Information-centric collaborative data collection for mobile devices in wireless sensor networks

The advancement of smart phones enables mobile users to collect data from their surrounding sensors using short-range wireless communication. However, the limited contact time and the wireless capacity constrain the amount of data to be collected by the mobile users. It is crucial for mobile users to collect sensing data that can maximize their data utility. In this paper, we propose a distributed algorithm to provide information-centric ubiquitous data collection for multiple mobile users. The mobile users construct data collection trees adaptively according to their dynamic moving speeds. They prioritize data collection according to the information value carried by the sensing data. The distributed algorithm can support smooth data collection and coordination among multiple mobile users. We evaluate the data utility, energy efficiency and scalability of our solution with extensive simulations. The results showed that our distributed algorithm can improve information value up to 50% and reduce energy consumption to half compared with the existing approach.

[1]  Cecilia Mascolo,et al.  Evolution and sustainability of a wildlife monitoring sensor network , 2010, SenSys '10.

[2]  Mani B. Srivastava,et al.  NAWMS: nonintrusive autonomous water monitoring system , 2008, SenSys '08.

[3]  Alhussein A. Abouzeid,et al.  Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric , 2006, IEEE Transactions on Robotics.

[4]  Nicholas R. Jennings,et al.  Decentralized control of adaptive sampling in wireless sensor networks , 2009, TOSN.

[5]  Edith C. H. Ngai,et al.  A Ubiquitous Publish/Subscribe Platform for Wireless Sensor Networks with Mobile Mules , 2012, 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems.

[6]  Leonidas J. Guibas,et al.  Data stashing: energy-efficient information delivery to mobile sinks through trajectory prediction , 2010, IPSN '10.

[7]  Hyung Seok Kim,et al.  Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks , 2003, SenSys '03.

[8]  Yunhao Liu,et al.  Exploiting Ubiquitous Data Collection for Mobile Users in Wireless Sensor Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.

[9]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

[10]  Stefan Valentin,et al.  Simulating wireless and mobile networks in OMNeT++ the MiXiM vision , 2008, SimuTools.

[11]  Ji Luo,et al.  Delay Tolerant Event Collection in Sensor Networks with Mobile Sink , 2010, 2010 Proceedings IEEE INFOCOM.

[12]  Leonidas J. Guibas,et al.  Predictive QoS routing to mobile sinks in wireless sensor networks , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[13]  Mani B. Srivastava,et al.  Context-aware sensor data dissemination for mobile users in remote areas , 2012, 2012 Proceedings IEEE INFOCOM.

[14]  Jianping Pan,et al.  Evaluating service disciplines for mobile elements in wireless ad hoc sensor networks , 2012, 2012 Proceedings IEEE INFOCOM.

[15]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

[16]  Jun Luo,et al.  Joint Sink Mobility and Routing to Maximize the Lifetime of Wireless Sensor Networks: The Case of Constrained Mobility , 2010, IEEE/ACM Transactions on Networking.

[17]  Waylon Brunette,et al.  Data MULEs: modeling a three-tier architecture for sparse sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[18]  Chang-Gun Lee,et al.  Partitioning based mobile element scheduling in wireless sensor networks , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..