Journal of Environmental Studies and Sciences

, Volume 6, Issue 1, pp 200–207

Autonomous real-time water quality sensing as an alternative to conventional monitoring to improve the detection of food, energy, and water indicators

  • Ziqian Dong
  • Fang Li
  • Babak Beheshti
  • Alan Mickelson
  • Marta Panero
  • Nada Anid
Article

Abstract

Advances in sensors and wireless sensor networks (WSNs) are enabling real-time environmental monitoring, which has the potential to provide a plethora of fine-grained data to assist in understanding the symbiosis between food, energy, and water (FEW) systems. This paper presents the advantages of autonomous real-time water quality monitoring systems over conventional systems and proposes cost-effective and feasible approaches to designing a system that autonomously collects environmental data by integrating digital and mechanical devices connected through various communication networks, both wired and wireless. More specifically, the autonomous sensing devices proposed include low-cost water quality sensors implemented on commercial hardware and cell-based biosensors using electric cell-substrate impedance sensing (ECIS), which are capable of detecting water and/or air toxicants in real time.

The paper discusses the key design considerations of the underlying WSN communication system supporting autonomous data transmission, including the spatial distribution of sensors, costs, and operation autonomy. Communication among connected devices (e.g., sensors) requires both precision timing and network security against attacks, as well as means to ensure the privacy and integrity of the data being collected and transmitted through the network. Preliminary results demonstrate the importance of precision timing and synchronization by using measured timing information and signal strength to identify man-in-the-middle attacks in fixed wireless networks and to locate the attack source using machine-learning approaches. Data modeling and recovery methods are presented to efficiently analyze and process sensing data to address the missing measurement issue caused by noise and device failure. The system proposed herein can serve as a valuable tool for real-time monitoring of FEW resources and can be broadly applied to efficient management of their sustainability.

Keywords

Water quality sensing Real-time Wireless sensor network Autonomous Cell-based sensors Indicators 

