Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks
Tóm tắt
Efficient clustering is a well-documented NP-hard optimization problem in wireless sensor networks (WSNs). Variety of computational intelligence techniques including evolutionary algorithms, reinforcement learning, artificial immune systems and recently, artificial bee colony (ABC) metaheuristic have been applied for efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to comparably poor exploitation cycle and requirement of certain control parameters. Thus, we propose an improved artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve exploitation capabilities of existing metaheuristic. Further, to enhance the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s t-distribution, which require only one control parameter to compute and store and therefore increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements; moreover, the use of first-of-its-kind compact Student’s t-distribution makes it suitable for limited hardware requirements of WSNs. Additionally, an energy-efficient clustering protocol based on iABC metaheuristic is presented, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads along with an optimal base station location to improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well-known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.
Tài liệu tham khảo
Abro AG, Mohamad-Saleh J (2012) Enhanced global-best artificial bee colony optimization algorithm. In: Sixth UKSim-AMSS European symposium on computer modeling and simulation, pp 95–100
Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14):2826–2841
Ari AAA, Yenke BO (2016) A power efficient cluster-based routing algorithm for wireless sensor networks: honeybees swarm intelligence based approach. J Netw Comput Appl
Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 3(3):325–349
Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: a survey. Wirel Commun IEEE 11(6):6–28
Attea BA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput 12(7):1950–1957
Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192(120):142
Chamam A, Pierre S (2010) A distributed energy-efficient clustering protocol for wireless sensor networks. Comput Electr Eng 36(2):303–312
Chen R (1984) Location problem with cost being sum of power of euclidean distances. J Comput Oper Res 11(3):285–294
Das S, Sugantha PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31
Das S, Abraham A, Konar A (2009) Metaheuristic clustering. Stud Comput Intell 178:252
Deng S, Li J, Shen L (2011) Mobility-based clustering protocol for wireless sensor networks with mobile nodes. Wirel Sens Syst IET 1(1):39–47
Ding Y, Chen R, Hao K (2016) A multi-path routing algorithm with dynamic immune clustering for event-driven wireless sensor networks. Neurocomputing
Ferranate Neri GI (2001) Compact optmization. In: Handbook of Optimization, ISRL 38, pp 337–364
Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111(17):871–882
Gao W, Liu LHS (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753
Gao W, Liu LHS (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybernet 43(3):1011–1024
Gaura E (2010) Wireless sensor networks: deployments and design frameworks. Springer, New York
Gonuguntla V, Mallipeddi R, Veluvolu KC (2015) Differential evolution with population and strategy parameter adaptation. Math Probl Eng 2015:287607. doi:10.1155/2015/287607
Guo P, Cheng JLW (2011) Global artificial bee colony search algorithm for numerical function optimization. Seventh Int Conf Nat Comput 3:1280–1283
Heinzelman WB, Chandrakasan AP, Balakrishnan H et al (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670
Hoang D, Yadav P, Kumar R, Panda S (2014) Real-time implementation of a harmony search algorithm-based clustering protocol for energy efficient wireless sensor networks. IEEE Trans Ind Inform 10(1):774–783
Jin Y, Wang L, Kim Y, Yang X (2008) Eemc: an energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks. Comput Netw 52(3):542–562
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 8(1):687–697
Khalil EA, Attea BA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evolut Comput 1(4):195–203
Kuila P, Jana PK (2014) Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng Appl Artif Intell 33:127–140
Kulkarni RV, Forster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. Commun Surv Tutor IEEE 13(1):68–96
Kumar D, Aseri TC, Patel R (2009) Eehc: energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput Commun 32(4):662–667
Larranaga P, Lozano JA (2001) Estimation of distribution algorithms: a new tool for evolutionary computation. Kluwer, Alphen aan den Rijn
Li G, Niu XXP (2013) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12(1):320–332
Liu Z, Zheng Q, Xue L, Guan X (2012) A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks. Future Gen Comput Syst 28(5):780–790
Mao SS, Zhao Cl W (2011) Unequal clustering algorithm for wsn based on fuzzy logic and improved aco. J China Univ Posts Telecommun 18(6):89–97
Mininno E, Cupertino DNF (2008) Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Trans Evol Computer 12(2):203–219
R Apostol MAM (2003) Sum of square of distance in m-space. The Mathematics Asso of America, pp 516–526
Ozturk C, Hancer E (2015) Dynamic clustering with improved binary artificial bee colony algorithm. Appl Soft Comput 28(69):80
Saleem M, Farooq M (2012) Beesensor: a bee-inspired power aware routing protocol for wireless sensor networks. In: Applications of evolutionary computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, New York, pp 81–90
Samrat L, Udgata AAS (2010) Artificial bee colony algorithm for small signal model parameter extraction of mesfet. Eng Appl Artif Intell 11:1573–1592
Selvakennedy S, Sinnappan S, Shang Y (2007) A biologically-inspired clustering protocol for wireless sensor networks. Comput Commun 30(14):2786–2801
Storn RPK (2010) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 23:689–694
Tyagi S, Kumar N (2012) A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks. J Netw Comput Appl 36(1):623–645
Walck C (1996) Handbook on statistical distributions for experimentalists. Internal report SUT-PFY/96–01. Stockholm
Yang J, Xu M, Zhao W, Xu B (2009) A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks. Sensors 10(5):4521–4540
Yi S, Heo J, Cho Y, Hong J (2007) Peach: power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Comput Commun 30(14):2842–2852
Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330
Younis O, Fahmy S (2004) Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379
Zhang R, Wu C (2011) An artificial bee colony algorithm for the job shop scheduling problem with random processing times. Entropy 13(9):1708–1729
Zhu C, Zheng C, Shu L, Han G (2012) A survey on coverage and connectivity issues in wireless sensor networks. J Netw Comput Appl 35:619–632