ABSTRACT
We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing the communication required. After the algorithm is run, each node can answer queries for its local region, or the nodes can efficiently transmit the parameters of the model to a user outside the network. We present an evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab, demonstrating that our distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.
- U. Bertele and F. Brioschi. Nonserial Dynamic Programming. Academic Press, New York, 1972. Google ScholarDigital Library
- R. Cowell, P. Dawid, S. Lauritzen, and D. Spiegelhalter. Probabilistic Networks and Expert Systems. Spinger, 1999. Google ScholarDigital Library
- G. Golub and C. Van Loan. Matrix Computations. Johns Hopkins, 1989.Google Scholar
- J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pister. System architecture directions for networked sensors. In Proceedings of ASPLOS, pages 93--104, November 2000. Google ScholarDigital Library
- C. Intanagonwiwat, D. Estrin, R. Govindan, and J.Heidemann. Impact of network density on data aggregation in wireless sensor networks. In ICDCS, July 2002. Google ScholarDigital Library
- C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MobiCom, August 2000. Google ScholarDigital Library
- P. Levis, N. Lee, A. Woo, M. Welsh, and D. Culler. TOSSIM: Accurate and scalable simulation of entire tinyos applications. In SenSys, November 2003. Google ScholarDigital Library
- S. Madden. The Design and Evaluation of a Query Processing Architecture for Sensor Networks. PhD thesis, UC Berkeley, 2003. Google ScholarDigital Library
- M. Hamilton et al. Habitat sensing array, first year report. http://cens.ucla.edu/Research/Applications/habitat_sensing.htm, 2003.Google Scholar
- P. McCullagh and J. Nelder. Generalized linear models. Chapman and Hall, 1983.Google ScholarCross Ref
- A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson. Wireless sensor networks for habitat monitoring. Technical Report IRB-TR-02-006, Intel Research, June 2002.Google ScholarDigital Library
- R. Nowak and U. Mitra. Boundary estimation in sensor networks: Theory and methods. In IPSN, 2003. Google ScholarDigital Library
- M. Paskin and C. Guestrin. Distributed inference in sensor networks. Technical Report IRB-TR-03-039, Intel Research, 2003.Google Scholar
- M. Paskin and G. Lawrence. Junction tree algorithms for solving sparse linear systems. Technical Report UCB/CSD-03-1271, U.C. Berkeley, 2003.Google Scholar
- G. Pottie and W. Kaiser. Wireless integrated network sen-sors. Communications of the ACM, 43(5):51--58, May 2000. Google ScholarDigital Library
- N. Priyantha, A. Miu, H. Balakrishnan, and S. Teller. The Cricket Compass for context-aware mobile applications. In Proceedings of ACM MOBICOM, Rome, Italy, July 2001. Google ScholarDigital Library
- S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, and S. Shenker. Ght: A geographic hash table for data-centric storage in sensornets, 2002. Google ScholarDigital Library
- Y. Yao and J. Gehrke. Query processing in sensor networks. In CIDR, 2003.Google Scholar
Index Terms
- Distributed regression: an efficient framework for modeling sensor network data
Recommendations
Lifetime and coverage guarantees through distributed coordinate-free sensor activation
MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networkingWireless Sensor Networks are emerging as a key sensing technology, with diverse military and civilian applications. In these networks, a large number of sensors perform distributed sensing of a target field. Each sensor is a small battery-operated ...
A distributed algorithm for energy-aware clustering in WSN
The clustering of sensor nodes of the Wireless Sensor Networks (WSNs) has received considerable research attention in recent time. The sensor devices of a WSN are severely resource constrained having limited operational lifetime. The clustering of ...
Distributed Algorithm for Lifetime Maximization in a Delay-Tolerant Wireless Sensor Network with a Mobile Sink
We propose an algorithm for maximizing the lifetime of a wireless sensor network when there is a mobile sink and the underlying application can tolerate some amount of delay in delivering the data to the sink. The algorithm is distributed, and in ...
Comments