Journal of Heuristics

, Volume 21, Issue 2, pp 233–256 | Cite as

Improved structures for data collection in static and mobile wireless sensor networks



In this paper we consider the problem of efficient data gathering in sensor networks for arbitrary sensor node deployments. The efficiency of the solution is measured by a number of criteria: total energy consumption, total transport capacity, latency and quality of the transmissions. We present a number of different constructions with various tradeoffs between aforementioned parameters. We provide theoretical performance analysis for our approaches, present their distributed implementation and discuss the different aspects of using each. We show that in many cases our output-sensitive approximation solution performs better than the currently known best results for sensor networks. We also consider our problem under the mobile sensor nodes environment, when the sensors have no information about each other. The only information a single sensor holds is its current location and future mobility plan. Our simulation results validate the theoretical findings.


Wireless sensor networks Optimization algorithms Approximation guarantees Data gathering 



The authors thank to Engineering and Physical Sciences Research Council (EPSRC), United Kingdom for providing support to the work on this paper and to the reviewers for their helpful comments.


  1. Andreae, T., Bandelt, H.-J.: Performance guarantees for approximation algorithms depending on parametrized triangle inequalities. SIAM J. Discret. Math. 8(1), 1–16 (1995)CrossRefMATHMathSciNetGoogle Scholar
  2. Awerbuch, B.: Optimal distributed algorithms for minimum weight spanning tree, counting, leader election, and related problems. ACM STOC 1987, 230–240 (1987)Google Scholar
  3. Basch, J.: Kinetic data structures. Ph.D. Dissertation, Stanford University (1999)Google Scholar
  4. Basch, J., Guibas, L.J., Hershberger, J.: Data structures for mobile data. In: SODA’97, pp. 747–756 (1997)Google Scholar
  5. Beier, R., Sanders, P., Sivadasan, N.: Energy optimal routing in radio networks using geometric data structures. In: ICALP’02, pp. 366–376 (2002)Google Scholar
  6. Ben-Shimol, Y., Dvir, A., Segal, M.: Splast: a novel approach for multicasting in mobile wireless ad hoc networks. In: IEEE PIMRC’04, pp. 1011–1015 (2004)Google Scholar
  7. Calinescu, G., Kapoor, S., Sarwat, M.: Bounded-hops power assignment in ad hoc wireless networks. Discret. Appl. Math. 154(9), 1358–1371 (2006)CrossRefMATHMathSciNetGoogle Scholar
  8. Chandrakasan, A., Amirtharajah, R., Cho, S., Goodman, J., Konduri, G., Kulik, J., Rabiner, W., Wang, A.: Design considerations for distributed microsensor systems. In: CICC’99, pp. 279–286 (1999)Google Scholar
  9. Chau, C.-K., Gibbens, R.J., Towsley, D.: Impact of directional transmission in large-scale multi-hop wireless ad hoc networks. In: INFOCOM, pp. 522–530 (2012)Google Scholar
  10. Clementi, A.E.F., Ferreira, A., Penna, P., Perennes, S., Silvestri, R.: The minimum range assignment problem on linear radio networks. In: ESA’00, pp. 143–154 (2000)Google Scholar
  11. Clementi, A.E.F., Penna, P., Silvestri, R.: On the power assignment problem in radio networks. Electron. Colloq. Comput. Complex. 7(054) (2000)Google Scholar
