MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models

The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical models--Markov Chains (MCs) and Hidden Markov Models (HMMs). We consider the problem of distributed indexing and semantic querying over such sensor models. Specifically, we are interested in efficiently answering (i) range queries: return all sensors that have observed an unusual sequence of symbols with a high likelihood, (ii) top-1 queries: return the sensor that has the maximum probability of observing a given sequence, and (iii) 1-NN queries: return the sensor (model) which is most similar to a query model. All the above queries can be answered at the centralized base station, if each sensor transmits its model to the base station. However, this is communicationintensive. We present a much m...

• Type: Conference
• Year: 2007
• Where: VLDB
• Authors: Arnab Bhattacharya, Anand Meka, Ambuj K. Singh
• Keywords: Database, VLDB 2007