Abstract
Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.
- Abdelbar, A. M., Andrews, E. A. M., and Wunsch III, D. C. 2003. Abductive reasoning with recurrent neural networks. Neural Netw. 16, 5-6, 665--673. Google ScholarDigital Library
- Aguero, J. R. and Vargas, A. 2005. Inference of operative configuration of distribution networks using fuzzy logic techniques---part II: Extended real-time model. IEEE Trans. Power Syst. 20, 3 (August), 1562--1569.Google Scholar
- Ahlswede, R., Cai, N., Li, S. R., and Yeung, R. W. 2000. Network information flow. IEEE Trans. Inf. Theory 46, 4 (July), 1204--1216. Google ScholarDigital Library
- Ahmed, M. and Pottie, G. 2005. Fusion in the context of information theory. In Distributed Sensor Networks, S. S. Iyengar and R. R. Brooks, Eds. CRC Press, Boca Raton, Chapter 22, 419--436.Google Scholar
- Ahn, J. and Krishnamachari, B. 2006. Fundamental scaling laws for energy-efficient storage and querying in wireless sensor networks. In Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'06). ACM, Florence, Italy, 334--343. Google ScholarDigital Library
- Akyildiz, I. F., Estrin, D., Culler, D. E., and Srivastava, M. B., Eds. 2003. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys'03). ACM, Los Angeles. Google Scholar
- Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cyirci, E. 2002. Wireless sensor networks: A survey. Comput. Netw. 38, 4 (March), 393--422. Google ScholarDigital Library
- Arbuckle, D., Howard, A., and Mataric, M. J. 2002. Temporal occupancy grids: A method for classifying spatio-temporal properties of the environment. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, Switzerland, 409--414.Google Scholar
- Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Sig. Proc. 50, 2 (February), 174--188. IEEE. Google ScholarDigital Library
- Aslam, J., Butler, Z., Constantin, F., Crespi, V., Cybenko, G., and Rus, D. 2003. Tracking a moving object with a binary sensor network. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys'03), 150--161. Google ScholarDigital Library
- Bajwa, W., Haupt, J., Sayeed, A., and Nowak, R. 2006. Compressive wireless sensing. Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 134--142. Google ScholarDigital Library
- Banon, G. 1981. Distinction between several subsets of fuzzy measures. Fuzzy Sets Syst. 5, 3 (May), 291--305.Google ScholarCross Ref
- Baran, R. H. 1989. A collective computation approach to automatic target recognition. In Proceedings of the International Joint Conference on Neural Networks. Vol. I. IEEE, Washington, D.C., 39--44.Google ScholarCross Ref
- Barros, J. and Servetto, S. D. 2006. Network information flow with correlated sources. IEEE Trans. Inf. Theory 52, 1 (January), 155--170. Google ScholarDigital Library
- Bass, T. 2000. Intrusion detection systems and multisensor data fusion. Comm. ACM 43, 4 (April), 99--105. ACM Press. Google ScholarDigital Library
- Bauer, F. and Varma, A. 1996. Distributed algorithms for multicast path setup in data networks. Trans. Netw. 4, 2 (April), 181--191. Google ScholarDigital Library
- Bayes, T. R. 1763. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society 53, 370--418.Google Scholar
- Bedworth, M. D. and O'Brien, J. C. 1999. The omnibus model: A new model for data fusion? In Proceedings of the 2nd International Conference on Information Fusion (FUSION'99). ISIF, Sunnyvale, 437--444.Google Scholar
- Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Advanced Applications in Pattern Recognition. Kluwer Academic Publishers, Norwell, MA. Google ScholarDigital Library
- Bhardwaj, M. and Chandakasan, A. P. 2002. Bounding the lifetime of sensor networks via optimal role assignment. In Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2002). USC/Information Sciences Institute, IEEE, New York, NY.Google Scholar
- Biswas, R., Thrun, S., and Guibas, L. J. 2004. A probabilistic approach to inference with limited information in sensor networks. Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 269--276. Google ScholarDigital Library
- Blatt, D. and Hero, A. 2004. Distributed maximum likelihood estimation for sensor networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04). Vol. 3. IEEE, Montreal, Canada, 929--932.Google Scholar
- Blum, P., Meier, L., and Thiele, L. 2004. Improved interval-based clock synchronization in sensor networks. Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 349--358. Google ScholarDigital Library
- Blumenthal, J., Timmermann, D., Buschmann, C., Fischer, S., Koberstein, J., and Luttenberger, N. 2006. Minimal transmission power as distance estimation for precise localization in sensor networks. In Proceedings of the 2006 International Conference on Communications and Mobile Computing (IWCMC'06). ACM, Vancouver, British Columbia, Canada, 1331--1336. Google ScholarDigital Library
- Bonfils, B. J. and Bonnet, P. 2003. Adaptive and decentralized operator placement for in-network query processing. In Information Processing in Sensor Networks 2nd International Workshop (IPSN'03), 47--62. Google ScholarDigital Library
- Bonissone, P. P. 1997. Soft computing: The convergence of emerging reasoning technologies. Soft Comput. 1, 1 (April), 6--18.Google ScholarCross Ref
- Boulis, A., Ganeriwal, S., and Srivastava, M. B. 2003a. Aggregation in sensor networks: An energy-accuracy trade-off. Ad Hoc Networks 1, 2-3 (September), 317--331. Special Issue on Sensor Network Protocols and Applications.Google ScholarCross Ref
- Boulis, A., Han, C.-C., and Srivastava, M. B. 2003b. Design and implementation of a framework for efficient and programmable sensor networks. In Proceedings of the 1st International Conference on Mobile Systems, Applications, and Services (MobiSys'03). USENIX, San Francisco, CA, 187--200. Google ScholarDigital Library
- Boyd, J. R. 1987. A discourse on winning and losing. Unpublished set of briefing slides available at Air University Library, Maxwell AFB, Alabama.Google Scholar
- Bracio, B. R., Horn, W., and Möller, D. P. F. 1997. Sensor fusion in biomedical systems. In Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 3. IEEE, Chicago, IL, 1387--1390.Google Scholar
- Brokmann, G., March, B., Romhild, D., and Steinke, A. 2001. Integrated multisensors for industrial humidity measurement. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. IEEE, Baden-Baden, Germany, 201--203.Google Scholar
- Brooks, R. R. and Iyengar, S. 1998. Multi-Sensor Fusion: Fundamentals and Applications with Software. Prentice Hall PTR, Upper Saddle River, NJ. Google ScholarDigital Library
- Brown, C., Durrant-Whyte, H., Leonard, J., Rao, B., and Steer, B. 1992. Distributed data fusion using Kalman filtering: A robotics application. In Data Fusion in Robotics and Machine Intelligence, M. A. Abidi and R. C. Gonzalez, Eds. Academic Press, Inc., San Diego, CA, Chapter 7, 267--309.Google Scholar
- Brown, R. G. and Hwang, P. Y. C. 1996. Introduction to Random Signals and Applied Kalman Filtering, 3rd ed. John Wiley & Sons, New York, NY.Google Scholar
- Buede, D. M. 1988. Shafer-Dempster and Bayesian reasoning: A response to “Shafer-Dempster reasoning with applications to multisensor target identification systems.” IEEE Trans. Syst., Man Cyber. 18, 6 (November/December), 1009--1011.Google ScholarCross Ref
- Cain, M. P., Stewart, S. A., and Morse, J. B. 1989. Object classification using multispectral sensor data fusion. In Proceedings of SPIE Sensor Fusion II. Vol. 1100. SPIE, Orlando, FL, 53--61.Google Scholar
- Castelaz, P. F. 1988. Neural networks in defense applications. In Proceedings of the IEEE International Conference on Neural Networks. Vol. II. IEEE, San Diego, CA, 473--480.Google ScholarCross Ref
- Chakrabarty, K., Iyengar, S. S., Qi, H., and Cho, E. 2002. Grid coverage for surveillance and target location in distributed sensor networks. IEEE Trans. Comput. 51, 12 (December), 1448--1453. Google ScholarDigital Library
