Data Collection for Time-Critical Applications in the Low-Duty-Cycle Wireless Sensor Networks

In low-duty-cycle wireless sensor networks, wireless nodes usually have two states: active state and dormant state. The necessary condition for a successful wireless transmission is that both the sender and the receiver are awake. In this paper, we study the problem: How fast can raw data be collected from all source nodes to a sink in low-duty-cycle WSNs with general topology? Both the lower and upper tight bounds are given for this problem. We use TDMA scheduling on the same frequency channel and present centralized and distributed fast data collection algorithms to find an optimal solution in polynomial time when no interfering links happen. If interfering links happen, multichannel scheduling is introduced to eliminate them. We next propose a novel Receiver-based Channel and Time Scheduling (RCTS) algorithm to obtain the optimal solution. Based on real trace, extensive simulations are conducted and the results show that the proposed RCTS algorithm is significantly more efficient than the link schedule on one channel and achieves the lower bound. We also evaluate the proposed data collection algorithms and find that RCTS is time-efficient and suffices to eliminate most of the interference in both indoor and outdoor environment for moderate size networks.

[1]  Tian He,et al.  Dynamic Switching-Based Data Forwarding for Low-Duty-Cycle Wireless Sensor Networks , 2011, IEEE Transactions on Mobile Computing.

[2]  Kris Steenhaut,et al.  Adaptive Learning Based Scheduling in Multichannel Protocol for Energy-Efficient Data-Gathering Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[3]  Bhaskar Krishnamachari,et al.  Fast Data Collection in Tree-Based Wireless Sensor Networks , 2012, IEEE Transactions on Mobile Computing.

[4]  Bo Jiang,et al.  Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links , 2009, IEEE Transactions on Computers.

[5]  Steve Goddard,et al.  Cross-Layer Analysis of the End-to-End Delay Distribution in Wireless Sensor Networks , 2009, 2009 30th IEEE Real-Time Systems Symposium.

[6]  Walter Willinger,et al.  Network topology generators: degree-based vs. structural , 2002, SIGCOMM '02.

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

[8]  Shouling Ji,et al.  Cell-based snapshot and continuous data collection in wireless sensor networks , 2013, TOSN.

[9]  Baoqing Li,et al.  Distributed Broadcast with Minimum Latency in Asynchronous Wireless Sensor Networks under SINR-Based Interference , 2013, Int. J. Distributed Sens. Networks.

[10]  Shaojie Tang,et al.  MENs: Multi-user Emergency Navigation System Using , 2011, Ad Hoc Sens. Wirel. Networks.

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

[12]  Shouling Ji,et al.  Distributed Data Collection in Large-Scale Asynchronous Wireless Sensor Networks Under the Generalized Physical Interference Model , 2013, IEEE/ACM Transactions on Networking.

[13]  Michael Segal,et al.  Improved Algorithms for Data-Gathering Time in Sensor Networks II: Ring, Tree, and Grid Topologies , 2009, Int. J. Distributed Sens. Networks.

[14]  Shaojie Tang,et al.  Delay Minimum Data Collection in the low-duty-cycle wireless sensor networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[15]  Andrea J. Goldsmith,et al.  Energy-delay tradeoffs for data collection in TDMA-based sensor networks , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[16]  Ying Zhang,et al.  Distributed Minimal Time Convergecast Scheduling in Wireless Sensor Networks , 2006, 26th IEEE International Conference on Distributed Computing Systems (ICDCS'06).

[17]  Shaojie Tang,et al.  Locating sensors in the forest: A case study in GreenOrbs , 2012, 2012 Proceedings IEEE INFOCOM.

[18]  Bhaskar Krishnamachari,et al.  Enhancing the Data Collection Rate of Tree-Based Aggregation in Wireless Sensor Networks , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[19]  Michael Segal,et al.  Improved Algorithms for Data-Gathering Time in Sensor Networks II: Ring, Tree and Grid Topologies , 2007, International Conference on Networking and Services (ICNS '07).

[20]  Do Young Eun,et al.  Smart sleep: Sleep more to reduce delay in duty-cycled wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[21]  Bhaskar Krishnamachari,et al.  Scheduling Algorithms for Tree-Based Data Collection in Wireless Sensor Networks , 2011, Theoretical Aspects of Distributed Computing in Sensor Networks.

[22]  Huang Lee,et al.  Towards Energy-Optimal and Reliable Data Collection via Collision-Free Scheduling in Wireless Sensor Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[23]  S. Shankar Sastry,et al.  Design and implementation of a sensor network system for vehicle tracking and autonomous interception , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[24]  Zhaohui Yuan,et al.  Sleep-aware mode assignment in wireless embedded systems , 2011, J. Parallel Distributed Comput..

[25]  Gang Zhou,et al.  VigilNet: An integrated sensor network system for energy-efficient surveillance , 2006, TOSN.

[26]  Yunhao Liu,et al.  Multiple task scheduling for low-duty-cycled wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[27]  Deborah Estrin,et al.  A wireless sensor network For structural monitoring , 2004, SenSys '04.

[28]  David E. Culler,et al.  Versatile low power media access for wireless sensor networks , 2004, SenSys '04.

[29]  Xiaodong Wang,et al.  Minimum Latency Broadcast Scheduling in Duty-Cycled Multihop Wireless Networks , 2012, IEEE Transactions on Parallel and Distributed Systems.

[30]  Xueyan Tang,et al.  Scheduling Sensor Data Collection with Dynamic Traffic Patterns , 2013, IEEE Transactions on Parallel and Distributed Systems.

[31]  Hui Song,et al.  Routing in intermittently connected sensor networks , 2008, 2008 IEEE International Conference on Network Protocols.