Abstract
In the wireless sensor network, lifetime enhancement of network is a critical design issue for a wide number of applications. Along with ultra-low power technology, the computational approach in the development of several disjoint covers of sensors, such that each cover must provide the coverage of all targets, can be a more effective means for increasing the life span of the network. This enforces to maximize the number of possible disjoint covers, among available sensors in the network. Effectively this problem can be treated as a Set-K-Cover problem, which has been proven to be NP-complete. To make the solution more power-efficient, in this paper, the complexity of the problem has increased at a further level by expecting not only the upper bound of covers but also making the covers redundant free and formed with the minimal number of sensors. This problem can be considered as a search of multiple components where each component itself carried a multi-dimensional characteristic. In a natural system where evolution is the fundamental principle in the development of any entity, such kind of multi-component complex problem doesn’t handle at one stage. First, an integral approximated solution evolves and later each component evolves separately. This is the reason that for the Set-K-Cover problem, single stages of the various successful evolutionary algorithms have shown their limitation in achieving the upper bound of covers and in delivering redundant free solutions. In the past, some kind of sequential local scanning process has been integrated with evolutionary computation in the iteration for each cover to improve the performance. But discovered covers fail to meet objectives of upper bound and/or redundant free covers with the minimal number of sensors. Hence this research has explored the possibility of finding the solution through more closer to natural way i.e. only through evolution only (without local scanning process). To meet the desired objectives, in this paper an ensemble evolutionary concept has proposed which has delivered redundant free, the upper bound of covers with the minimum number of sensors. Evolutionary Ensemble architecture works at a different level in a cascaded manner to maximize the total possible number of disjoint covers and their refinement along with that there is a feedback mechanism to explore the new covers further if there is any. Based on the natural extinction process, an extinct operator has also introduced in the Genetic algorithm to increase the convergence rate and better exploration. Instead of fitness-oriented, equal opportunity for every parent in offspring creation was introduced to increase the diversity level. The performance results on different simulated networks confirm that without fail the proposed solution has achieved all objectives whereas various variants of Differential evolution and Genetic algorithms and Particle Swarm Optimization fail to deliver desired performances.
Similar content being viewed by others
References
Winkler M, Street M, Tuchs KD, Wrona K (2012) Wireless sensor networks for military purposes. In: Filippini D (eds) Autonomous sensor networks. Springer series on chemical sensors and biosensors (methods and applications), vol 13. Springer, Berlin
Đurišić MP, Tafa Z (2012)”A survey of military applications of wireless sensor networks. In: 2012 mediterranean conference on embedded computing (MECO)
Erciyes K (2019) Case study: environment monitoring by a wireless sensor network. In: Distributed real-time systems. Computer communications and networks. Springer, Cham
Bajrami X, Murturi I (2018) An efficient approach to monitoring environmental conditions using a wireless sensor network and NodeMCU. e & i Elektrotechnik und Informationstechnik 135(3):294–301
Yang J, Zhang C, Li X (2010) Integration of wireless sensor networks in environmental monitoring cyber infrastructure. Wirel Netw 16(4):1091–1108
Grover K, Kahali D, Verma S, Subramanian B (2020) WSN-based system for forest fire detection and mitigation. In: Subramanian B, Chen SS, Reddy K (eds) Emerging technologies for agriculture and environment. Lecture notes on multidisciplinary industrial engineering. Springer, Singapore
Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710
Ramson SRJ, Moni DJ (2017)Applications of wireless sensor networks—a survey. In: 2017 international conference on innovations in electrical, electronics, instrumentation and media technology (ICEEIMT)
Huang YM, Hsieh MY, Sandnes FE (2008) Wireless sensor networks and applications. In: Mukhopadhyay S, Huang R (eds) Sensors. Lecture notes electrical engineering, vol 21. Springer, Berlin
Aznoli F, Navimipour NJ (2017) Deployment strategies in the wireless sensor networks: systematic literature review, classification, and current trends. Wirel Pers Commun 95(2):819–846