References

  1. Arampatzis T, Lygeros J, Manesis S (2005) A survey of applications of wireless sensors and Wireless Sensor Networks. 2005 I.E. International Symposium on Intelligent Control & 13th Mediterranean Conference on Control and Automation, Limassol, Cyprus 1-2:719–724Google Scholar
  2. Arduino (2015), https://www.arduino.cc/. Accessed 30 December 2015
  3. Baronti P, Pillai P, Chook VWC, Chessa S, Gotta A, Hu YF (2007) Wireless sensor networks: a survey on the state of the art and the 802.15.4 and ZigBee standards. Computer Communications 30(7):1655–1695CrossRefGoogle Scholar
  4. Beheshti BD, Michel H (2011a) A proposed API for the information plane of the WSN Integrated Technical Reference Model (I-TRM). Proceedings of SDR’11 Wireless Innovation Conference and Exposition. Washington DC.Google Scholar
  5. Beheshti BD, Michel H (2011b) A proposed API for the control plane of the WSN Integrated Technical Reference Model (I-TRM). Emerging Technologies for a Smarter World (CEWIT): pp 1–5Google Scholar
  6. Buzby JC, Farah-Wells H, Hyman J (2014) The estimated amount, value, and calories of postharvest food losses at the retail and consumer levels in the United States. USDA-ERS economic information bulletin, (121). http://www.endhunger.org/PDFs/2014/USDA-FoodLoss-2014.pdf. Accessed 20 June 2015
  7. Candès EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? Information theory. IEEE Transactions on 52(12):5406–5425Google Scholar
  8. Candès EJ, Wakin MB (2008) An introduction to compressive sampling. Signal processing magazine, IEEE 25(2):21–30CrossRefGoogle Scholar
  9. Curtis TM, Tabb J, Romeo L, Schwager SJ, Widder MW, Van der Schalie WH (2009) Improved cell sensitivity and longevity in a rapid impedance-based toxicity sensor. J. Appl. Toxicol. 29:374–380CrossRefGoogle Scholar
  10. Dong Z, Anand S, Chandramouli R (2011) Estimation of missing RTTs in computer networks: matrix completion vs compressed sensing. Computer networks 55(15):3364–3375CrossRefGoogle Scholar
  11. Dong Z, Espejo R, Wan Y, Zhuang W (2015) Detecting and locating man-in-the-middle attacks in fixed wireless networks, CIT. Journal of computing and information technology 23(4):283–293CrossRefGoogle Scholar
  12. Dursch A, Yen DC, Shih DH (2004) Bluetooth technology: an exploratory study of the analysis and implementation frameworks. Computer standards & interfaces 26(4):263–277CrossRefGoogle Scholar
  13. FAO (2014) Food and Agriculture Organization of the United Nations. The water-energy-food nexus: a new approach in support of food security and sustainable agriculture; FAO, Rome, 2014. http://www.fao.org/nr/water/docs/FAO_nexus_concept.pdf. Accessed 20 June 2015
  14. Hunter PR, Zmirou-Navier D, Hartemann P (2009) Estimating the impact on health of poor reliability of drinking water interventions in developing countries. Science of the total environment 407:2621–2624CrossRefGoogle Scholar
  15. ISO 11784 (2004) Radio frequency identification of animals—code structureGoogle Scholar
  16. ISO 11785 (1996) Radio frequency identification of animals—technical conceptGoogle Scholar
  17. ISO 14223 (2003) Radiofrequency identification of animals—Advanced transpondersGoogle Scholar
  18. ISO/IEC 14443 (2008b) Identification cards—contactless integrated circuit cards—proximity cardsGoogle Scholar
  19. ISO/IEC 15693 (2008c) Identification cards—contactless integrated circuit cards—vicinity cards.Google Scholar
  20. ISO/IEC 18000 (2008d) Information technology—radio frequency identification for item managementGoogle Scholar
  21. ISO/IEC 7816 (2008a) Identification cards—integrated circuit cardsGoogle Scholar
  22. Jackson T, Mansfield K, Saafi M, Colman T, Romine P (2008) Measuring soil temperature and moisture using wireless MEMS sensors. Measurement 41:381–390CrossRefGoogle Scholar
  23. Jalava M, Kummu M, Porkka M, Siebert S, Varis O (2014) Diet change—a solution to reduce water use? Environmental research letters 9(7), p.074016Google Scholar
  24. Joshi H, Michel H (2008) Integrated technical reference model and sensor network architecture. International Conference on Wireless Networks. Las Vegas, NVGoogle Scholar
  25. Kovacs G (2003) Electronic sensors with living cellular components. Proceedings of the IEEE–PIEEE 91:915–929CrossRefGoogle Scholar
  26. Lei JF, Martin LC, Will HA (1997) Advances in thin film sensor technologies for engine applications. InASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition 1997 Jun 2 (pp. V004T15A036-V004T15A036). American Society of Mechanical EngineersGoogle Scholar
  27. Morgan J (2014) A simple explanation of the “Internet of Things” http://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/. Accessed 04 January 2016Google Scholar
  28. Pancrazio JJ, Whelan JP, Borkholder DA, Ma W, Stenger DA (1999) Development and application of cell-based biosensors. Ann. Biomed. Eng. 27:697–711CrossRefGoogle Scholar
  29. Postel SL (2000) Entering an era of water scarcity: the challenges ahead. Ecological applications 10(4):941–948CrossRefGoogle Scholar
  30. Qu X, Brame J, Li Q, Alvarez PJ (2013) Nanotechnology for a safe and sustainable water supply: enabling integrated water treatment and reuse. Accounts of chemical research. 46:834–843CrossRefGoogle Scholar
  31. Rijsberman FR (2006) Water scarcity: fact or fiction? Agricultural water management 80(1):5–22CrossRefGoogle Scholar
  32. Seckler D, Barker R, Amarasinghe U (1999) Water scarcity in the twenty-first century. International journal of water resources development 15(1–2):29–42CrossRefGoogle Scholar
  33. UNEP, UNCCD; GEO 5 (2014) Global environmental outlook; Managing increasing pressures on land; http://www.unep.org/geo/pdfs/geo5/GEO-5_LAND-small.pdf. Accessed 20 June 2015
  34. Vörösmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, et al. (2010) Global threats to human water security and river biodiversity. Nature 467:555–561CrossRefGoogle Scholar
  35. Wise KD (2007) Integrated sensors, MEMS, and microsystems: reflections on a fantastic voyage. Sens. Actuat. A-Physical. 136:39–50CrossRefGoogle Scholar
  36. World Economic Forum (2011) Water security—the water-food-energy-climate nexus; Island Press. Washington, Covelo, LondonGoogle Scholar
  37. Zennaro M, Floros A, Dogan G, Sun T, Cao Z, Huang C, Bahader M, Ntareme H, Bagula A (2009) On the design of a water quality wireless sensor network (wqwsn): an application to water quality monitoring in Malawi. InParallel Processing Workshops, 2009. ICPPW’09. IEEE International Conference: 330–336Google Scholar
  38. Zhang X, Li F, Nordin AN, Tarbell J, Voiculescu I (2015) Toxicity studies using mammalian cells and impedance spectroscopy method. Sensing and bio-sensing research 3:112–121CrossRefGoogle Scholar

Copyright information

© AESS 2016

Authors and Affiliations

  1. 1.School of Engineering and Computing SciencesNew York Institute of TechnologyNew YorkUSA
  2. 2.Department of Electrical, Computer, and Energy EngineeringUniversity of ColoradoBoulderUSA

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