- Chakravarty, P. and Jarvis, R. 2005. Multiple target tracking for surveillance: A particle filter approach. In Proceedings of the 2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP'05). IEEE, Melbourne, Australia, 181--186.Google Scholar
- Chan Yet, W. and Qidwai, U. 2005. Intelligent sensor network for obstacle avoidance strategy. In Proceedings of the 4th IEEE Conference on Sensors. IEEE, Irvine.Google Scholar
- Chen, C., Ali, A. M., and Wang, H. 2006a. Design and testing of robust acoustic arrays for localization and enhancement of several bird sources. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 268--275. Google ScholarDigital Library
- Chen, H., Mineno, H., and Mizuno, T. 2006b. A meta-data-based data aggregation scheme in clustering wireless sensor networks. In Proceedings of the 7th International Conference on Mobile Data Management (MDM'06). IEEE, Nara, Japan, 154--154. Google ScholarDigital Library
- Chen, Y. P., Liestman, A. L., and Liu, J. 2006c. A hierarchical energy-efficient framework for data aggregation in wireless sensor networks. IEEE Trans. Vehic. Tech. 55, 3 (May), 789--796.Google ScholarCross Ref
- Cheng, Y. and Kashyap, R. L. 1988. Comparison of Bayesian and Dempster's rules in evidence combination. In Maximum-Entropy and Bayesian Methods in Science and Engineering, G. J. Erickson and C. R. Smith, Eds. Klewer, Dordrecht, Netherlands, 427--433.Google Scholar
- Chew, P. and Marzullo, K. 1991. Masking failures of multidimentional sensors. In Proceedings of the 10th Symposium on Reliable Distributed Systems. IEEE, Pisa, Italy, 32--41.Google Scholar
- Ci, S. and Sharif, H. 2005. Performance comparison of kalman filter based approaches for energy efficiency in wireless sensor networks. In Proceedings of the 3rd ACS/IEEE International Conference on Computer Systems and Applications (AICCSA'05). IEEE, Cairo, Egypt, 58--65. Google ScholarDigital Library
- Ci, S., Sharif, H., and Nuli, K. 2004. A UKF-based link adaptation scheme to enhance energy efficiency in wireless sensor networks. In Proceedings of the 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'04). Vol. 4. IEEE, Barcelona, Spain, 2483--2488.Google Scholar
- Ciancio, A. and Ortega, A. 2004. A distributed wavelet compression algorithm for wireless sensor networks using lifting. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04). Vol. 4. IEEE, Montreal, Canada.Google Scholar
- Ciancio, A., Pattem, S., Ortega, A., and Krishnamachari, B. 2006. Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 309--316. Google ScholarDigital Library
- Cimander, C., Carlsson, M., and Mandenius, C. 2002. Sensor fusion for on-line monitoring of yoghurt fermentation. J. Biotech. 99, 3 (November), 237--248.Google ScholarCross Ref
- Coates, M. 2004. Distributed particle filters for sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 99--107. Google ScholarDigital Library
- Cohen, N. H., Purakayastha, A., Turek, J., Wong, L., and Yeh, D. 2001. Challenges in flexible aggregation of pervasive data. IBM Research Report RC 21942 (98646), IBM Research Division, Yorktown Heights, NY (January).Google Scholar
- Coué, C., Fraichard, T., Bessiere, P., and Mazer, E. 2002. Multi-sensor data fusion using Bayesian programming: An automotive application. In IEEE/RSJ International Conference on Intelligent Robots and System. Vol. 1. IEEE, Lausanne, Switzerland, 141--146.Google Scholar
- Crisan, D. and Doucet, A. 2002. A survey of convergence results on particle filtering methods for practitioners. IEEE Trans. Sig. Proc. 50, 3 (March), 736--746. Google ScholarDigital Library
- Cui, X., Hardin, T., Ragade, R., and Elmaghraby, A. 2004. A swarm-based fuzzy logic control mobile sensor network for hazardous contaminants localization. In Proceedings of the 1st IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS'04). IEEE, Fort Lauderdale, 194--203.Google Scholar
- Dasarathy, B. V. 1997. Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc. IEEE 85, 1 (January), 24--38.Google ScholarCross Ref
- Dasarathy, B. V. 2000. More the merrier…or is it?---sensor suite augmentation benefits assessment. In Proceedings of the 3rd International Conference on Information Fusion (Fusion 2000). Vol. 2. IEEE, Paris, France, WEC3/20--WEC3/25.Google ScholarCross Ref
- Dasarathy, B. V. 2001. What, where, why, when, and how? Inform. Fus. 2, 2 (January), 75--76. Editorial.Google ScholarCross Ref
- Dasgupta, K., Kukreja, M., and Kalpakis, K. 2003. Topology-aware placement and role assignment for energy-efficient information gathering in sensor networks. In Proceedings of the 8th IEEE International Symposium on Computers and Communication (ISCC'03). Vol. 1. IEEE, Antalya, Turkey, 341--348. Google ScholarDigital Library
- Data Fusion Server. 2004. {Online} Available: http://www.data-fusion.org.Google Scholar
- de Campos, L. M., Gamez, J. A., and Moral, S. 2002. Partial abductive inference in Bayesian belief networks---an evolutionary computation approach by using problem-specific genetic operators. IEEE Trans. Evolut. Computat. 6, 2 (April), 105--131. Google ScholarDigital Library
- Dempster, A. P. 1968. A generalization of Bayesian inference. J. Royal Stat. Soc., Series B 30, 205--247.Google Scholar
- Dhillon, S. S., Chakrabarty, K., and Iyengar, S. S. 2002. Sensor placement for grid coverage under imprecise detections. In Proceedings of the 5th International Conference on Information Fusion (Fusion 2002). Vol. 2. IEEE, Annapolis, Maryland, 1581--1587.Google Scholar
- Ding, M., Cheng, X., and Xue, G. 2003. Aggregation tree construction in sensor networks. In Proceedings of the 58th IEEE Vehicular Technology Conference (VTC-Fall 2003). Vol. 4. IEEE, Orlando, 2168--2172.Google Scholar
- Doucet, A., Vo, B.-N., Andrieu, C., and Davy, M. 2002. Particle filtering for multi-target tracking and sensor management. In Proceedings of the 5th International Conference on Information Fusion (Fusion 2002). Vol. 1. IEEE, Annapolis, Maryland, 474--481.Google Scholar
- Duarte, M. F., Wakin, M. B., Baron, D., and Baraniuk, R. G. 2006. Universal distributed sensing via random projections. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 177--185. Google ScholarDigital Library
- Durrant-Whyte, H. F. 1988. Sensor models and multisensor integration. Inter. J. Robotics Res. 7, 6 (December), 97--113. Google ScholarDigital Library
- Elfes, A. 1987. Sonar-based real-world mapping and navigation. IEEE J Robotics Automat. RA-3, 3 (June), 249--265.Google Scholar
- Elfes, A. 1989. Using occupancy grids for mobile robot perception and navigation. IEEE Comput. 22, 6 (June), 46--57. Google ScholarDigital Library
- Elmenreich, W. 2002. Sensor fusion in time-triggered systems. Ph.D. thesis, Institut für Technische Informatik, Vienna University of Technology, Vienna, Austria.Google Scholar
- Elson, J. and Parker, A. 2006. Tinker: A tool for designing data-centric sensor networks. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 350-- 357. Google ScholarDigital Library
- Fang, L., Du, W., and Ning, P. 2005. A beacon-less location discovery scheme for wireless sensor networks. In Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2005), 161--171.Google Scholar
- Figueiredo, C. M., Nakamura, E. F., and Loureiro, A. A. 2004. Multi: A hybrid adaptive dissemination protocol for wireless sensor networks. In Proceedings of the 1st International Workshop on Algorithmic Aspects of Wireless Sensor Networks (ALGOSENSORS 2004). Lecture Notes in Computer Science, vol. 3121. Springer, Turku, Finland, 171--186.Google ScholarCross Ref
- Filippidis, A., Jain, L. C., and Martin, N. 2000. Fusion of intelligent agents for the detection of aircraft in SAR images. IEEE Trans. Patt. Anal. Mach. Intell. 22, 4 (April), 378--384. Google ScholarDigital Library
- Forney, G. D. 1973. The viterbi algorithm. Proc. IEEE 61, 3 (March), 268--278.Google ScholarCross Ref
