Unlike traditional analytical optimization techniques, bio-inspired multi-objective optimization has recently enjoyed an intense interest regarding wireless sensor network (WSN) issues. Network lifetime and target coverage are among the major concerns in many well-established scenarios of WSNs, such as routing and node deployment. For set covers scenario in WSNs, however, little attention has been paid to the role of multi-objective requirements. In this paper, we take a step toward exploring the feasibility of such bio-inspired algorithms for solving multi-objective set covers problem in WSNs. The main contributions of this study are twofold. First, we extend the set covers problem and address it with three issues: network lifetime, target coverage, and network connectivity as a multi-objective set covers (MOSC) formulation. To the best of our knowledge, this is the first effort to define such a general multi-objective set covers problem. Second, we design and elaborate four well-known multi-objective optimization algorithms from evolutionary and swarm intelligence community to tackle the formulated MOSC problem. All characteristic components of the adopted algorithms are tailored specifically to handle the formulated problem. Further, a self-adaptive heuristic mutation operator is proposed to attain and emphasize the strength of the algorithms in terms of network lifetime and coverage probability. Extensive simulations are performed to test and demonstrate the performance of the designed algorithms to tackle the problem appropriately.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Abdulhalim MF, Attea BA (2015) Multi-layer genetic algorithm for maximum disjoint reliable set covers problem in wireless sensor networks. Wirel Pers Commun 80(1):203–227
Aitsaadi N, Achir N, Boussetta K, Pujolle G (2010) Multi-objective WSN deployment: quality of monitoring, connectivity and lifetime. In: 2010 IEEE international conference on communications (ICC). IEEE, pp 1–6
Attea BA, Khalil EA, Özdemir S (2014) Biologically inspired probabilistic coverage for mobile sensor networks. Soft Comput 18(11):2313–2322
Attea BA, Khalil EA, Cosar A (2015a) Multi-objective evolutionary routing protocol for efficient coverage in mobile sensor networks. Soft Comput 19(10):2983–2995
Attea BA, Khalil EA, Özdemir S, Yıldız O (2015b) A multi-objective disjoint set covers for reliable lifetime maximization of wireless sensor networks. Wirel Pers Commun 81(2):819–838
Cardei I, Cardei M (2008) Energy-efficient connected-coverage in wireless sensor networks. Int J Sens Netw 3(3):201–210
Cardei M, Du DZ (2005) Improving wireless sensor network lifetime through power aware organization. Wirel Netw 11(3):333–340
Cardei M, MacCallum D, Cheng MX, Min M, Jia X, Li D, Du DZ (2002) Wireless sensor networks with energy efficient organization. J Interconnect Netw 3(03n04):213–229
Carrabs F, Cerulli R, D’Ambrosio C, Raiconi A (2017) Exact and heuristic approaches for the maximum lifetime problem in sensor networks with coverage and connectivity constraints. RAIRO Oper Res 51(2017):607–625
Cheng CT, Leung H (2012) A multi-objective optimization framework for cluster-based wireless sensor networks. In: 2012 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC). IEEE, pp 341–347
Coello CAC, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, vol 242. Kluwer Academic, New York
Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279
Deb K (2001) Multi-objective optimization using evolutionary algorithms, vol 16. Wiley, Hoboken
Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature. Springer, Berlin, pp 849–858
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Elfes A (1987) Sonar-based real-world mapping and navigation. IEEE J Robot Autom 3(3):249–265
Fei Z, Li B, Yang S, Xing C, Chen H, Hanzo L (2017) A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms, and open problems. IEEE Commun Surv Tutor 19(1):550–586
Ferentinos KP, Tsiligiridis TA (2007) Adaptive design optimization of wireless sensor networks using genetic algorithms. Comput Netw 51(4):1031–1051
Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evol Comput 18(1):82–97
