http://iet.metastore.ingenta.com
1887

Lifetime maximisation of disjoint wireless sensor networks using multiobjective genetic algorithm

Lifetime maximisation of disjoint wireless sensor networks using multiobjective genetic algorithm

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Wireless Sensor Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

One of the lifetime maximisation methods for wireless sensor network (WSN) depends on organising the dense sensors into groups which can work in a cooperative sequential manner. Each group contains a subset of sensors that cover all the monitored area and is called a complete cover or simply a cover. Increasing the number of organised covers and maximising the covers lifetime enable longer network lifetime. Here, the authors investigate the WSN lifetime problem as a two-objective optimisation problem. The first objective is to find the maximum number of covers. The second objective considers the problem of wasted energy. Minimising the wasted energy in the critical sensors is achieved by defining a difference factor (DF). The DF is an indication of the difference between the critical sensor lifetime and the cover lifetime. This second objective is compared with other choices in the literature such as minimising the overlapping and minimising the variance. This optimisation problem is addressed using non-dominated sorting genetic algorithm-II (NSGA-II). Simulation results are conducted for the network lifetime when using one-objective and different two-objective optimisation problem. The choice of DF as the second objective is proved to overcome drawbacks of other second objectives choices.

References

    1. 1)
      • 1. Contreras, W., Ziavras, S.: ‘Wireless sensor network-based pattern matching technique for the circumvention of environmental and stimuli-related variability in structural health monitoring’, IET Wirel. Sens. Syst., 2016, 6, (1), pp. 2633.
    2. 2)
      • 2. Zonouz, E.A., Xing, L., Vokkarane, M.V., et al: ‘Hybrid wireless sensor networks: a reliability, cost and energy-aware approach’, IET Wirel. Sens. Syst., 2016, 6, (2), pp. 4248.
    3. 3)
      • 3. Yeh, L.-W., Wang, Y.-C., Tseng, Y.-C.: ‘Ipower: an energy conservation system for intelligent buildings by wireless sensor networks’, Int. J. Sensor Netw., 2009, 5, (1), pp. 110.
    4. 4)
      • 4. Mikhaylov, K., Tervonen, J., Heikkila, J., et al: ‘Wireless sensor networks in industrial environment: real-life evaluation results’. 2nd Baltic Congress on Future Internet Communications (BCFIC 2012), Vilniaus Gedimino Technikos Universitetas Vilnius, Lithuania, April 2012, pp. 17.
    5. 5)
      • 5. Durisic, M.P., Tafa, Z., Dimic, G., et al: ‘A survey of military applications of wireless sensor networks’. 2012 Mediterranean Conf. on Embedded Computing (MECO 2012), Montenegro, June 2012, pp. 196199.
    6. 6)
      • 6. Jin, R., Che, Z., Wang, Z., et al: ‘Battery optimal scheduling based on energy balance in wireless sensor networks’, IET Wirel. Sens. Syst., 2015, 5, (6), pp. 277282.
    7. 7)
      • 7. Slijepcevic, S., Potkonjak, M.: ‘Power efficient organization of wireless sensor networks’. Proc. IEEE Int. Conf. on Communications, Helsinki, Finland, 2001, pp. 472476.
    8. 8)
      • 8. Cardei, I., Cardei, M.: ‘Energy-efficient connected-coverage in wireless sensor networks’, Int. J. Sensor Netw., 2008, 3, (3), pp. 201210.
    9. 9)
      • 9. Ashouri, M., Zali, Z., Mousavi, S.R., et al: ‘New optimal solution to disjoint set K-coverage for lifetime extension in wireless sensor networks’, IET Wirel. Sens. Syst., 2012, 2, (1), pp. 3139.
    10. 10)
      • 10. Njoya, A.N., Thron, C., Barry, J., et al: ‘Efficient scalable sensor node placement algorithm for fixed target coverage applications of wireless sensor networks’, IET Wirel. Sens. Syst., 2017, 7, (2), pp. 4454.
