Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

doi:10.1007/s40436-016-0135-8

Advances in Manufacturing ›› 2016, Vol. 4 ›› Issue (1): 66-78.doi: 10.1007/s40436-016-0135-8

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

Ling Wang^1,2, Lu An¹, Hao-Qi Ni¹, Wei Ye¹, Panos M. Pardalos², Min-Rui Fei¹

1 Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;
2 Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, Florida 32611, USA

收稿日期:2014-04-08 修回日期:2016-01-28 出版日期:2016-03-25 发布日期:2016-02-24
通讯作者: Ling Wang E-mail:wangling@shu.edu.cn
基金资助:
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61304031,61304143), the Innovation Program of Shanghai Municipal Education Commission (Grant No. 14YZ007), the Key Project of Science and Technology Commission of Shanghai Municipality (Grant Nos. 14JC1402200, 14DZ1206302), the Key Project of Shanghai Municipal Commission of Economy and Informatization, and the Research Foundation for the Doctoral Program of Higher Education of China (Grant No. 20103108120008).

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

Ling Wang^1,2, Lu An¹, Hao-Qi Ni¹, Wei Ye¹, Panos M. Pardalos², Min-Rui Fei¹

1 Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;
2 Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, Florida 32611, USA

Received:2014-04-08 Revised:2016-01-28 Online:2016-03-25 Published:2016-02-24
Contact: Ling Wang E-mail:wangling@shu.edu.cn
Supported by:
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61304031,61304143), the Innovation Program of Shanghai Municipal Education Commission (Grant No. 14YZ007), the Key Project of Science and Technology Commission of Shanghai Municipality (Grant Nos. 14JC1402200, 14DZ1206302), the Key Project of Shanghai Municipal Commission of Economy and Informatization, and the Research Foundation for the Doctoral Program of Higher Education of China (Grant No. 20103108120008).

摘要/Abstract

摘要： The reliability and real time of industrial wireless sensor networks (IWSNs) are the absolute requirements for industrial systems, which are two foremost obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliability, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolution harmony search (MOBDEHS) is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deployment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOBDEHS is an effective tool for multi-objective node placement problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II (NSGA-II) and modified NSGA-II.

关键词: Industrial wireless sensor networks (IWSNs), Node placement, Harmony search, Differential evolution, Pareto, Real time, Reliability

Abstract: The reliability and real time of industrial wireless sensor networks (IWSNs) are the absolute requirements for industrial systems, which are two foremost obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliability, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolution harmony search (MOBDEHS) is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deployment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOBDEHS is an effective tool for multi-objective node placement problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II (NSGA-II) and modified NSGA-II.

Key words: Industrial wireless sensor networks (IWSNs), Node placement, Harmony search, Differential evolution, Pareto, Real time, Reliability

Ling Wang, Lu An, Hao-Qi Ni, Wei Ye, Panos M. Pardalos, Min-Rui Fei. Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search[J]. Advances in Manufacturing, 2016, 4(1): 66-78.

参考文献

1. Akerberg J, Gidlund M, Bjorkman M (2011) Future research challenges in wireless sensor and actuator networks targeting industrial automation. The 9th IEEE International Conference on Industrial Informatics (INDIN), 26-29 July 2011, Caparica, Lisbon, pp 410-415

2. Miorandi D, Uhlemann E, Vitturi S et al (2007) Guest editorial: special section on wireless technologies in factory and industrial automation-Part I. IEEE Trans Ind Inf 3:95-98

3. Tan KK, Huang SN, Zhang Y et al (2009) Distributed fault detection in industrial system based on sensor wireless network. Comput Stand Interfaces 31:573-578

4. Korkua S, Jain H, Lee WJ et al (2010) Wireless health monitoring system for vibration detection of induction motors. IEEE industrial and commercial power systems technical conference (I&CPS), 9-13 May 2010, Tallahassee, FL, USA, pp 1-6

5. Bayindir R, Cetinceviz Y (2011) A water pumping control system with a programmable logic controller (PLC) and industrial wireless modules for industrial plants-An experimental setup. ISA Trans 50:321-328

6. Salvadori F, Campos MD, Sausen PS et al (2009) Monitoring in industrial systems using wireless sensor network with dynamic power management. IEEE Trans Instrum Meas 58:3104-3111