- Fowler, M. L. and Chen, M. 2005. Fisher-information-based data compression for estimation using two sensors. IEEE Trans. Aerospace Electr. Syst. 41, 3 (July), 1131--1137.Google ScholarCross Ref
- Frank, C. and Römer, K. 2005. Algorithms for generic role assignment in wireless sensor networks. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys'05), 230--242. Google ScholarDigital Library
- Frankel, C. B. 1999. Such order from confusion sprung: Adaptive competence and affect regulation. Ph.D. thesis, Pacific Graduate School of Psychology, Palo Alto, CA.Google Scholar
- Frankel, C. B. and Bedworth, M. D. 2000. Control, estimation and abstraction in fusion architectures: Lessons from human information processing. In Proceedings of the 3rd International Conference on Information Fusion (Fusion 2000). Vol. 1. IEEE, Paris, France, MoC5/3--MoC5/10.Google ScholarCross Ref
- Friedlander, D. S. 2005. Semantic information extraction. In Distributed Sensor Networks, S. S. Iyengar and R. R. Brooks, Eds. CRC Press, Boca Raton, Chapter 21, 409--417.Google Scholar
- Friedlander, D. S. and Phoha, S. 2002. Semantic information fusion for coordinated signal processing in mobile sensor networks. Int. J. High Perf. Comput. Appl. 16, 3 (Fall), 235--241.Google ScholarDigital Library
- Gagvani, N. and Silver, D. 2000. Shape-based volumetric collision detection. In Proceedings of the 2000 IEEE Symposium on Volume Visualization. IEEE, Salt Lake City, Utah, 57--61. Google ScholarDigital Library
- Gallager, R. G. 1968. Information Theory and Reliable Communication. Wiley, New York. Google ScholarDigital Library
- Ganeriwal, S., Kumar, R., and Srivastava, M. B. 2003. Timing-sync protocol for sensor networks. In Proceedings of the 1st International Conference on Embedded Networks Sensor Systems (SenSys'03), 138--149. Google ScholarDigital Library
- Gao, J. B. and Harris, C. J. 2002. Some remarks on Kalman filters for the multisensor fusion. Information Fusion 3, 3 (September), 191--201.Google ScholarCross Ref
- Garvey, T. D., Lowrance, J. D., and Fischler, M. A. 1981. An inference technique for integrating knowledge from disparate sources. In Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI'81). William Kaufmann, Vancouver, British Columbia, Canada, 319--325.Google Scholar
- Geoscience and Remote Sensing Society. 2004. {Online} Available: http://www.dfc-grss.org.Google Scholar
- Gilks, W., Richardson, S., and Spie, D., Eds. 1996. Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC, London, UK.Google Scholar
- Grime, S. and Durrant-Whyte, H. F. 1994. Data fusion in decentralized sensor networks. Contr. Eng. Pract. 2, 5 (October), 849--863.Google ScholarCross Ref
- Gu, L., Jia, D., Vicaire, P., Yan, T., Luo, L., Tirumala, A., Cao, Q., He, T., Stankovic, J. A., Abdelzaher, T., and Krogh, B. H. 2005. Lightweight detection and classification for wireless sensor networks in realistic environments. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys'05), 205--217. Google ScholarDigital Library
- Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., and Madden, S. 2004. Distributed regression: an efficient framework for modeling sensor network data. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 1--10. Google ScholarDigital Library
- Gummadi, R., Li, X., Govindan, R., Shahabi, C., and Hong, W. 2005. Energy-efficient data organization and query processing in sensor networks. SIGBED Review 2, 1, 7--12. Google ScholarDigital Library
- Gunnarsson, F. and Gustafsson, F. 2003. Positioning using time-difference of arrival measurements. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol. 6. IEEE, Hong Kong, 553--556.Google Scholar
- Guo, D. and Wang, X. 2004. Dynamic sensor collaboration via sequential Monte Carlo. IEEE J. Selec. Areas Comm. 22, 6 (August), 1037--1047. Google ScholarDigital Library
- Guo, D., Wang, X., and Chen, R. 2005. New sequential Monte Carlo methods for nonlinear dynamic systems. Statis. Comput. 15, 2 (April), 135--147. Google ScholarDigital Library
- Gupta, I., Riordan, D., and Sampalli, S. 2005. Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the 3rd Annual Communication Networks and Services Research Conference (CNSR'05). IEEE, Halifax, Canada, 255--260. Google ScholarDigital Library
- Halgamuge, M. N., Guru, S. M., and Jennings, A. 2003. Energy efficient cluster formation in wireless sensor networks. In Proceedings of the 10th International Conference on Telecommunications (ICT'03). Vol. 2. IEEE, Papeete, French Polynesia, 1571--1576.Google Scholar
- Hall, D. L. 1992. Mathematical Techniques in Multisensor Data Fusion. Artech House, Norwood, MA. Google ScholarDigital Library
- Hall, D. L. and Llinas, J. 1997. An introduction to multi-sensor data fusion. Proc. IEEE 85, 1 (January), 6--23.Google ScholarCross Ref
- Hartl, G. and Li, B. 2004. Loss inference in wireless sensor networks based on data aggregation. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 396--404. Google ScholarDigital Library
- Hartl, G. and Li, B. 2005. infer: A Bayesian inference approach towards energy efficient data collection in dense sensor networks. In Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05). IEEE, Washington, 371--380. Google ScholarDigital Library
- Haupt, J. and Nowak, R. 2006. Signal reconstruction from noisy random projections. IEEE Trans. Inform. Theory 52, 9 (September), 4036--4048. Google ScholarDigital Library
- He, T., Blum, B. M., Stankovic, J. A., and Abdelzaher, T. 2004. AIDA: Adaptive application independent data aggregation in wireless sensor network. ACM Trans. Embed. Comput. Syst. 3, 2 (May), 426--457. Special issue on Dynamically Adaptable Embedded Systems. Google ScholarDigital Library
- Heidemann, J., Silva, F., and Estrin, D. 2003. Matching data dissemination algorithms to application requirements. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys'03), 218--229. Google ScholarDigital Library
- Heinzelman, W., Chandrakasan, A., and Balakrishnan, H. 2000. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS'00). IEEE, Maui, 8020--8029. Google ScholarDigital Library
- Hellerstein, J. M., Hong, W., Madden, S., and Stanek, K. 2003. Beyond average: Towards sophisticated sensing with queries. In Information Processing in Sensor Networks: 2nd International Workshop (IPSN'03), 63--79. Google ScholarDigital Library
- Hoang, A. T. and Motani, M. 2005a. Collaborative broadcasting and compression in cluster-based wireless sensor networks. In Proceeedings of the Second European Workshop on Wireless Sensor Networks (EWSN'05). IEEE, Istanbul, Turkey, 197--206.Google Scholar
- Hoang, A. T. and Motani, M. 2005b. Exploiting wireless broadcast in spatially correlated sensor networks. In Proceedings of the 2005 IEEE International Conference on Communications (ICC'05). Vol. 4. IEEE, Seoul, Korea, 2807--2811.Google Scholar
- Hongyang, C., Ping, D., Yongjun, X., and Xiaowei, L. 2005. A robust location algorithm with biased extended Kalman filtering of TDOA data for wireless sensor networks. In Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing (WCNM'05). Vol. 2. IEEE, Wuhan, China, 883--886.Google Scholar
- Hoover, A. and Olsen, B. D. 1999. A real-time occupancy map from multiple video streams. In Proceedings of the IEEE International Conference on Robotics and Automation. Vol. 3. IEEE, Detroit, Michigan, 2261--2266.Google Scholar
- Hoover, A. and Olsen, B. D. 2000. Sensor network perception for mobile robotics. In Proceedings of the IEEE International Conference on Robotics and Automation. Vol. 1. IEEE, San Fransico, California, 342--347.Google Scholar
- Hu, L. and Evans, D. 2004. Localization for mobile sensor networks. In Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom'04). ACM, Philadelphia, PA, USA, 45--57. Google ScholarDigital Library
- Hua, G. and Chen, C. W. 2005. Distributed source coding in wireless sensor networks. In Proceedings of the 2nd International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (Qshine'05). IEEE, Orlando. Google ScholarDigital Library