    11. 11)
      • 11. Razali, M.N., Salleh, S., Mohamadi, H.: ‘Solving priority-based target coverage problem in directional sensor networks with adjustable sensing ranges’, Wirel. Pers. Commun., 2017, 95, (2), pp. 847872.
    12. 12)
      • 12. Panigrahi, B., De, S., Panda, B.S., et al: ‘Network lifetime maximising distributed forwarding strategies in ad hoc wireless sensor networks’, IET Commun., 2012, 6, (14), pp. 21382148.
    13. 13)
      • 13. Cheng, C.T., Leung, H.: ‘A multi-objective optimization framework for cluster-based wireless sensor networks’. 2012 Int. Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery, Sanya (CyberC’ 2012), China, October 2012, pp. 341347.
    14. 14)
      • 14. Li, J., AlRegib, G.: ‘Network lifetime maximization for estimation in multihop wireless sensor networks’, IEEE Trans. Signal Process., 2009, 57, (7), pp. 24562466.
    15. 15)
      • 15. Huynh, T.T., Tran, T.N., Tran, C.H., et al: ‘Delay constraint energy-efficient routing based on Lagrange relaxation in wireless sensor networks’, IET Wirel. Sens. Syst., 2017, 7, (5), pp. 138145.
    16. 16)
      • 16. Rawat, P., Singh, K.D., Chaouchi, H., et al: ‘Wireless sensor networks: a survey on recent developments and potential synergies’, J. Supercomput., 2014, 68, (1), pp. 148.
    17. 17)
      • 17. Kosunalp, S., Chu, Y., Mitchell, P.D., et al: ‘Use of Q-learning approaches for practical medium access control in wireless sensor networks’, Eng. Appl. Artif. Intell., 2016, 55, pp. 146154.
    18. 18)
      • 18. Annie, U.R., Kasmir Raja, S.V., Antony, J., et al: ‘Energy-efficient predictive congestion control for wireless sensor networks’, IET Wirel. Sens. Syst., 2015, 5, (3), pp. 115123.
    19. 19)
      • 19. Sagar, A.K., Lobiyal, D.K.: ‘Coverage and lifetime maximization of wireless sensor network with multi-objective evolutionary algorithm’, Int. J. Sci. Eng. Res., 2014, 5, (6), pp. 11941203.
    20. 20)
      • 20. Attea, B.A.A.: ‘Multi-objective set cover problem for reliable and efficient wireless sensors network’, Iraqi J. Sci., 2015, 56, (2A), pp. 11471160.
    21. 21)
      • 21. Li, J., Hou, X., Su, D., et al: ‘Fuzzy power-optimised clustering routing algorithm for wireless sensor networks’, IET Wirel. Sens. Syst., 2017, 7, (5), pp. 130137.
    22. 22)
      • 22. Bara'a, A.A., Khalil, E.A., Ozdemir, S., et al: ‘A multi-objective disjoint set covers for reliable lifetime maximization of wireless sensor networks’, Wirel. Pers. Commun., 2015, 81, (2), pp. 819838.
    23. 23)
      • 23. Wang, B.: ‘Coverage problems in sensor networks: survey’, ACM Comput. Surv., 2011, 43, (4), pp. 153.
    24. 24)
      • 24. Deif, D.S., Gadallah, Y.: ‘Classification of wireless sensor networks deployment techniques’, IEEE Commun. Surv. Tutor., 2014, 16, (2), pp. 834855.
    25. 25)
      • 25. Cardei, M., Wu, J., Lu, M., et al: ‘Maximum network lifetime in wireless sensor networks with adjustable sensing ranges’. Proc. IEEE Intl. Conf. on Wireless and Mobile Computing Networking and Communications (WiMob'05), Canada, August 2005, vol. 3, pp. 438445.
    26. 26)
      • 26. Liao, C.C.: ‘Multiobjective evolutionary algorithm for lifetime extension and loading balancing of wireless sensor networks’. 3rd Int. Conf. on Innovations in Bio-Inspired Computing and Applications (IBICA 2012), Taiwan, September 2012, pp. 116121.
    27. 27)
      • 27. Cardei, M., Du, D.Z.: ‘Improving wireless sensor network lifetime through power aware organization’, Wirel. Netw., 2005, 11, (3), pp. 333340.
    28. 28)
      • 28. Lai, C., Ting, C., Ko, R.: ‘An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications’. IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, 2007, pp. 35313538.
