REVIEW OF RFID OPTIMAL TAG COVERAGE ALGORITHMS

REVIEW OF RFID OPTIMAL TAG COVERAGE ALGORITHMS Adel Muhsin Elewe, Khalid bin Hasnan and Azli bin Nawawi Faculty of Mechanical and Manufactuing Engineeing, Univesiti Tun Hussein Onn Malaysia, Pait Raja, Batu Pahat, Joho, Malaysia E-Mail: adelmuhsin2@gmail.com ABSTRACT Radio Fequency Identification (RFID) system is a technology that use lage numbe of tags communicates with small numbe of eades. This situation leads to the poblem of detemining the eadability of Passive RFID Tanspondes based on the limited ange of the eade-to-tag communication. Fo this eason seveal algoithms have been developed in ode to optimize RFID tag coveage fo impoving functional pocedues. Natue Inspied Algoithms applied to find RFID Optimal tag coveage. Paticle Swam Optimization (PSO) algoithm is used as an optimization technique because its fast in opeation speeds, easy to implement and fewe paametes need to be adjusted. To impove accuacy, maximize the tacking pecision and minimize the eade consumption it's hybidized with many techniques. The atificial bee colony algoithm (ABC) is anothe optimization algoithm which is distinguished as a simple algoithm with high flexibility, stong obustness, few contol paametes, ease of combination with othe methods, ability to handle the objective with stochastic natue, fast convegence, and both exploation and exploitation. Finally the bacteial foaging optimization (BFO) as a global optimization algoithm optimizes the local minima, diection of movement, andomness, swaming and attaction/ epelling. All these algoithms pesented in this pape. Keywods: tag coveage, natue inspied algoithms. INTRODUCTION RFID systems ae a evolutionay element. It can ead multiple tags at the same time with a lage data stoage capacity wheeas the system can stoe a tag seial numbe which is used to identify the objects globally and uniquely. An RFID stand fo Radio Fequency Identification is a communication medium with non-light sight and employs the adio fequencies to tansmit o eceive signals. Radio Fequency Identification (RFID) tag is electonic piece chaacteized as a noncontact automatic identification technology. RFID tags utilize the RF signal to use it as an infomation tansfe medium and enegy souce to exchange the infomation with the measued objects (Hasnan et al. n.d.). An RFID system consists of thee main components: a tag (o tansponde), a eade (o inteogato) and a middlewae. The tansponde usually located on the object to be identified. It is made of a chip and an antenna with a unique code to povide unique object identification. The inteogato o eade emits adio signals and eceive signal in etun fom the tag. A eade typically contains a tansmitte and eceive (adio fequency module), a contol unit and an antenna. Additional inteface (RS 485, RS 232, etc.) ae fitted with eades to enable them to fowad the data eceived to othe systems such as PC, obot contol system, etc... The last component is the middlewae which can povide the pimay link between RFID eades and databases (Nawawi et al. 2011). Thee ae two types of RFID tags: Active and Passive. An active tag is an expensive cicuit because it equipped with a battey. Its size is lage but it can pefom complicated pocessing fo this eason it used fo lage goods such as containe in pot o cas in paking aea. While passive tag is small, light, cheap, and poweed by the adio fom eade. It has no stoage fo pocessing histoy fo this eason it is used fo small items such as goods in a etail waehouse to identify the ID and some embedded infomation. RFID Netwok Planning (RNP) is vey cucial in deploying the RFID system which is pesent an acceptable Quality of Sevice (QoS) by accomplish seveal objectives such as maximizing tag coveage, maximizing economic efficiency, minimizing eade's intefeence and achieving equal load balance in all eades. Theefoe enhancing eadability of tag coveage epesents the most cucial among othe RNP objectives. Fo this eason RNP optimization can be fomed by investigating the best location and powe setting fo each eade. As a esult, many algoithms wee employed