Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure.

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

Similar content being viewed by others

References

  1. Keynote, 2012 mobile user survey (2012).

  2. St-Hilaire, M. (2009). Topological planning and design of UMTS mobile networks: A survey. Wireless Communications and Mobile Computing, 9(7), 948.

    Article  Google Scholar 

  3. Deruyck, M., Vereecken, W., Joseph, W., Lannoo, B., Pickavet, M., & Martens, L. (2009). Reducing the power consumption in wireless access networks: Overview and recommendations. Progress in Electromagnetics Research, 132, 255.

    Article  Google Scholar 

  4. Valberg, P., van Deventer, T., & Repacholi, M. (2007). Workgroup report: Base stations and wireless networks-radio frequency (RF) exposures and health consequences. Environ Health Perspect, 2007, 416–424.

    Google Scholar 

  5. Russo, P., Cerri, G., & Vespasiani, V. (2010). A numerical coefficient for evaluation of the environmental impact of electromagnetic fields radiated by base stations for mobile communications. Bioelectromagnetics, 2010, 613–621.

    Article  Google Scholar 

  6. Joseph, W., Frei, P., Roosli, M., Thuroczy, G., Gajsek, P., Trcek, T., et al. (2010). Comparison of personal radio frequency electromagnetic field exposure in different urban areas across Europe. Environmental Research, 110, 658.

    Article  Google Scholar 

  7. Joseph, W., Verloock, L., Goeminne, F., Vermeeren, G., & Martens, L. (2010). Assessment of general public exposure to LTE and RF sources present in an urban environment, Bioelectromagnetics, 31, 576.

    Article  Google Scholar 

  8. Verloock, L., Joseph, W., Vermeeren, G., & Martens, L. (2010). Procedure for assessment of general public exposure from WLAN in offices and in wireless sensor network testbed. Health Physics 98, 628.

    Article  Google Scholar 

  9. Ran, M., & Ezra, Y. B. (2011). Green femtocell based on UWB technologies, novel applications of the UWB technologies, pp. 175–194.

  10. Unger, P., Schack, M., & Kurner, T. (2007). Minimizing the electromagnetic exposure using hybrid (DVB-H/UMTS) networks. Broadcasting, IEEE Transactions on, 53(1), 418.

    Article  Google Scholar 

  11. Plets, D., Joseph, W., Vanhecke, K., Tanghe, E., & Martens, L. (2012). Coverage prediction and optimization algorithms for indoor environments. EURASIP Journal on Wireless Communications and Networking, Special Issue on Radio Propagation, Channel Modeling, and Wireless, Channel Simulation Tools for Heterogeneous Networking Evaluation.

  12. Plets, D., Joseph, W., Vanhecke, K., Tanghe, E., & Martens, L. (2010). Development of an accurate tool for path loss and coverage prediction in indoor environments, in Antennas and Propagation (EuCAP). In Proceedings of the fourth European conference on, 2010, pp. 1–5.

  13. Liu, N., Plets, D., Joseph, W., & Martens, L. (2014). Hybrid multi-objective network planning optimization algorithm. In Proceedings of the European conference on the use of modern information and communication technologies, Vol. 302, pp. 73–85.

  14. Sotiroudis, S., Goudos, S., Gotsis, K., Siakavara, K., & Sahalos, J. (2013). Application of a composite differential evolution algorithm in optimal neural network design for propagation path-loss prediction in mobile communication systems. Antennas and Wireless Propagation Letters, IEEE, 12, 364.

    Article  Google Scholar 

  15. Vilovic, I., Burum, N., & Sipus, Z. (2007). Design of an indoor wireless network with neural prediction model. In Sntennas and propagation, 2007. EuCAP 2007. The second European conference on, 2007, pp. 1–5.

  16. Nagy, L. (2007), Indoor radio coverage optimization for WLAN, in 2nd European conference on antennas and propagation (EuCAP 2007).

  17. Vilovic, I., Burum, N., & Sipus, Z. (2009). Ant colony approach in optimization of base station position. In Antennas and propagation, 2009. EuCAP 2009. 3rd European conference on 2009, pp. 2882–2886.

  18. Storn, R., & Price, K. (1997). Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    Article  MATH  MathSciNet  Google Scholar 

  19. Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano 1992.

  20. Lee, J. W., Choi, B. S., & Lee, J. J. (2011). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. Industrial Informatics, IEEE Transactions on, 7(3), 419.

    Article  Google Scholar 

  21. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the fourth IEEE international conference on neural networks (Perth, Australia, 1995), pp. 1942–1948.

  22. Elkamchouchi, H., Elragal, H., & Makar, M. (2007). Cellular radio network planning using particle swarm optimization. In: Radio science conference, 2007. NRSC 2007. National, pp. 1–8.

  23. Bhattacharya, I., & Roy, U. K. (2010). Optimal placement of readers in an RFID network using particle swarm optimization. International Journal of Computer Networks & Communications, 2, 225.

    Article  Google Scholar 

  24. Chen, H., Zhu, Y., Hu, K., & Ku, T. (2011). RFID network planning using a multi-swarm optimizer. Journal of Network and Computer Applications, 34(3), 888.

