Multiple task scheduling for low-duty-cycled wireless sensor networks

For energy conservation, a wireless sensor network is usually designed to work in a low-duty-cycle mode, in which a sensor node keeps active for a small percentage of time during its working period. In applications where there are multiple data delivery tasks with high data rates and time constraints, low-duty-cycle working mode may cause severe transmission congestion and data loss. In order to alleviate congestion and reduce data loss, the tasks need to be carefully scheduled to balance the workloads among the sensor nodes in both spatial and temporal dimensions. This paper studies the load balancing problem, and proves it is NP-Complete in general network graphs. Two efficient scheduling algorithms to achieve load balance are proposed and analyzed. Furthermore, a task scheduling protocol is designed relying on the proposed algorithms. To the best of our knowledge, this paper is the first one to tackle multiple task scheduling for low-duty-cycled sensor networks. The simulation results show that the proposed algorithms greatly improve the network performance in most scenarios.

[1]  Geoffrey Werner-Allen Optimizing High-Resolution Signal Collection in Wireless Sensor Networks , 2008 .

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

[3]  Matt Welsh,et al.  Lance: optimizing high-resolution signal collection in wireless sensor networks , 2008, SenSys '08.

[4]  Ying Zhang,et al.  A Learning-based Adaptive Routing Tree for Wireless Sensor Networks , 2006, J. Commun..

[5]  Chenyang Lu,et al.  Dynamic Conflict-free Query Scheduling for Wireless Sensor Networks , 2006, Proceedings of the 2006 IEEE International Conference on Network Protocols.

[6]  Cristina V. Lopes,et al.  Adaptive Low Power Listening for Wireless Sensor Networks , 2007, IEEE Transactions on Mobile Computing.

[7]  Milind Dawande,et al.  Link scheduling in sensor networks: distributed edge coloring revisited , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[8]  Chenyang Lu,et al.  A Holistic Approach to Decentralized Structural Damage Localization Using Wireless Sensor Networks , 2008, 2008 Real-Time Systems Symposium.

[9]  James R. Zeidler,et al.  Distributed Opportunistic Scheduling for Ad-Hoc Communications Under Delay Constraints , 2010, 2010 Proceedings IEEE INFOCOM.

[10]  Matt Welsh,et al.  Fidelity and yield in a volcano monitoring sensor network , 2006, OSDI '06.

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

[12]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[13]  Yang Xiao,et al.  A Survey of Energy-Efficient Scheduling Mechanisms in Sensor Networks , 2006, Mob. Networks Appl..

[14]  Ness B. Shroff,et al.  On the Construction of a Maximum-Lifetime Data Gathering Tree in Sensor Networks: NP-Completeness and Approximation Algorithm , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[15]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[16]  Ion Stoica,et al.  Adaptive Distributed Time-Slot Based Scheduling for Fairness in Multi-Hop Wireless Networks , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[17]  Bhaskar Krishnamachari,et al.  Delay efficient sleep scheduling in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  Tian He,et al.  Data forwarding in extremely low duty-cycle sensor networks with unreliable communication links , 2007, SenSys '07.

[19]  Philip Levis,et al.  TOSSIM: A Simulator for TinyOS Networks , 2003 .

[20]  Wei Hong,et al.  A macroscope in the redwoods , 2005, SenSys '05.

[21]  Shaojie Tang,et al.  Canopy closure estimates with GreenOrbs: sustainable sensing in the forest , 2009, SenSys '09.

[22]  Yantao Pan,et al.  Energy-efficient lifetime maximization and sleeping scheduling supporting data fusion and QoS in Multi-SensorNet , 2007, Signal Process..

[23]  Maleq Khan,et al.  A fast distributed approximation algorithm for minimum spanning trees , 2007, Distributed Computing.

[24]  Yunhao Liu,et al.  Underground Structure Monitoring with Wireless Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[25]  Jianzhong Li,et al.  Distributed Data Aggregation Scheduling in Wireless Sensor Networks , 2009, IEEE INFOCOM 2009.

[26]  Jinhui Xu,et al.  Spatiotemporal Delay Control for Low-Duty-Cycle Sensor Networks , 2009, 2009 30th IEEE Real-Time Systems Symposium.