in ode to detemine the best locations fo eades (Nawawi et al. 2015). TAG COVERAGE Tag density and stuctue of tag distibutions epesent an effective Factos which affect ead ange fo RFID. Its pimaily depends on the popeties of the undelying RFID technology. The ange in the eade-totag communication and the signal fom tag to eade play a majo ole in tag coveage function. The objective function fo minimizing tag coveage is as follows (Nawawi et al. 2015). p ( ) (1) C min i d i 1 But thee is no obvious coelation elated to the position and powe of eades. This poblem Fiis equation; (Nawawi et al. 2015). Applied fo detemining the powe eceived at tag: 7706

P P. Gt. P 2 (4 d ) ( ) / (2) RFID TAG COVERAGE OPTIMIZATION Optimal tag coveage consists of identifying a minimal set of eades that cove all tags pesent in the system, detecting maximum numbe of tags with the coelation of the cost consideation (Hasnan et al. 2012) the Optimal tag coveage minimizing edundant epots fom multiple eades by using a minimal set of eades in the system which is cove all pesented tags. Fo this eason this objective occupy the highest pioity and weighting (Nawawi et al. 2015) (Ma et al. 2014), (Nawawi et al. 2015) poposed mathematical model fo RFID tag coveage optimization as shown in equation below, the main issue in this equation is to minimize the diffeence between the theshold powe and the powe eceives at tag. the function is fomulated as the sum of the diffeence between the desied powe levels Rq and the actual eceived powe Pi j of each tag in each element: system. This algoithm is inspied by the movement of bids and fishes in thei own goups PSO advantages is fast in opeation speed, easy to implement and fewe paametes need to be adjusted woking on vaious optimization poblems and finally it is easy to modify/alte and uses less memoy and paamete to wok with in ode to fulfill diffeent needs. Algoithm (below) povides a pseudocode of the Paticle Swam Optimization algoithm: P Q i 1 ij q i 1 j 1 f ( ) (3) Whee Ni is the numbe of tags in the ith element. All the powe levels in this function ae expessed in dbm. Using this objective function the algoithm ties to locate the RFID eades close to the egions whee the desied coveage level is highe, wheeas the aeas whee a lowe coveage is equested ae taken into account by a pope incease of the adiated powe. OPTIMIZATION TECHNIQUES Optimization is a pocess, o methodology fo impoving functional pocedues such as finding the maximum o minimum of a function in ode to highest achievable pefomance unde the given constaints. Atificial Intelligence (AI) techniques intoduced an inteesting application in engineeing. Optimization techniques epesent a poweful set of tools which can be used to find optimal solutions of many kinds of poblems. In RFID system, optimization and seach technique was vey helpful in solving poblems of lage seach spaces, high complexity, seaching ill-stuctued spaces. Fo this eason Natue Inspied Algoithms applied in this aea. This study will pesent oveview of the natue inspied algoithms used in RFID tag coveage optimization. Natue inspied computation techniques include evolutionay algoithm (EA) and swam intelligence (SI). SI includes fou diffeent algoithms, namely atificial bee colony (ABC), ant colony optimization (ACO), paticle swam optimization (PSO), bacteial foaging optimization (BFO) and (Hasnan et al. 2013) a) Paticle swam optimization (PSO) algoithm Paticle Swam Optimization (PSO) algoithm is an optimization technique that is based on a population PSO is tends to fall easily into the local minima (Pasopoulos and Vahatis, 2002), also it cannot adjust the velocity sufficiently. In ode to ovecome the PSO poblems, the eseaches poposed some modifications in paametes and hybidized PSO. Many eseaches apply this method to impove tag coveage level. (Niu et al. 2007), pesents a multi-swam coopeative paticle swam optimize MCPSO. The pefomances of the poposed algoithms ae compaed with the standad PSO and its vaiants demonstated the supeioity of MCPSO. (Chen and Zhu 2008) develop a mathematical model based on the application