Amutha J, Sharma S (2019) WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: review, approaches and open issues. Wirel Pers Commun. https://doi.org/10.1007/s11277-019-06903-z
Wang X, Wang S, Ma J (2006) Dynamic deployment optimization in wireless sensor networks. In: Huang DS, Li K, Irwin GW (eds) Intelligent control and automation. Lecture notes in control and information sciences, vol 344. Springer, Berlin
Rout RR, Ghosh SK (2013) Enhancement of lifetime using duty cycle and network coding in wireless sensor networks. IEEE Trans Wirel Commun 12(2):656–667
Cardei M, MacCallum D, Cheng X, Min M, Jia X, Li D, Du DZ (2002) Wireless sensor networks with energy efficient organization. J Interconnect Netw 3(3/4):213–229
Stine J, de Veciana G (2002) Improving energy efficiency of centrally controlled wireless data networks. Wirel Netw 8(6):681–700
Benini L, Castelli G, Macii A, Macii E et al (2000) A discrete-time battery model for high-level power estimation. In: Design, automation and test in Europe conference, pp 35–39 (2000)
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422
Buczak AL, Jin Y, Darabi H, Jafari M (1999) Genetic algorithm based sensor network optimization for target tracking. Intell Eng Syst Artif Neural Netw 9:349–354
Sausen PS, Spohn MA, Perkusich A (2010) Broadcast routing in wireless sensor networks with dynamic power management and multi-coverage backbones. Inf Sci 180(5):653–663
Berman P, Calinescu G, Shah C, Zelikovsky A (2004) Power efficient monitoring management in sensor networks. In: Proceedings of the wireless communications and networking conference, vol 4, pp 2329–2334 (2004)
Tretyakova A, Seredynski F, Guinand F (2017) Scheduling sensors activity in wireless sensor networks. In: Nguyen N, Papadopoulos G, Jędrzejowicz P, Trawiński B, Vossen G (eds) Computational collective intelligence. ICCCI 2017. Lecture notes in computer science, vol 10448. Springer, Cham
Subir H, Amrita G, Sanjib S, Avishek D, Sipra D (2009) A lifetime enhancing node deployment strategy in WSN. In: Lee Y, Kim T, Fang W, Ślęzak D (eds) Future generation information technology. FGIT 2009
Halder S, Dasbit S (2014) Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes. J Netw Comput Appl 38(1):106–124
Garey MR, Johnson DS (1979) Computers and intractability. Freeman, San Francisco
Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, pp 1–10
Intanagonwiwat C, Govindan R, Estrin D (2000) Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: MobiCom’00: proceedings of the sixth annual international conference on mobile computing and networking. ACM, pp 56–67
Kumar M, Dutta K (2016) LDAT: LFTM based data aggregation and transmission protocol for wireless sensor networks. J Trust Manag 3:2
Liang H, Yang S, Li L et al (2019) Research on routing optimization of WSNs based on improved LEACH protocol. EURASIP J Wirel Commun Netw 2019:194
He Z, Lee BS, Wang XS (2008) Aggregation in sensor networks with a user-provided quality of service goal. Inf Sci 178(9):2128–2149
Marcelloni F, Vecchio M (2010) Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Inf Sci 180(10):1924–1941
Ok C, Lee S, Mitra P, Kumara S (2010) Distributed routing in wireless sensor networks using energy welfare metric. Inf Sci 180(9):1656–1670
Benini L, Castelli G, Macii A, Macii E et al (2000) A discrete-time battery model for high-level power estimation. In: Design, automation and test in Europe conference, pp 35–39
Krishnamachari L, Estrin D, Wicker S (2002) The impact of data aggregation in wireless sensor networks. In: Proceedings of the 22nd international conference on distributed computing systems workshop, 2002, pp 575–578
Wang J, Gao Y (2019) Energy efficient routing algorithm with mobile sink support for wireless sensor networks. Sensors (Basel) 19(7):1494
Slijepcevic S, Potkonjak M (2001) Power efficient organization of wireless sensor networks. In: Proceedings of the IEEE international conference on communications, vol 2, 2001, pp 472–476
Lai CC, Ting CK, Ko RS (2007) An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In: Proceedings of the 2007 congress on evolutionary computation, 2007, pp 3531–3538
Xu Y, Fang J, Zhu W (2013) Differential evolution for lifetime maximization of heterogeneous wireless sensor networks. Math Probl Eng. https://doi.org/10.1155/2013/172783
Singh M, Nagarathna P (2012) Improvement in life span of WSN using genetic algorithm with new fitness function. In: ICECT 2012. IEEE
Ting C-K, Liao C-C (2010) A memetic algorithm for extending wireless sensor network lifetime. Inf Sci 180:4818–4833
Nagarathna P, Manjula R (2016) Maximization of WSN life using hybrid evolutionary programming. Int J Wirel Inf Netw 23(3):246–256
Liao C-C, Ting C-K (2018) A novel integer-coded memetic algorithm for the setk-cover problem in wireless sensor networks. IEEE Trans Cybern 48(8):2245–2258
Kane EA, Higham TE (2015) Complex systems are more than the sum of their parts: using integration to understand performance, biomechanics, and diversity. Integr Comp Biol 55(1):146–165
