Top

Wireless Networks

Published in:

11-05-2019

Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage

Author: Ali Jameel Al-Mousawi

Published in: Wireless Networks | Issue 8/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Evolutionary intelligence has become one of the most important directions that improve the performance and effectiveness of automated systems such as communication systems, robotics and engineering industries. Today, there are many applications of evolutionary intelligence in many engineering fields and the most important fields related to computation and informatics engineering as a part of electrical and communication engineering, as modern engineering applications are involved in these fields. The sensor network is the main data source in the world of smart systems nowadays. Additionally, it has become a field of science used in the development of the rest of scientific applications. The need to use evolutionary intelligence in sensor networks has emerged because of the problems encountered by different types of sensor networks. This paper represents a comprehensive scientific review of the role of evolutionary intelligence in sensor networks and its implications for this important part of engineering applications. This paper discusses the theoretical, mathematical and practical application of evolutionary computing with the use of evolutionary algorithms and the improvements resulting from the application of evolutionary intelligence in sensor networks. The content of this paper will review the most important of the evolutionary intelligence from principles, algorithms and applications. The problems facing the types of sensor network has been solved using evolutionary algorithms. After reviewing the evolutionary intelligence and its details in the sensor network, a performance evaluation is presented in the paper at the end of each of the targeted areas of the sensor network. This performance evaluation represents the measure of the quality of improvements provided by evolutionary intelligence in sensor network field with graphical analysis studies to demonstrate the effect of evolutionary algorithms on the sensor network.

previous article Recent advances in consensus protocols for blockchain: a survey

next article New metrics to modify BGP routes based on SDN

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef

Yu, X., Wu, P., Han, W., & Zhang, W. (2013). A survey on wireless sensor network infrastructure for agriculture. Computer Standards & Interfaces, 35(1), 59–64.

Wang, P., Hou, H., He, X., Wang, C., Xu, T., & Li, Y. (2015). Survey on application of wireless sensor network in smart grid. Procedia Computer Science, 52, 1212–1217.

Serpen, G., Li, J., & Liu, L. (2013). AI-WSN: Adaptive and intelligent wireless sensor network. Procedia Computer Science, 20, 406–413.

Marsh, D., Tynan, R., O’Kane, D., & O’Hare, G. M. (2004). Autonomic wireless sensor networks. Engineering Applications of Artificial Intelligence, 17(7), 741–748.MATH

Federici, F., Alesii, R., Colarieti, A., Faccio, M., Graziosi, F., Gattulli, V., et al. (2014). Design of wireless sensor nodes for structural health monitoring applications. Procedia Engineering, 87, 1298–1301.

AL-Mousawi, A. J., & AL-Hassani, H. K. (2017). A survey in wireless sensor network for explosives detection. Computers & Electrical Engineering, 72, 682–701.

Sharma, D., Liscano, R., & Shah-Heydari, S. (2013). Enhancing collection tree protocol for mobile wireless sensor networks. Procedia Computer Science, 21, 416–423.

10.

Tuna, G., Güngör, V. Ç., & Potirakis, S. M. (2015). Wireless sensor network-based communication for cooperative simultaneous localization and mapping. Computers & Electrical Engineering, 41, 407–425.

11.

Benini, L., Farella, E., & Guiducci, C. (2006). Enabling technology for ambient intelligence. Microelectronics Journal, 37(12), 1639–1649.

12.

Rademacher, S., Schmitt, K., & Wöllenstein, J. (2015). Wireless gas sensor network for the spatially resolved measurement of hazardous gases in case of a disaster. Procedia Engineering, 120, 310–314.

13.

Kumar, S. P. L. (2017). State of The art-intense review on artificial intelligence systems application in process planning and manufacturing. Engineering Applications of Artificial Intelligence, 65, 294–329.

14.

Ganesan, D., Cerpa, A., Ye, W., Yan, Y., Zhao, J., & Estrin, D. (2004). Networking issues in wireless sensor networks. Journal of Parallel and Distributed Computing, 64(7), 799–814.

15.

Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4(3), 303–334.

16.

Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

17.

Serpen, G., Li, J., & Liu, L. (2013). AI-WSN: Adaptive and intelligent wireless sensor network. Procedia Computer Science, 20, 406–413.

18.

