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International audienceWe introduce the Stochastic Quality Calculus in order to model and reason about distributed processes that rely on each other in order to achieve their overall behaviour. The calculus supports broadcast communication in a truly concurrent setting. Generally distributed delays are associated with the outputs and at the same time the inputs impose constraints on the waiting times. Consequently, the expected inputs may not be available when needed and therefore the calculus allows to express the absence of data.The communication delays are expressed by general distributions and the resulting semantics is given in terms of Generalised Semi-Markov Decision Processes. By restricting the distributions to be continuous and by allowing truly concurrent communication we eliminate the non-determinism and arrive at Generalised Semi-Markov Processes (GSMPs); further restriction to exponential distributions gives rise to numerically analysable GSMPs, in particular using techniques from stochastic model checking

Abstract-We present a probabilistic model for the network setup phase of the Lightweight Medium Access Protocol (LMAC) for concurrent Wireless Sensor Networks. In the network setup phase, time slots are allocated to the individual sensors through resolution of successive collisions. The setup phase involving collisions should preferably be as short as possible for efficiency and energy consumption reasons. This concurrent stochastic process has inherent internal nondeterminism, and we model it using combinatorics. The setup phase is modeled by a discrete time Markov chain such that we can apply results from the theory of phase type distributions. Having obtained our model we are able to find optimal protocol parameters. We have simultaneously developed a simulation model, partly to verify our analytical derivations and partly to be able to deal with systems of excessively high order or stiff systems that might cause numerical challenges. Our abstracted model has a state space of limited size where the number of states are of the order n+r+1 n , where n is number of sensors, and r is the maximum back off time. We have developed a tool, named LMAC analyzer, on the MATLAB platform to assist automatic generation and analysis of the model.