Skip to main content

Advertisement

SpringerLink
Log in

An Energy Efficient Middleware Architecture for Processing Spatial Alarms on Mobile Clients

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Time based alarms are used by many on a daily basis. Spatial alarms extend the very same idea to location based triggers, which are fired whenever a mobile user enters the spatial region of the location alarms. Spatial alarms provide critical capabilities for many mobile location based applications ranging from personal assistants, inventory tracking to industrial safety warning systems. In this paper we present a middleware architecture for energy efficient processing of spatial alarms on mobile clients, while maintaining low computation and storage costs. Our approach to spatial alarms provides two systematic methods for minimizing energy consumption on mobile clients. First, we introduce the concept of safe distance to reduce the number of unnecessary mobile client wakeups for spatial alarm evaluation, enabling mobile clients to sleep for longer intervals of time in the presence of active spatial alarms. We show that our safe distance techniques can significantly minimize the energy consumption on mobile clients compared to periodic wakeups while preserving the accuracy and timeliness of spatial alarms. Second, we develop a suite of techniques for minimizing the number of location triggers to be checked for spatial alarm evaluation upon each wakeup. This further reduces the computation cost and energy expenditure on mobile clients. We evaluate the scalability and energy-efficiency of our approach using a road network simulator. Our spatial alarms middleware architecture offers significant improvements on battery lifetime of mobile clients, while maintaining high quality of spatial alarm services, especially compared to the conventional approach of periodic wakeup and checking all alarms upon wakeup.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. US Census Bureau, Tiger, tiger/line and tiger-related products. Available at http://www.census.gov/geo/www/tiger/

  2. Aurenhammer F (1991) Voronoi diagrams a survey of a fundamental geometric data structure. ACM Comput Surv 23(3):345–405

    Article  Google Scholar 

  3. Bartlett JF, Brakmo LS, Farkas KI, Hamburgen WR, Mann T, Viredaz MA, Waldspurger CA, Wallach DA (2000) The itsy pocket computer. Technical Report 2000/6, COMPAQ Western Research Laboratory

  4. Bazinette V, Cohen NH, Ebling MR, Hunt GDH, Lei H, Purakayastha A, Stewart G, Wong L, Yeh DL (2001) An intelligent notification system. IBM research report

  5. Cherkassky BV, Goldberg AV, Radzik T (1993) Shortest paths algorithms: theory and experimental evaluation. Math Program 73:129–174

    MathSciNet  Google Scholar 

  6. Dey AK, Abowd GD (2000) Cybreminder: a context-aware system for supporting reminders. In: Handheld and ubiquitous computing: second international symposium, HUC 2000, Bristol, UK proceedings

  7. Dijkstra EW (1959) A note on two problems in connection with graphs. Numerische Math 1:269–271

    Article  MATH  MathSciNet  Google Scholar 

  8. Erwig M (2000) The graph voronoi diagram with applications. In: Networks, pp 156–163

  9. Flautner K, Mudge T (2002) Vertigo: automatic performance-setting for linux. SIGOPS Oper Syst Rev 36(SI):105–116

    Article  Google Scholar 

  10. Flinn J, Satyanarayanan M (1999) Energy-aware adaptation for mobile applications. In: SOSP ’99: proceedings of the seventeenth ACM symposium on operating systems principles. ACM Press, New York, NY, USA, pp 48–63

    Chapter  Google Scholar 

  11. Fortune S (1997) Voronoi diagrams and delaunay triangulations. In: Goodman JE and OŠRourke J (eds) Handbook of discrete and computational geometry. CRC, Boca Raton, pp 377–388

    Google Scholar 

  12. Graf M, Winter S (2003) Netzwerk-voronoi-diagramme. Österr Z Vermess Geoinf 91:166–174

    Google Scholar 

  13. Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: SIGMOD ’84: proceedings of the 1984 ACM SIGMOD international conference on management of data. ACM, New York, pp 47–57

    Chapter  Google Scholar 

  14. Heidemann J, Shah D (2000) Location-aware scheduling with minimal infrastructure. In: USENIX conference proceedings. USC/Information Sciences Institute, USENIX, San Diego, pp 131–138

    Google Scholar 

  15. Kim SW, Kim MC, Park SH, Jin YK, Choi WS (2004) Gate reminder: a design case of a smart reminder. In: DIS ’04: Proceedings of the 2004 conference on designing interactive systems. ACM, New York, pp 81–90

    Chapter  Google Scholar 

  16. Ludford PJ, Frankowski D, Reily K, Wilms K, Terveen L (2006) Because i carry my cell phone anyway: functional location-based reminder applications. In: CHI ’06: proceedings of the SIGCHI conference on human factors i computing systems. ACM, New York, pp 889–898

    Chapter  Google Scholar 

  17. Mokbel MF, Xiong X, Aref WG (2004) Sina: scalable incremental processing of continuous queries in spatio-temporal databases. In: In SIGMOD, pp 623–634

  18. Mudge T (2001) Power: a first-class architectural design constraint. In: Computer. Michigan Univ., Ann Arbor, pp 52–58

  19. Prabhakar S, Xia Y, Kalashnikov DV, Aref WG, Hambrusch SE (2002) Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects. IEEE Trans Comput 51(10):1124–1140

    Article  MathSciNet  Google Scholar 

  20. Sadeh NM, Gandon FL, Kwon OB (2005) Ambient intelligence: the mycampus experience. In: CMU-ISRI-05-123

  21. Sohn T, Li KA, Lee G, Smith I, Scott J, Griswold WG (2005) Place-its: a study of location-based reminders on mobile phones. In: UbiComp 2005: ubiquitous computing

  22. Zhang J, Zhu M, Papadias D, Tao Y, Lee DL (2003) Location-based spatial queries. In: In SIGMOD, pp 443– 454

Download references

Acknowledgements

This work is partially supported by grants from NSF CISE under CNS, NetSE, and CyberTrust, a grant from AFOSR, an IBM SUR grant, and an IBM faculty award, and an Intel research council grant. We would also like to thank Ramesh Chandran, Kipp Jones, Calton Pu, and the mobile computing group within the DiSL lab for the valuable discussions and help with the experiments.

Author information

Authors and Affiliations

  1. College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Anand Murugappan & Ling Liu

Authors
  1. Anand Murugappan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ling Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand Murugappan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Murugappan, A., Liu, L. An Energy Efficient Middleware Architecture for Processing Spatial Alarms on Mobile Clients. Mobile Netw Appl 15, 543–561 (2010). https://doi.org/10.1007/s11036-010-0221-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-010-0221-2

Keywords

Search

Navigation