ABSTRACT
As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in unprecedented sensing capabilities. A key challenge has become how to make these emerging wireless sensor networks more sustainable and easier to maintain over increasingly prolonged deployments.
In this paper, we report the findings from a one year deployment of an automated wildlife monitoring system for analyzing the social co-location patterns of European badgers (Meles meles) residing in a dense woodland environment.
We describe the stages of its evolution cycle, from implementation, deployment and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We report preliminary descriptive analyses of a subset of the data collected, demonstrating the significant potential our system has to generate new insights into badger behavior. The main lessons learned were: the need to factor in the maintenance costs while designing the system; to look carefully at software and hardware interactions; the importance of a rapid initial prototype deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.
- Wavetrend TG 100 Domino Tag. http://www.wavetrend.net.Google Scholar
- J. Aschoff. Circadian Clocks. North Holland Press, 1965.Google Scholar
- G. Barrenetxea, F. Ingelrest, G. Schaefer, M. Vetterli, O. Couach, and M. Parlange. SensorScope: Out-of-the-Box Environmental Monitoring. In Proceedings of IPSN '08, pages 332--343, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- J. Beutel, S. Gruber, A. Hasler, R. Lim, A. Meier, C. Plessl, I. Talzi, L. Thiele, C. Tschudin, M. Woehrle, and M. Yuecel. PermaDAQ: A Scientific Instrument for Precision Sensing and Data Recovery in Environmental Extremes. In Proceedings of IPSN '09, pages 265--276, Washington, DC, USA, 2009. IEEE Computer Society. Google ScholarDigital Library
- V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10):P10008, 2008.Google ScholarCross Ref
- M. Buettner, G. V. Yee, E. Anderson, and R. Han. X-MAC: A Short Preamble MAC Protocol for Duty-cycled Wireless Sensor Networks. In Proceedings of SenSys '06, pages 307--320, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- S. Chatterjea and P. Havinga. An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks. In Proceedings of DCOSS'08, pages 60--78, Santorini, Greece, June 2008. Google ScholarDigital Library
- M. Durvy, J. Abeillé, P. Wetterwald, C. O'Flynn, B. Leverett, E. Gnoske, M. Vidales, G. Mulligan, N. Tsiftes, N. Finne, and A. Dunkels. Making Sensor Networks IPv6 Ready. In Proceedings of SenSys'08, pages 421--422, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- V. Dyo and C. Mascolo. Efficient Node Discovery in Mobile Wireless Sensor Networks. In Proceedings of DCOSS '08, pages 60--78, Santorini, Greece, June 2008. Google ScholarDigital Library
- E. Ekici, Y. Gu, and D. Bozdag. Mobility-based Communication in Wireless Sensor Networks. IEEE Communications Magazine, 44(7):56--62, July 2006. Google ScholarDigital Library
- A. Gorlick. Turtles to Test Wireless Network, July 2007.Google Scholar
- J. W. Hui and D. E. Culler. IP is Dead, Long Live IP for Wireless Sensor Networks. In Proceedings of SenSys '08, pages 15--28, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- X. Jiang, J. Taneja, J. Ortiz, A. Tavakoli, P. Dutta, J. Jeong, D. Culler, P. Levis, and S. Shenker. An Architecture for Energy Management in Wireless Sensor Networks. SIGBED Review, 4(3):31--36, 2007. Google ScholarDigital Library
- L. P. Kaelbling, M. L. Littman, and A. P. Moore. Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research, 4:237--285, 1996. Google ScholarCross Ref
- R. E. Kenward. A Manual for Wildlife Radio Tagging (Biological Techniques). Academic Press, 2 edition, 2001.Google Scholar
- D. W. Macdonald and C. Newman. Population Dynamics of Badgers (Meles meles) in Oxfordshire, U.K.: Numbers, Density and Cohort Life Histories, and a Possible Role of Climate Change in Population Growth. Journal of Zoology, 256(01):121--138, 2002.Google ScholarCross Ref
