Optimal data collection in wireless sensor networks with correlated energy harvesting

  • Kishor PatilEmail author
  • Koen De Turck
  • Dieter Fiems


We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate.


Value of information Correlated energy harvesting Markov process Sensor networks 


  1. 1.
    Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805CrossRefGoogle Scholar
  2. 2.
    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29:1645–1660CrossRefGoogle Scholar
  3. 3.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  4. 4.
    Akyildiz IF, Vuran MC (2010) Wireless sensor networks . Wiley, UKCrossRefGoogle Scholar
  5. 5.
    Ammari HM, Gomes N, Jacques M, Maxim B, Yoon D (2015) A survey of sensor network applications and architectural components. Ad Hoc & Sensor Wireless Networks 25(1–2): 1–44Google Scholar
  6. 6.
    Sundmaeker H, Guillemin P, Friess P, Woelfflé S (2010) Vision and challenges for realising the Internet of Things European commissionGoogle Scholar
  7. 7.
    Sudevalayam S, Kulkarni P (2011) Energy harvesting sensor nodes: survey and implications. IEEE Commun Surv Tutorials 13(3):443–461CrossRefGoogle Scholar
  8. 8.
    Polastre J, Hill J, Culler D (2004) Versatile low power media access for wireless sensor networks categories and subject descriptors. In: Proceedings of the 2nd international conference on embedded networked sensor systems (SenSys ’04), pp 95– 107Google Scholar
  9. 9.
    Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, pp 3005–3014Google Scholar
  10. 10.
    Anastasi G, Conti M, Di Francesco M, Passarella A (2009) Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw 7(3):537–568CrossRefGoogle Scholar
  11. 11.
    Ganeriwal S, Tsigkogiannis I, Shim H, Tsiatsis V, Srivastava MB, Ganesan D (2009) Estimating clock uncertainty for efficient duty-cycling in sensor networks. IEEE/ACM Trans Netw 17(3):843–856CrossRefGoogle Scholar
  12. 12.
    Wang X, Xing G, Zhang Y, Lu C, Pless R, Gill C (2003) Integrated coverage and connectivity configuration in wireless sensor networks. In: Proceedings of the First International Conference on Embedded Networked Sensor Systems (SenSys ’03), pp 28–39Google Scholar
  13. 13.
    Paradiso JA, Starner T (2005) Energy scavenging for mobile and wireless electronics. IEEE Pervasive Comput 4(1):18–27CrossRefGoogle Scholar
  14. 14.
    Zhou Z, Du C, Shu L, Hancke G, Niu J, Ning H (2016) An energy-balanced heuristic for mobile sink scheduling in hybrid WSNs. IEEE Trans Ind Inf 12(1):28–40CrossRefGoogle Scholar
  15. 15.
    Bi Y, Niu J, Sun L, Huangfu W, Sun Y (2007) Moving schemes for mobile sinks in wireless sensor networks. In: Proceedings of the IEEE international performance computing, and communications conference, pp 101–108Google Scholar
  16. 16.
    Tunca C, Isik S, Donmez MY, Ersoy C (2014) Distributed mobile sink routing for wireless sensor networks: a survey. IEEE Commun Surv Tutorials 16(2):877–897CrossRefGoogle Scholar
  17. 17.
    Nazir B, Hasbullah H (2010) Mobile sink based routing protocol (MSRP) for prolonging network lifetime in clustered wireless sensor network. In: Proceedings of the 2010 international conference on computer applications and industrial electronics, pp 624–629Google Scholar
  18. 18.
    Gu Y, Ji Y, Li J, Zhao B (2013) ESWC Efficient scheduling for the mobile sink in wireless sensor networks with delay constraint. IEEE Trans Parallel Distrib Syst 24(7):1310–1320CrossRefGoogle Scholar
  19. 19.
    Ren X, Liang W, Xu W (2015) Data collection maximization in renewable sensor networks via time-slot scheduling. IEEE Trans Comput 64(7):1870–1883MathSciNetCrossRefGoogle Scholar
  20. 20.
    Huang S-C, Chang H-Y (2017) A farmland multimedia data collection method using mobile sink for wireless sensor networks. Multimedia Tools and Applications 76(19):19463–19478CrossRefGoogle Scholar
  21. 21.
    Yang Y, Miao Y (2017) A path planning method for mobile sink in farmland wireless sensor network. In: Proceedings of the IEEE 2nd information technology networking, electronic and automation control conference, pp 1157–1160Google Scholar
  22. 22.
    Taherian M, Maeen M, Haghparast M (2017) Promoting the quality level of signaling in railway transportation system taking advantage from wireless sensor networks technology. COMPUTERS, 6(3).
  23. 23.
    Lei J, Yates R, Greenstein L (2009) A generic model for optimizing single-hop transmission policy of replenishable sensors. IEEE Trans Wirel Commun 8(2):547–551CrossRefGoogle Scholar
  24. 24.
    Seyedi A, Sikdar B (2008) Modeling and analysis of energy harvesting nodes in wireless sensor networks. In: Proceedings of the Forty-Sixth Annual Allerton Conference, Allerton House, IL, USAGoogle Scholar
  25. 25.
