The size of Büchi Automata(BA) is a key factor during converting Linear-Time Temporal Logic(LTL) formulae to BA in model checking. Most algorithms for generating BA from LTL formulas involved intermediate automata, degeneralization algorithm and simplification of the formulas, but size of BA and time of converting can be reduced further. In this paper, we present an improved Tableau-based algorithm, which converts LTL formulae to Transition-based Büchi Automata(TBA) more efficiently. The algorithm is geared towards being used in model checking in on-the-fly fashion. By attaching the satisfy information of ∪-formula on states and transitions, we can decide whether the sequences of the BA are acceptable by using only one acceptance condition set, not multiple ones. Binary Decision Diagrams(BDD) is used to describe BA and simplify formulae. A better data structure, syntax Directed Acyclic Graph(DAG), is adopted in the algorithm. The size of product BA and computational complexity can be reduced significantly by using on-the-fly degeneralization. The algorithm can expand the state nodes while detecting the validity of nodes, removing the invalid nodes and combining equivalent states and transitions. Compared with other recent conversion tools, the algorithm proposed in this paper runs faster, with the smaller size of BA.

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This paper presents a general view of the target tracking in Wireless Sensor Networks (WSN) with proper deployment of it. Target tracking task in WSN can be more accurately and efficiently accomplished, given the circumstance where the WSN is more proper deployed, for example, the coverage with clearer border and less holes, sensors with less movement, and connectivity well preserved. To prove this thought, several protocols of target tracking and various aspects of deployment in WSN are investigated.