Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

This survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. The marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of artificial systems' problems has been shaped into an important field with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. The process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.

[1]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[2]  Sanyou Zeng,et al.  Evolvable Systems: From Biology to Hardware, 7th International Conference, ICES 2007, Wuhan, China, September 21-23, 2007, Proceedings , 2007, ICES.

[3]  Ganesh K. Venayagamoorthy,et al.  Bio-inspired node localization in wireless sensor networks , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[5]  Robert A. Johnson Learning, Memory, and Foraging Efficiency in Two Species of Desert Seed‐Harvester Ants , 1991 .

[6]  Walter Lang,et al.  Advanced Bio-inspired Plausibility Checking in a Wireless Sensor Network Using Neuro-immune Systems: Autonomous Fault Diagnosis in an Intelligent Transportation System , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[7]  Wang Jinghua,et al.  Data Aggregation and Routing in Wireless Sensor Networks Using Improved Ant Colony Algorithm , 2009, 2009 International Forum on Computer Science-Technology and Applications.

[8]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[9]  Gianluca Tempesti,et al.  Fault Tolerance Using Dynamic Reconfiguration on the POEtic Tissue , 2007, IEEE Transactions on Evolutionary Computation.

[10]  Kay Römer,et al.  The design space of wireless sensor networks , 2004, IEEE Wireless Communications.

[11]  Gheorghe Paun,et al.  Introduction to Membrane Computing , 2006, Applications of Membrane Computing.

[12]  William Leventon,et al.  Cover story: synthetic skin , 2002 .

[13]  Eylem Ekici,et al.  SAMAC: A Cross-Layer Communication Protocol for Sensor Networks with Sectored Antennas , 2010, IEEE Transactions on Mobile Computing.

[14]  Robin De Keyser,et al.  A Theoretical Study on Modeling the Respiratory Tract With Ladder Networks by Means of Intrinsic Fractal Geometry , 2010, IEEE Transactions on Biomedical Engineering.

[15]  Li Li,et al.  Hybrid Learning Algorithm for Effective Coverage in Wireless Sensor Networks , 2008, 2008 Fourth International Conference on Natural Computation.

[16]  Peter Corke,et al.  Networked Cows : Virtual fences for controlling cows , 2004 .

[17]  Jianhua Li,et al.  Combinatorial optimization for wireless sensor networks , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..

[18]  Hitoshi Iba,et al.  Learning polynomial feedforward neural networks by genetic programming and backpropagation , 2003, IEEE Trans. Neural Networks.

[19]  Albrecht Schmidt,et al.  Applying wearable sensors to avalanche rescue , 2003, Comput. Graph..

[20]  Zhiming Wu,et al.  A TDMA scheduling scheme for many-to-one communications in wireless sensor networks , 2007, Comput. Commun..

[21]  Weng-Long Chang,et al.  Fast parallel molecular algorithms for DNA-based computation: factoring integers , 2005, IEEE Transactions on NanoBioscience.

[22]  Kirk Martinez,et al.  Sensor web for glaciers , 2004 .

[23]  Kenneth N. Lodding The Hitchhiker’s Guide to Biomorphic Software , 2004, ACM Queue.

[24]  W. Leventon Synthetic skin , 2002 .

[25]  Neil Genzlinger A. and Q , 2006 .

[26]  R.R. Selmic,et al.  Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection , 2008, 2007 IEEE International Conference on Networking, Sensing and Control.

[27]  Paul Marrow,et al.  Nature-Inspired Computing Technology and Applications , 2000 .

[28]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[29]  Yi Pan,et al.  An adaptive genetic fuzzy multi-path routing protocol for wireless ad-hoc networks , 2005, Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network.

[30]  M. Marks,et al.  A Survey of Multi-Objective Deployment in Wireless Sensor Networks , 2023, Journal of Telecommunications and Information Technology.

[31]  Venkatesh Saligrama,et al.  Efficient Sensor Management Policies for Distributed Target Tracking in Multihop Sensor Networks , 2008, IEEE Transactions on Signal Processing.

[32]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[33]  Ian F. Akyildiz,et al.  XLP: A Cross-Layer Protocol for Efficient Communication in Wireless Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[34]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[35]  Antonio Alfredo Ferreira Loureiro,et al.  Simulating large wireless sensor networks using cellular automata , 2005, 38th Annual Simulation Symposium.

[36]  Jun Zhang,et al.  Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks , 2010, IEEE Transactions on Evolutionary Computation.

[37]  Erkki Mäkinen,et al.  A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks , 2009, IEEE Transactions on Neural Networks.

[38]  Sarat Kumar Patra,et al.  Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[39]  Luca Maria Gambardella,et al.  An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem , 2000, INFORMS J. Comput..