of two poweful optimization techniques known as Evolutionay Algoithms (EAs) and Swam Intelligence (SI). It obtains a supeio solution in tems of optimization accuacy and computation obustness. Dingyi, Yunlong, and HanNing, 2008, optimize RFID eades' deployment using Paticle Swam Optimization (PSO) algoithm fo taget tacking in Electonic Poduct Code (EPC). They maximize the tacking pecision and minimize the eade consumption. (Bhattachaya and Roy 2010) poposed an RFID netwok based on Paticle Swam Optimization (PSO) fo eade placement technique. The esults show the effectiveness of this algoithm in achieving the optimal solution. (Di Giampaolo et al. 2010) apply the Paticle Swam Optimization algoithm in complex RFID eades fo a system in lage aeas. The numeical esults show the effectiveness of the method. Chen, et al., and 2011pesent a novel multi-swam paticle swam optimize called 7707

PS2O. This algoithm extends the single population PSO to the inteacting multi-swams model and enhances dynamical update equations. It poves to be supeio fo planning RFID netwoks than PSO and multi-swam coopeative PSO (MCPSO). (Gong et al. 2012) developed a novel paticle swam optimization (PSO) algoithm with a tentative eade elimination (TRE) opeato. The mechanism of this algoithm is to delete eades duing the seach pocess of PSO and ecove it in ode to adjust the numbe of eades used to enhance the oveall pefomance of RFID netwok. Expeimental esults show that the poposed algoithm is capable of achieving highe coveage and using fewe eades than the othe algoithms. Han and (Feng and Qi 2012) pesent a novel optimization algoithm, namely the multi-community GA- PSO. It applied on the complicated RFID netwok planning poblem of lage-scale system. The poposed algoithm enhances PSO algoithm wok. (Suiya 2013) apply PSO and genetic algoithm (GA) to the model fomulations to seach fo feasible solutions to the coveage RNP poblem. (Kuo et al. 2013) poposed a hybid of atificial immune system (AIS) and paticle swam optimization (PSO)-based suppot vecto machine (SVM) (HIP SVM) fo optimizing SVM paametes. They indicated that HIP SVM can achieve highest accuacy compaed to those of AIS SVM and PSO SVM. (Chen et al. 2014) used multiobjective EA and SI algoithms to find all the Paeto optimal solutions and to achieve the optimal planning solutions. The multiobjective paticle swam optimization (MOPSO). Simulation esults show that multiobjective atificial bee colony algoithm MOABC poves to be moe supeio fo planning RFID netwoks than NSGA-II and MOPSO in tems of optimization accuacy and computation obustness. (Nawawi et al. 2015) developed a method to detemine the optimum setting fo PSO paametes. Two sessions of Design of Expeiment (DOE) analysis wee embedded in the optimization pocess. It manages to geneate high quality esults fo this eason the poposed method (PSO and DOE combination) cause to become as a obust and efficient optimization system. b) Bee colony algoithm (ABC) The atificial bee colony algoithm (ABC) is an optimization algoithm based on the intelligent foaging behavio of honey bee swam, poposed by Kaaboga in 2008-2009. Algoithm (below) povides a pseudocode of the Bee colony algoithm: The main advantages of the ABC algoithm ove othe optimization methods fo solving optimization ae simplicity, high flexibility, stong obustness, few contol paametes, ease of combination with othe methods, ability to handle the objective with stochastic natue, fast convegence, and both exploation and exploitation (Le Dinh et al. 2013). both of exploation and exploitation ae necessay fo the population-based optimization algoithms to investigate the vaious unknown egions in the solution space in ode to discove the global optimum. To find bette solutions based on exploation and exploitation in ode to achieve good optimization pefomance by keeping the two abilities in good balance. Fo this eason (Zhu and Kwong 2010) modify ABC algoithm and pesent a new algoithm named Gbestguided ABC (GABC) algoithm. (Ma et al. 2014) applied a novel optimization algoithm, namely, hieachical atificial bee colony