Chang, F.-C., & Huang, H.-C. (2016). A survey on intelligent sensor network and its applications. Journal of Network Intelligence, 1(1), 1–5.MathSciNet

19.

Kulkarni, R. V., Forster, A., & Venayagamoorthy, G. K. (2011). Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 13(1), 68–96.

20.

Jabbar, S., Iram, R., Minhas, A. A., Shafi, I., Khalid, S., & Ahmad, M. (2013). Intelligent optimization of wireless sensor networks through bio-inspired computing: Survey and future directions. International Journal of Distributed Sensor Networks, 2013, 13, 421084.

21.

Jerison, H. (1973). Evolution of the brain and intelligence (pp. iv–ii). London: Academic Press.

22.

Nguyen, T. T., Yang, S., & Branke, J. (2012). Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation, 6, 1–24.

23.

Ahmed, Y. E. E., Adjallah, K. H., Stock, R., & Babikier, S. F. (2016). Wireless sensor network lifespan optimization with simple, rotated, order and modified partially matched crossover genetic algorithms. IFAC-PapersOnLine, 49(25), 182–187.

24.

Aguilar-Rivera, R., Valenzuela-Rendón, M., & Rodríguez-Ortiz, J. J. (2015). Genetic algorithms and Darwinian approaches in financial applications: A survey. Expert Systems with Applications, 42(21), 7684–7697.

25.

Yi, L., & Wanli, K. (2011). A new genetic programming algorithm for building decision tree. Procedia Engineering, 15(2011), 3658–3662.

26.

Cai, J., & Thierauf, G. (1996). Evolution strategies for solving discrete optimization problems. Advances in Engineering Software, 25(2–3), 177–183.

27.

Balkaya, Ç. (2013). An implementation of differential evolution algorithm for inversion of geoelectrical data. Journal of Applied Geophysics, 98, 160–175.

28.

Holmes, J. H., Durbin, D. R., & Winston, F. K. (2000). The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence in Medicine, 19(1), 53–74.

29.

Bensmaine, A., Dahane, M., & Benyoucef, L. (2013). A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computers & Industrial Engineering, 66(3), 519–524.

30.

Saranya, S., & Princy, M. (2012). Routing techniques in sensor network—A survey. Procedia Engineering, 38, 2739–2747.

31.

Liang, Z., Jianmin, X. U., & Lingxiang, Z. (2007). Application of genetic algorithm in dynamic route guidance system. Journal of Transportation Systems Engineering and Information Technology, 7(3), 45–48.

32.

Gupta, S. K., Kuila, P., & Jana, P. K. (2016). Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Computers & Electrical Engineering, 56, 544–556.

33.

Bhatia, T., Kansal, S., Goel, S., & Verma, A. K. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers & Electrical Engineering, 56, 441–455.

34.

Bayraklı, S., & Erdogan, S. Z. (2012). Genetic algorithm based energy efficient clusters (GABEEC) in wireless sensor networks. Procedia Computer Science, 10, 247–254.

35.

Yan, W., Xin-xin, S., & Yan-ming, S. U. (2011). Study on the application of genetic algorithms in the optimization of wireless network. Procedia Engineering, 16, 348–355.

36.

Gong, G., Liu, Y., & Qian, M. (2001). An adaptive simulated annealing algorithm. Stochastic Processes and their Applications, 94(1), 95–103.MathSciNetMATH

37.

Shahi, B., Dahal, S., Mishra, A., Kumar, S. V., & Kumar, C. P. (2016). A review over genetic algorithm and application of wireless network systems. Procedia Computer Science, 78, 431–438.

38.

Bari, A., Wazed, S., Jaekel, A., & Bandyopadhyay, S. (2009). A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks, 7, 665–676.

39.

Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46(2014), 198–226.

40.

Nayebi, A., & Sarbazi-Azad, H. (2011). Performance modelling of the LEACH protocol for mobile wireless sensor networks. Journal of Parallel and Distributed Computing, 71, 812–821.MATH

41.

Geetha, V., Kallapur, P. V., & Tellajeera, S. (2012). Clustering in wireless sensor networks: Performance comparison of leach & leach-C protocols using ns2. Procedia Technology, 4, 163–170.

42.

Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.

43.

