Abstract
Proper sensor placement is crucial for maximizing the usability of large-scale sensor networks. Specially, the total sensible area covered by a sensor network can be maximized if we optimally arrange all sensors. To address this coverage optimization problem, this paper studies a typical sensor network—camera network. In this network, both locations and orientations of the cameras can be adjusted. An interesting constraint is the moving distance limitation. It transforms the optimization into a constrained problem. To tackle this problem, we investigate as possible solutions three variations of the particle swarm optimization (PSO) algorithm, namely the absorbing PSO, the penalty PSO, and the reflecting PSO. They are tested against several benchmarks. The experiments show that the PSO can be effectively applied on optimizing the coverage of the constrained camera network. And it can be easily adapted for coverage optimization of general sensor networks. The statistical analysis shows that the performances of the above three algorithms are in descending order. The results further prove that the absorbing PSO is an optimal choice for improving the coverage of the aforementioned sensor network.
Similar content being viewed by others
References
AlRashidi MR (2009) A survey of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13(4):913–918
Carlisle A, Doizier G (2001) An off- the-shelf PSO. In: Proceedings of the workshop particle swarm optimization, Indianapolis, IN
Clerc M, Kennedy J (2002) The particle system—exploration, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):53–58
Eberhart RC, Shi Y (1998) Evolving artificial neural networks. In: Proceedings of the international conference neural networks and brain, Beijing, People’s Republic of China
Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the CEC01, 2001, vol 1, pp 81–86
Erdem UM, Sclaroff S (2006) Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements. Comput Vis Image Underst 103(3):156–169
Hörster E, Lienhart R (2009) Optimal placement of multiple visual sensors. In: Agahajan H, Cavallaro A (eds) Multi-camera networks-principles and application, Elsevier, Burlington, MA, 2009, pp 117–138
Hsieha Y-C, Lee Y-C, You P-S, Chen T-C (2009) An immune based two-phase approach for the multiple-type surveillance camera location problem. Expert Syst Appl 36(7):10634–10639
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948
Khodier MM, Al-Aqeel M (2009) Linear and circular array optimization: a study using particle swarm intelligence. Prog Electromagn Res B 15:347–373. doi:10.2528/PIERB09033101
Mendis C, Guru S, Halgamuge S, Fernando S (2006) Optimized sink node path using particle swarm optimization. In: Proceedings of the 20th international conference on advanced information networking and applications, pp 388–394
Mittal A, Davis LS (2008) A general method for sensor planning in multi-sensor systems: extension to random occlusion. Int J Comput Vision 76(1):31–52
Noel M, Joshi P, Jannett T (2006) Improved maximum likelihood estimation of target position in wire-less sensor networks using particle swarm optimization. In: Proceedings of the 3rd international conference on information technology: new generations, pp 274–279
Park J, Choi K, Allstot DJ (2003) Parasitic-aware design and optimization of a fully integrated CMOS wideband amplifier. In: Proceedings of the ASP-DAC, pp 904–907
Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problems. In: Sincak P et al. (eds) Intelligent technologies—theory and application: new trends in intelligent technologies, vol 76 of Frontiers in artificial intelligence and applications, IOS Press, Amsterdam, pp 214–220
Poli R (2008) Analysis of the publications on the applications of particle swarm optimization. J Artif Evol Appl. doi:10.1155/2008/685175
Pompili D, Melodia T, Akyildiz IF (2006) Deployment analysis in underwater acoustic wireless sensor networks. In: Proceedings of the 1st ACM international workshop on underwater networks, Los Angeles, CA, USA, 2006, pp 48–55
Pulido GT, Coello CAC (2004) A constraint-handling mechanism for particle swarm optimization. In: Proceedings of the CEC04, Portland, OR, USA, 2004, vol 2, pp 1396–1403
Robinson J, Rahmat-Samii Y (2004) Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag 52(2):397–407
Sugisaka M, Fan X (2004) An effective search method for nn-based face detection using PSO. In: Proceedings of the SICE 04, 2004, vol 3, pp 2742–2745
Tao D (2007) Research on coverage control and cooperative processing method for video sensor networks. Doctoral dissertation, Beijing University of Posts and Telecommunications, Beijing
Tao D, Ma H, Liu L (2007) A virtual potential field based coverage-enhancing algorithm for directional sensor networks. J Softw 18(5):1152–1163
Xiao RB, Xu YC, Amos M (2007) Two hybrid compaction algorithms for the layout optimization problem. BioSystems 90(2):560–567
Xu R, Anagnostopoulos GC, Wunsch DC (2007) Multiclass cancer classification using semisupervised ellipsoid artmap and particle swarm optimization with gene expression data. IEEE/ACM Trans Computat Biol Bioinforma 4(1):65–77
Xu Y, Lei B, Sun S, Dong F, Chai C (2010) Three particle swarm algorithms to improve coverage of camera networks with mobile nodes. In: Proceedings of the IEEE international conference on bio-inspired computing: theories and applications, 23–26 Sept 2010, Changsha, China
Xu Y-C, Lei B, Hendriks EA (2011) Camera network coverage improving by particle swarm optimization. EURASIP J Image Video Process 2011:10. doi:10.1155/2011/458283 (Article ID 458283)
Yang J-M, Chen Y-P, Homg J-T, Kao C-Y (1997) Applying family competition to evolution strategies for constrained optimization. Lect Notes Comput Sci 1213:201–211
Yuan Huang F, Jun Li R, Xia Liu H, Li R (2006) A modified particle swarm algorithm combined with fuzzy neural network with application to financial risk early warning. In: IEEE Asia–Pacific conference on services computing 2006, pp 168–173
Zhao J, Cheung SC, Nguyen T (2008) Optimal camera network configurations for visual tagging. IEEE J Sel Top Sign Process 2(4):464–479
Zou Y, Chakrabarty K (2004) Sensor deployment and target localization in distributed sensor networks. ACM Trans Embed Comput Syst 3(1):61–91
Acknowledgments
The project is supported by the National Natural Science Foundation of China (60972162, 61272237, 61102155), the Science Foundation of Hubei Provincial Department of Education (Q20101205), Natural Science Foundation of Hubei Province (2011CBD180), and the Science Funding of CTGU (KJ 2009B014).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by A.-A. Tantar.
Rights and permissions
About this article
Cite this article
Xu, YC., Lei, B. & Hendriks, E.A. Constrained particle swarm algorithms for optimizing coverage of large-scale camera networks with mobile nodes. Soft Comput 17, 1047–1057 (2013). https://doi.org/10.1007/s00500-012-0978-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-012-0978-2