Mobile Element Scheduling with Dynamic Deadlines

Wireless networks have historically considered support for mobile elements's an extra overhead. However, recent research has provided the means by which a network can take advantage of mobile elements. Particularly in the case of wireless sensor networks, mobile elements can be deliberately built into the system to improve the lifetime of the network and act as mechanical carriers of data. The mobile element, whose mobility is controlled, visits the nodes to collect their data before their buffers are full. In general, the spatio-temporal dynamics of the sensed phenomenon may require sensor nodes to collect samples at different rates, in which case, some nodes need to be visited more frequently than others. This work formulates the problem of scheduling the mobile element in the network so that there is no data loss due to buffer overflow. The problem is shown to be NP-complete and an integer-linear-programming formulation is given. Finally, some computationally practical algorithms for a single mobile and for the case of multiple mobiles are presented and their performances compared

[1]  Mani B. Srivastava,et al.  Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines , 2004, 25th IEEE International Real-Time Systems Symposium.

[2]  Jerry Zhao,et al.  Habitat monitoring: application driver for wireless communications technology , 2001, CCRV.

[3]  Adam Meyerson,et al.  Approximation algorithms for deadline-TSP and vehicle routing with time-windows , 2004, STOC '04.

[4]  Gaurav S. Sukhatme,et al.  Call and response: experiments in sampling the environment , 2004, SenSys '04.

[5]  Martin W. P. Savelsbergh,et al.  Local search in routing problems with time windows , 1984 .

[6]  Gaurav S. Sukhatme,et al.  Studying the feasibility of energy harvesting in a mobile sensor network , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[7]  F. Bullo,et al.  Decentralized algorithms for vehicle routing in a stochastic time-varying environment , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[8]  Deborah Estrin,et al.  Controllably mobile infrastructure for low energy embedded networks , 2006, IEEE Transactions on Mobile Computing.

[9]  Zygmunt J. Haas,et al.  The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way) , 2003, MobiHoc '03.

[10]  Jun Luo,et al.  Joint mobility and routing for lifetime elongation in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[11]  Ellen W. Zegura,et al.  A message ferrying approach for data delivery in sparse mobile ad hoc networks , 2004, MobiHoc '04.

[12]  Deborah Estrin,et al.  A Collaborative Approach to In-Place Sensor Calibration , 2003, IPSN.

[13]  Eugene L. Lawler,et al.  The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization , 1985 .

[14]  N. Biggs.  THE TRAVELING SALESMAN PROBLEM A Guided Tour of Combinatorial Optimization , 1986 .

[15]  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..

[16]  Deborah Estrin,et al.  Cyclops: in situ image sensing and interpretation in wireless sensor networks , 2005, SenSys '05.

[17]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..

[18]  Michel Gendreau,et al.  Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching , 1999, Transp. Sci..

[19]  Mostafa H. Ammar,et al.  Message ferrying: proactive routing in highly-partitioned wireless ad hoc networks , 2003, The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems, 2003. FTDCS 2003. Proceedings..

[20]  Gaurav S. Sukhatme,et al.  Task allocation for event-aware spatiotemporal sampling of environmental variables , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[22]  Yong Wang,et al.  Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet , 2002, ASPLOS X.

[23]  Ashutosh Sabharwal,et al.  Using Predictable Observer Mobility for Power Efficient Design of Sensor Networks , 2003, IPSN.

[24]  James W. Layland,et al.  Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment , 1989, JACM.

[25]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[26]  Mani B. Srivastava,et al.  RAGOBOT: A New Platform for Wireless Mobile Sensor Networks , 2005, DCOSS.

[27]  Paolo Toth,et al.  The Vehicle Routing Problem , 2002, SIAM monographs on discrete mathematics and applications.

[28]  Bhaskar Krishnamachari,et al.  Learning-enforced time domain routing to mobile sinks in wireless sensor fields , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[29]  Harilaos N. Psaraftis,et al.  Dynamic vehicle routing: Status and prospects , 1995, Ann. Oper. Res..

[30]  Gaurav S. Sukhatme,et al.  Networked infomechanical systems: a mobile embedded networked sensor platform , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..