Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks

Energy-efficient clustering protocols are much sought specially for low-power, multi-functional Wireless Sensor Networks (WSNs). With the application of Computational Intelligence (CI) based approaches, various metaheuristics have been developed for energy-efficient clustering in WSNs. Artificial Bee Colony (ABC) is one such metaheuristic which arose much interest over other population-based metaheuristics for solving optimization problems in WSNs due to its ease of implementation and adaptive nature. However, its solution search equation, which is poor at exploitation process, contributes to its insufficiency. Thus, we present an improved Artificial Bee Colony (iABC) metaheuristic with an improved solution search equation to improve its exploitation capabilities. Additionally, in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student's-t distribution. The proposed metaheuristic maintains 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. Further, an energy efficient clustering protocol BeeCluster based on iABC metaheuristic is introduced, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads (CHs) and improves 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.

[1]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[2]  Samuel Pierre,et al.  A distributed energy-efficient clustering protocol for wireless sensor networks , 2010, Comput. Electr. Eng..

[3]  Amit Konar,et al.  Metaheuristic Clustering , 2009, Studies in Computational Intelligence.

[4]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[5]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[6]  Prasanta K. Jana,et al.  Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach , 2014, Eng. Appl. Artif. Intell..

[7]  Yookun Cho,et al.  PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks , 2007, Comput. Commun..

[8]  Neeraj Kumar,et al.  A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks , 2013, J. Netw. Comput. Appl..

[9]  Muddassar Farooq,et al.  BeeSensor: A Bee-Inspired Power Aware Routing Protocol for Wireless Sensor Networks , 2009, EvoWorkshops.

[10]  Sanyang Liu,et al.  A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning , 2013, IEEE Transactions on Cybernetics.

[11]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems , 2015, Appl. Soft Comput..

[12]  Peng Guo,et al.  Global artificial bee colony search algorithm for numerical function optimization , 2011, 2011 Seventh International Conference on Natural Computation.

[13]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[14]  Lingling Huang,et al.  A global best artificial bee colony algorithm for global optimization , 2012, J. Comput. Appl. Math..

[15]  Guoqiang Li,et al.  Development and investigation of efficient artificial bee colony algorithm for numerical function optimization , 2012, Appl. Soft Comput..

[16]  Sanyang Liu,et al.  Improved artificial bee colony algorithm for global optimization , 2011 .

[17]  A. Rezaee Jordehi,et al.  Particle swarm optimisation (PSO) for allocation of FACTS devices in electric transmission systems: A review , 2015 .

[18]  David Naso,et al.  Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[19]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[20]  Lewis Girod,et al.  Wireless Sensor Networks: Deployments and Design Frameworks , 2010 .

[21]  Ganesh K. Venayagamoorthy,et al.  Computational Intelligence in Wireless Sensor Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[22]  Wei Zhao,et al.  A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks , 2009, Sensors.

[23]  N. Al-KarakiJ.,et al.  Routing techniques in wireless sensor networks , 2004 .

[24]  Bara'a Ali Attea,et al.  Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks , 2011, Swarm Evol. Comput..

[25]  Siba K. Udgata,et al.  Artificial bee colony algorithm for small signal model parameter extraction of MESFET , 2010, Eng. Appl. Artif. Intell..

[26]  Song Mao,et al.  Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO , 2011 .

[27]  Yi Shang,et al.  A biologically-inspired clustering protocol for wireless sensor networks , 2007, Comput. Commun..

[28]  A. Rezaee Jordehi,et al.  Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems , 2015 .

[29]  Rui Zhang,et al.  An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times , 2011, Entropy.

[30]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC's in power systems , 2015 .

[31]  Yan Jin,et al.  EEMC: An Energy-Efficient Multi-Tier Clustering Algorithm for Large-Scale Wireless Sensor Networks , 2006, 2006 International Conference on Wireless Communications, Networking and Mobile Computing.

[32]  Junita Mohamad-Saleh,et al.  Enhanced Global-Best Artificial Bee Colony Optimization Algorithm , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.

[33]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[34]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[35]  K JanaPrasanta,et al.  Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks , 2015 .

[36]  S. Deng,et al.  Mobility-based clustering protocol for wireless sensor networks with mobile nodes , 2011, IET Wirel. Sens. Syst..

[37]  Bara'a Ali Attea,et al.  A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks , 2012, Appl. Soft Comput..

[38]  A. Rezaee Jordehi,et al.  An efficient chaotic water cycle algorithm for optimization tasks , 2015, Neural Computing and Applications.

[39]  R. B. Patel,et al.  EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks , 2009, Comput. Commun..

[40]  Rammohan Mallipeddi,et al.  Differential Evolution with Population and Strategy Parameter Adaptation , 2015 .

[41]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[42]  Rajesh Kumar,et al.  Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks , 2014, IEEE Transactions on Industrial Informatics.

[43]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[44]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[45]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[46]  Xin-Ping Guan,et al.  A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks , 2012, Future Gener. Comput. Syst..

[47]  V. KulkarniR.,et al.  Computational Intelligence in Wireless Sensor Networks , 2011 .

[48]  Mingzhe Liu,et al.  Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm , 2015, Neurocomputing.

[49]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[50]  Elena Gaura,et al.  Wireless Sensor Networks:Deployments and Design Frameworks: Deployments and Design Frameworks , 2010 .