optimization, called HABC on RFID tag coveage. The poposed algoithm applied with multilevel model. The highe-level species can be aggegated by the subpopulations fom lowe level. Each subpopulation in the bottom level employs the canonical ABC. They appoved that this algoithm supeio, in tems of optimization accuacy and computation obustness. (Bacanin et al. 2015) pefom multi-objective RFID optimization with the ABC algoithm hybidized with heuistic. This appoach uses a fom of collaboative hybid. This hybid type has thee possible stuctues: multi-stage, whee fist algoithm acts as the global optimize, wheeas the second algoithm pefoms the local seach, sequential, whee both algoithms ae un altenatively until one of the convegence citeia is met and paallel, whee two o moe algoithms ae un simultaneously seaching on the same population. The esults find excellent quality solutions. (Tuba et al. 2015) also pesents a new hybid ABC algoithm fo RFID tag coveage. They incopoated genetic opeato into the basic atificial bee colony algoithm (GI-ABC) which is poduce a supeio esults. 7708

c) Bacteial foaging optimization (BFO) The bacteial foaging optimization (BFO) poposed by Passino in the yea 2002. Bacteial foaging optimization algoithm (BFOA) as a global optimization algoithm has diffeent set of advantages egading local minima, diection of movement, andomness, swaming, attaction/ epelling and so on. Algoithm (below) povides a pseudo code of the Bacteial foaging optimization: swim length of bacteia is adapted to exploitation state as a smalle one. If a bacteium s fitness is unchanged, the swim length adjusts to lage one and this bacteium entes in exploation state. (Chen et al. 2011) also use the selfadaptive bacteial foaging optimization (SABFO) to optimize tag coveage poblems in dynamic RFID netwoks. The esults show that the SABFO obtains supeio solutions than the oiginal BFO method. Table-1 shows a summay of RFID Tag coveage algoithms: Table-1. Summay of RFID tag coveage algoithms. It has been epoted to supass many poweful optimization algoithms in tems of convegence speed and final accuacy. (Chen et al. 2010) popose the multicolony bacteia foaging optimization (MC-BFO) applied in complex RFID netwok planning poblem. The mechanism of this appoach is to extend the single population bacteial foaging algoithm based on elating the chemotactic behavio of single bacteial cell to the inteacting multi-colony model by the cell-to-cell communication of bacteial community. The esults show that the MC-BFO obtains supeio solutions fo RNP poblem in tems of optimization accuacy and computation obustness. (Liu et al. 2011) developed a selfadaptive bacteial foaging optimization (SABFO) in which swim length of individual bacteium adjusts dynamically duing seach to balance the exploation/ exploitation tade off. The mechanism of bacteium which discoves a bette fitness based on pomising domain, Yea Autho (s) Algoithm 2007 Ben, et al. (MCPSO) 2008 Hanning Chen and Yunlong Zhu (EAs-SI), 2008 Dingyi, Yunlong, and HanNing (EPC-PSO) 2010 Indajit Bhattachaya and Uttam Kuma Roy (PSO) 2010 Di Giampaolo, et al. (PSO) 2010 Guopu Zhu and Sam Kwong (GABC) 2010 Hanning Chen et al. (MC-BFO) 2011 Chen, et al. (PS2O) 2011 Liu et al. (SABFO) 2011 Hanning Chen et al. (SABFO) 2012 Gong, et al. (PSO-TRE) 2012 Han and Jie (GA-PSO) 2013 Suiya (GA-PSO) 2013 Kuo et al. (PSO-SVM) 2014 Chen et al. (MOPSO) 2014 Lianbo Ma et al. (HABC) 2015 Nawawi (PSO-DOE) 2015 Nebojsa Bacanin et al. (HABC) 2015 Milan tuba et al. (GI-ABC) EVALUATION OF ALGORITHMS Because of the complex and difficult engineeing poblems such as dimensions, vaiables, and time inceases. natue inspied algoithms ae designed to optimize numeical benchmak functions, multi objective functions and solve NP-had poblems fo lage numbe of vaiables, dimensions, etc. the big challenge fo evaluating the pefomance of these algoithms is the high dimensions. This poblem is temed as cuse of dimensionality as shown in Table-2. 7709

