An environmental energy harvesting framework for sensor networks

ABSTRACT

Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.

REFERENCES

• 1: Panasonic solar cells technical handbook. http://www.solarbotics.net/library/datasheets/sunceram.pdf.
• 2: Dimitri Bertsekas , Robert Gallager, Data networks, Prentice-Hall, Inc., Upper Saddle River, NJ, 1987
• 3: Deborah Estrin , Ramesh Govindan , John Heidemann, Embedding the Internet: introduction, Communications of the ACM, v.43 n.5, p.38-41, May 2000 [doi>10.1145/332833.332836]
• 4: Indranil Gupta , Robbert van Renesse , Kenneth P. Birman, Scalable Fault-Tolerant Aggregation in Large Process Groups, Proceedings of the 2001 International Conference on Dependable Systems and Networks (formerly: FTCS), p.433-442, July 01-04, 2001
• 5: S Haykin, Adaptive filter theory, Prentice-Hall, Inc., Upper Saddle River, NJ, 1986
• 6: James San Jacinto Mountains Reserve. http://www.jamesreserve.edu.
• 7: K. Kalpakis, K. Dasgupta, and P. Namjoshi. Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Technical Report UMBC-TR-02-13, Department of Computer Science and Electrical Engineering,University of Maryland, Baltimore County, August 2002. Accepted for publication in Computer Networks.
• 8: Samuel Madden , Robert Szewczyk , Michael J. Franklin , David Culler, Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks, Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications, p.49, June 20-21, 2002
• 9: C. Melhuish. The ecobot project. www.ias.uwe.ac.uk/energy_autonomy/EcoBot_web_page.html.
• 10: R. Min, M. Bhardwaj, S. Cho, A. Sinha, E. Shih, A. Wang, and A. Chandrakasan. An architecture for a power-aware distributed microsensor node. In IEEE Workshop on Signal Processing Systems (SiPS '00), October 2000.
• 11: R. A. F. Mini, B. Nath, and A. A. F. Loureiro. A probabilistic approach to predict the energy consumption in wireless sensor networks. In IV Workshop de Comunicaçĕo sem Fio e Computaçĕo Móvel, Sĕo Paulo, Brazil, October 23-25 2002.
• 12: Mica wireless measurement system. Datasheet, Crossbow Technology Inc. http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/MICA.pdf.
• 13: Jan M. Rabaey , M. Josie Ammer , Julio L. da Silva , Danny Patel , Shad Roundy, PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking, Computer, v.33 n.7, p.42-48, July 2000 [doi>10.1109/2.869369]
• 14: K. Pister. Smart dust: Autonomous sensing and communication in a cubic millimeter. http://robotics.eecs.berkeley.edu/˜pister/SmartDust/.
• 15: V. Raghunathan, C. Schurgers, S. Park, and M. Srivastava. Energy aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2):40--50, March 2002.
• 16: M. Rahimi, H. Shah, G. S. Sukhatme, J. Heidemann, and D. Estrin. Studying the feasibility of energy harvesting in a mobile sensor network. In IEEE Int'l Conference on Robotics and Automation, 2003.
• 17: R. Ramanathan and R. Hain. Topology control of multihop wireless networks using transmit power adjustment. In INFOCOM (2), pages 404--413, 2000.
• 18: Curt Schurgers , Vlasios Tsiatsis , Saurabh Ganeriwal , Mani Srivastava, Optimizing Sensor Networks in the Energy-Latency-Density Design Space, IEEE Transactions on Mobile Computing, v.1 n.1, p.70-80, January 2002 [doi>10.1109/TMC.2002.1011060]
• 19: R. C. Shah and J. M. Rabaey. Energy aware routing for low energy ad hoc sensor networks. In Proc. IEEE Wireless Communications and Networking Conference (WCNC), volume 1, pages 350--355, Orlando, FL, March, 17-21 2002.
• 20: Suresh Singh , Mike Woo , C. S. Raghavendra, Power-aware routing in mobile ad hoc networks, Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking, p.181-190, October 25-30, 1998, Dallas, Texas, United States [doi>10.1145/288235.288286]
• 21: Mani Srivastava , Richard Muntz , Miodrag Potkonjak, Smart kindergarten: sensor-based wireless networks for smart developmental problem-solving enviroments, Proceedings of the 7th annual international conference on Mobile computing and networking, p.132-138, July 2001, Rome, Italy [doi>10.1145/381677.381690]
• 22: P. Stoica and R. Moses. Introduction to Spectral Analysis. Prentice Hall, 1997.
• 23: David Tennenhouse, Proactive computing, Communications of the ACM, v.43 n.5, p.43-50, May 2000 [doi>10.1145/332833.332837]
• 24: Ya Xu , John Heidemann , Deborah Estrin, Geography-informed energy conservation for Ad Hoc routing, Proceedings of the 7th annual international conference on Mobile computing and networking, p.70-84, July 2001, Rome, Italy [doi>10.1145/381677.381685]
• 25: M. Younis , M. Youssef , K. Arisha, Energy-Aware Routing in Cluster-Based Sensor Networks, Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'02), p.129, October 11-16, 2002
• 26: J. Zhao, R. Govindan, and D. Estrin. Residual energy scans for monitoring wireless sensor networks. In IEEE Wireless Communications and Networking Conference (WCNC'02), volume 1, pages 356--362, Orange County Convention Center, Orlando, FL, USA, 17-21 March 2002.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.4 Distributed Systems

Additional Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.2 Network Protocols
          Subjects: Routing protocols

General Terms:
Algorithms, Design

Keywords:
energy aware routing, energy harvesting, sensor networks, task scheduling