7. Li TT, Jia TJ, Fei MR et al (2011) Time delay characteristic of industrial wireless networks based on IEEE 802.15.4a. Int J Autom Comput 8:170-176

8. Toscano E, Bello LL (2012) Multichannel superframe scheduling for IEEE 802.15.4 industrial wireless sensor networks. IEEE Trans Ind Inf 8:337-350

9. Song J, Han S, Mok AK et al (2008) Wireless HART: applying wireless technology in real-time industrial process control. IEEE real-time and embedded technology and applications symposium, 22-24 April 2008, St Louis, Missouri, pp 377-386

10. Agha KA, Bertin MH, Dang T et al (2009) Which wireless technology for industrial wireless sensor networks? The development of OCARI technology. IEEE Trans Ind Electron 56:4266-4278

11. Bai H, Atiquzzaman M (2003) Error modeling schemes for fading channels in wireless communications: a survey. IEEE Commun Surv Tutorials 5:2-9

12. Howitt I, Manges WW, Kuruganti PT et al (2006) Wireless industrial sensor networks: framework for QoS assessment and QoS management. ISA Trans 45:347-359

13. Yu K, Gidlund M, Åkerberg J et al (2011) Reliable and low latency transmission in industrial wireless sensor networks. Proc Comput Sci 5:866-873

14. Xing Z, Zeng P, Wang H (2008) Reliability, capacity, and energy efficiency: a comprehensively optimized MAC protocol for industrial wireless sensor networks. Advances in Communication Systems and Electrical Engineering, pp 139-154

15. Balasubramanian K, Anil Kumar GS, Manimaran G et al (2006) A novel real-time MAC protocol exploiting spatial and temporal channel diversity in wireless industrial networks. High Perf Comput 4297:534-546

16. Willig A (2005) Redundancy concepts to increase transmission reliability in wireless industrial LANs. IEEE Trans Ind Inf 1:173-182

17. Chang CY, Sheu JP, Chen YC et al (2009) An obstacle-free and power-efficient deployment algorithm for wireless sensor networks. IEEE Trans Syst Man Cybern Part A 39:795-806

18. Quang PTA, Kim DS (2012) Enhancing real-time delivery of gradient routing for industrial wireless sensor networks. IEEE Trans Ind Inf 8:61-68

19. Heo J, Hong J, Cho Y (2009) EARQ: energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Trans Ind Inf 5:3-11

20. Yoo S, Chong PK, Kim D et al (2010) Guaranteeing real-time services for industrial wireless sensor networks with IEEE 802.15.4. IEEE Trans Ind Electr 57:3868-3876

21. Boginski VL, Commander CW, Pardalos PM et al (2011) Sensors: theory, algorithms, and applications. Springer, New York

22. Sorokin A, Boyko N, Boginski V et al (2009) Mathematical programming techniques for sensor networks. Algorithms 2:565-581

23. 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. IEEE Trans Comput 56:710-715

24. Wu Q, Rao NSV, Du X et al (2007) On efficient deployment of sensors on planar grid. Comput Commun 30:2721-2734

25. Ferentinos KP, Tsiligiridis TA (2010) A memetic algorithm for optimal dynamic design of wireless sensor networks. Comput Commun 33:250-258

26. Aitsaadi N, Achir N, Boussetta K et al (2010) Multi-objective WSN deployment: quality of monitoring, connectivity and lifetime. 2010 IEEE international conference on communications (ICC), 23-27 May 2010, Cape Town, pp 1-6

27. Konstantinidis A, Yang K (2012) Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Appl Soft Comput 12:1847-1864

28. Ferentinos KP, Tsiligiridis TA (2007) Adaptive design optimization of wireless sensor networks using genetic algorithms. Comput Netw 51:1031-1051

29. Jia J, Chen J, Chang G et al (2009) Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Comput Math Appl 57:1756-1766

30. Wang L, Fu XP, Fang JT et al (2011) Optimal node placement in industrial wireless sensor networks using adaptive mutation probability binary particle swarm optimization algorithm. The 7th international conference on natural computation, 26-28 July 2011, Shanghai, China, pp 2199-2203

