Decentralized spatial data mining for geosensor networks

Advances in distributed sensing and computing technology offer new, reliable, and costeffective means to collect fine-grained spatiotemporal data. Conventional spatiotemporal data mining procedures, however, are based on centralized models of information processing, where sophisticated and powerful central systems collate and process global information. By contrast, decentralized spatial computing systems require new techniques for in-network knowledge discovery. This chapter introduces the notion of decentralized spatial data mining, where individual sensor-enabled computing nodes possess only local knowledge about their immediate neighborhood, but derive global knowledge through local collaboration and information exchange. The chapter then presents four strategies for decentralized spatial data mining, illustrating the concept of decentralization with three simple decentralized algorithms for the classical spatial data mining task of clustering.

[1]  Leonidas J. Guibas,et al.  Wireless sensor networks - an information processing approach , 2004, The Morgan Kaufmann series in networking.

[2]  Michael F. Worboys,et al.  Monitoring qualitative spatiotemporal change for geosensor networks , 2006, Int. J. Geogr. Inf. Sci..

[3]  Ujjwal Maulik,et al.  Clustering distributed data streams in peer-to-peer environments , 2006, Inf. Sci..

[4]  Michael F. Worboys,et al.  GIS - a computing perspective (2. ed.) , 2004 .

[5]  Matt Welsh,et al.  Deploying a wireless sensor network on an active volcano , 2006, IEEE Internet Computing.

[6]  Martin Mauve,et al.  A survey on position-based routing in mobile ad hoc networks , 2001, IEEE Netw..

[7]  Deborah Estrin,et al.  Geography-informed energy conservation for Ad Hoc routing , 2001, MobiCom '01.

[8]  Nikolaus Correll,et al.  Collective Inspection of Regular Structures using a Swarm of Miniature Robots , 2004, ISER.

[9]  Ran Wolff,et al.  Distributed Data Mining in Peer-to-Peer Networks , 2006, IEEE Internet Computing.

[10]  Wendi B. Heinzelman,et al.  Flooding Strategy for Target Discovery in Wireless Networks , 2005 .

[11]  Alexandros Labrinidis,et al.  Report from the first workshop on geo sensor networks , 2004, SGMD.

[12]  Deborah Estrin,et al.  Rumor Routing Algorithm For Sensor Networks , 2002 .

[13]  Anantha Chandrakasan,et al.  MobiCom poster: top five myths about the energy consumption of wireless communication , 2003, MOCO.

[14]  Michael F. Worboys,et al.  Monitoring dynamic spatial fields using responsive geosensor networks , 2005, GIS '05.

[15]  GuoYing,et al.  Transforming Agriculture through Pervasive Wireless Sensor Networks , 2007 .

[16]  David O'Sullivan,et al.  Geographic Information Analysis , 2002 .

[17]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[18]  Ran Wolff,et al.  Association rule mining in peer-to-peer systems , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Michael F. Worboys,et al.  GIS : a computing perspective , 2004 .

[20]  Wendi B. Heinzelman,et al.  Adaptive protocols for information dissemination in wireless sensor networks , 1999, MobiCom.

[21]  Nancy A. Lynch,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[22]  Ran Wolff,et al.  In-Network Outlier Detection in Wireless Sensor Networks , 2006, ICDCS.

[23]  Deborah Estrin,et al.  Geographical and Energy Aware Routing: a recursive data dissemination protocol for wireless sensor networks , 2002 .

[24]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[25]  Hillol Kargupta,et al.  K-Means Clustering Over a Large, Dynamic Network , 2006, SDM.

[26]  Leonidas J. Guibas,et al.  Sensing, tracking and reasoning with relations , 2002, IEEE Signal Process. Mag..

[27]  Brad Karp,et al.  GPSR : Greedy Perimeter Stateless Routing for Wireless , 2000, MobiCom 2000.

[28]  Martin Vetterli,et al.  Locating mobile nodes with EASE: learning efficient routes from encounter histories alone , 2006, IEEE/ACM Transactions on Networking.

[29]  Ranga Raju Vatsavai,et al.  Trends in Spatial Data Mining , 2022 .

[30]  David Tse,et al.  Mobility increases the capacity of ad hoc wireless networks , 2002, TNET.

[31]  Deborah Estrin,et al.  Embedding the Internet: introduction , 2000, Commun. ACM.

[32]  Peter I. Corke,et al.  Transforming Agriculture through Pervasive Wireless Sensor Networks , 2007, IEEE Pervasive Computing.

[33]  Torben Bach Pedersen,et al.  Spatio-temporal Rule Mining: Issues and Techniques , 2005, DaWaK.

[34]  Wendi B. Heinzelman,et al.  Flooding Strategy for Target Discovery in Wireless Networks , 2003, MSWIM '03.

[35]  Wolfgang Kellerer,et al.  (Auto) mobile communication in a heterogeneous and converged world , 2001, IEEE Wirel. Commun..

[36]  Philip K. Chan,et al.  Advances in Distributed and Parallel Knowledge Discovery , 2000 .