- Ihler, A. T., Fisher I, J. W., Moses, R. L., and Willsky, A. S. 2005. Nonparametric belief propagation for self-localization of sensor networks. IEEE J. Select. Areas Comm. 23, 4 (April), 809--819. Google ScholarDigital Library
- INFOCOM, Ed. 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2005). IEEE, Miami, USA.Google Scholar
- Intanagonwiwat, C., Estrin, D., Govindan, R., and Heidemann, J. 2002. Impact of network density on data aggregation in wireless sensor networks. In Proceedings of the 22nd IEEE International Conference on Distributed Computing Systems (ICDCS'02). IEEE, Vienna, Austria, 457--458. Google ScholarDigital Library
- Intanagonwiwat, C., Govindan, R., and Estrin, D. 2000. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MobiCom'00). ACM Press, Boston, MA, 56--67. Google ScholarDigital Library
- Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., and Silva, F. 2003. Directed diffusion for wireless sensor networking. IEEE/ACM Trans. Netw. 11, 1 (February), 2--16. Google ScholarDigital Library
- International Society of Information Fusion. 2004. {Online} Available: http://www.inforfusion.org.Google Scholar
- IPSN, Ed. 2005. Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN'05). IEEE, Los Angeles.Google Scholar
- Isard, M. and Blake, A. 1996. Contour tracking by stochastic propagation of conditional density. In 4th European Conference on Computer Vision (ECCV'96), B. F. Buxton and R. Cipolla, Eds. Lecture Notes in Computer Science, vol. 1064. Springer, Cambridge, UK, 343--356. Google ScholarDigital Library
- Isla, D. A. and Blumberg, B. M. 2002. Object persistence for synthetic creatures. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems. ACM Press, Bologna, Italy, 1356--1363. Google ScholarDigital Library
- Iyengar, S. S., Chakrabarty, K., and Qi, H. 2001. Introduction to special issue on “distributed sensor networks for real-time systems with adaptive configuration.” J. Franklin Inst. 338, 6 (September), 651-- 653.Google ScholarCross Ref
- Jacobs, O. L. R. 1993. Introduction to Control Theory, 2nd ed. Oxford University Press, Oxford, UK.Google Scholar
- Jain, A., Chang, E. Y., and Wang, Y.-F. 2004. Adaptive stream resource management using Kalman filters. In Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD'04). ACM, Paris, France, 11--22. Google ScholarDigital Library
- Jayasimha, D. N. 1994. Fault tolerance in a multisensor environment. In Proceedings of the 13th Symposium on Reliable Distributed Systems. IEEE, Dana Point, CA, 2--11.Google ScholarCross Ref
- Jazwinski, A. H. 1970. Stochastic Processes and Filtering Theory. Academic Press, New York.Google Scholar
- Jin, G. and Nittel, S. 2006. Ned: An efficient noise-tolerant event and event boundary detection algorithm in wireless sensor networks. In Proceedings of the 7th International Conference on Mobile Data Management (MDM'06). IEEE, Washington, DC, 153--161. Google ScholarDigital Library
- Ju, H. and Cui, L. 2005. Easipc: A packet compression mechanism for embedded WSN. In Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'05). IEEE, Hong Kong, China, 394--399. Google ScholarDigital Library
- Julier, S. J. and Uhlmann, J. K. 1997. New extension of the Kalman filter to nonlinear systems. In Signal Processing, Sensor Fusion, and Target Recognition VI. Vol. 3068. SPIE, San Diego, 182--193.Google Scholar
- Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Engin. 82, 35--45.Google ScholarCross Ref
- Kalpakis, K., Dasgupta, K., and Namjoshi, P. 2003. Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Comput. Netw. 42, 6 (August), 697--716. Google ScholarDigital Library
- Keppens, J., Shen, Q., and Schafer, B. 2005. Probabilistic abductive computation of evidence collection strategies in crime investigation. In Proceedings of the 10th International Conference on Artificial Intelligence and Law (ICAIL'05). ACM, Bologna, Italy, 215--224. Google ScholarDigital Library
- Kessler et al., 1992. Functional description of the data fusion process. Tech. rep., Naval Air Development Center, Warminster, PA, USA. January. Report prepared for the Office of Naval Technology.Google Scholar
- Klein, L. A. 1993. Sensor and Data Fusion Concepts and Applications. Vol. TT14. SPIE Optical Engineering Press. Google ScholarDigital Library
- Kochhal, M., Schwiebert, L., and Gupta, S. 2003. Role-based hierarchical self organization for wireless ad hoc sensor networks. In Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications (WSNA'03). ACM, San Diego, CA, 98--107. Google ScholarDigital Library
- Kohonen, T. 1997. Self-Organizing Maps. Springer-Verlag, Secaucus, NJ, USA. Google ScholarDigital Library
- Kokar, M. M., Bedworth, M. D., and Frankel, C. B. 2000. A reference model for data fusion systems. In Sensor Fusion: Architectures, Algorithms and Applications IV. SPIE, Orlando, FL, 191--202.Google Scholar
- Kokar, M. M., Tomasik, J. A., and Weyman, J. 1999. A formal approach to information fusion. In Proceedings of the 2nd International Conference on Information Fusion (Fusion'99). Vol. 1. ISIF, Sunnyvale, 133-- 140.Google Scholar
- Kotz, D. and Gray, R. S. 1999. Mobile agents and the future of the internet. ACM SIGOPS Oper. Syst. Rev. 33, 3 (July), 7--13. Google ScholarDigital Library
- Kreucher, C., Kastella, K., and Hero III, A. O. 2005. Sensor management using an active sensing approach. Sig. Proc. 85, 3 (March), 607--624. Google ScholarDigital Library
- Krishnamachari, B., Estrin, D., and Wicker, S. 2002. The impact of data aggregation in wireless sensor networks. In International Workshop of Distributed Event Based Systems (DEBS). IEEE, Vienna, Austria, 575--578. Google ScholarDigital Library
- Krishnamachari, B. and Iyengar, S. 2004. Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans. Comput. 53, 3 (March), 241--250. Google ScholarDigital Library
- Kulik, J., Heinzelman, W., and Balakrishnan, H. 2002. Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Networks 8, 2/3 (March-May), 169--185. Google ScholarDigital Library
- Kumar, R., Wolenetz, M., Agarwalla, B., Shin, J., Hutto, P., Paul, A., and Ramachandran, U. 2003. Dfuse: A framework for distributed data fusion. In Proceedings of the 1st International Conference on Embedded Networks Sensor Systems (SenSys'03), 114--125. Google ScholarDigital Library
- Kumar, V. P. and Desai, U. B. 1996. Image interpretation using Bayesian networks. IEEE Trans. Patt. Anal. Mach. Intell. 18, 1 (January), 74--77. Google ScholarDigital Library
- Kusuma, J., Doherty, L., and Ramchandran, K. 2001. Distributed compression for sensor networks. In Proceedings of the 2001 International Conference on Image Processing (ICIP'01). Vol. 1. IEEE, Thessaloniki, Greece, 82--85.Google Scholar
- Lee, C. C. 1990. Fuzzy logic in control systems: Fuzzy logic controller---part i. IEEE Trans. Syst., Man Cyber. 20, 2 (March--April), 404--418.Google Scholar
- Levis, P. and Culler, D. 2002. Maté: A tiny virtual machine for sensor networks. In Proceedings of the 10th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). ACM Press, San Jose, CA, 85--95. Google ScholarDigital Library
- Lewis, T. W. and Powers, D. M. W. 2002. Audio-visual speech recognition using red exclusion and neural networks. In Proceedings of the 25th Australasian Conference on Computer Science. Australian Computer Society, Inc., Melbourne, Victoria, Australia, 149--156. Google ScholarDigital Library
- Li, S., Lin, Y., Son, S. H., Stankovic, J. A., and Wei, Y. 2004. Event detection services using data service middleware in distributed sensor networks. Telecomm. Syst. 26, 2--4 (June), 351--368.Google ScholarDigital Library
- Li, T., Ekpenyong, A., and Huang, Y. 2006. Source localization and tracking using distributed asynchronous sensor. IEEE Trans. Sig. Proc. 54, 10 (October), 3991--4003. Google ScholarDigital Library
