A bridging model for parallel computation
Communications of the ACM
IBM Systems Journal
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Proceedings of the sixth workshop on I/O in parallel and distributed systems
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
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Communications of the ACM
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OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
International Journal of Sensor Networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Experiences on Processing Spatial Data with MapReduce
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Sensor Networks—Motes, Smart Spaces, and Beyond
IEEE Pervasive Computing
Impact of correlation in node locations on the performance of distributed compression
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Design of large-scale agricultural wireless sensor networks: email from the vineyard
International Journal of Sensor Networks
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IEEE Wireless Communications
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Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement
sMapReduce: a programming pattern for wireless sensor networks
Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications
Static type checking of Hadoop MapReduce programs
Proceedings of the second international workshop on MapReduce and its applications
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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.