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A type of energy-efficient data gathering method based on single sink moving along fixed points

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Abstract

A type of data gathering method based on one mobile Sink moving along the fixed traverse points (DGFP) is proposed in this paper. An optimal trajectory for the mobile Sink is built with the help of sensing and coverage models of the sensor node. Moreover, a sleep scheduling strategy is executed to further reduce energy consumption on idle listening. Sensors could go into light sleeping or deep sleeping mode when the Sink is far away from their communication ranges. Simulation results show that, DGFP could not only enhance network coverage, but also balance energy consumption compared with VNP, RP-UG and MobiCluster methods.

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References

  1. Di Francesco M, Das SK, Anastasi G (2011) Data collection in wireless sensor networks with mobile elements: a survey[J]. ACM Trans Sens Netw 8(1):1–31

    Article  Google Scholar 

  2. Sidhik A, Mammu K, Unai H-J, Sainz N, de la Iglesia I (2015) Cross-layer cluster-based energy-efficient protocol for wireless sensor networks[J]. Sensors 15(4):8314–8336

    Google Scholar 

  3. Salarian H, Chin K-W, Naghdy F (2014) An energy-efficient mobile-sink path selection strategy for wireless sensor networks[J]. IEEE Trans Veh Technol 63(5):2407–2419

    Article  Google Scholar 

  4. Abo-Zahhad M, Member S, Ahmed SM, Sabor N, Sasaki S (2015) Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks[J]. IEEE Sensors J 15(8):4576–4586

    Article  Google Scholar 

  5. Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks[J]. Wirel Pers Commun 78(1):231–246

    Article  Google Scholar 

  6. Abba S, Lee J-A (2015) An autonomous self-aware and adaptive fault tolerant routing technique for wireless sensor networks[J]. Sensors 15(8):20316–20354

    Article  Google Scholar 

  7. Lai Y, Xie J, Lin Z, Wang T, Liao M (2015) Adaptive data gathering in mobile sensor networks using speedy mobile elements[J]. Sensors 15(9):23218–23248

    Article  Google Scholar 

  8. Xie R, Liu A, Gao J (2016) A residual energy aware schedule scheme for WSNs employing adjustable awake/sleep duty cycle[J]. Wirel Pers Commun. doi:10.1007/s11277-016-3428-0

    Google Scholar 

  9. He S, Chen J, Yau DKY, Sun Y (2012) Cross-layer optimization of correlated data gathering in wireless sensor networks[J]. IEEE Trans Mob Comput 11(11):1678–1691

    Article  Google Scholar 

  10. Liang W, Schweitzer P, Xu Z (2013) Approximation algorithms for capacitated minimum spanning forest problems in wireless sensor networks with a mobile Sink[J]. IEEE Trans Comput 62(10):1932–1944

    Article  MathSciNet  MATH  Google Scholar 

  11. Gao S, Zhang H, Das SK (2011) Efficient data collection in wireless sensor networks with path-constrained mobile Sinks[J]. IEEE Trans Mob Comput 10(4):592–608

    Article  Google Scholar 

  12. Shah RC, Roy S, Jain S, Brun W (2003) Data MULEs: modeling a three-tier architecture for sparse sensor networks[C]. IEEE International Workshop on Sensor Network Protocols and Applications, Anchorage, AK, 2003. 30–41

  13. Tashtarian F, Moghaddam MHY, Sohraby K, Effati S (2015) On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile Sinks[J]. IEEE Trans Veh Technol 64(7):3177–3189

    Google Scholar 

  14. Mehrabi A, Kim K (2016) Maximizing data collection throughput on a path in energy harvesting sensor networks using a mobile Sink[J]. IEEE Trans Mob Comput 15(3):690–704

    Article  Google Scholar 

  15. Mehrabi A, Kim K, Member S (2015) Maximizing data collection throughput on a path in energy harvesting sensor networks using a mobile Sink[J]. IEEE Trans Mob Comput 14(8):1–16

    Google Scholar 

  16. Khan M, Gansterer W, Haring G (2013) Static vs. mobile Sink: the influence of basic parameters on energy efficiency in wireless sensor networks[J]. Elsevier Comput Commun 36(9):965–978

    Article  Google Scholar 

  17. Yu G, Ji Y, Li J, Zhao B (2013) ESWC: efficient scheduling for the mobile Sink in wireless sensor networks with delay constraint[J]. IEEE Trans Parallel Distrib Syst 24(7):1310–1320

    Article  Google Scholar 

  18. Liu Y, Dong M, Ota K, Liu A (2016) ActiveTrust: secure and trustable routing in wireless sensor networks[J]. IEEE Trans Infor Forensics and Secur 11(9):2013–2027

    Article  Google Scholar 

  19. Wang J, Zhang Z, Xia F (2013) An energy efficient stable election-based routing algorithm for wireless sensor networks[J]. Sensors 13:14301–14320

    Article  Google Scholar 

  20. Chen T-S, Tsai H-W, Chang Y-H, Chen T-C (2013) Geographic converge cast using mobile Sink in wireless sensor networks[J]. Comput Commun 36(4):445–458