- Li, X., Kim, Y. J., Govindan, R., and Hong, W. 2003. Multi-dimensional range queries in sensor networks. In Proceedings of the 1st International Conference on Embedded Network Sensor Systems (SenSys '03), 63--75. Google ScholarDigital Library
- Liang, Q. and Ren, Q. 2005a. Energy and mobility aware geographical multipath routing for wireless sensor networks. In 2005 IEEE Wireless Communications and Networking Conference (WCNC'05). Vol. 3. IEEE, New Orleans, 1867--1871.Google Scholar
- Liang, Q. and Ren, Q. 2005b. An energy-efficient MAC protocol for wireless sensor networks. In 2005 IEEE Global Telecommunications Conference (GLOBECOM'05). Vol. 1. IEEE, St. Louis.Google Scholar
- Liu, J., Cheong, E., and Zhao, F. 2005. Semantics-based optimization across uncoordinated tasks in networked embedded systems. In Proceedings of the 5th ACM International Conference On Embedded Software (EMSOFT 2005), W. Wolf, Ed. ACM, Jersey City, 273--281. Google ScholarDigital Library
- Liu, J., Zhang, Y., and Zhao, F. 2006. Robust distributed node localization with error management. In Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'06). ACM, Florence, Italy, 250--261. Google ScholarDigital Library
- Liu, J. and Zhao, F. 2005. Towards semantic services for sensor-rich information systems. In Proceedings of the 2nd International Conference on Broadband Networks (BROADNETS 2005). IEEE, Boston, 967--974.Google Scholar
- Luo, H. and Pottie, G. 2005. A study on combined routing and source coding with explicit side information in sensor networks. In 2005 IEEE Global Telecommunications Conference (GLOBECOM'05). Vol. 5. IEEE, St. Louis.Google Scholar
- Luo, R. C. and Kay, M. G. 1992. Data fusion and sensor integration: State-of-the-art 1990s. In Data Fusion in Robotics and Machine Intelligence, M. A. Abidi and R. C. Gonzalez, Eds. Academic Press, Inc., San Diego, CA, Chapter 3, 7--135.Google Scholar
- Luo, R. C. and Kay, M. G., Eds. 1995. Multisensor Integration and Fusion for Intelligent Machines and Systems, Reissue edition Computer Engineering and Computer Science. Ablex Publishing, New Jersey, USA.Google Scholar
- Luo, R. C., Yih, C.-C., and Su, K. L. 2002. Multisensor fusion and integration: Approaches, applications, and future research directions. IEEE Sensors J. 2, 2 (April), 107--119.Google ScholarCross Ref
- Luo, X., Dong, M., and Huang, Y. 2006. On distributed fault-tolerant detection in wireless sensor networks. IEEE Trans. Comput. 55, 1 (January), 2006. Google ScholarDigital Library
- Madden, S. R., Franklin, M. J., Hellerstein, J. M., and Hong, W. 2002. TAG: a Tiny AGgregation service for ad-hoc sensor networks. ACM SIGOPS Oper. Syst. Rev. 36, SI (Winter), 131--146. Google ScholarDigital Library
- Madden, S. R., Franklin, M. J., Hellerstein, J. M., and Hong, W. 2005. TinyDB: An acqusitional query processing system for sensor networks. ACM Trans. Database Syst. 30, 1 (March), 122--173. Google ScholarDigital Library
- Manzo, M., Roosta, T., and Sastry, S. 2005. Time synchronization attacks in sensor networks. In Proceedings of the 3rd ACM Workshop on Security of Ad Hoc and Sensor Networks (SASN'05). ACM, Alexandria, VA, 107--116. Google ScholarDigital Library
- Marco, D. and Neuhoff, D. L. 2004. Reliability vs. efficiency in distributed source coding for field-gathering sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 161--168. Google ScholarDigital Library
- Markin, M., Harris, C., Bernhardt, M., Austin, J., Bedworth, M., Greenway, P., Johnston, R., Little, A., and Lowe, D. 1997. Technology foresight on data fusion and data processing. Publication of The Royal Aeronautical Society.Google Scholar
- Marzullo, K. 1984. Maintaining the time in a distributed system: An example of a loosely-coupled distributed service. Ph.D. thesis, Stanford University, Department of Electrical Engineering, Stanford, CA. Google ScholarDigital Library
- Marzullo, K. 1990. Tolerating failures of continuous-valued sensors. ACM Trans. Comput. Syst. (TOCS) 8, 4 (November), 284--304. Google ScholarDigital Library
- Mascolo, C. and Musolesi, M. 2006. SCAR: Context-aware adaptive routing in delay tolerant mobile sensor networks. In Proceeding of the 2006 International Conference on Communications and Mobile Computing (IWCMC'06). ACM, Vancouver, Canada, 533--538. Google ScholarDigital Library
- Megerian, S., Koushanfar, F., Qu, G., Veltri, G., and Potkonjak, M. 2002. Exposure in wireless sensor networks: Theory and practical solutions. Wireless Netw. 8, 5 (September), 443--454. Google ScholarDigital Library
- Meguerdichian, S., Koushanfar, F., Potkonjak, M., and Srivastava, M. 2001a. Coverage problems in wireless ad-hoc sensor networks. In Proceedings of IEEE Infocom 2001. Vol. 3. IEEE, Anchorage, AK, 1380--1387.Google Scholar
- Meguerdichian, S., Slijepcevic, S., Karayan, V., and Potkonjak, M. 2001b. Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure. In Proceedings of the 2001 ACM International Symposium on Mobile Ad Hoc Networking & Computing. ACM Press, Long Beach, CA, 106--116. Google ScholarDigital Library
- Meier, L., Blum, P., and Thiele, L. 2004. Internal synchronization of drift-constraint clocks in ad-hoc sensor networks. In Proceedings of the 5th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'04). ACM, Tokyo, Japan, 90--97. Google ScholarDigital Library
- Mhatre, V. and Rosenberg, C. 2004. Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In Proceedings of the 2004 IEEE International Conference on Communications (ICC'04). Vol. 6. IEEE, Paris, France, 3646--3651.Google Scholar
- Miguez, J. and Artes-Rodriguez, A. 2006. A Monte Carlo method for joint node location and maneuvering target tracking in a sensor network. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06). Vol. 4. IEEE, Toulouse, France.Google Scholar
- Mini, R. A., Loureiro, A. A., and Nath, B. 2004. The distinctive design characteristics of a wireless sensor network: The energy map. Comput. Comm. 7, 10 (June), 935--945. Google ScholarDigital Library
- Mooney, R. J. 2000. Integrating abduction and induction in machine learning. In Abduction and Induction, Essays on their Relation and Integration, P. A. Flach and A. C. Kakas, Eds. Applied Logic Series. Kluwer, New York, Chapter 12, 336p.Google Scholar
- Moshiri, B., Asharif, M. R., and HoseinNezhad, R. 2002. Pseudo information measure: A new concept for extension of Bayesian fusion in robotic map building. Inform. Fusion 3, 1 (March), 51--68.Google ScholarCross Ref
- Nakamura, E. F., de Oliveira, H. A., Pontello, L. F., and Loureiro, A. A. 2006. On demand role assignment for event-detection in sensor networks. In Proceedings of the 11th IEEE International Symposium on Computers and Communication (ISCC'06), P. Bellavista, C.-M. Chen, A. Corradi, and M. Daneshmand, Eds. IEEE Computer Society, Cagliari, Italy, 941--947. Google ScholarDigital Library
- Nakamura, E. F., Figueiredo, C. M., and Loureiro, A. A. 2005a. Information fusion for data dissemination in self-organizing wireless sensor networks. In Proceedings of the 4th International Conference on Networking (ICN 2005), P. Lorenz and P. Dini, Eds. Lecture Notes in Computer Science, vol. 3420. Springer-Verlag GmbH, Reunion Island, France, 585--593. Google ScholarDigital Library
- Nakamura, E. F., Nakamura, F. G., Figueiredo, C. M., and Loureiro, A. A. 2005b. Using information fusion to assist data dissemination in wireless sensor networks. Telecomm. Syst. 30, 1--3 (November), 237-- 254.Google ScholarDigital Library
- Nelson, M. and Gailly, J.-L. 1995. The Data Compression Book, 2nd ed. M & T Books, New York, NY, USA. Google ScholarDigital Library
- Niu, R. and Varshney, P. K. 2006. Target location estimation in sensor networks with quantized data. IEEE Trans. Sig. Proc. 54, 12 (December), 4519--4528. Google ScholarDigital Library
- Nordlund, P.-J., Gunnarsson, F., and Gustafsson, F. 2002. Particle filters for positioning in wireless networks. In Proceedings of the XI European Signal Processing Conference (EURSIPCO'02). Vol. II. TeSA, Toulouse, France, 311--314.Google Scholar