- D. W. Macdonald, C. Newman, C. D. Buesching, and P. J. Johnson. Male-biased Movement in a High-density Population of the Eurasion Badger (Meles meles). Journal of Mammalogy, pages 1077--1086, 2008.Google Scholar
- D. W. Macdonald, P. Riordan, and F. Mathews. "Biological Hurdles to the Control of TB in Cattle: A Test of Two Hypotheses Concerning Wildlife to Explain the Failure of Control". Biological Conservation, 131(2):268--286, 2006. Infectious Disease and Mammalian Conservation.Google ScholarCross Ref
- G. Mainland, D. C. Parkes, and M. Welsh. Decentralized, Adaptive Resource Allocation for Sensor Networks. In Proceedings of NSDI '05, pages 315--328, Berkeley, CA, USA, 2005. USENIX Association. Google ScholarDigital Library
- A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson. Wireless Sensor Networks for Habitat Monitoring. In Proceedings of WSNA '02, pages 88--97, New York, NY, USA, 2002. ACM. Google ScholarDigital Library
- K. Martinez, J. K. Hart, and R. Ong. Deploying a Wireless Sensor Network in Iceland. In Proceedings of GSN '09, pages 131--137, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarDigital Library
- E. Neal and C. Cheeseman. Badgers. Poyser Books, 1996.Google Scholar
- C. E. Perkins and P. Bhagwat. Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers. SIGCOMM Comput. Commun. Rev., 24:234--244, October 1994. Google ScholarDigital Library
- C. Sadler and M. Martonosi. Data Compression Algorithms for Energy-constrained Devices in Delay Tolerant Networks. In ACM Conference on Embedded Network Sensor Systems (SenSys), 2006. Google ScholarDigital Library
- L. Selavo, A. Wood, Q. Cao, T. Sookoor, H. Liu, A. Srinivasan, Y. Wu, W. Kang, J. Stankovic, D. Young, and J. Porter. LUSTER: Wireless Sensor Network for Environmental Research. In SenSys'07: Proceedings of the 5th international conference on Embedded networked sensor systems, pages 103--116, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- P. Sikka, P. Corke, P. Valencia, C. Crossman, D. Swain, and G. Bishop-Hurley. Wireless Adhoc Sensor and Actuator Networks on the Farm. In Proceedings of IPSN '06, pages 492--499, 2006. Google ScholarDigital Library
- R. Szewczyk, J. Polastre, A. Mainwaring, and D. Culler. Lessons From A Sensor Network Expedition. In Proceedings of EWSN '04, pages 307--322, 2004.Google ScholarCross Ref
- G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, and W. Hong. A macroscope in the redwoods. In SenSys '05: Proceedings of the 3rd international conference on Embedded networked sensor systems, pages 51--63, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- G. Werner-Allen, S. Dawson-Haggerty, and M. Welsh. Lance: Optimizing High-resolution Signal Collection in Wireless Sensor Networks. In Proceedings of SenSys '08, pages 169--182, 2008. Google ScholarDigital Library
- P. Zhang, C. M. Sadler, S. A. Lyon, and M. Martonosi. Hardware Design Experiences in ZebraNet. In Proceedings of SenSys '04, pages 227--238, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
Index Terms
- Evolution and sustainability of a wildlife monitoring sensor network
Recommendations
WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring
The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. ...
Wildlife and environmental monitoring using RFID and WSN technology
SenSys '09: Proceedings of the 7th ACM Conference on Embedded Networked Sensor SystemsWireless Sensor Networks enable scientists to collect information about the environment with a granularity unseen before, while providing numerous challenges to software designers. Since sensor devices are often powered by small batteries, which take ...
Network performance of a wireless sensor network for temperature monitoring in vineyards
PE-WASUN '11: Proceedings of the 8th ACM Symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networksWireless sensor networks (WSNs) are an emerging technology which can be used for outdoor environmental monitoring. This paper presents challenges that arose from the development and deployment of a WSN for environmental monitoring as well as network ...
Comments