    Michelusi N, Stamatiou K, Zorzi M (2013) Transmission policies for energy harvesting sensors with time-correlated energy supply. IEEE Trans Commun 61(7):2988–3001CrossRefGoogle Scholar
  26. 26.
    Lee P, Eu ZA, Han M, Tan HP (2011) Empirical modeling of a solar-powered energy harvesting wireless sensor node for time-slotted operation. In: 2011 IEEE wireless communications and networking conference, pp 179–184Google Scholar
  27. 27.
    Ho CK, Khoa PD, Ming PC (Nov 2010) Markovian models for harvested energy in wireless communications. In: Proceedings of the 2010 IEEE International Conference on Communication Systems (ICCS), pp 311–315Google Scholar
  28. 28.
    Naderi MY, Basagni S, Chowdhury KR (2012) Modeling the residual energy and lifetime of energy harvesting sensor nodes. In: Proceedings of the 2012 IEEE Global Communications Conference (GLOBECOM)Google Scholar
  29. 29.
    De Cuypere E, De Turck K, Fiems D (2012) Stochastic modelling of energy harvesting for low power sensor nodes. In: Proceedings of the 7th international conference on queueing theory and networking applications (QTNA, p 2012Google Scholar
  30. 30.
    De Cuypere E, De Turck K, Fiems D (2018) A queueing model of an energy harvesting sensor node with data buffering. Telecommun Syst 67(2):281–295CrossRefGoogle Scholar
  31. 31.
    Gelenbe E, Kadioglu YM (2015) Energy loss through standby and leakage in energy harvesting wireless sensors. In: 2015 IEEE 20th international workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), pp 231–236Google Scholar
  32. 32.
    Gelenbe E (2014) A sensor node with energy harvesting. SIGMETRICS Performance Evaluation Review 42 (2):37–39CrossRefGoogle Scholar
  33. 33.
    Tan L, Tang S (2017) Energy harvesting wireless sensor node with temporal death: novel models and analyses. IEEE/ACM Trans Netw 25(2):896–909MathSciNetCrossRefGoogle Scholar
  34. 34.
    Dimitriou I, Alouf S, Jean-Marie A (2015) A Markovian queueing system for modeling a smart green base station. In: Proceedings of the 12th European Workshop on Computer Performance Engineering (EPEW 2015), pp 3–18, Madrid, SpainGoogle Scholar
  35. 35.
    Abu Alsheikh M, Hoang DT, Niyato D, Tan HP, Lin S (2015) Markov decision processes with applications in wireless sensor networks: a survey. IEEE Commun Surv Tutorials 17(3):1239–1267CrossRefGoogle Scholar
  36. 36.
    Rao VS, Prasad RV, Niemegeers IGMM (2015) Optimal task scheduling policy in energy harvesting wireless sensor networks. In: Proceedings of the 2015 IEEE Wireless Communications and Networking Conference (WCNC), pp 1030–1035Google Scholar
  37. 37.
    Lei L, Kuang Y, Shen XS, Yang K, Qiao J, Zhong Z (2016) Optimal reliability in energy harvesting industrial wireless sensor networks. IEEE Trans Wirel Commun 15(8):5399– 5413CrossRefGoogle Scholar
  38. 38.
    Zordan D, Melodia T, Rossi M (2016) On the design of temporal compression strategies for energy harvesting sensor networks. IEEE Trans Wirel Commun 15(2):1336–1352CrossRefGoogle Scholar
  39. 39.
    Mitici M, Goseling J, de Graaf M, Boucherie RJ (2016) Energy-efficient data collection in wireless sensor networks with time constraints. Perform Eval 102:34–52CrossRefGoogle Scholar
  40. 40.
    Patil K, De Turck K, Fiems D (2018) A two-queue model for optimising the value of information in energy-harvesting sensor networks. Perform Eval 119:27–42CrossRefGoogle Scholar
  41. 41.
    Sachidananda V, Khelil A, Suri N (2010) Quality of information in wireless sensor networks: a survey. In: Proceedings of the 15th International Conference on Information Quality (ICIQ’10), pp 193–207Google Scholar
  42. 42.
    Bisdikian C, Kaplan LM, Srivastava MB (2013) On the quality and value of information in sensor networks. ACM Transactions on Sensor Networks 9(4):48CrossRefGoogle Scholar
  43. 43.
    Ngai EC-H, Gunningberg P (2014) Quality-of-information-aware data collection for mobile sensor networks. Pervasive Mob Comput 11:203–215CrossRefGoogle Scholar
  44. 44.
    Patil K, De Turck K, Fiems D (2016) Optimal data collection in hybrid energy-harvesting sensor networks. In: Proceedings of the 23rd international conference on Analytical and Stochastic Modelling Techniques and Applications (ASMTA2016), Cardiff, WalesGoogle Scholar
  45. 45.
    Blondia C, Casals O (1992) Statistical multiplexing of VBR sources: a matrix-analytic approach. Perform Eval 16(1–3):5–20CrossRefGoogle Scholar

Copyright information

© Institut Mines-Télécom and Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Telecommunications and Information ProcessingGhent UniversityGhentBelgium
  2. 2.Central SupélecLaboratoire des Signaux et SystèmesGif-sur-YvetteFrance

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