    29. 29)
      • 29. Liao, C., Ting, C.: ‘Extending wireless sensor network lifetime through order-based genetic algorithm’. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2008), Singapore, 2008, pp. 14341439.
    30. 30)
      • 30. Cardei, M., Thai, M.T., Li, Y., et al: ‘Energy-efficient target coverage in wireless sensor networks’. Proc. IEEE Infocom, Miami, FL, USA, 2005.
    31. 31)
      • 31. Mir, A.K., Zubair, M., Qureshi, I.M.: ‘Lifetime maximization of wireless sensor networks using particle swarm optimization’, Turk. J. Electr. Eng. Comput. Sci., 2016, 24, pp. 160170.
    32. 32)
      • 32. Zhang, H., Hou, J.C.: ‘Maintaining sensing coverage and connectivity in large sensor networks’, Ad Hoc Sens. Wirel. Netw., 2005, 1, (1–2), pp. 89124.
    33. 33)
      • 33. Kim, H., Han, Y.H., Min, S.G.: ‘Maximum lifetime scheduling for target coverage in wireless sensor networks’. 6th Int. Wireless Communications and Mobile Computing Conf., France, June 2010, pp. 99103.
    34. 34)
      • 34. Cao, Y., Huang, L., Xing, K., et al: ‘Local maximum lifetime algorithms for strong k-barrier coverage with coordinated sensors’. IEEE 3rd Int. Conf. on Communication Software and Networks (ICCSN 2011), China, May 2011, pp. 7176.
    35. 35)
      • 35. Sengupta, S., Das, S., Nasir, M., et al: ‘An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2012, 42, (6), pp. 10931102.
    36. 36)
      • 36. Diongue, D., Thiare, O.: ‘ALARM: an energy aware sleep scheduling algorithm for lifetime maximization in wireless sensor networks’. IEEE Symp. on Wireless Technology and Applications (ISWTA 2013), Malaysia, September 2013, pp. 7479.
    37. 37)
      • 37. Sagar, A.K., Lobiyal, D.K.: ‘A multi-objective optimization approach for lifetime and coverage problem in wireless sensor network’, Intell. Comput. Netw. Inform., 2014, 243, pp. 343350.
    38. 38)
      • 38. Hu, X.M., Zhang, J., Yu, Y., et al: ‘Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks’, IEEE Trans. Evol. Comput., 2010, 14, (5), pp. 766781.
    39. 39)
      • 39. Sengupta, S., Das, S., Nasir, M., et al: ‘Energy-efficient differentiated coverage of dynamic objects using an improved evolutionary multi-objective optimization algorithm with fuzzy-dominance’. 2012 IEEE Congress on Evolutionary Computation (CEC), Australia, June 2012, pp. 18.
    40. 40)
      • 40. Zhong, J.H., Zhang, J.: ‘Energy-efficient local wake-up scheduling in wireless sensor networks’. Proc. of IEEE Congress on Evolutionary Computation (CEC), USA, June 2011, pp. 22802282.
    41. 41)
      • 41. Huang, C.F., Tseng, Y.C.: ‘The coverage problem in a wireless sensor network’, Mob. Netw. Appl., 2005, 10, (4), pp. 519528.
    42. 42)
      • 42. Deb, K., Pratap, A., Agarwal, S., et al: ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, IEEE Trans. Evol. Comput., 2002, 6, (2), pp. 182197.
    43. 43)
      • 43. Coello, C.A.C.: ‘An updated survey of GA-based multiobjective optimization techniques’, ACM Comput. Surv., 2000, 32, (2), pp. 109143.
    44. 44)
      • 44. Fei, Z., Li, B., Yang, S., et al: ‘A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms, and open problems’, IEEE Commun. Surv. Tutor., 2017, 19, (1), pp. 550586.
    45. 45)
      • 45. Zhan, Z.H., Zhang, J., Du, K.J., et al: ‘Extended binary particle swarm optimization approach for disjoint set covers problem in wireless sensor networks’. IEEE Conf. on Technologies and Applications of Artificial Intelligence (TAAI 2012), Taiwan, November 2012, pp. 327331.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2017.0069
Loading

Related content

content/journals/10.1049/iet-wss.2017.0069
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address