    Article  Google Scholar 

  21. Yu F, Park S, Lee E, Kimiet S-H (2010) Elastic routing: a novel geographic routing for mobile Sinks in wireless sensor networks[J]. IET Commun 4(6):716–727

    Article  Google Scholar 

  22. Jain S, Shah RC, Brunette W, Borriello G, Roy S (2006) Exploiting mobility for energy efficient data collection in sensor networks[J]. Mob Netw Appl 11(3):327–339

    Article  Google Scholar 

  23. Chakrabarti A, Sabharwal A, Aazhang B (2006) Communication power optimization in a sensor network with a path-constrained mobile observer[J]. ACM Trans Sens Netw 2(3):219–240

    Article  Google Scholar 

  24. Konstantopoulos C, Pantziou G, Gavalas D, Mpitziopoulos A, Mamalis B (2012) A rendezvous-based approach enabling energy-efficient sensory data collection with mobile Sinks[J]. IEEE Trans Parallel Distrib Syst 23(5):809–817

    Article  Google Scholar 

  25. de Freitas EP, Heimfarth T, Vinel A, Wagner FR, Pereira CE, Larsson T (2013) Cooperation among wirelessly connected static and mobile sensor nodes for surveillance applications[J]. Sensors 13(10):12903–12928

    Article  Google Scholar 

  26. Somasundara A, Ramamoorthy A, Srivastava M (2004) Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines[C]. Proc. 25th IEEE Int’l Real-Time Systems Symp, Lisbon, Portugal, December 5–8, 2004. 296–305

  27. Gao S, Hongke Z (2011) Optimal path selection for mobile Sink in delay-guaranteed sensor networks[J]. Chin J Electron 39(4):1–6

    Google Scholar 

  28. Shi Y, Hou YT (2008) Theoretical results on base station movement problem for sensor network[C]. The 27th Conference on Computer Communications, INFOCOM 2008, Phoenix, AZ, USA, 13–18, April, 2008. 376–384

  29. Xing G, Wang T, Xie Z (2008) Rendezvous planning in wireless sensor networks with mobile elements[J]. IEEE Trans Mob Comput 7(11):1–14

    Article  Google Scholar 

  30. Yang Q, He S, Li J, Chen J, Sun Y (2015) Energy-efficient probabilistic area coverage in wireless sensor networks[J]. IEEE Trans Veh Technol 64(1):367–377

    Article  Google Scholar 

  31. He S, Shin D-H, Zhang J, Chen J, Sun Y (2016) Full-view area coverage in camera sensor networks: dimension reduction and near-optimal solutions[J]. IEEE Trans Veh Technol 65(9):7448–7461

    Article  Google Scholar 

  32. Zhang Y, Sun X, Wang B (2016) Efficient algorithm for K-Barrier coverage based on integer linear programming[J]. China Commun 13(7):16–23

    Article  Google Scholar 

  33. Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks[J]. J Internet Technol 16(1):171–178

    Google Scholar 

  34. Liu Y, Liu A, Hu Y, Li Z, Choi Y-J (2016) FFSC: an energy efficiency communications approach for delay minimizing in internet of things[J]. IEEE Access 4:3775–3793

    Google Scholar 

Download references

Acknowledgments

The subject is sponsored by the National Natural Science Foundation of P. R. China (61373017, 61373138, 61672297), Jiangsu Natural Science Foundation for Excellent Young Scholar (BK20160089), Open Project of Provincial Key Laboratory for Computer Information Processing Technology of Soochow University (KJS1327), Open Project of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201517), A Project Funded by the Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).

Author contributions

Chao Sha proposed the main ideas of the DGFP algorithm while Jian-mei Qiu designed and conducted the simulations of the protocol. Meng-ye Qiang and Shu-yan Li analyzed the data, results and verified the theory. Ru-chuan Wang served as advisor to the above authors and gave suggestions on simulations, performance evaluation and writing. The manuscript write up was a combined effort from the five Authors.

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Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, 157# No.66 Xin Mofan Road, Nanjing, Jiangsu, China, 210003

    Chao Sha, Jian-mei Qiu, Shu-yan Li, Meng-ye Qiang & Ru-chuan Wang

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China, 215006

    Chao Sha & Ru-chuan Wang

  3. Jiangsu high Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu, China, 210003

    Chao Sha & Ru-chuan Wang

  4. Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China, 210044

    Chao Sha

  5. School of Engineering and Computation, New York Institute of Technology, New York, NY, 11568-8000, USA

    Shu-yan Li & Meng-ye Qiang

Authors
  1. Chao Sha
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  2. Jian-mei Qiu
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  3. Shu-yan Li
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  4. Meng-ye Qiang
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  5. Ru-chuan Wang
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Correspondence to Chao Sha.

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Sha, C., Qiu, Jm., Li, Sy. et al. A type of energy-efficient data gathering method based on single sink moving along fixed points. Peer-to-Peer Netw. Appl. 11, 361–379 (2018). https://doi.org/10.1007/s12083-016-0534-4

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  • DOI: https://doi.org/10.1007/s12083-016-0534-4

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