- Novák, V., Perfilieva, I., and Mockor, J. 1999. Mathematical Principles of Fuzzy Logic. The International Series in Engineering and Computer Science. Kluwer Academic Publishers, Norwell, MA.Google Scholar
- Nowak, R., Mitra, U., and Willett, R. 2004. Estimating inhomogeneous fields using wireless sensor networks. IEEE J. Select. Areas Comm. 22, 6 (August), 999--1006. Google ScholarDigital Library
- Nowak, R. D. 2003. Distributed em algorithms for density estimation and clustering in sensor networks. IEEE Trans. Sig. Proc. 51, 8 (August), 2245--2253. Google ScholarDigital Library
- Olfati-Saber, R. 2005. Distributed kalman filter with embedded consensus filters. In 44th IEEE Conference on Decision and Control(CDC'05). IEEE, Seville, Spain, 8179--8184.Google ScholarCross Ref
- Pagac, D., Nebot, E. M., and Durrant-Whyte, H. 1998. An evidential approach to map-building for autonomous vehicles. IEEE Trans. Robotics Autom. 14, 4 (August), 623--629.Google ScholarCross Ref
- Pan, H., Anastasio, Z.-P., and Huang, T. 1998. A hybrid NN-Bayesian architecture for information fusion. In Proceedings of the 1998 International Conference on Image Processing (ICIP'98). Vol. 1. IEEE, Chicago, IL, 368--371.Google Scholar
- Pattem, S., Krishnamachari, B., and Govindan, R. 2004. The impact of spatial correlation on routing with compression in wireless sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 28--35. Google ScholarDigital Library
- Patwari, N., Hero, A. O., Perkins, M., Correal, N. S., and O'Dea, R. J. 2003. Relative location estimation in wireless sensor networks. IEEE Trans. Sig. Proc. 51, 8 (August), 2137--2148. Google ScholarDigital Library
- Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, USA. Google ScholarDigital Library
- Peirce, C. S. 1955. Abduction and induction. In Philosophical Writings of Peirce, C. S. Peirce and J. Buchler, Eds. Dover, New York, Chapter 11, 150--156.Google Scholar
- Petrovic, D., Shah, R. C., Ramchandran, K., and Rabaey, J. 2003. Data funneling: Routing with aggregation and compression for wireless sensor networks. In Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications (SNPA 2003). IEEE, Anchorage, AK, 156--162.Google ScholarCross Ref
- Pinto, A. J., Stochero, J. M., and de Rezende, J. F. 2004. Aggregation-aware routing on wireless sensor networks. In Proceedings of the IFIP TC6 9th International Conference on Personal Wireless Communications (PWC'04). Lecture Notes in Computer Science, vol. 3260. Springer, Delft, The Netherlands, 238--247.Google ScholarCross Ref
- Pohl, C. and van Genderen, J. L. 1998. Multisensor image fusion in remote sensing: Concepts, methods and applications. Int. J. Remote Sens. 19, 5 (March), 823--854.Google ScholarCross Ref
- Polastre, J., Hill, J., and Culler, D. 2004. Versatile low power media access for wireless sensor networks. In SenSys'04, J. A. Stankovic, A. Arora, and R. Govindan, Eds. ACM, Baltimore, MD, 95--107. Google ScholarDigital Library
- Poor, H. V. 1994. An Introduction to Signal Detection and Estimation, 2nd ed. Springer, New York. Google ScholarDigital Library
- Pottie, G. J. and Kaiser, W. J. 2000. Wireless integrated network sensors. Comm. ACM 43, 5 (May), 51--58. Google ScholarDigital Library
- Pradhan, S. S., Kusuma, J., and Ramchandran, K. 2002. Distributed compression in a dense microsensor network. IEEE Sig. Proc. Mag. 19, 2 (March), 51--60.Google ScholarCross Ref
- Pradhan, S. S. and Ramchandran, K. 2003. Distributed source coding using syndromes (DISCUS): design and construction. IEEE Trans. Inform. Theory 49, 3 (March), 626--643. Google ScholarDigital Library
- Provan, G. M. 1992. The validity of Dempster-Shafer belief functions. Int. J. Approx. Reasoning 6, 3 (May), 389--399. Google ScholarDigital Library
- Psounis, K. 1999. Active networks: Applications, security, safety and architectures. IEEE Comm. Surv. 2, 1 (First Quarter), 2--16. Google ScholarDigital Library
- Qi, H., Wang, X., Iyengar, S. S., and Chakrabarty, K. 2002. High performance sensor integration in distributed sensor networks using mobile agents. Int. J. High Perf. Comput. Appl. 16, 3 (Fall), 325--335.Google Scholar
- Rabbat, M., Haupt, J., Singh, A., and Nowak, R. 2006. Decentralized compression and predistribution via randomized gossiping. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 51--59. Google ScholarDigital Library
- Rabbat, M. and Nowak, R. D. 2004. Distributed optimization in sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 20--27. Google ScholarDigital Library
- Rachlin, Y., Negi, R., and Khosla, P. 2006. On the interdependence of sensing and estimation complexity in sensor networks. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 160--167. Google ScholarDigital Library
- Ramamoorthy, A., Jain, K., Chou, P. A., and Effros, M. 2006. Separating distributed source coding from network coding. IEEE Trans. Inf. Theory 52, 6 (June), 2785--2795. Google ScholarDigital Library
- Ramchandran, K., Sztipanovits, J., Hou, J. C., and Pappas, T. N., Eds. 2004. Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04). ACM, Berkeley, CA. Google Scholar
- Rangwala, S., Gummadi, R., Govindan, R., and Psounis, K. 2006. Interference-aware fair rate control in wireless sensor networks. In Proceedings of the 2006 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM'06). ACM, Pisa, Italy, 63--74. Google ScholarDigital Library
- Rao, N. S. V. 2001. On fusers that perform better than the best sensor. IEEE Trans. Patt. Anal. Mach. Intell. 23, 8 (August), 904--909. Google ScholarDigital Library
- Ratnasamy, S., Karp, B., Shenker, S., Estrin, D., Govindan, R., Yin, L., and Yu, F. 2003. Data-centric storage in sensornets with GHT, a geographic hash table. Mobile Networks and Applications (MONET) 8, 4 (August), 427--442. Google ScholarDigital Library
- Raviraj, P., Sharif, H., Hempel, M., and Ci, S. 2005. MOBMAC---an energy efficient and low latency MAC for mobile wireless sensor networks. In Proceedings of the 2005 Systems Communications. IEEE, Montreal, Canada, 370--375. Google ScholarDigital Library
- Redi, J., Balakrishnan, H., and Zhao, F., Eds. 2005. Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys'05). ACM, San Diego, CA. Google Scholar
- Rhee, I., Warrier, A., Aia, M., and Min, J. 2005. Z-mac: A hybrid MAC for wireless sensor networks. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys'05), 90--101. Google ScholarDigital Library
- Ribo, M. and Pinz, A. 2001. A comparison of three uncertainty calculi for building sonar-based occupancy grids. Robotics and Autonomous Systems 35, 3--4 (June), 201--209.Google ScholarCross Ref
- Römer, K., Blum, P., and Meier, L. 2005. Time synchronization and calibration in wireless sensor networks. In Handbook of Sensor Networks: Algorithms and Architectures, I. Stojmenovic, Ed. John Wiley & Sons, Hoboken, NJ, 199--237.Google Scholar
- Rosenblatt, F. 1959. Two theorems of statistical separability in the perceptron. In Mechanization of Thought Processes. National Physical Laboratory, London, UK, 421--456.Google Scholar
- Roth, M. R. 1990. Survey of neural network technology for automatic target recognition. IEEE Trans. Neural Netw. 1, 1 (March), 28--33.Google ScholarDigital Library
- Saligrama, V., Alanyali, M., and Savas, O. 2006. Distributed detection in sensor networks with packet losses and finite capacity links. IEEE Trans. Sig. Proc. 54, 11 (November), 4118--4132. Google ScholarDigital Library
- Sam, D., Nwankpa, C., and Niebur, D. 2001. Decision fusion of voltage stability indicators for small sized power systems. In IEEE Power Engineering Society Summer Meeting. Vol. 3. IEEE, Vancouver, British Columbia, Canada, 1658--1663.Google Scholar
- Santini, S. and Römer, K. 2006. An adaptive strategy for quality-based data reduction in wireless sensor networks. In Proceedings of the 3rd International Conference on Networked Sensing Systems (INSS 2006). TRF, Chicago, IL, 29--36.Google Scholar
