Abstract
Sensors deployment has played an important role in many engineering applications, and the key goal is aimed at achieving an optimal surveillance region with a set of sensors. In this paper, a probabilistic strategy was chosen as the sensing model and a Gaussian probability distribution was employed, furthermore an accumulative probability for all the utilized sensors was presented and an optimal deployment on meshed planar grid was proposed. It was proved that the deployment problem was NP-complete, and an approach for approximating this solution should be resorted to intelligent methods. Particle swarm optimization (PSO) was a widely used artificial intelligent tool, and hereby an improved discrete PSO (DPSO) was proposed for solving the deployment problem, and which was based on integer coding, and the initialization, positions and velocities updating were distinct with the traditional PSO. In final, the deployment was investigated respectively by using uniform sensors (binary coding problem) and combinational sensors (multivariate integer coding problem), which were indicated to the core structure of proposed DPSO.
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References
Ahmed N, Kanhere SS, Jha S (2005) Probabilistic coverage in wireless sensor networks [C] Local Comput Netw 8:681
Akbarzadeh V, Gagné C, Parizeau M et al (2013) Probabilistic sensing model for sensor placement optimization based on line-of-sight coverage [J]. Instrum Meas 62(2):293–303
Akyildiz IF, Su W, Sankarasubramaniam Y et al (2002) A survey on sensor networks [J]. Commun Mag IEEE 40(8):102–114
Albayrak M, Allahverdi N (2011) Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms [J]. Expert Syst Appl 38(3):1313–1320
Ammari HM, Das SK (2012) Centralized and clustered k-coverage protocols for wireless sensor networks [J]. Comput IEEE Trans 61(1):118–133
Antunes P, Lima H, Varum H et al (2012) Optical fiber sensors for static and dynamic health monitoring of civil engineering infrastructures: abode wall case study [J]. Measurement 45(7):1695–1705
Caetano E, Silva S, Bateira J (2011) A vision system for vibration monitoring of civil engineering structures [J]. Exp Tech 35(4):74–82
Cardei M, Wu J (2006) Energy-efficient coverage problems in wireless ad-hoc sensor networks [J]. Comput Commun 29(4):413–420
Carter B, Ragade R (2009) A probabilistic model for the deployment of sensors [C]. In: IEEE sensors applications symposium, IEEE, pp 7–12
Dingxing Z, Ming X, Yingwen C et al (2006) Probabilistic coverage configuration for wireless sensor networks [C]. In: Wireless communications, networking and mobile computing (WiCOM 2006), International conference on IEEE, pp 1–4
Du H, Ni Q, Pan Q et al (2014) An improved particle swarm optimization-based coverage control method for wireless sensor network [M]. In: Advances in swarm intelligence. Springer International Publishing, Berlin, pp 114–124
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory [C]. In: Proceedings of the sixth international symposium on micro machine and human science, vol 1, pp 39–43
Farshidianfar A, Saghafi A, Kalami SM et al (2012) Active vibration isolation of machinery and sensitive equipment using H ∞ control criterion and particle swarm optimization method [J]. Meccanica 47(2):437–453
Garey MR, Johnson DS (2002) Computers and intractability [M]. WH Freeman, New York
Gupta V, Chung TH, Hassibi B, et al (2006) On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage [J]. Automatica 42(2):251–260
Hefeeda M, Ahmadi H (2007) A probabilistic coverage protocol for wireless sensor networks [C]. In: IEEE international conference network protocols (ICNP), pp 41–50
Ke WC, Liu BH, Tsai MJ (2007) Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP-complete [J]. IEEE Trans Comput (5):710–715
Kennedy J, Kennedy JF, Eberhart RC et al (2001) Swarm intelligence [M]. Morgan Kaufmann, Burlington
Krose BJA, Bunschoten R (1999) Probabilistic localization by appearance models and active vision [C]. Robot Autom 3:2255–2260
Liu CL (1968) Introduction to combinatorial mathematics [M]. McGraw-Hill, Pennsylvania
Liu B, Towsley D (2004) A study of the coverage of large-scale sensor networks [C]. In: IEEE international conference on mobile ad-hoc and sensor systems, pp 475–483
Rapaić MR, Kanović Ž, Jeličić ZD (2008) Discrete particle swarm optimization algorithm for solving optimal sensor deployment problem [J]. J Autom Control 18(1):9–14
Rogers A, Farinelli A, Jennings NR (2010) Self-organising sensors for wide area surveillance using the max-sum algorithm [M]. In: Self-organizing architectures. Springer, Berlin, pp 84–100
Wang YC, Tseng YC (2008) Distributed deployment schemes for mobile wireless sensor networks to ensure multilevel coverage [J]. Parallel Distrib Syst 19(9):1280–1294
Wu Q, Rao NSV, Du X, et al (2007) On efficient deployment of sensors on planar grid [J]. Comput Commun 30(14):2721–2734
Yi TH, Li HN (2012) Methodology developments in sensor placement for health monitoring of civil infrastructures [J]. Int J Distrib Sens Netw. doi:10.1155/2012/612726
Zhang W, Wang G, Xing Z, et al (2005) Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks [J]. Artif Intell 161(1):55–87
Zhou Z, Das S, Gupta H (2004) Connected k-coverage problem in sensor networks [C]. In: Proceedings of the 13th international conference on computer communications and networks, IEEE, pp 373–378
Zou D, Gao L, Li S, et al (2011) Solving 0–1 knapsack problem by a novel global harmony search algorithm [J]. Appl Soft Comput 11(2):1556–1564
Acknowledgements
This research is completely supported by National Key Research and Development Program “Research on Vibration Control Technology for Established Industrial Building Structures”, which is sponsored by Ministry of Science and Technology of the P. R. China and the Grant No. is 2016YFC0701302; and it is also launched as preparation for the revising work of ‘Code for design of vibration isolation’ (national code of P. R. China). Team colleagues in China National Machinery Industry Corporation (SINOMACH) and Technology Research Center of Engineering Vibration Control (EVCC) in China IPPR International Engineering Co., Ltd (IPPR) are gratefully acknowledged.
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Jian, X., Tong-Yi, Z., Wei, H. et al. Improved discrete particle swarm optimization for solving the practical sensors deployment. Evolving Systems 8, 221–231 (2017). https://doi.org/10.1007/s12530-017-9184-x
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DOI: https://doi.org/10.1007/s12530-017-9184-x