Data Aggregation and Forwarding Route Control for Efficient Data Gathering in Dense Mobile Wireless Sensor Networks

This chapter presents a data gathering method considering geographical distribution of data values for reducing traffic in dense mobile wireless sensor networks. First, we present our previous method (DGUMA) which is a data gathering method that efficiently gathers sensor data using mobile agents in dense mobile wireless sensor networks. Second, we introduce an extended method of DGUMA, named DGUMA/DA (DGUMA with Data Aggregation), that exploits geographical distribution of data values in order to further reduce traffic. Finally, we analyze DGUMA/DA and confirm the effectiveness of the method through some simulation experiments.

[1]  Mirco Musolesi,et al.  The Rise of People-Centric Sensing , 2008, IEEE Internet Comput..

[2]  Chongqing Zhang.  Cluster-based Routing Algorithms Using Spatial Data Correlation for Wireless Sensor Networks , 2010, J. Commun..

[3]  Takahiro Hara,et al.  Data gathering using mobile agents in dense mobile wireless sensor networks , 2011, MoMM '11.

[4]  Rui Zhang,et al.  PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems , 2010, 2010 Proceedings IEEE INFOCOM.

[5]  Deborah Estrin,et al.  MobiSense — mobile network services for coordinated participatory sensing , 2009, 2009 International Symposium on Autonomous Decentralized Systems.

[6]  Raghupathy Sivakumar,et al.  A scalable correlation aware aggregation strategy for wireless sensor networks , 2005, First International Conference on Wireless Internet (WICON'05).

[7]  Ramesh Govindan,et al.  The impact of spatial correlation on routing with compression in wireless sensor networks , 2008, TOSN.

[8]  Sajal K. Das,et al.  Routing Correlated Data in Wireless Sensor Networks: A Survey , 2007, IEEE Network.

[9]  Rajesh K. Gupta,et al.  Path Planning of Data Mules in Sensor Networks , 2011, TOSN.

[10]  Engin Zeydan,et al.  Energy-efficient routing for correlated data in wireless sensor networks , 2012, Ad Hoc Networks.

[11]  Deborah Estrin,et al.  Examining micro-payments for participatory sensing data collections , 2010, UbiComp.

[12]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[13]  Mohamed A. Sharaf,et al.  TiNA: a scheme for temporal coherency-aware in-network aggregation , 2003, MobiDe '03.

[14]  Chuang Lin,et al.  Effective Data Aggregation Supported by Dynamic Routing in Wireless Sensor Networks , 2010, 2010 IEEE International Conference on Communications.

[15]  Takahiro Hara,et al.  Data Gathering Considering Geographical Distribution of Data Values in Dense Mobile Wireless Sensor Networks , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[16]  Gen-Huey Chen,et al.  Correlated Data Gathering With Double Trees in Wireless Sensor Networks , 2012, IEEE Sensors Journal.

[17]  Jun Sun,et al.  Compressive data gathering for large-scale wireless sensor networks , 2009, MobiCom '09.

[18]  Torsten Braun,et al.  BLR: beacon-less routing algorithm for mobile ad hoc networks , 2004, Comput. Commun..

[19]  Neeraj Suri,et al.  An adaptive and composite spatio-temporal data compression approach for wireless sensor networks , 2011, MSWiM '11.

[20]  Sajal K. Das,et al.  Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey , 2011, TOSN.

[21]  Klaus Wehrle,et al.  Towards Scalable Mobility in Distributed Hash Tables , 2006, Sixth IEEE International Conference on Peer-to-Peer Computing (P2P'06).

[22]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[23]  Lars Kulik,et al.  Optimizing query processing using selectivity-awareness in Wireless Sensor Networks , 2009, Comput. Environ. Urban Syst..

[24]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.