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Issue No.01 - January (2010 vol.9)
pp: 127-139
Ryo Sugihara , University of California, San Diego, La Jolla
Rajesh K. Gupta , University of California, San Diego, La Jolla
ABSTRACT
A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.
INDEX TERMS
Wireless sensor networks, controlled mobility, data mule, motion planning, scheduling.
CITATION
Ryo Sugihara, Rajesh K. Gupta, "Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks", IEEE Transactions on Mobile Computing, vol.9, no. 1, pp. 127-139, January 2010, doi:10.1109/TMC.2009.113
REFERENCES
[1] I. Vasilescu, K. Kotay, D. Rus, M. Dunbabin, and P.I. Corke, “Data Collection, Storage, and Retrieval with an Underwater Sensor Network,” Proc. Conf. Embedded Networked Sensor Systems (SenSys '05), pp. 154-165, 2005.
[2] M. Todd, D. Mascarenas, E. Flynn, T. Rosing, B. Lee, D. Musiani, S. Dasgupta, S. Kpotufe, D. Hsu, R. Gupta, G. Park, T. Overly, M. Nothnagel, and C. Farrar, “A Different Approach to Sensor Networking for SHM: Remote Powering and Interrogation with Unmanned Aerial Vehicles,” Proc. Sixth Int'l Workshop Structural Health Monitoring, 2007.
[3] R. Sugihara and R.K. Gupta, “Improving the Data Delivery Latency in Sensor Networks with Controlled Mobility,” Proc. Int'l Conf. Distributed Computing in Sensor Systems (DCOSS '08), 2008.
[4] A. Kansal, A.A. Somasundara, D.D. Jea, M.B. Srivastava, and D. Estrin, “Intelligent Fluid Infrastructure for Embedded Networks,” Proc. MobiSys, pp. 111-124, 2004.
[5] R. Sugihara and R.K. Gupta, “Data Mule Scheduling in Sensor Networks: Scheduling under Location and Time Constraints,” Technical Report CS2007-0911, Univ. of California, San Diego, 2007.
[6] R.C. Shah, S. Roy, S. Jain, and W. Brunette, “Data MULEs: Modeling a Three-Tier Architecture for Sparse Sensor Networks,” Proc. First IEEE Int'l Workshop Sensor Network Protocols and Applications, pp. 30-41, 2003.
[7] A.A. Somasundara, A. Kansal, D.D. Jea, D. Estrin, and M.B. Srivastava, “Controllably Mobile Infrastructure for Low Energy Embedded Networks,” IEEE Trans. Mobile Computing, vol. 5, no. 8, pp. 958-973, Aug. 2006.
[8] A.A. Somasundara, A. Ramamoorthy, and M.B. Srivastava, “Mobile Element Scheduling with Dynamic Deadlines,” IEEE Trans. Mobile Computing, vol. 6, no. 4, pp. 395-410, Apr. 2007.
[9] M. Ma and Y. Yang, “SenCar: An Energy Efficient Data Gathering Mechanism for Large Scale Multihop Sensor Networks,” Proc. Int'l Conf. Distributed Computing in Sensor Systems (DCOSS '06), pp. 498-513, 2006.
[10] M. Ma and Y. Yang, “SenCar: An Energy Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks,” IEEE Trans. Parallel and Distributed System, vol. 18, no. 10, pp. 1476-1488, Oct. 2007.
[11] A. Chakrabarti, A. Sabharwal, and B. Aazhang, “Using Predictable Observer Mobility for Power Efficient Design of Sensor Networks,” Proc. Second Int'l Symp. Information Processing in Sensor Networks (IPSN '03), pp. 129-145, 2003.
[12] A. Vahdat and D. Becker, “Epidemic Routing for Partially-Connected Ad Hoc Networks,” Technical Report CS-2000-06, Duke Univ., 2000.
[13] P. Juang, H. Oki, Y. Wang, M. Martonosi, L.S. Peh, and D. Rubenstein, “Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet,” Proc. Ann. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS-X), pp. 96-107, 2002.
[14] W. Zhao and M. Ammar, “Message Ferrying: Proactive Routing in Highly-Partitioned Wireless Ad Hoc Networks,” Proc. IEEE Workshop Future Trends in Distributed Computing Systems, pp. 308-314, 2003.
[15] W. Zhao, M. Ammar, and E. Zegura, “A Message Ferrying Approach for Data Delivery in Sparse Mobile Ad Hoc Networks,” Proc. ACM MobiHoc, pp. 187-198, 2004.
[16] W. Zhao, M. Ammar, and E. Zegura, “Controlling the Mobility of Multiple Data Transport Ferries in a Delay-Tolerant Network,” Proc. IEEE INFOCOM, pp. 1407-1418, 2005.
[17] M.M.B. Tariq, M. Ammar, and E. Zegura, “Message Ferry Route Design for Sparse Ad Hoc Networks with Mobile Nodes,” Proc. ACM MobiHoc, pp. 37-48, 2006.
[18] J.R. Jackson, “Scheduling a Production Line to Minimize Maximum Tardiness,” Research Report 43, Management Science Research Project, Univ. of California, Los Angeles, 1955.
[19] C.L. Liu and J.W. Layland, “Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment,” J. ACM, vol. 20, no. 1, pp. 46-61, 1973.
[20] J.A. Stankovic, M. Spuri, M.D. Natale, and G.C. Buttazzo, “Implications of Classical Scheduling Results for Real-Time Systems,” Computer, vol. 28, no. 6, pp. 16-25, June 1995.
[21] B. Simons and M. Sipser, “On Scheduling Unit-Length Jobs with Multiple Release Time/Deadline Intervals,” Operations Research, vol. 32, no. 1, pp. 80-88, 1984.
[22] C. Shih, J.W.S. Liu, and I.K. Cheong, “Scheduling Jobs with Multiple Feasible Intervals,” Proc. Ninth IEEE Int'l Conf. Embedded and Real-Time Computing Systems and Applications (RTCSA '03), pp.53-71, 2003.
[23] J.-J. Chen, J. Wu, C. Shih, and T.-W. Kuo, “Approximation Algorithms for Scheduling Multiple Feasible Interval Jobs,” Proc. 11th IEEE Int'l Conf. Embedded and Real-Time Computing Systems and Applications (RTCSA '05), pp. 11-16, 2005.
[24] J.W.S. Liu, Real-Time Systems. Prentice Hall, 2000.
[25] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
[26] J. Löfberg, “YALMIP: A Toolbox for Modeling and Optimization in MATLAB,” Proc. IEEE Int'l Symp. Computer Aided Control Systems Design (CACSD '04), http://control.ee.ethz.ch/joloefyalmip.php , 2004.
[27] J.F. Sturm, “Using SeDuMi 1.02, a MATLAB Toolbox for Optimization over Symmetric Cones,” Optimization Methods and Software, vols. 11-12, pp. 625-653, 1999.
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