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Parallel processing of data from very large-scale wireless sensor networks

In this paper we explore the problems of storing and reasoning about data collected from very large-scale wireless sensor networks (WSNs). Potential worldwide deployment of WSNs for, e.g., environmental monitoring purposes could yield data in amounts of petabytes each year. Distributed database solutions such as BigTable and Hadoop are capable of dealing with storage of such amounts of data. However, it is far from clear whether the associated MapReduce programming model is suitable for processing of sensor data. This is because typical applications MapReduce is used for, currently are relational in nature, whereas for sensing data one is usually interested in spatial structure of data instead. We show that MapReduce can indeed be used to develop such applications, and also describe in detail a general architecture for service platform for storing and processing of data obtained from massive WSNs.