Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed embedded devices (nodes), which communicate with one another via radio. Over the last decade improvements in hardware and a steady decrease in cost have encouraged the application of WSNs in areas such as industrial control, security and environmental monitoring. However, despite increasing popularity, the design of end-to-end software for WSNs is still an expert task since the choice of middleware protocols heavily influences the performance of resource-constrained WSNs. As a consequence, WSN designers resort to discrete event simulation prior to deploying networks. While such simulations are reasonably accurate, they tend to be computationally expensive to run, especially for large networks. This particularly limits the number of distinct protocol configurations that engineers can test in advance of construction and hence their final setup may be suboptimal. To mitigate this effect we discuss how highly efficient mean-field techniques can be brought to bear on models of wireless sensor networks. In particular, we consider the practical modelling issues involved in constructing appropriately realistic Population CTMC (PCTMC) models of WSN protocols.
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