    Article  Google Scholar 

  25. Holland, J. H. (1975). Adaptation in nature and artificial systems. Ann Arbo: The University of Michigan Press.

    Google Scholar 

  26. Jourdan, D., & de Weck, O. (2004). Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. In Vehicular technology conference, 2004. VTC 2004-Spring. 2004 IEEE 59th, Vol. 5, pp. 2466–2470.

  27. Yun, Z., Lim, S., & Iskander, M. (2008). An integrated method of ray tracing and genetic algorithm for optimizing coverage in indoor wireless networks. Antennas and Wireless Propagation Letters, IEEE, 7, 145.

    Article  Google Scholar 

  28. Koutitas, G., & Samaras, T. (2010). Exposure minimization in indoor wireless networks. Antennas and Wireless Propagation Letters, IEEE, 9, 199.

    Article  Google Scholar 

  29. Cerri, G., De Leo, R., Micheli, D., & Russo, P. (2004). Base-station network planning including environmental impact control, Communications. IEE Proceedings-2004, 151(3), 197.

  30. Develi, I., & Yazlik, E. N. (2012). Optimum antenna configuration in MIMO systems: A differential evolution based approach. Wireless Communications and Mobile Computing, 12(6), 473.

    Article  Google Scholar 

  31. Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. Evolutionary Computation, IEEE Transactions on, 10(6), 646.

    Article  Google Scholar 

  32. Qin, A. K., & Suganthan, P. (2005), Self-adaptive differential evolution algorithm for numerical optimization. In Evolutionary computation, 2005. The 2005 IEEE congress on 2005, Vol. 2, pp. 1785–1791.

  33. Goudos, S. K., Zaharias, D. Z., & Traianos, V. Y. (2010). Application of a differential evolution algorithm with strategy adaptation to the design of multi-band microwave filters for wireless communications. Progress in Electromagnetics Research, 109, 123.

    Article  Google Scholar 

  34. Goudos, S. K., Siakavara, K., Samaras, T., Vafiadis, E., & Sahalos, J. (2011). Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation. Antennas and Wireless Propagation Letters, IEEE, 10, 670.

    Article  Google Scholar 

  35. Wang, Y., Cai, Z., & Zhang, Q. (2011). Differential evolution with composite trial vector generation strategies and control parameters. Evolutionary Computation, IEEE Transactions on, 15(1), 55.

    Article  MathSciNet  Google Scholar 

  36. Tanghe, E., Joseph, W., Verloock, L., & Martens, L. (2008). The industrial indoor channel: Large-scale and temporal fading at 900, 2400, and 5200 MHz. Wireless Communications, IEEE Transactions on, 7(7), 2740.

    Article  Google Scholar 

  37. Saunders, S. R. (1999). Antennas and propagation for wireless communication systems. New York: Wiley.

    Google Scholar 

  38. Plets, D., Pakparvar, M., Joseph, W., & Martens, L. (2013). Influence of intra-network interference on quality of service in wireless LANs. In Broadband multimedia systems and broadcasting (BMSB), 2013 IEEE international symposium on 2013, pp. 1–5.

  39. Deruyck, M., Vereecken, W., Joseph, W., Lannoo, B., Pickavet, M., & Martens, L. (2012). Reducing the power consumption in wireless access networks: Overview and recommendations. Progress in Electromagnetics Research, 132, 255.

    Article  Google Scholar 

  40. Deruyck, M., Joseph, W., Lannoo, B., Colle, D., & Martens, L. (2013). Designing energy-efficient wireless access networks: LTE and LTE-advanced. Internet Computing, IEEE, 17(5), 39.

    Article  Google Scholar 

  41. Erceg, V., & Schumacher, L., et al. (2004), IEEE P802. 11 wireless LANs, TGn channel models, doc.: IEEE 2004.

  42. Bultitude, R. J. C. (1987). Measurement, characterization and modeling of indoor 800/900 MHz radio channels for digital communications. Communications Magazine, IEEE, 25(6), 5.

    Article  Google Scholar 

  43. Plets, D., Joseph, W., Vanhecke, K., Tanghe, E., & Martens, L. (2013). Simple indoor path loss prediction algorithm and validation in living lab setting. Wireless Personal Communications, 68(3), 535.

    Article  Google Scholar 

  44. Leung, K., Clark, M., McNair, B., Kostic, Z., Cimini, L., & Winters, J. (2007). Outdoor IEEE 802.11 cellular networks: Radio and MAC design and their performance. Vehicular Technology, IEEE Transactions on, 56(5), 2673.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the IWT-SBO SymbioNets Project.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Ning Liu

  2. Department of Information Technology, WiCa, Ghent University/iMinds, Gaston Crommenlaan 8, Box 201, 9050, Ghent, Belgium

    Ning Liu, David Plets, Luc Martens & Wout Joseph

  3. Radiocommunications Laboratory, Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

    Sotirios K. Goudos

Authors
  1. Ning Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Plets
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sotirios K. Goudos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luc Martens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wout Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ning Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, N., Plets, D., Goudos, S.K. et al. Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity. Wireless Netw 21, 841–857 (2015). https://doi.org/10.1007/s11276-014-0822-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-014-0822-y

Keywords

Search

Navigation