of Collective Adaptive Systems

“…Examples of systems which have been studied using this approach include an emergency egress system [18], smart buildings [19], data flows in wireless sensor networks [8] swarm robots [17], and internet worm attacks [6].…”