The Community for Technology Leaders
RSS Icon
Issue No.01 - January (2012 vol.11)
pp: 86-99
Özlem Durmaz Incel , Bogazici University, Istanbul
Amitabha Ghosh , Princeton University, Princeton
Bhaskar Krishnamachari , University of Southern California, Los Angeles
Krishnakant Chintalapudi , Microsoft Research, Bangalore
We investigate the following fundamental question—how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.
Convergecast, TDMA scheduling, multiple channels, power control, routing trees.
Özlem Durmaz Incel, Amitabha Ghosh, Bhaskar Krishnamachari, Krishnakant Chintalapudi, "Fast Data Collection in Tree-Based Wireless Sensor Networks", IEEE Transactions on Mobile Computing, vol.11, no. 1, pp. 86-99, January 2012, doi:10.1109/TMC.2011.22
[1] S. Gandham, Y. Zhang, and Q. Huang, “Distributed Time-Optimal Scheduling for Convergecast in Wireless Sensor Networks,” Computer Networks, vol. 52, no. 3, pp. 610-629, 2008.
[2] K.K. Chintalapudi and L. Venkatraman, “On the Design of MAC Protocols for Low-Latency Hard Real-Time Discrete Control Applications over 802.15.4 Hardware,” Proc. Int'l Conf. Information Processing in Sensor Networks (IPSN '08), pp. 356-367, 2008.
[3] I. Talzi, A. Hasler, G. Stephan, and C. Tschudin, “PermaSense: Investigating Permafrost with a WSN in the Swiss Alps,” Proc. Workshop Embedded Networked Sensors (EmNets '07), pp. 8-12, 2007.
[4] S. Upadhyayula and S.K.S. Gupta, “Spanning Tree Based Algorithms for Low Latency and Energy Efficient Data Aggregation Enhanced Convergecast (DAC) in Wireless Sensor Networks,” Ad Hoc Networks, vol. 5, no. 5, pp. 626-648, 2007.
[5] T. Moscibroda, “The Worst-Case Capacity of Wireless Sensor Networks,” Proc. Int'l Conf. Information Processing in Sensor Networks (IPSN '07), pp. 1-10, 2007.
[6] T. ElBatt and A. Ephremides, “Joint Scheduling and Power Control for Wireless Ad-Hoc Networks,” Proc. IEEE INFOCOM, pp. 976-984, 2002.
[7] Ö. Durmaz Incel and B. Krishnamachari, “Enhancing the Data Collection Rate of Tree-Based Aggregation in Wireless Sensor Networks,” Proc. Ann. IEEE Comm. Soc. Conf. Sensor, Mesh and Ad Hoc Comm. and Networks (SECON '08), pp. 569-577, 2008.
[8] Y. Wu, J.A. Stankovic, T. He, and S. Lin, “Realistic and Efficient Multi-Channel Communications in Wireless Sensor Networks,” Proc. IEEE INFOCOM, pp. 1193-1201, 2008.
[9] A. Ghosh, Ö. Durmaz Incel, V.A. Kumar, and B. Krishnamachari, “Multi-Channel Scheduling Algorithms for Fast Aggregated Convergecast in Sensor Networks,” Proc. IEEE Int'l Conf. Mobile Adhoc and Sensor Systems (MASS '09), pp. 363-372, 2009.
[10] V. Annamalai, S.K.S. Gupta, and L. Schwiebert, “On Tree-Based Convergecasting in Wireless Sensor Networks,” Proc. IEEE Wireless Comm. and Networking Conf. (WCNC '03), vol. 3, pp. 1942-1947, 2003.
[11] X. Chen, X. Hu, and J. Zhu, “Minimum Data Aggregation Time Problem in Wireless Sensor Networks,” Proc. Int'l Conf. Mobile Ad-Hoc and Sensor Networks (MSN '05), pp. 133-142, 2005.
[12] W. Song, F. Yuan, and R. LaHusen, “Time-Optimum Packet Scheduling for Many-to-One Routing in Wireless Sensor Networks,” Proc. IEEE Int'l Conf. Mobile Ad-Hoc and Sensor Systems (MASS '06), pp. 81-90, 2006.
[13] H. Choi, J. Wang, and E. Hughes, “Scheduling for Information Gathering on Sensor Network,” Wireless Networks, vol. 15, pp. 127-140, 2009.
[14] N. Lai, C. King, and C. Lin, “On Maximizing the Throughput of Convergecast in Wireless Sensor Networks,” Proc. Int'l Conf. Advances in Grid and Pervasive Computing (GPC '08), pp. 396-408, 2008.
[15] M. Pan and Y. Tseng, “Quick Convergecast in ZigBee Beacon-Enabled Tree-Based Wireless Sensor Networks,” Computer Comm., vol. 31, no. 5, pp. 999-1011, 2008.
[16] W. Song, H. Renjie, B. Shirazi, and R. LaHusen, “TreeMAC: Localized TDMA MAC Protocol for Real-Time High-Data-Rate Sensor Networks,” J. Pervasive and Mobile Computing, vol. 5, no. 6, pp. 750-765, 2009.
[17] G. Zhou, C. Huang, T. Yan, T. He, J. Stankovic, and T. Abdelzaher, “MMSN: Multi-Frequency Media Access Control for Wireless Sensor Networks,” Proc. IEEE INFOCOM, pp. 1-13, 2006.
[18] Y. Kim, H. Shin, and H. Cha, “Y-MAC: An Energy-Efficient Multi-Channel MAC Protocol for Dense Wireless Sensor Networks,” Proc. Int'l Conf. Information Processing in Sensor Networks (IPSN '08), pp. 53-63, Apr. 2008.
[19] B. Krishnamachari, D. Estrin, and S.B. Wicker, “The Impact of Data Aggregation in Wireless Sensor Networks,” Proc. Int'l Conf. Distributed Computing Systems Workshops (ICDCSW '02), pp. 575-578, 2002.
[20] J. Zander, “Performance of Optimum Transmitter Power Control in Cellular Radio Systems,” IEEE Trans. on Vehicular Technology, vol. 41, no. 1, pp. 57-62, Feb. 1992.
[21] P. Kyasanur and N.H. Vaidya, “Capacity of Multi-Channel Wireless Networks: Impact of Number of Channels and Interfaces,” Proc. ACM MobiCom, pp. 43-57, 2005.
[22] G. Sharma, R.R. Mazumdar, and N.B. Shroff, “On the Complexity of Scheduling in Wireless Networks,” Proc. ACM MobiCom, pp. 227-238, 2006.
[23] X. Lin and S. Rasool, “A Distributed Joint Channel-Assignment, Scheduling and Routing Algorithm for Multi-Channel Ad-Hoc Wireless Networks,” Proc. IEEE INFOCOM, pp. 1118-1126, 2007.
[24] C.H. Papadimitriou, “The Complexity of the Capacitated Tree Problem,” Networks, vol. 8, no. 3, pp. 217-230, 1978.
[25] H. Dai and R. Han, “A Node-Centric Load Balancing Algorithm for Wireless Sensor Networks,” Proc. IEEE Conf. Global Telecomm. (GlobeCom '03), pp. 548-552, 2003.
[26] M. Zuniga and B. Krishnamachari, “An Analysis of Unreliability and Asymmetry in Low-Power Wireless Links,” ACM Trans. Sensor Networks, vol. 3, no. 2, p. 7, 2007.
[27] J. Grönkvist and A. Hansson, “Comparison between Graph-Based and Interference-Based STDMA Scheduling,” Proc. ACM MobiHoc, pp. 255-258, 2001.
16 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool