Intelligent Optimization of Wireless Sensor Networks through Bio

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  • Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2013, Article ID 421084, 13 pageshttp://dx.doi.org/10.1155/2013/421084

    Review ArticleIntelligent Optimization ofWireless Sensor Networks throughBio-Inspired Computing: Survey and Future Directions

    Sohail aaraia Iramidliinhas ImranShaShehzadhalid andueethmad

    Bahria University Wireless Research Center, Bahria University, 44220 Islamabad, Pakistan

    Correspondence should be addressed to Sohail Jabbar; sjabbar.research@gmail.com

    Received 14 July 2012; Revised 15 December 2012; Accepted 17 December 2012

    Academic Editor: Jiman Hong

    Copyright 2013 Sohail Jabbar et al. is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

    1. Introduction

    Aer exhaustive effort in gaining maximum productivityfrom brainstorming solutions, researchers are now incliningtowards biologically inspired solutions. Each and every cre-ation of the One has the invitation to the researchers andthinkers to ponder. is creation is perfect in its own lifeand needs. e ultimate ideological convergence of scientiststo this perfect creation for nding the relevant traces ofproblems and their solutions is in fact moving to the best.Giving insight attention to the nature and the creation givesthe glow to thoughts due to its miraculous architecture. Ibnal-Haitham, a 10th-century mathematician, astronomer, andphysicist, invented the rst pin-hole camera by understand-ing the mechanism that light enters the eye, rather thanleaving it [1]. In the 9th century, Abbas ibn Firnas was therst person to make a real attempt to construct a yingmachine and y. He designed a winged apparatus, roughlyresembling a bird costume [2]. Moreover, Newtons theory ofgravity, Wright brothers inuenced work of ying, Einsteins

    theory of relativity, and John von Neumanns abstract modelof self-reproduction are just the names of few. e intenseexploitation of these miraculous creations of the One inHumans problems solving has now organized as a completeeld naming biological-inspired computing. e word com-puting encompasses the activities of using and improvingthe computer technology, computer hardware, and computersoware. e technique of formulating the views, ideas,technologies, and algorithms for unknotting the issues aswell as introducing the innovation in computer technology,hardware, and soware that are motivated by the biologicalsystems is called biologically inspired computing or bio-inspired computing. Its typical consortium is shown in Tables1 and 2. ree key subelds are loosely knit together to formthe core of bio-inspired computing which are connectionism,social behavior, and emergence. ough biological systemsexhibit complex, intelligent, and organize behavior, they arecomprised of simple elements governed by simple rules. It ishighly cost effective to hire the complex and in-networkedprocessing-based simple rules inman-made systems.e real

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    effort that comes in this synergistic solution is in ndingthe similar patterns of issues in articial (man-made) andreal (natural) creation and later their solutions. is invitesthe interdisciplinary research which is an essential driver forinnovation.

    e rest of the paper is organized as follows. Section 2summarizes the intelligent optimization in biological sys-tems. Bio-inspired intelligent optimization for non-biologicalsystem is discussed in Section 3. Section 4 shows the opti-mization in wireless sensor networks domain. In continua-tion to this, in Section 5 a connection is tried to be establishedbetween real and articial systems to show how differentarticial systems have been developed from the real systemand what are the constituents of this establishment. Solutionof various problems from the wireless sensor networksdomain from different bio-inspired systems is discussed andtabulated in Section 6. Future direction and conclusion aregiven in the subsequent sections.

    2. Intelligent Optimization inBiological Systems

    e real creation of any natural system has marvelous traitsin it. e source of inspiration is the traits which providea guiding light to lead us towards the optimal solutionfor inspired systems. As the traditional optimization prob-lems need more memory and computational power andare directly exponentially related with the problem size,optimization can be an interdisciplinary approach for ndingthe best solution of a problem among alternatives. Bettercomputer systems and improved performance of computa-tional tasks such as exibility, adaptability, decentralization,and fault tolerance can be achieved in the form of bio-inspired algorithms from the principles evolved fromNature.As far as modeling of a problem with respect to the bio-inspired algorithms is concerned it enables the problem tosolve according to the principles evolved from nature byunderstanding natures rule the complex problems. Figure1 below shows the optimization techniques classication tosolve optimization issues based on three categories, that is,search space, problem, and form of objective function.

    One of the key features of biological systems in searchingthe best solution is optimization. Most of the time, optimiza-tion process needs iteration of working sessions. Foragingbehavior in ants and bees systems, ocking, herding, andschooling process in birds, animals, and sh, respectively,are some of the typical examples of iteration of workingsessions for the optimal respective solutions. Optimizationis the scientic discipline that raises the winning ag for thesolution which gives the optimal result among all availablealternatives in resolving the particular problem. Swarm intel-ligence (SI) is one of the aspiring solutions from bio-inspiredcomputing for such heuristic optimization problems, forexample, routing. Swarm refers to the large no. of insects orother small organized entities, especially when they are inmotion. Global intelligent behavior to which the individualsare entirely unknown is emerged due to the local, self-organized, and decentralized interaction of swarms agents.

    Search

    space

    Problem

    Objective

    function

    Optimization

    Discrete

    Combinatorial

    Continuous

    Global

    Mixed integer

    Dynamic

    Multiobjective

    Linear

    Nonlinear

    Convex

    Quadratic

    Stochastic

    Nonconvex

    F 1: Optimization categories.

    Natural examples of SI include ant colony, bird ocking,animal herding, bacterial growth, sh schooling, drop water,and reies. Examples of algorithms under the head of SI areAnt Colony Optimization (ACO), River Formation Dynam-ics (RFD), Particle SwarmOptimization (PSO), GravitationalSearch Algorithm (GSA), Intelligent Water Drop (IWD),Charged System Search (CSS) and Stochastic DiffusionSearch (SDS). Among all genus under swarm intelligence,autonomously social and extremely well-organized as well ascolony-based life is of Ants. Inspired algorithm from ants isAnt Colony Optimization. Number of combinatorial opti-mization problems are targeted by theACO technique such asasymmetric travelling salesman [3], graph coloring problem[4], and load balancing in telecommunication networks [5].Ant Colony Optimization has been formalized into a meta-heuristic for combinatorial optimization problem. A meta-heuristic is a set of algorithmic concepts that can be used todene heuristic methods applicable to wide set of differentproblems. Examples of meta-heuristic include tabu search[68], simulated annealing [9, 10], iterative local search[11], evolutionary computation [1215], and ant colonyoptimization [1619]. For their applications in differentelds for the optimal solutions, their nature is customizedaccordingly. For example, in case ofWireless Sensor Network(WSN), which is an energy, memory, and computationalpower constraint network, the proposed heuristic algorithmmust be customized enough to cater for these constraints.

    3. Bio-Inspired Intelligent Optimization forNonbiological System

    Survival of the best is the key driving force behind theoptimization in articial systems. From nothing to existenceof global village clearly portrays this endeavor of movingto the best. e inuence of biology and life sciences isa source of inspiration sin