  12. Clementi, A.E.F., Penna, P., Silvestri, R.: The power range assignment problem in radio networks on the plane. In: STACS’00, pp. 651–660 (2000)Google Scholar
  13. Dolev, S., Segal, M., Shpungin, H.: Bounded-hop energy-efficient liveness of flocking swarms. IEEE Trans. Mob. Comput. 12(3), 516–528 (2013)CrossRefGoogle Scholar
  14. Dolev, S., Segal, M., Shpungin, H.: Bounded-hop energy-efficient liveness of flocking swarms. IEEE Trans. Mob. Comput. 12(3), 516–528 (2013)CrossRefGoogle Scholar
  15. Elkin, M., Lando, Y., Nutov, Z., Segal, M., Shpungin, H.: Novel algorithms for the network lifetime problem in wireless settings. ACM Wirel. Netw. 17(2), 397–410 (2011)CrossRefGoogle Scholar
  16. Funke, S., Soren Laue, S.: Bounded-hop energy-efficient broadcast in low-dimensional metrics via coresets. In: STACS’07, vol. 4393, pp. 272–283 (2007)Google Scholar
  17. Gao, J., Guibas, L., Hersheberger, J., Zhang, L., Zhu, A.: Geometric spanner for routing in mobile networks. In: ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc01), pp. 45–55 (2001)Google Scholar
  18. Gao, J., Guibas, L.J., Nguyen, A.: Deformable spanners and applications. Comput. Geom. Theory Appl. 35(1), 2–19 (2006)CrossRefMATHMathSciNetGoogle Scholar
  19. Gupta, P., Kumar, P.R.: The capacity of wireless networks. IEEE Trans. Inf. Theory 46(2), 388–404 (2000)CrossRefMATHMathSciNetGoogle Scholar
  20. Halldórsson, M.M., Mitra, P.: Distributed connectivity of wireless networks. In: PODC, pp. 205–214 (2012)Google Scholar
  21. Hassin, Y., Peleg, D.: Sparse communication networks and efficient routing in the plane. Distrib. Comput. 14(4), 205–215 (2001)CrossRefGoogle Scholar
  22. Huang, H., Richa, A.W., Segal, M.: Approximation algorithms for the mobile piercing set problem with applications to clustering in ad-hoc networks. MONET 9(2), 151–161 (2004)Google Scholar
  23. Huang, H., Richa, A.W., Segal, M.: Dynamic coverage in ad-hoc sensor networks. Mobile Netw. Appl. 10(1–2), 9–17 (2005)CrossRefGoogle Scholar
  24. Ji, S., Beyah, R.A., Li, Y.: Continuous data collection capacity of wireless sensor networks under physical interference model. In: IEEE MASS’11, pp. 222–231 (2011)Google Scholar
  25. Jovicic, A., Viswanath, P., Kulkarni, S.R.: Upper bounds to transport capacity of wireless networks. IEEE Trans. Inf. Theory 50(11), 2555–2565 (2004)CrossRefMATHMathSciNetGoogle Scholar
  26. Khuller, S., Raghavachari, B., Young, N.: Balancing minimum spanning and shortest path trees. In: SODA’93, pp. 243–250 (1993)Google Scholar
  27. Kirousis, L.M., Kranakis, E., Krizanc, D., Pelc, A.: Power consumption in packet radio networks. Theoret. Comput. Sci. 243(1–2), 289–305 (2000)CrossRefMATHMathSciNetGoogle Scholar
  28. Kothapalli, K., Scheideler, C., Onus, M., Richa, A.W.: Constant density spanners for wireless ad-hoc networks. In: SPAA’05, pp. 116–125 (2005)Google Scholar
  29. Ramanan, K., Baburaj, E.: Data gathering algorithms for wireless sensor networks: a survey. In: IJASUC, 1 (2010)Google Scholar
  30. Li, X., Yan, S., Xu, C., Nayak, A., Stojmenovic, I.: Localized delay-bounded and energy-efficient data aggregation in wireless sensor and actor networks. Wirel. Commun. Mobile Comput. 11(12), 1603–1617 (2011)CrossRefGoogle Scholar
  31. Li, X.-Y., Wang, Y., Wang, Y.: Complexity of data collection, aggregation, and selection for wireless sensor networks. IEEE Trans. Comput. 60(3), 386–399 (2011)CrossRefMathSciNetGoogle Scholar