He J, Xiong N, Xiao Y, Pan Y (2010) A reliable energy efficient algorithm for target coverage in wireless sensor networks. In: 2010 IEEE 30th international conference on distributed computing systems workshops (ICDCSW). IEEE, pp 180–188
Hu XM, Zhang J, Yu Y, Chung HSH, Li YL, Shi YH, Luo XN (2010) Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks. IEEE Trans Evol Comput 14(5):766–781
Huang CF, Tseng YC (2005) The coverage problem in a wireless sensor network. Mob Netw Appl 10(4):519–528
Jameii SM, Faez K, Dehghan M (2013) Multi-objective energy efficient optimization algorithm for coverage control in wireless sensor networks. Int J Comput Sci Eng Inf Technol 3(4):25–33
Jia J, Chen J, Chang G, Wen Y, Song J (2009a) Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Comput Math Appl 57(11–12):1767–1775
Jia J, Chen J, Chang G, Tan Z (2009b) Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Comput Math Appl 57(11–12):1756–1766
Jourdan DB, de Weck OL (2004) Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. In: 2004 IEEE 59th on vehicular technology conference, 2004. VTC 2004-Spring, vol 5. IEEE, pp 2466–2470
Kang CW, Chen JH (2009) An evolutionary approach for multi-objective 3d differentiated sensor network deployment. In: International conference on computational science and engineering, 2009. CSE’09, vol 1. IEEE, pp 187–193
Kennedy J (2006) Swarm intelligence. In: Zomaya AY (ed) Handbook of nature-inspired and innovative computing. Springer, Boston, MA, pp 187–219
Konstantinidis A, Yang K, Zhang Q, Zeinalipour-Yazti D (2010) A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Comput Netw 54(6):960–976
Lai CC, Ting CK, Ko RS (2007) September. An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In: IEEE Congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 3531–3538
Le Berre M, Hnaien F, Snoussi H (2011) Multi-objective optimization in wireless sensors networks. In: 2011 international conference on microelectronics (ICM). IEEE, pp 1–4
Li X (2003) A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: E. Cantú-Paz et al (eds) Genetic and evolutionary computation—GECCO 2003. Proceedings, Part I, LNCS, vol 2723. Springer, pp 37–48
Molina G, Alba E, Talbi EG (2008) Optimal sensor network layout using multi-objective metaheuristics. J UCS 14(15):2549–2565
Özdemir S, Attea BA, Khalil ÖA (2013a) Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks. Wirel Pers Commun 71(1):195–215
Özdemir S, Attea BA, Khalil ÖA (2013b) Multi-objective clustered-based routing with coverage control in wireless sensor networks. Soft Comput 17(9):1573–1584
Rajagopalan R (2010) Multi-objective optimization algorithms for sensor network design. In: 2010 IEEE 11th annual on wireless and microwave technology conference (WAMICON). IEEE, pp 1–4
Slijepcevic S, Potkonjak M (2001) Power efficient organization of wireless sensor networks. In: IEEE international conference on communications, 2001. ICC 2001, vol 2. IEEE, pp 472–476
Woehrle M, Brockhoff D, Hohm T, Bleuler S (2010) Investigating coverage and connectivity trade-offs in wireless sensor networks: the benefits of MOEAs. In: Ehrgott M, Naujoks B, Stewart T, Wallenius J (eds) Multiple criteria decision making for sustainable energy and transportation systems. Springer, Berlin, pp 211–221
Yang S, Dai F, Cardei M, Wu J (2005) On multiple point coverage in wireless sensor networks. In: IEEE international conference on mobile adhoc and sensor systems conference, 2005. IEEE, p 8-pp
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. ETH Zurich, Zurich
Zou Y, Chakrabarty K (2004) Sensor deployment and target localization in distributed sensor networks. ACM Trans Embed Comput Syst (TECS) 3(1):61–91
Conflict of interest
Authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Communicated by V. Loia.
About this article
Cite this article
Attea, B.A., Abbas, M.N., Al-Ani, M. et al. Bio-inspired multi-objective algorithms for connected set K-covers problem in wireless sensor networks. Soft Comput 23, 11699–11728 (2019). https://doi.org/10.1007/s00500-018-03721-6
- Evolutionary algorithms
- Multi-objective optimization
- Particle swarm optimization
- Set covers problem
- Self-adaptive heuristic