AOBA: Recognizing Object Behavior in Pervasive Urban Management

Accurately recognizing the object´s behavior from the uncertain sensor data is a key issue of Internet of Things application. For example, in urban management monitoring system, it is necessary to have an autonomous analyzing module that can online monitor object´s behavior based on environmental monitoring information in order to prevent an emergent situation in advance. In this work, we present an approximate object´s behavior analysis method, called AOBA, which can recognize behavioral patterns of the hybrid objects which include patrolman, watering cart, street lamp etc. In intelligent urban management. AOBA consists of two phases: filtering phase and recognizing phase. In the filtering phase, a -approximate pre-matching algorithm based on q-grams distance is introduced to select possible pattern rapidly, which can discard huge amount insignificant or dirty data; in the recognizing phase, aiming to the temporal and the spatial characteristics of sensor data, an improved bit-parallel string matching algorithm is proposed to recognize the k-approximate multiple patterns over event sequences selected by the filtering phase. Experiments on real urban monitoring data and synthetic data show that the proposed method can efficiently discriminate object´s behavior. Compared with the existing method, the proposed method provides a fault-tolerant approximate pattern recognition solution.