Skip to main content

Advertisement

Log in

Strong minimum energy \(2\)-hop rooted topology for hierarchical wireless sensor networks

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

Abstract

Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the sum of the transmit powers of all the sensors is minimum subject to the constraint that the resulting topology containing only bidirectional links is strongly connected. This problem is known to be NP-hard. However, most of the wireless sensor networks are hierarchical in nature, where shorter path lengths are preferred for communication between any two nodes. Given a set of sensors and a specified sensor, say \(r\), the strong minimum energy \(2\)-hop rooted topology problem (\(2\)h-SMERT) is to assign transmit power to each sensor such that the sum of the transmit powers of all the sensors is minimum subject to the constraints that the resulting topology containing only bidirectional links is strongly connected and the length of the path from \(r\) to any other node is at most \(2\). We prove that the \(2\)h-SMERT problem is NP-hard. We also prove that \(2\)h-SMERT problem is APX-hard. We then show that \(2\)h-SMERT problem is not approximable within a factor of \(\frac{1}{2}(1-\epsilon ) \ln n\) unless NP \(\subseteq DTIME(n^{\log \log n})\). On the positive side, we propose a \(2(1+ \ln n)\)-approximation algorithm for the \(2\)h-SMERT problem. We also study two special cases of the \(2\)h-SMERT problem, namely, the \(d\)-rest-\(2\)h-SMERT problem and the \(k\)-int-\(2\)h-SMERT problem. We propose a \(2(1+ \ln d)\)-approximation algorithm for the \(d\)-rest-\(2\)h-SMERT problem. The \(k\)-int-\(2\)h-SMERT problem is NP-hard for arbitrary \(k\). However, for fixed constant \(k\), we propose a \(\frac{k+1}{2}\)-approximation algorithm for the \(k\)-int-\(2\)h-SMERT problem and obtain a polynomial time optimal algorithm for the \(k\)-int-\(2\)h-SMERT problem for \(k=2\).

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Alfandari L, Paschos VT (1999) Approximating minimum spanning tree of depth two. Int Trans Oper Res 6:607–622

    Article  MathSciNet  Google Scholar 

  • Althaus E, Funke S, Har-peled S, Ramos EA, Skutella M (2005) Approximating \(k\)-hop minimum spanning trees. Oper Res Lett 33:115–120

    Article  MATH  MathSciNet  Google Scholar 

  • Alimonti P, Kann V (1997) Hardness of approximating problems on cubic graphs, In: Proceedings of of 3rd Italian conference on algorithms and complexity, Rome, Lecturer notes in computer Science, vol 1203, pp 288–298

  • Ausiello G, Crescenzi P, Gambosi G, Kann V, Marchetti-Speccamela A, Protasi M (1999) Complexity and approximation. Springer, Berlin

    Book  MATH  Google Scholar 

  • Bilò D, Proietti G (2008) On the complexity of minimizing interference in ad-hoc and sensor networks. Theor Comput Sci 402:43–55

    Article  MATH  Google Scholar 

  • Calinescu G (2003) Wan P (2003) Range assignment for high connectivity in wireless adhoc networks, ADHOC-NOW 2003 LNCS, vol 2865, pp 235–246

  • Cheng X, Narahari B, Simha R, Cheng MX, Liu D (2003) Strong minimum energy topology in wireless sensor networks: NP-completeness and heuristics. IEEE Trans Mob Computing 2(3):248–256

    Article  Google Scholar 

  • Clementi AEF, Penna P, Silvestri R (1999) Hardness results for the power range assignment problem in packet radio networks. In: Proceedings of third international workshop on randomization and approximation in computer science (APPROX 1999), LNCS, vol 1671, pp 195–208, Springer, July 1999

  • Clementi AEF, Huiban G, Penna P, Rossi G, Verhoeven YC (2002) Some recent theoretical advances and open questions on energy consumption in ad-hoc wireless networks. In: Proceedings of 3rd workshop on approximation and randomization algorithms in communication networks (ARACNE), pp 23–38

  • Estrin D, Govindan R, Heidemann JS, Kumar S (1999) Next century challenges: scalable coordination in sensor networks. In: Mobile computing and networking, Seattle, WA, USA

  • Fuchs B (2005) On the hardness of range assignment problems. Electronic colloquium on complexity. Report no 113

  • Jia X, Kim D, Makki S, Wan PJ, Yi CW (2005) Power assignment for k-connectivity in wireless ad hoc networks. J Comb Optim 9(2):213–222

    Article  MATH  MathSciNet  Google Scholar 

  • Lloyd EL, Liu R, Marathe MV, Ramanathan R, Ravi SS (2005) Algorithmic aspects of topology control problems for ad hoc networks. Mob Netw Appl 10:19–34

    Article  Google Scholar 

  • Nutov Z (2008) Approximating minimum-power k-connectivity. In: Coudert D et al (eds) ADHOC-NOW 2008, LNCS, vol 5198, Springer, Berlin, pp 86–93

  • Pottie GJ, Kaiser WJ (2000) Wireless integrated network sensors. Commun ACM 43(5):51–58

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous referees for their helpful comments leading to improvements in the presentation of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. S. Panda.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Panda, B.S., Pushparaj Shetty, D. Strong minimum energy \(2\)-hop rooted topology for hierarchical wireless sensor networks. J Comb Optim 30, 1077–1094 (2015). https://doi.org/10.1007/s10878-013-9683-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-013-9683-z

Keywords

Navigation