- Savvides, A., Han, C., and Strivastava, M. B. 2003. The n-hop multilateration primitive for node localization. Mobile Netw. Appl. 8, 4 (August), 443--451. Google ScholarDigital Library
- Scaglione, A. and Servetto, S. D. 2002. On the interdependence of routing and data compression in multihop sensor networks. In Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MobiCom'02). ACM, Atlanta, GA, 140--147. Google ScholarDigital Library
- Schmid, U. and Schossmaier, K. 2001. How to reconcile fault-tolerant interval intersection with the Lipschitz condition. Distrib. Comput. 14, 2 (April), 101--111. Google ScholarDigital Library
- Schmitt, T., Hanek, R., Beetz, M., Buck, S., and Radig, B. 2002. Cooperative probabilistic state estimation for vision-based autonomous mobile robots. IEEE Trans. Robotics Autom. 18, 5 (October), 670--684.Google ScholarCross Ref
- Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ.Google Scholar
- Shah, S. F. A., Ribeiro, A., and Giannakis, G. B. 2005. Bandwidth-constrained MAP estimation for wireless sensor networks. In Conference Record of the 39th Asilomar Conference on Signals, Systems and Computers. IEEE, Pacific Grove, CA, 215--219.Google Scholar
- Sharaf, M. A., Beaver, J., Labrinidis, A., and Chrysanthis, P. K. 2003. TiNA: A scheme for temporal coherency-aware in-network aggregation. In Proceedings of the 3rd ACM International Workshop on Data Engineering for Wireless and Mobile Access. ACM Press, San Diego, CA, 69--76. Google ScholarDigital Library
- Sheng, B., Li, Q., and Mao, W. 2006. Data storage placement in sensor networks. In Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'06). ACM, Florence, Italy, 344--355. Google ScholarDigital Library
- Sheng, X. and Hu, Y.-H. 2005. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Trans. Sig. Proc. 53, 1 (January), 44--53. Google ScholarDigital Library
- Sheng, X., Hu, Y. H., and Ramanathan, P. 2005. Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN'05), 181--188. Google ScholarDigital Library
- Shu, H. and Liang, Q. 2005. Fuzzy optimization for distributed sensor deployment. In 2005 IEEE Wireless Communications and Networking Conference (WCNC'05). Vol. 3. IEEE, New Orleans, LA, 1903-- 1908.Google Scholar
- Shulsky, A. N. and Schmitt, G. J. 2002. Silent Warfare: Understanding the World of Intelligence, 3 ed. Brasseys, Inc., New York, NY.Google Scholar
- Siaterlis, C. and Maglaris, B. 2004. Towards multisensor data fusion for DoS detection. In Proceedings of the 2004 ACM Symposium on Applied Computing. ACM Press, Nicosia, Cyprus, 439--446. Google ScholarDigital Library
- Sichitiu, M. L. and Ramadurai, V. 2004. Localization of wireless sensor networks with a mobile beacon. In Proceedings of the 1st IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS 2004). IEEE, Fort Lauderdale, FL, 174--183.Google Scholar
- Sidhu, T., Cruder, O., and Huff, G. 1997. An abductive inference technique for fault diagnosis in electrical power transmission networks. IEEE Trans. Power Deliv. 12, 1 (January), 515--522.Google ScholarCross Ref
- Singh, A., Nowak, R., and Ramanathan, P. 2006. Active learning for adaptive mobile sensing networks. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 60--68. Google ScholarDigital Library
- Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Jordan, M., and Sastry, S. 2004. Kalman filtering with intermittent observations. IEEE Trans. Autom. Cont. 49, 9 (September), 1453--1464.Google ScholarCross Ref
- Smith, S. W. 1999. The Scientist and Engineer's Guide to Digital Signal Processing, 2nd ed. California Technical Publishing, San Diego, CA. Google ScholarDigital Library
- Sohrabi, K., Gao, J., Ailawadhi, V., and Pottie, G. J. 2000. Protocols for self-organization of a wireless sensor network. IEEE Pers. Comm. 7, 5 (October), 16--27.Google ScholarCross Ref
- Spanos, D., Olfati-Saber, R., and Murray, R. M. 2005. Approximate distributed Kalman filtering in sensor networks with quantifiable performance. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN'05), 133--139. Google ScholarDigital Library
- Srinivasan, T., Chandrasekar, R., and Vijaykumar, V. 2006. A fuzzy, energy-efficient scheme for data centric multipath routing in wireless sensor networks. In 2006 IFIP International Conference on Wireless and Optical Communications Networks. IEEE, Bangalore, India.Google Scholar
- Stankovic, J. A., Gibbons, P. B., Wicker, S. B., and Paradiso, J. A., Eds. 2006. Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06). ACM, Nashville, TN. Google Scholar
- Stann, F. and Heidemann, J. 2005. BARD: Bayesian-assisted resource discovery in sensor networks. In 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2005), 866--877.Google Scholar
- Steinberg, A. N., Bowman, C. L., and White, F. E. 1999. Revisions to the JDL data fusion model. In Proceedings of the SPIE. Vol. 3719. SPIE, Orlando, FL, 430--441.Google Scholar
- Tang, C. and Raghavendra, C. S. 2005. Wavelet based source broadcast for in-network processing in sensor networks unknown side information. In 2005 IEEE Global Telecommunications Conference (GLOBECOM'05). Vol. 1. IEEE, St. Louis, USA.Google Scholar
- Tang, C., Raghavendra, C. S., and Prasanna, V. K. 2003. An energy efficient adaptive distributed source coding scheme in wireless sensor networks. In Proceedings of the 2003 IEEE International Conference on Communications (ICC'03). Vol. 1. IEEE, Anchorage, AK, 732--737.Google Scholar
- Tenney, R. R. and Sandell Jr., N. R. 1981. Detection with distributed sensors. IEEE Trans. Aerosp. Electron. Syst. 17, 4 (July), 501--510.Google Scholar
- Tian, D. and Georganas, N. D. 2002. A coverage-preserving node scheduling scheme for large wireless sensor networks. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA'02). ACM Press, Atlanta, GA, 32--41. Google ScholarDigital Library
- Tsymbal, A., Puuronen, S., and Patterson, D. W. 2003. Ensemble feature selection with the simple Bayesian classification. Inform. Fusion 4, 2 (June), 87--100.Google ScholarCross Ref
- U.S. Department of Defense. 1991. Data fusion lexicon. Published by Data Fusion Subpanel of the Joint Directors of Laboratories. Tecnichal Panel for C3 (F.E. White, Code 4202, NOSC, San Diego, CA).Google Scholar
- van Renesse, R. 2003. The importance of aggregation. In Future Directions in Distributed Computing: Research and Position Papers, A. Schiper, A. A. Shvartsman, H. Weatherspoon, and B. Y. Zhao, Eds. Lecture Notes in Computer Science, vol. 2584. Springer, Bologna, Italy, 87--92. Google ScholarDigital Library
- Varshney, P. K. 1997. Distributed Detection and Data Fusion. Springer, New York, USA. Google ScholarDigital Library
- Vercauteren, T., Guo, D., and Wang, X. 2005. Joint multiple target tracking and classification in collaborative sensor networks. IEEE J. Sel. Areas Comm. 23, 4 (April), 714--723. Google ScholarDigital Library
- Wagner, R. S., Baraniuk, R. G., Du, S., Johnson, D. B., and Cohen, A. 2006. An architecture for distributed wavelet analysis and processing in sensor networks. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 243--250. Google ScholarDigital Library
- Wald, L. 1999. Some terms of reference in data fusion. IEEE Trans. Geosci. Remote Sens. 13, 3 (May), 1190--1193.Google Scholar
- Wallace, J., Pesch, D., Rea, S., and Irvine, J. 2005. Fuzzy logic optimisation of MAC parameters and sleeping duty-cycles in wireless sensor networks. In 62nd Vehicular Technology Conference, 2005. VTC-2005-Fall. Vol. 3. IEEE, Dallas, TX, 1824--1828.Google Scholar
- Waltz, E. L. and Llinas, J. 1990. Multisensor Data Fusion. Artech House, Norwood, MA. Google ScholarDigital Library
- Welch, G. and Bishop, G. 2001. An introduction to the Kalman filter. In SIGGRAPH 2001 Course Notes. ACM, Los Angeles, CA. Course 8. Google ScholarDigital Library