Bar-Yam Y (2002) General features of complex systems. In: Knowledge management, organizational intelligence and learning, and complexity, vol I
Gregory TR (2008) The evolution of complex organs. Evol Educ Outreach 1:358–389
Cardei M, Thai MT, Li Y, Wu W (2005) Energy-efficient target coverage in wireless sensor networks. In: Proceedings of the IEEE INFOCOM. IEEE, pp 1976–1984
Cardei M, Du DZ (2005) Improving wireless sensor network lifetime through power aware organization. Wirel Netw 11(3):333–340
Abrams Z, Goel A, Plotkin S (2004) Set K-cover algorithms for energy efficient monitoring in wireless sensor networks. In: IPSN’04, April 26–27, 2004, Berkeley, California, USA
AlShawi IS (2012) Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm. IEEE Sens J 12(10):3010–3018
Rout RR (2013) Enhancement of lifetime using duty cycle and network coding in wireless sensor networks. IEEE Wirel Commun 12(2):656–667
Wang C-F (2014) A network lifetime enhancement method for sink relocation and its analysis in wireless sensor networks. IEEE Sens J 14(6):1932–1943
Saha D, Das N (2013) Distributed area coverage by connected set cover partitioning in wireless sensor networks. In: First international workshop on sustainable monitoring through cyber-physical systems (SuMo-CPS), in conjunction with ICDCN, Mumbai, India
Yu J, Chen Y, Ma L, Huang B, Cheng X (2016) On connected target k-coverage in heterogeneous wireless sensor networks. Sensors 16:104. https://doi.org/10.3390/s16010104
Raza U, Bogliolo A, Freschi V, Lattanzi E, Murphy AL (2016) A two-prong approach to energy-efficient WSNs: wake-up receivers plus dedicated, model-based sensing. Ad Hoc Netw 45:1–12
Mohamed SM, HamzaIman HS, Saroi A (2017) Coverage in mobile wireless sensor networks (M-WSN): a survey. Comput Commun 110:133–150
Elhoseny M, Tharwat A, Yuan X, Hassanien AE (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153
Potthuri S, Shankar T, Rajesh A (2018) Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Eng J 9(4):655–663
Tchakonté DT, Simeu E, Tchuente M (2018) Lifetime optimization of wireless sensor networks with sleep mode energy consumption of sensor nodes. Wirel Netw. https://doi.org/10.1007/s11276-018-1783-3
Ahmed MM, Houssein EH, Hassanien AE (2019) Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun Syst 72:1–17
Katti A (2019) Target coverage in random wireless sensor networks using cover sets. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.05.006
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Fogel LJ (1999) Intelligence through simulated evolution: forty years of evolutionary programming. Wiley, Hoboken
Beyer H-G, Schwefel H-P (2002) Evolution strategies: a comprehensive introduction. J Natural Comput 1(1):3–52
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, a bradford book. The MIT Press, Cambridge
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. IV, pp 1942–1948. https://doi.org/10.1109/icnn.1995.488968
Müller SD, Marchetto J, Airaghi S, Koumoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6(1):16–29
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Singh MK (2013) A new optimization method based on adaptive social behavior: ASBO. In: Kumar MA, Selvarani R, Kumar T (eds) Proceedings of international conference on advances in computing. Advances in intelligent systems and computing, vol 174. Springer, New Delhi
Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications. IEEE Trans Ind Electron 43(5):519–534
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Sarker RA, Elsayed SM, Ray T (2014) Differential evolution with dynamic parameters selection for optimization problems. IEEE Trans Evol Comput 18(5):689–707
Chivian E, Bernstein A (eds) (2008) Sustaining life: how human health depends on biodiversity. Center for Health and the Global Environment, Oxford University Press, New York
Darwin C (1859) On the origin of species by means of natural selection, or preservation of favoured races in the struggle for life. John Murray, London
Raup DM (1994) The role of extinction in evolution. Proc Natl Acad Sci USA 91:6758–6763
Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: Evolutionary programming VII. Springer, Berlin, pp 591–600
Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859–871
Prakasha S, Raju GT, Singh MK (2016) Cluster optimisation in information retrieval using self-exploration-based PSO. Int J Intell Eng Inform 4(1):91–115
Bonyadi MR (2019) A theoretical guideline for designing an effective adaptive particle swarm. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2019.2906894
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Singh, M.K. Discovery of redundant free maximum disjoint Set-k-Covers for WSN life enhancement with evolutionary ensemble architecture. Evol. Intel. 13, 611–630 (2020). https://doi.org/10.1007/s12065-020-00374-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-020-00374-z