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An improved energy efficient system for IoT enabled precision agriculture

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Abstract

Energy efficiency in wireless sensor network is a well studied research problem. A recent research study on IoT based precision agriculture has reported that designing the energy efficient data aggregation at the base station is an open research problem. The paper is motivated by recent developments in the energy efficient models and algorithms to maintain energy requirement at the base station. The base station is energy constrained device and it has to survive maintaining the energy requirements from various sensors and gateway modules. Therefore base station uses energy harvesting to maintain energy neutrality. There are two important research questions to study; how to estimate the solar energy to maintain the energy neutrality at the base station having solar backup for energy harvesting and how to estimate the energy requirements at the base station at random point of time. The first question has been answered quite extensively. In this paper, we are trying to address the second question of how to estimate the energy requirements at the base station of IoT enabled precision agriculture. In precision agriculture, numerous types of sensors including soil, moisture, temperature, wind direction, wind speed, camera, drone etc. are used to continuously monitor the field and connect to the base station. Hence base station has different modes of power requirements such as low power, medium power, and high power modes based on different types of wireless communication medium at random point of time. The paper proposes a novel product density model to estimate the energy requirements at the base station. Moreover, an Improved Duty Cycling algorithm is proposed using residual energy parameter. The performance of the proposed Improved Duty Cycling is compared with two other algorithms and the proposed algorithm shows an improvement in terms of average energy consumption, residual energy performance, throughput etc.

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References

  • Akhlaq A, Sheltami T, Shakshuki E (2014) C3: an energy-efficient protocol for coverage, connectivity, and communication in WSNs. Int J Pers Ubiquitous Comput Springer 18(5):1117–1133. https://doi.org/10.1007/s00779-013-0719-2

    Article  Google Scholar 

  • Bajaber F, Awan I (2010) Energy efficient clustering protocol to enhance lifetime of wireless sensor networks. J Ambient Hum Comput 1(4):239–248. https://doi.org/10.1007/s12652-010-0019x

    Article  Google Scholar 

  • Beheshtiha SS, Tan H P, Sabaei M (2012) Opportunistic routing with adaptive harvesting- aware duty cycling in energy harvesting WSN. In: Proceedings of 15th international symposium on wireless personal media communications (WPMC), pp 90–94

  • Briante O, Mandalari AM, Molinaro A, Ruggeri G, Zarate J Alonso, Vazyuez GF (2014) Duty-cycle optimization for machine to machine area networks based on frame slotted Aloha with energy harvesting capabilities. In: Proceedings of 20th European wireless conference, June 2014, Barcelona, Spain

  • Chetlapalli V, Iyer KSS, Patil, S, Agrawal H (2016) Estimation of diurnal bandwidth requirement using random point process and product density. In: Proceedings of future technologies conference (FTC), San Francisco, pp 206–212. https://doi.org/10.1109/FTC.2016.7821612

  • Corke P, Valencia P, Sikka P, Wark T, Overs L (2007) Long-duration solar-powered wireless sensor networks. In: Proceedings of the 4th workshop on embedded networked sensors EmNets’07, June 2007, pp 33–37. https://doi.org/10.1145/1278972.1278980

  • Gnanambigai J, Rengarajan N, Anbukkarasi K (2012) LEACH and its descendant protocols: a survey. Int J Commun Comput Technol 1(3):15–20

    Google Scholar 

  • Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless micro sensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences (HICSS’00), Hawaii, USA, Jan 2000. https://doi.org/10.1109/hicss.2000.926982

  • Hsu J, Kansal A, Zahedi S, Shrivastava M, Raghunathan V (2006) Adaptive duty-cycling for energy Harvesting. In: Proceedings of international symposium on low power electronics and design (ISLPED’06), pp 180–185. https://doi.org/10.1145/1165573.1165616

  • Iyer KSS, Saksena VN (1970) A stochastic model on the growth of Cells in Cancer. Biometrics 26(3):401–410

    Article  Google Scholar 

  • Jia B, Liu S, Guan Y, Li W, Ren W (2017) The fusion model of multi domain context information for the internet of things. Wirel Commun Mob Comput. https://doi.org/10.1155/2017/6274824

    Article  Google Scholar 

  • Jin Y, Wang L, Kim Y, Yang X (2006) EEMC: an energy-efficient multi-tier clustering algorithm for large scale wireless sensor networks. In: Proceedings of the 2006 IEEE international conference on wireless communication networking and mobile computing, September 2006. https://doi.org/10.1109/wicom.2006.269

  • Kamalinejad P, Mahapatra C, Sheng Z, Mirabbasi S, Leung VCM, Guan YL (2015) Wireless energy harvesting for the internet of things. IEEE Commun Mag 53:6. https://doi.org/10.1109/mcom.2015.7120024

    Article  Google Scholar 

  • Kansal A, Shrivastava M (2003) An environmental energy harvesting framework for sensor networks. In: Proceedings of international symposium on low power electronics and design 2003. ISLPED’03. https://doi.org/10.1109/lpe.2003.1231958