Natue inspied algoithm Table-2. Evaluation of algoithms. Max Dim. Min Dim. Numbe of benchmak functions PSO 1000 100 6 ABC 30 10 5 BFOA 10 4 4 It is cleae that the potays show the vaious existing of Evaluated Dimensions of Natue Inspied Algoithms used in RFID ove continuous unimodal o multimodal benchmak test poblems(agawal 2014). CONCLUSIONS RFID tag coveage objective is the most cucial among othe RNP objectives because it's a vey complex optimization poblem due to the high dimensional chaacteistic. This pape pesents diffeent algoithms which developed to quick convegence of the optimal solution and eduction of the computational time. It has been solved successfully vaious constained and unconstained multi-objective optimization poblems. The optimization pogess concens with the investigating of a satisfactoy citeion to assess the pefomances of the tag coveage eadability. REFERENCES Agawal, P., 2014. Natue-Inspied Algoithms : State-of- At, Poblems and Pospects., 100(14), pp. 14-21. Bacanin, N., Tuba, M and Stumbege, I. 2015. RFID Netwok Planning by ABC Algoithm Hybidized with Heuistic fo Initial Numbe and Locations of Reades. Bhattachaya, I. and Roy, U.K., 2010. Optimal Placement of Reades in an RFID Netwok Using Paticle Swam Optimization. Intenational jounal of Compute Netwoks & Communications, 2(6), pp. 225-234. Chen, H. et al. 2011. Dynamic RFID netwok optimization using a self-adaptive bacteial foaging algoithm. Intenational Jounal of Atificial Intelligence, 7(A11), pp.219 231. Chen, H. et al., 2014. Multiobjective RFID netwok optimization using multiobjective evolutionay and swam intelligence appoaches. Mathematical Poblems in Engineeing, 2014. Chen, H. and Zhu, Y. 2008. RFID netwoks planning using evolutionay algoithms and swam intelligence. In Wieless Communications, Netwoking and Mobile Computing, 2008. WiCOM 08. 4 th Intenational Confeence on. pp. 1-4. Chen, H., Zhu, Y. and Hu, K. 2010. Multi-colony bacteia foaging optimization with cell-to-cell communication fo RFID netwok planning. Applied Soft Computing, 10(2), pp. 539-547. Le Dinh, L., Vo Ngoc, D. and Vasant, P. 2013. Atificial bee colony algoithm fo solving optimal powe flow poblem. The Scientific Wold Jounal, 2013. Feng, H. and Qi, J. 2012. Optimal RFID netwoks planning using a hybid evolutionay algoithm and swam intelligence with multi-community population stuctue. In Advanced Communication Technology (ICACT), 2012 14 th Intenational Confeence on. pp. 1063 1068. Di Giampaolo, E., Fonì, F. and Maocco, G. 2010. RFID-netwok planning by paticle swam optimization. In Antennas and Popagation (EuCAP), 2010 Poceedings of the Fouth Euopean Confeence on. pp. 1-5. Gong, Y.-J. et al. 2012. Optimizing RFID netwok planning by using a paticle swam optimization algoithm with edundant eade elimination. Industial Infomatics, IEEE Tansactions on, 8(4), pp.900 912. Hasnan, K. et al. 2012. Implementation of RFID System fo Impoving the Inventoy Management System in Unijoh Sdn Bhd. Planning (RNP), 2013. Hasnan, K. et al. 2013. Optimization of RFID eal-time locating system. Kuo, R.J. et al. 2013. Hybid of atificial immune system and paticle swam optimization-based suppot vecto machine fo Radio Fequency Identification-based positioning system. Computes and Industial Engineeing. 64(1), pp. 333-341. Liu, W.. B. et al. 2011. RFID netwok scheduling using an adaptive bacteial foaging algoithm. Jounal of Computational Infomation Systems, 7(4), pp.1238 1245. Available at: http://www.scopus.com/inwad/ecod.ul?eid=2-s2.0-79955742873&patneid=40&md5=4d5ccd66a01d76621e de81c21a75ca89. Ma, L. et al. 2014. Hieachical atificial bee colony algoithm fo RFID netwok planning optimization. The Scientific Wold Jounal, 2014, p.941532. Available at: http://www.pubmedcental.nih.gov/aticleende.fcgi?atid =3921959&tool=pmcentez&endetype=abstact. Nawawi, A., Hasnan, K. and Ahmad Baeduan, S. 2011. The application of RFID technology to captue and ecod poduct and pocess data fo evese logistics soting activity. Poceeding of the 12 th Intenational Confeence on QiR (Quality in Reseach). (July), pp. 4-7. Nawawi, A. 2015. A Modified Technique in RFID Netwok Planning and Optimization. 7710

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