  32. Li, Y., Thai, M.T., Wang, F., Du, D.-Z.: On the construction of a strongly connected broadcast arborescence with bounded transmission delay. IEEE Trans. Mob. Comput. 5(10), 1460–1470 (2006)CrossRefGoogle Scholar
  33. Milyeykovsky, V., Segal, M., Shpungin, H.: Location, location, location: Using central nodes for efficient data collection in wsns. In: IEEE WIOPT (2013)Google Scholar
  34. Monma, C.L., Suri, S.: Transitions in geometric minimum spanning trees. Discret. Comput. Geom. 8, 265–293 (1992)CrossRefMATHMathSciNetGoogle Scholar
  35. Pahlavan, K., Levesque, A.H.: Wireless information networks. Wiley-Interscience, London (1995)Google Scholar
  36. Rajendran, V., Obraczka, K., Garcia-Luna-Aceves, J.J.: Energy-efficient collision-free medium access control for wireless sensor networks. In: SenSys’03, pp. 181–192 (2003)Google Scholar
  37. Redmond, C., Yukich, J.: Asymptotics for euclidean functionals with power-weighted edges. Stochast. Process. Appl. 61(2), 289–304 (1996)CrossRefMATHMathSciNetGoogle Scholar
  38. Segal, M., Shpungin, H.: Improved multi-criteria spanners for ad-hoc networks under energy and distance metrics. In: IEEE INFOCOM’10, pp. 6–10. Also in ACM Trans. Sensor Netw. (to appear) (2013)Google Scholar
  39. Segal, M., Shpungin, H.: On construction of minimum energy \(k\)-fault resistant topology. Ad Hoc Netw. 7(2), 363–373 (2009)CrossRefGoogle Scholar
  40. Shpungin, H., Segal, M.: Low-energy fault-tolerant bounded-hop broadcast in wireless networks. IEEE/ACM Trans. Netw. 17(2), 582–590 (2009)CrossRefGoogle Scholar
  41. Shpungin, H., Segal, M.: Near optimal multicriteria spanner constructions in wireless ad-hoc networks. IEEE/ACM Trans. Netw. 18(6), 1963–1976 (2010)CrossRefGoogle Scholar
  42. Steele, J.M.: Probability and problems in euclidean combinatorial optimization. Stat. Sci. 8(1), 48–56 (1993)CrossRefGoogle Scholar
  43. van Hoesel, L., Havinga, P.: A lightweight medium access protocol (lmac) for wireless sensor networks: reducing preamble transmissions and transceiver state switches. In: INSS’04, pp. 205–208 (2004)Google Scholar
  44. Wan, P.-J., Calinescu, G., Li, X., Frieder, O.: Minimum-energy broadcast routing in static ad hoc wireless networks. In: INFOCOM’01, pp. 1162–1171 (2001)Google Scholar
  45. Xie, L.-L., Kumar, P.R.: A network information theory for wireless communication: scaling laws and optimal operation. IEEE Trans. Inf. Theory 50(5), 748–767 (2004)CrossRefMATHMathSciNetGoogle Scholar
  46. Xie, L.-L., Kumar, P.R.: On the path-loss attenuation regime for positive cost and linear scaling of transport capacity in wireless networks. IEEE Trans. Inf. Theory 52(6), 2313–2328 (2006)CrossRefMATHMathSciNetGoogle Scholar
  47. Xue, F., Xie, L.-L., Kumar, P.R.: The transport capacity of wireless networks over fading channels. IEEE Trans. Inf. Theory 51(3), 834–0847 (2005)CrossRefMATHMathSciNetGoogle Scholar
  48. Yun, Y., Xia, Y., Behdani, B., Smith, J.C.: Distributed algorithm for lifetime maximization in a delay-tolerant wireless sensor network with a mobile sink. IEEE Trans. Mob. Comput. 12(10), 1920–1930 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Computer LaboratoryCambridge UniversityCambridgeUK
  2. 2.Department of Communication Systems EngineeringBen-Gurion University of the NegevBeershebaIsreal

Personalised recommendations