Formal analysis of communication protocols for wireless sensor systems

Sensor technology is an increasingly popular area of research due to the prevalent use of sensor devices. With the need for accurate, detailed data sensors are increasingly often used together in sensor networks. As the size of these sensor networks grows, so does the importance of efficient methods for their analysis for the prevention of system errors and discovery of design flaws. The increasing number of sensor devices leads to an exponential increase is the state space of the associated model. As such models of realistic systems are decreasingly often small enough for their verification to be feasible. Symmetry reduction techniques developed over the last 30 years, have been shown to be effective in reducing the state space explosion problem, particularly in the case of heterogeneous sensor systems, which contain many identical sensor devices. In this thesis we present our approach to verifying Ctrl-MAC, a novel wireless network protocol that supports bidirectional communication of multiple simultaneous physical properties. We explore the extent to which symmetry reduction can aid the model checking process for a sensor network communication protocol. We present our results, and suggest statistical approaches based on our observations of the protocol. We investigate the use of automated tools for the application of symmetry reduction, in particular GRIP, which is well suited for symmetry reduction of wireless sensor network systems. Models of communication protocols often require the use of synchronisation to model the interaction between devices. We present GRIP 3.0, a new version of the tool, which provides support for the use of synchronised transition statements. We provide results from practical work, coupled together with a discussion of drawbacks and future improvements

Formal modeling of robot behavior with learning

We present formal specification and verification of a robot moving in a complex network, using temporal sequence learning to avoid obstacles. Our aim is to demonstrate the benefit of using a formal approach to analyze such a system as a complementary approach to simulation. We first describe a classical closed-loop simulation of the system and compare this approach to one in which the system is analyzed using formal verification. We show that the formal verification has some advantages over classical simulation and finds deficiencies our classical simulation did not identify. Specifically we present a formal specification of the system, defined in the Promela modeling language and show how the associated model is verified using the Spin model checker. We then introduce an abstract model that is suitable for verifying the same properties for any environment with obstacles under a given set of assumptions. We outline how we can prove that our abstraction is sound: any property that holds for the abstracted model will hold in the original (unabstracted) model