Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

A joint optimization of data ferry trajectories and communication powers of ground sensors for long-term environmental monitoring

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

Abstract

Recently, various hybrid wireless sensor networks which consist of several robotic vehicles and a number of static ground sensors have been investigated. In this kind of system, the main role of the mobile nodes is to deliver the messages produced by the sensor nodes, and naturally their trajectory control becomes a significant issue closely related to the performance of the entire system. Previously, several communication power control strategies such as topology control are investigated to improve energy-efficiency of wireless sensor networks. However, to the best of our knowledge, no communication power control strategy has been investigated in the context of the hybrid wireless sensor networks. This paper introduces a new strategy to utilize the communication power control in multiple data ferry assisted wireless sensor network for long-term environmental monitoring such that the lifetime of the sensor network is maximized. We formally define the problem of our interest and show it is NP-hard. We further prove there exists no approximation algorithm for the problem which can produce a feasible solution for every possible problem instance even though there is a feasible solution. Then, we propose heuristic algorithms along with rigorous theoretical performance analysis for both the single data ferry case and the multiple data ferry case under certain condition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Anastasi G, Conti M, Francesco MD (2009) Reliable and energy-efficient data collection in sparse sensor networks with mobile elements. J Perform Eval 66:791–810

    Article  Google Scholar 

  • Anastasi G, Conti M, Francesco MD, Passaarella A (2009) Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw 7:537–568

    Article  Google Scholar 

  • Boloni L, Turgut D (2008) Should I send now or send later? A decision-theoretic approach to transmission scheduling in sensor networks with mobile sinks. Wirel Commun Mobile Comput 8(3):385–403

    Article  Google Scholar 

  • Cardei M, Thai MT, Li Y, Wu W (2005) Energy-efficient target coverage in wireless sensor networks. In: Proceedings of the 24st IEEE international conference on computer communications (INFOCOM 2005), Miami, FL, March 13–17

  • Christofides N (1976) Worst-case Analysis of a New Heuristic for the Travelling Salesman Problem, Report 388. Graduate School of Industrial Administration, CMU

  • Ciullo D, Celik GD, Modiano E (2010) Minimizing transmission energy in sensor networks via trajectory control. In: Proceedings of the 8th international symposium on modeling and optimization in mobile, ad hoc and wireless networks (WiOpt), pp 132–141

  • CliffsNotes.com, Mean Value Theorem, June 26, 2013. http://www.cliffsnotes.com/math/calculus/calculus/applications-of-the-derivative/mean-value-theorem

  • Dumitrescua A, Mitchell JSB (2003) Approximation algorithms for TSP with neighborhoods in the plane. J Algorithms 48(1):135–159

    Article  MathSciNet  Google Scholar 

  • Dumitrescu A, Mitchell JSB (2001) Approximation Algorithms for TSP with Neighborhoods in the Plane. In: Proceedings of the 12th annual ACM-SIAM symposium on discrete algorithms (SODA)

  • Even G, Garg N, Konemann J, Ravi R, Sinha A (2004) Min–max tree covers of graphs. Oper Res Lett 32(4):309–315

    Article  MathSciNet  MATH  Google Scholar 

  • Frederickson GN, Hecht MS, Kim CE (1978) Approximation algorithms for some routing problems. SIAM J Comput 7:178–193

    Article  MathSciNet  Google Scholar 

  • Henkel D, Brown TX (2008) Towards autonomous data ferry route design through reinforcement learning. In: Proceedings of the 2008 international symposium on a world of wireless, mobile and multimedia networks (WOWMOM), pp 1–6

  • Jenkins A, Henkel D, Brown T (2007) Sensor data collection through gateways in a highly mobile mesh network. In: Proceedings of IEEE wireless communications and networking conference (WCNC)

  • Jun H, Zhao W, Ammar MH, Zeura EW, Lee C (2007) Trading latency for energy in densely deployed wirless adhoc networks using message ferrying. Ad Hoc Netw 5:441–461

    Article  Google Scholar 

  • Kim D, Uma RN, Abay BH, Wu W, Wang W, Tokuta AO (2014) Minimum latency multiple data MULE trajectory planning in wireless sensor networks. IEEE Trans Mobile Comput (TMC) 13(4):838–851

    Article  Google Scholar 

  • Kim D, Abay BH, Uma RN, Wu W, Wang W, Tokuta AO (March 2012) Minimizing data collection latency in wireless sensor network with multiple mobile elements. In: Proceedings of the 31st IEEE international conference on computer communications (INFOCOM 2012), pp 504–512

  • Li Y, Cheng MX, Wu W (2005) Optimal topology control for balanced energy consumption in ad hoc wireless networks. J Parallel Distrib Comput (JPDC) 65(2):124–131