- Whitehouse, K., Liu, J., and Zhao, F. 2006. Semantic streams: A framework for composable inference over sensor data. In 3rd European Workshop on Wireless Sensor Networks (EWSN'06), K. Römer, H. Karl, and F. Mattern, Eds. Lecture Notes in Computer Science, vol. 3868. Springer, Zurich, Switzerland, 5--20. Google ScholarDigital Library
- Widrow, B. and Hoff, M. E. 1960. Adaptive switching circuits. 1960 IRE Western Electric Show and Convention Record 4, 96--104.Google Scholar
- Willett, R., Martin, A., and Nowak, R. 2004. Backcasting: Adaptive sampling for sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04), 124--133. Google ScholarDigital Library
- Wong, Y., Wu, J., Ngoh, L., and Wong, W. 2004. Collaborative data fusion tracking in sensor networks using monte carlo methods. In Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks (LCN'04). IEEE, Tampa, FL, 563--564. Google ScholarDigital Library
- Wongngamnit, C. and Angluin, D. 2001. Robot localization in a grid. Inform. Proc. Lett. 77, 5--6 (March), 261--267. Google ScholarDigital Library
- Woo, A., Tong, T., and Culler, D. 2003. Taming the underlying challenges of reliable multihop routing in sensor networks. In Proceedings of the 1st International Conference on Embedded Network Sensor Systems (SenSys'03), 14--27. Google ScholarDigital Library
- Wu, Q., Rao, N. S., Barhen, J., Iyengar, S. S., Vaishnavi, V. K., Qi, H., and Chakrabarty, K. 2004. On computing mobile agent routes for data fusion in distributed sensor networks. IEEE Trans. Knowl. Data Eng. 16, 6 (June), 740--753. Google ScholarDigital Library
- Xiao, J., Ribeiro, A., Luo, Z., and Giannakis, G. B. 2006a. Distributed compression-estimation using wireless sensor networks. IEEE Sig. Proc. Mag. 23, 4 (July), 27--41.Google Scholar
- Xiao, L., Boyd, S., and Lall, S. 2005. A scheme for robust distributed sensor fusion based on average consensus. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN'05), 63--70. Google ScholarDigital Library
- Xiao, L., Boyd, S., and Lall, S. 2006b. A space-time diffusion scheme for peer-to-peer least-squares estimation. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN'06), 168--176. Google ScholarDigital Library
- Xiong, Z., Liveris, A. D., and Cheng, S. 2004. Distributed source coding for sensor networks. IEEE Sig. Proc. Mag. 21, 5 (September), 80--94.Google Scholar
- Xu, Y. and Qi, H. 2004. Distributed computing paradigms for collaborative signal and information processing in sensor networks. J. Para. Distrib. Comput. 64, 8 (August), 945--959. Google ScholarDigital Library
- Yang, C.-L., Bagchi, S., and Chappell, W. J. 2005a. Location tracking with directional antennas in wireless sensor networks. In 2005 IEEE MTT-S International Microwave Symposium Digest. IEEE, Long Beach, CA.Google Scholar
- Yang, Z., Guo, D., and Wang, X. 2005b. Blind decoding of multiple description codes over OFDM systems via sequential Monte Carlo. EURASIP J. Wireless Comm. Netw. 2005, 2, 141--154. Google ScholarDigital Library
- Yao, Y. and Gehrke, J. 2002. The cougar approach to in-network query processing in sensor networks. Sigmod Rec. 31, 3 (September), 9--18. Google ScholarDigital Library
- Yiyao, L., Venkatesh, Y. V., and Ko, C. C. 2001. A knowledge-based neural network for fusing edge maps of multi-sensor images. Inform. Fusion 2, 2 (June), 121--133.Google ScholarCross Ref
- Yu, B., Sycara, K., Giampapa, J. A., and Owens, S. R. 2004. Uncertain information fusion for force aggregation and classification in airborne sensor networks. In AAAI-04 Workshop on Sensor Networks. AAAI Press, San Jose, CA.Google Scholar
- Yuan, Y. and Kam, M. 2004. Distributed decision fusion with a random-access channel for sensor network applications. IEEE Trans. Instr. Meas. 53, 4 (August), 1339--1344.Google ScholarCross Ref
- Yuen, D. C. K. and MacDonald, B. A. 2002. A comparison between extended Kalman filtering and sequential Monte Carlo techniques for simultaneous localisation and map-building. In Proceedings of the 2002 Australasian Conference on Robotics and Automation, W. Friedrich and P. Lim, Eds. ARAA, Auckland, New Zealand, 111--116.Google Scholar
- Yusuf, M. and Haider, T. 2005. Energy-aware fuzzy routing for wireless sensor networks. In IEEE International Conference on Emerging Technologies (ICET'05). IEEE, Islamiabad, Pakistan, 63--69.Google Scholar
- Zadeh, L. A. 1994. Fuzzy logic and soft computing: Issues, contentions and perspectives. In Proceedings of the 3rd International Conference on Fuzzy Logic, Neural Nets and Soft Computing. Fuzzy Logic Systems Institute, Iisuka, Japan, 1--2. Google ScholarDigital Library
- Zeng, Z. and Ma, S. 2002. Head tracking by active particle filtering. In Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition (FGR'02). IEEE, Washington, D.C., USA, 82--87. Google ScholarDigital Library
- Zhang, X. and Wicker, S. B. 2005. Robustness vs. efficiency in sensor networks. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN'05), 225--230. Google ScholarDigital Library
- Zhao, F. and Guibas, L. J., Eds. 2003. Information Processing in Sensor Networks: 2nd International Workshop (IPSN'03). Lecture Notes in Computer Science, vol. 2634. Springer, Palo Alto, CA.Google Scholar
- Zhao, F., Liu, J., Liu, J., Guibas, L., and Reich, J. 2003a. Collaborative signal and information processing: An information directed approach. Proc. IEEE 91, 8 (August), 1199--1209.Google ScholarCross Ref
- Zhao, F., Shin, J., and Reich, J. 2002a. Information-driven dynamic sensor collaboration for tracking applications. IEEE Sig. Proc. Mag. 19, 2 (March), 61--72.Google Scholar
- Zhao, J., Govindan, R., and Estrin, D. 2002b. Residual energy scans for monitoring wireless sensor networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC'02). Vol. 1. IEEE, Orlando, FL, 356--362.Google Scholar
- Zhao, J., Govindan, R., and Estrin, D. 2003b. Computing aggregates for monitoring wireless sensor networks. In Proceedings of the 1st IEEE International Workshop on Sensor Network Protocols and Applications (SNPA 2003). IEEE, Anchorage, AK, 139--148.Google Scholar
- Zhou, B., Ngoh, L. H., Lee, B. S., and Fu, C. P. 2004. A hierarchical scheme for data aggregation in sensor networks. In Proceedings of the 12th IEEE International Conference on Networks (ICON'04). Vol. 2. IEEE, Singapore, 525--529.Google Scholar
- Zhou, C. and Krishnamachari, B. 2003. Localized topology generation mechanisms for self-configuring sensor networks. In 2003 IEEE Global Telecommunications Conference (GLOBECOM'03). Vol. 22. IEEE, San Francisco, CA, 1269--1273.Google Scholar
- Zhu, Y., Vedantham, R., Park, S.-J., and Sivakumar, R. 2005. A scalable correlation aware aggregation strategy for wireless sensor networks. In Proceedings of the 1st International Conference on Wireless Internet (WICON'05). IEEE, Budapest, Hungary, 122--129. Google ScholarDigital Library
Index Terms
- Information fusion for wireless sensor networks: Methods, models, and classifications
Recommendations
Information fusion in wireless sensor networks
SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of dataWireless sensor networks (WSNs) are commonly treated as a distributed database system that is accessed by means of a query language. However, the computation of such queries are usually performed by information fusion techniques. Information fusion has ...
The reliability analysis of wireless sensor networks based on the energy restrictions
The Wireless Sensor Networks WSNs are composed of sensor nodes and sink nodes, and the network of sensor nodes is energy constrained, so the reliability of the WSNs is closely related to the energy of the nodes. According to the characteristics of the ...
Nodes Deployment Algorithm Based on Data Fusion and Evidence Theory in Wireless Sensor Networks
AbstractWireless sensor networks have been widely researched and developed in recent years. The node deployment problem is a multi-dimensional nonlinear optimization problem with continuous discrete variables. In order to improve the coverage effect of ...
Comments