  • Kansal A, Potter D, Shrivastava M (2004) Performance aware tasking for environmentally powered sensor networks. ACM SIGMETRICS Perform Eval Rev 32(1):223–234. https://doi.org/10.1145/1005686.1005714

    Article  Google Scholar 

  • Kansal A, Hsu J, Zadehi S, Shrivastava MB (2007) Power management in energy harvesting sensor networks. ACM Trans Embed Comput Syst. https://doi.org/10.1145/1274858.1274870

    Article  Google Scholar 

  • Kumar D, Aseri TC, Patel RB (2009) EEHC: energy-efficient heterogenous clustered scheme for wireless sensor networks. Comput Commun Elsevier 32(4):662–667. https://doi.org/10.1016/j.comcom.2008.11.025

    Article  Google Scholar 

  • Lee D, Chung K (2010) Adaptive duty cycled based congestion control for home automation networks. IEEE Trans Consum Electron 56(1):42–47

    Article  Google Scholar 

  • Li W, Jia B, Saruwatari S, Watanabe T (2016) Waterfalls partial aggregation in wireless sensor networks. Int J Distrib Sens Netw. https://doi.org/10.1155/2016/2392149

    Article  Google Scholar 

  • Li W, Jia B, Li Q, Wang J (2018) An energy efficient and lifetime aware routing protocol in ad hoc networks. In: Vaidya J, Li J (eds) Algorithms and architectures for parallel processing lecture notes in computer science, vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_30

    Google Scholar 

  • Lina Xu, O’Hare GMP, Collier R (2017) A smart and balanced energy-efficient multi-hop clustering algorithm (Smart-BEEM) for MIMO IoT systems in future networks. MDPI Sens 17(7):574. https://doi.org/10.3390/s17071574

    Article  Google Scholar 

  • Lindsey S, Raghvendra CS (2002) PEGASIS: power-efficient gathering in sensor information systems. In: Proceedings of the aerospace conference, pp 1125–1130, March 2002. https://doi.org/10.1109/aero.2002.1035242

  • Manjeshwar A, Agrawal DP (2000) TEEN: a protocol for enhanced efficiency in wireless sensor networks. In: Proceedings of 15th international parallel and distributed processing symposium, pp 305–312, IEEE, San Francisco, Calif, USA, April 2000. https://doi.org/10.1109/ipdps.2001.925197

  • Oliveria CHS, Ghamri-Doudane Y, Lohier S (2013) A duty-cycled self adaption algorithm for the 802.15.4 wireless sensor networks. In: IEEE Global information infrastructure and networking symposium (GIIS’13). https://doi.org/10.1109/giis.2013.6684349

  • Shaikh FK, Zeadally S (2016) Energy harvesting in wireless sensor networks: a comprehensive review. Renew Sustain Energy Rev 55:1041–1054. https://doi.org/10.1016/j.rser.2015.11.010

    Article  Google Scholar 

  • Sharma N, Gummeson J, Irwin D, Shenoy P (2010) Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems. In: Proceedings of 7th Annual ieee communications society conference on sensor, mesh and ad hoc communications and networks (SECON), July 2010. https://doi.org/10.1109/secon.2010.5508260

  • Srinivasan SK, Iyer KSS (1966) Random processes associated with random points on a line. Zastosowania Matematicae Appl Math VIII:221–230

    MathSciNet  MATH  Google Scholar 

  • Vasisht D, Kapetanovic Z, Won J, Xinxin JJ, Chandra R, Sinha S, Kapoor A, Sudarshan M, Stratman S (2017) Farm-beats: an IoT platform for data driven agriculture. In: Proceedings of 14th (USENIX) symposium on networks systems designs and implementations (NSDI), Boston, MA, pp 515–529

  • Vigorito CM (2007) Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In: Proceedings of the 4th annual IEEE communications society conference on sensor, mesh and ad hoc communication and networks, San Diego, CA, USA

  • Younis O, Fahmy S (2004) HEED: a hybrid energy-efficient distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379. https://doi.org/10.1109/TMC.2004.41

    Article  Google Scholar 

  • Zheng G, Zhi-Jun Y, Min H, Wen-Hua Q (2019) Energy efficient analysis of an IEEE 802.11 PCF MAC protocol based on WLAN. J Ambient Hum Comput 10(5):1727–1737. https://doi.org/10.1007/s12652-018-0684-8

    Article  Google Scholar 

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Acknowledgements

We are thankful to Microsoft FarmBeats PI for his valuable comments during the work.

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Correspondence to Himanshu Agrawal.

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Agrawal, H., Dhall, R., Iyer, K.S.S. et al. An improved energy efficient system for IoT enabled precision agriculture. J Ambient Intell Human Comput 11, 2337–2348 (2020). https://doi.org/10.1007/s12652-019-01359-2

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