31. Wang L, Ye W, Mao YF et al (2013) The node placement of large-scale industrial wireless sensor networks based on binary differential evolution harmony search algorithm. Int J Innov Comput Inf Control 9:955-970

32. Wang L, Xu Y, Mao YF et al (2010) A discrete harmony search algorithm. Commun Comput Inf Sci 98:37-43

33. Turky AM, Abdullah S (2014) A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf Sci 272:84-95

34. Hasan BHF, Doush IA, Maghayreh EA (2014) Hybridizing harmony search algorithm with different mutation operators for continuous problems. Appl Math Comput 232:1166-1182

35. Landa-Torres I, Manjarres D, Salcedo-Sanz S (2013) A multiobjective grouping harmony search algorithm for the optimal distribution of 24-hour medical emergency units. Expert Syst Appl 40:2343-2349

36. Rahmati SHA, Hajipour V, Niaki STA (2013) A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl Soft Comput 13:1728-1740

37. Nekooei K, Farsangi MM, Nezamabadi-Pour H (2013) An improved multi-objective harmony search for optimal placement of DGs in distribution systems. IEEE Trans Smart Grid 4:557-567

38. Laifa A, Medoued A (2013) Optimal FACTS location to enhance voltage stability using multi-objective harmony search. The 3rd international conference on electric power and energy conversion systems (EPECS), 2-4 Oct. 2013, Istanbul, pp 1-6

39. Luo Q, Wu J, Sun X (2012) Optimal design of groundwater remediation systems using a multi-objective fast harmony search algorithm. Hydrogeol J 20:1497-1510

40. Kougias IP, Theodossiou NP (2013) Multiobjective pump scheduling optimization using harmony search algorithm (HSA) and polyphonic HAS. Water Resour Manag 27:1249-1261

41. Fesanghary M, Asadi S, Geem ZW (2012) Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm. Build Environ 49:245-250

42. Sivasubramani S, Swarup KS (2011) Environmental/economic dispatch using multi-objective harmony search algorithm. Electric Power Syst Res 81:1778-1785

43. Jeddi B, Vahidinasab V (2014) A modified harmony search method for environmental/economic load dispatch of real-world power systems. Energy Convers Manag 78:661-675

44. Sivasubramani S, Swarup KS (2011) Multi-objective harmony search algorithm for optimal power flow problem. Int J Electr Power Energy Syst 33:745-752

45. Geem ZW (2005) Harmony search in water pump switching problem. Lect Notes Comput Sci 3612:445-445

46. Chinchuluun A, Pardalos PM, Migdalas A et al (2008) Pareto optimality, game theory and equilibria. Springer, New York

47. Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182-197

48. Li YF, Sansavini G, Zio E (2013) Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks. Reliab Eng Syst Safety 111:195-205

49. Wang L, Fu XP, Menhas MI et al (2010) A modified binary differential evolution algorithm. Lect Notes Comput Sci 6329:49-57

50. Wang L, Fu XP, Mao YF (2012) A novel modified binary differential evolution algorithm and its applications. Neurocomputing 98:55-75

51. Wu CY, Tseng KY (2010) Topology optimization of structures using modified binary differential evolution. Struct Multidiscip Optim 42:939-953

52. Jeyadevi S, Baskar S, Babulal CK et al (2011) Solving multiobjective optimal reactive power dispatch using modified NSGAII. Int J Electr Power Energy Syst 33:219-228

53. Goh CK, Tan KC (2009) A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans Evol Comput 13:103-127

[1]	Xi-Nong En, Yi-Min Zhang, Xian-Zhen Huang. Time-variant reliability analysis of a continuous system with strength deterioration based on subset simulation[J]. Advances in Manufacturing, 2019, 7(2): 188-198.
[2]	Nikita Voinov, Igor Chernorutsky, Pavel Drobintsev, Vsevolod Kotlyarov. An approach to net-centric control automation of technological processes within industrial IoT systems[J]. Advances in Manufacturing, 2017, 5(4): 388-393.
[3]	H. Xu T. Suni V. Vuorinen J. Li H. Heikkinen P. Monnoyer M. Paulasto-Kro¨ ckel. Wafer-level SLID bonding for MEMS encapsulation[J]. Advances in Manufacturing, 2013, 1(3): 226-235.

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价