Zhou, Y., Li, X., & Gao, L. (2013). A differential evolution algorithm with intersecting mutation operator. Applied Soft Computing, 13(1), 390–401.

44.

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.MathSciNetMATH

45.

Potthuri, S., Shankar, T., & Rajesh, A. (2016). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Engineering Journal, 9(4), 655–663.

46.

Sumithra, S., & Victoire, T. A. A. (2015). Differential evolution algorithm with diversified vicinity operator for optimal routing and clustering of energy efficient wireless sensor networks. The Scientific World Journal, 2015, 3, 729634.

47.

Raguraman, P., Ramasundaram, M., & Balakrishnan, V. (2018). Localization in wireless sensor networks: A dimension based pruning approach in 3D environments. Applied Soft Computing, 68, 219–232.

48.

Sun, W., & Su, X. (2011). Wireless sensor network node localization based on genetic algorithm. In 2011 IEEE 3rd international conference on communication software and networks (pp. 316–319).

49.

Schmitt, L. M. (2001). Theory of genetic algorithms. Theoretical Computer Science, 259(1–2), 1–61.MathSciNetMATH

50.

Carter, J. N. (2003). Chapter 3, Introduction to using genetic algorithms. In M. Nikravesh, F. Aminzadeh, & L. A. Zadeh (Eds.), Developments in petroleum science (Vol. 51, pp. 51–76). Elsevier.

51.

Banzhaf, W. (2001). Artificial intelligence: Genetic programming. In International encyclopedia of the social & behavioral sciences (pp. 789–792). Pergamon.

52.

Tam, V., Cheng, K.-Y., & Lui, K.-S. (2006). Improving localization in wireless sensor networks with an evolutionary algorithm. In IEEE consumer communications and networking conference (CCNC) 2006 (pp. 137–141). Las Vegas, NV, USA.

53.

Li, Z., Zhou, X., & Li, S. (2005). Issues of wireless sensor network management. Lecture notes in computer science (pp. 355–36 l).

54.

Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57–66. https://doi.org/10.1109/2.940014.CrossRef

55.

Flathagen, J., & Korsnes, R. (2010). Localization in wireless sensor networks based on Ad hoc routing and evolutionary computation. In 2010, Milcom military communications conference, CA (pp. 1062–1067).

56.

Tam, V., Cheng, K. -Y., & Lui, K.-S. (2006). Improving localization in wireless sensor networks with an evolutionary algorithm. CCNC. In 2006 3rd IEEE consumer communications and networking conference, 2006 (pp. 137–141). Las Vegas, NV, USA, 2006.

57.

Mohamed, S. M., Hamza, H. S., & Saroit, I. A. (2017). Coverage in mobile wireless sensor networks (M-WSN): A survey. Computer Communications, 110, 133–150.

58.

Vecchio, M., & López-Valcarce, R. (2015). Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach. Journal of Network and Computer Applications, 48, 1–13.

59.

Li, X. -Y., Wan, P. -J., & Frieder, O. (2002). Coverage in wireless ad-hoc sensor networks. In 2002 IEEE international conference on communications. Conference proceedings. ICC 2002 (Cat. No.02CH37333), New York, NY, USA (Vol. 5, pp. 3174–3178).

60.

Li, M., Liu, S., Zhang, L., Wang, H., Meng, F., & Bai, L. (2012). Non-dominated sorting genetic algorithms-based on multi-objective optimization model in the water distribution system. Procedia Engineering, 37, 309–313.

61.

Jie, J., Jian, C., Chang, G. R., & Ying-You, W. E. N. (2008). Efficient cover set selection in wireless sensor networks. Acta Automatica Sinica, 34(9), 1157–1162.

Title: Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage
Author: Ali Jameel Al-Mousawi
Publication date: 11-05-2019
Publisher: Springer US
Published in: Wireless Networks / Issue 8/2020
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI: https://doi.org/10.1007/s11276-019-02008-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 8/2020

Detecting Byzantine attack in cognitive radio networks using machine learning

CAR: heuristics for the inventory routing problem

Gradient-based adaptive modeling for IoT data transmission reduction

Catora: congestion avoidance through transmission ordering and resource awareness in delay tolerant networks

Implementation and parametric analysis of single and dual band planar filtering antennas for WLAN applications

Privacy preserving semantic trajectory data publishing for mobile location-based services