[40]  Fred W. Glover,et al.  Tabu Search , 1997, Handbook of Heuristics.

[41]  Hugo Terashima-Marín,et al.  On the possibility to design intelligent sensor systems after excitable media models: An agent-based simulation , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[42]  Li Zhi,et al.  The Multi-Objective Routing Optimization of WSNs Based on an Improved Ant Colony Algorithm , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[43]  Grzegorz Rozenberg,et al.  The mathematical theory of L systems , 1980 .

[44]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[45]  Imed Bouazizi,et al.  ARA-the ant-colony based routing algorithm for MANETs , 2002, Proceedings. International Conference on Parallel Processing Workshop.

[46]  Mohammad Najmud Doja,et al.  Swarm intelligent power-aware detection of unauthorized and compromised nodes in MANETs , 2008 .

[47]  ČernýV. Thermodynamical approach to the traveling salesman problem , 1985 .

[48]  Jung-Tang Huang,et al.  A Miniaturized Hilbert Inverted-F Antenna for Wireless Sensor Network Applications , 2010, IEEE Transactions on Antennas and Propagation.

[49]  Chris Roadknight,et al.  Self-organising Sensor networks. , 2003 .

[50]  Yunhao Liu,et al.  Rendered Path: Range-Free Localization in Anisotropic Sensor Networks With Holes , 2007, IEEE/ACM Transactions on Networking.

[51]  Hui Cheng,et al.  Genetic Algorithms With Immigrants and Memory Schemes for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[52]  S. Wolfram Statistical mechanics of cellular automata , 1983 .

[53]  W. Marsden I and J , 2012 .

[54]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[55]  J. Podpora,et al.  Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks , 2008, IEEE Sensors Journal.

[56]  Dario Pompili,et al.  Overview of networking protocols for underwater wireless communications , 2009, IEEE Communications Magazine.

[57]  Sohail Jabbar,et al.  Computational Intelligence Based Optimization of Energy aware Routing in WSN , 2011 .

[58]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[59]  Fernando José Von Zuben,et al.  Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System , 2011, IEEE Transactions on Neural Networks.

[60]  Kashif Saleem,et al.  Proposed Nature Inspired Self-Organized Secure Autonomous Mechanism for WSNs , 2009, 2009 First Asian Conference on Intelligent Information and Database Systems.

[61]  Russ Abbott,et al.  Challenges for biologically-inspired computing , 2005, GECCO '05.

[62]  B. P. Vijaya Kumar,et al.  Dynamic clustering for Wireless Sensor Networks: A Neuro-Fuzzy technique approach , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[63]  Adel Said Elmaghraby,et al.  A swarm-based fuzzy logic control mobile sensor network for hazardous contaminants localization , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).

[64]  Guolong He,et al.  Universal Perceptron and DNA-Like Learning Algorithm for Binary Neural Networks: LSBF and PBF Implementations , 2009, IEEE Transactions on Neural Networks.

[65]  Hao Wu,et al.  An Improved Coverage Scheme Based on Cellular Automata in WSN , 2010, 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing.

[66]  Helena Ramalhinho Dias Lourenço,et al.  Iterated Local Search , 2001, Handbook of Metaheuristics.

[67]  Peter Secretan Learning , 1965, Mental Health.

[68]  Jason Brownlee,et al.  On biologically inspired computation , 2005 .

[69]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[70]  Klaus H. Hinrichs,et al.  A Generic Rendering System , 2002, IEEE Trans. Vis. Comput. Graph..

[71]  Yong Wang,et al.  Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet , 2002, ASPLOS X.

[72]  S. Wicker,et al.  Termite: ad-hoc networking with stigmergy , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[73]  Andrew Adamatzky,et al.  Genetic approaches to search for computing patterns in cellular automata , 2009, IEEE Computational Intelligence Magazine.

[74]  H. Sharif,et al.  Artificial Immune System based image pattern recognition in energy efficient Wireless Multimedia Sensor Networks , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[75]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[76]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..

[77]  V. Cerný Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[78]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[79]  Jingwen Tian,et al.  Wireless Sensor Network for Community Intrusion Detection System Based on Improved Genetic Algorithm Neural Network , 2009, 2009 International Conference on Industrial and Information Systems.

[80]  Jin-Kao Hao,et al.  Tabu Search for Graph Coloring, T-Colorings and Set T-Colorings , 1999 .

[81]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[82]  Jean-Yves Le Boudec,et al.  An artificial immune system approach with secondary response for misbehavior detection in mobile ad hoc networks , 2005, IEEE Transactions on Neural Networks.

[83]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[84]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.