    Article  Google Scholar 

  • Li Y, Guo L, Prasad S (2010) An energy-efficient distributed algorithm for minimum-latency aggregation scheduling in wireless sensor networks. In Proceedings of the 30th international conference on distributed computing systems (ICDCS 2010), Genoa, Italy, June 21–25

  • Ma M, Yang Y (2007) SenCar: an energy-efficeint data gathering mechanism for large-scale multihop sensor networks. IEEE Trans Parallel Distrib Syst (TPDS) 18(10):1476–1488

    Article  MathSciNet  Google Scholar 

  • Mitchell JSB (2007) A PTAS for TSP with neighborhoods among fat regions in the plane. In Proceedings of the 18th annual ACM-SIAM symposium on discrete algorithms (SODA), pp 11–18

  • Mitchell JSB (2010) A constant-factor approximation algorithm for TSP with pairwise-disjoint connected neighborhoods in the plane. In: Proceedings of the annual symposium on computational geometry (SoCG)

  • Muskett RR, Romanovsky VE (2011) Alaskan permafrost groundwater storage changes derived from grace and ground measurements. Remote Sens 3(2):378–397

    Article  Google Scholar 

  • Papadimitriou CH (1977) The euclidean traveling salesman problem is NP-complete. Theor Comput Sci (TCS) 4(3):237–244

    Article  MathSciNet  MATH  Google Scholar 

  • Pearre B, Brown TX (2012) Model-free trajectory optimisation for unmanned aircraft serving as data ferries for widespread sensors. Remote Sens 4:2971–3000

    Article  Google Scholar 

  • Pearre B, Brown TX (2010) Model-free trajectory optimization for wireless data ferries among multiple sources. In: IEEE globecom 2010 workshop on wireless networking for unmanned aerial vehicles (Wi-UAV 2010)

  • Pearre B, Brown TX (2011) Fast, scalable, model-free trajectory optimization for wireless data ferries. In: Proceedings of IEEE international conference on computer communications and networks (ICCCN), pp 370–377

  • Pearre B, Brown TX (2012) Energy conservation in sensor network data ferrying: a reinforcement metalearning approach. In: Proceedings of the IEEE global communications conference (GLOBECOM 2012), December 3–7

  • Perovich DK, Grenfell TC, Richter-Menge JA, Light B, Tucker WB III, Eicken H (2003) Thin and thinner: sea ice mass balance measurements during sheba. J Geophys Res 108(C3):26.1–26.21

    Article  Google Scholar 

  • Somasundara AA, Ramamoorthy A, Srivastava MB (2004) Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines. In: Proceedings of the 25th IEEE international real-time systems symposium (RTSS), pp 296–305

  • Somasundara AA, Kansal A, Jea DD, Estrin D, Srivastava MB (2006) Controllably mobile infrastructure for low energy embedded networks. IEEE Trans Mobile Comput 5(8):958–973

    Article  Google Scholar 

  • Somasundara AA, Ramamoorthy A, Srivastava MB (2007) Mobile element scheduling with dynamic deadlines. IEEE Trans Mobile Comput 6(4):395–410

    Article  Google Scholar 

  • Sugihara R, Gupta RK (2009) Optimizing energy-latency trade-off in sensor networks with controlled mobility. In: Proceedings of the 28st IEEE international conference on computer communications (INFOCOM 2009), pp 1476–1488

  • Tekdas O, Lim J, Terzis A, Isler V (2008) Using mobile robots to harvest data from sensor fields. IEEE Wirel Commun Spec Issue Wirel Commun Netw Robot 16:22–28

    Google Scholar 

  • Xue L, Kim D, Zhu Y, Li D, Wang W, Tokuta AO (2014) Multiple heterogeneous data ferry trajectory planning in wireless sensor networks. In: Proceedings of the 33rd IEEE international conference on computer communications (INFOCOM 2014), April 27, 2014–May 2, Toronto, Canada

  • Yang Y, Lin M, Xu J, Xie Y (2007) Minimum spanning tree with neighborhoods. In: Proceedings of the 3rd international conference on algorithmic aspects in information and management (AAIM ’07), Portland, OR, USA, June 6–8

  • Yuan B, Orlowska M, Sadiq S (2007) On the optimal robot routing problem in wireless sensor networks. IEEE Trans Knowl Data Eng (TKDE) 19(9):1252–1261

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported in part by US National Science Foundation (NSF) CREST No. HRD-1345219. It was also partly supported by National Natural Science Foundation of China 530 under Grant No. 11471005. This paper was jointly supported by National Natural Science Foundation of China under Grant 91124001, the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China 10XNJ032.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, D., Wang, W., Li, D. et al. A joint optimization of data ferry trajectories and communication powers of ground sensors for long-term environmental monitoring. J Comb Optim 31, 1550–1568 (2016). https://doi.org/10.1007/s10878-015-9840-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-015-9840-7

Keywords

Navigation