6 – Sensor Networks

Publisher Summary This chapter analyzes typical sensor network scenario, where sensors measure a data field (temperature) and the results of their measurements are transported across the network to a certain designated node called the sink. This is referred to as data gathering, and it is a relevant problem in various sensor network settings, where information from the network, in its coded form, is needed at a central base station node for storage, monitoring, or control purposes. It studies the interaction between data representation at nodes, rate allocation, and routing and node placement for gathering of correlated data in sensor networks. The results of work show that a joint consideration of these issues provides important improvements in the overall data gathering. Moreover, it considers the problem of energy efficient optimal node placement, data gathering of spatio-temporally correlated processes under delay constraints, and defines distortion measure that includes the effects of both spatial approximation and delay.

[1]  Toby Berger,et al.  Multiterminal Source Coding with High Resolution , 1999, IEEE Trans. Inf. Theory.

[2]  Baltasar Beferull-Lozano,et al.  Scaling laws for correlated data gathering , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[3]  Martin Vetterli,et al.  On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[4]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[5]  Michael Gastpar,et al.  To code, or not to code: lossy source-channel communication revisited , 2003, IEEE Trans. Inf. Theory.

[6]  Baltasar Beferull-Lozano,et al.  Power-efficient sensor placement and transmission structure for data gathering under distortion constraints , 2006, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[7]  Krishna M. Sivalingam,et al.  Data gathering in sensor networks using the energy*delay metric , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[8]  Kannan Ramchandran,et al.  On distributed sampling of bandlimited and non-bandlimited sensor fields , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Kannan Ramchandran,et al.  Distributed source coding using syndromes (DISCUS): design and construction , 2003, IEEE Trans. Inf. Theory.

[10]  Michael Gastpar,et al.  Power, spatio-temporal bandwidth, and distortion in large sensor networks , 2005, IEEE Journal on Selected Areas in Communications.

[11]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[12]  Deborah Estrin,et al.  Coping with irregular spatio-temporal sampling in sensor networks , 2004, CCRV.

[13]  Ramesh Govindan,et al.  Scale-free aggregation in sensor networks , 2005, Theor. Comput. Sci..

[14]  S. PradhanS.,et al.  Distributed source coding using syndromes (DISCUS) , 2006 .

[15]  Deborah Estrin,et al.  Simultaneous Optimization for Concave Costs: Single Sink Aggregation or Single Source Buy-at-Bulk , 2003, SODA '03.

[16]  S.D. Servetto,et al.  Efficient network architectures for sensor reachback , 2004, International Zurich Seminar on Communications, 2004.

[17]  Baltasar Beferull-Lozano,et al.  Lossy network correlated data gathering with high-resolution coding , 2005, IEEE Transactions on Information Theory.

[18]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.

[19]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.

[20]  Gregory J. Pottie,et al.  Wireless integrated network sensors , 2000, Commun. ACM.

[21]  Mingyan Liu,et al.  On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of Its Data , 2003, IPSN.

[22]  Sergio Verdú,et al.  The source-channel separation theorem revisited , 1995, IEEE Trans. Inf. Theory.

[23]  Toby Berger,et al.  Lossy Source Coding , 1998, IEEE Trans. Inf. Theory.

[24]  Martin Vetterli,et al.  Power efficient gathering of correlated data: optimization, NP-completeness and heuristics , 2003, MOCO.

[25]  Bernd Girod,et al.  Compression with side information using turbo codes , 2002, Proceedings DCC 2002. Data Compression Conference.

[26]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.