A novel optimal RFID network planning by MC-GPSO

Indian Journal of Science and Technology, Vol 8(17), IPL068, August 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 A Novel Optimal RFID Network Planning by MC-GPSO Khalid Hasnan*, Aftab Ahmed, Badrul-aisham, Qadir Bakhsh, Kashif Hussain and Kamran Latif Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia; [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] Abstract The fast development of RFID technology having challenging issues of the optimal deployment of RFID network are tags coverage, interference, economic efficiency and load balance. In this paper the novel approach of Multi-Colony Global Particle Swarm Optimization (MC-GPSO) algorithm was used to deploy minimum number of reader which covers all tags with minimum interference efect in large scale basis. The main aim of this algorithm is to divide the swarm in to multi-colony for achieving the optimal results as compared to the basic PSO. Simulation results show the optimal solution of RFID Network Planning (RNP). Keywords: MC-GPSO, PSO, RFID, RNP 1. Introduction he RFID wireless communication system has been used for real time identiication and data collection of entities at global level since last more than half century. Currently the RFID technology most commonly used in toll collection, tracking the luggage at airport, inventory management at retail shop, warehousing, logistics and supply chain management. he RFID system has three basic components such as tag, reader and Middleware. he RFID tag is small electronic chip data carrying device used to attach with items for identiication and tracking. he RFID reader is a device used for data collection from tagged items by electromagnetic wave and transmits to the middleware. he Middleware transmit and receive data directly from the interrogator, carry out a business-related process, stores the data as per the requirement, sends data to the enterprise applications. he working principle of RFID system described as, when the reader send an electromagnetic wave through their antenna towards the tagged items, tags in the range transmit back a radio wave signal towards the reader through their embedded antenna along with the data stored in it. hen reader transmits that data *Author for correspondence to the middleware which updates the information of business sotware1. However RFID is an enabling and promising technology, even though it has issues during deployment of the RFID system among various working environment. he fast development of RFID technology having challenging issues of the optimal deployment of RFID network are tags coverage, interference; economic eiciency and load balance2–5. Due to the limited range and high cost of RFID reader the following constrained are to be considered before deploying the reader network. (1) How many minimum readers are required to cover all tags? (2) Where to deploy these minimum readers? (3) What parameters are to be set for each reader? Owing to the development of computer applications in every ield the latest scientiic approach is used to compute any task by using various computer sotware’s and programming to fulill the particular computation in very short time. he Artiicial Intelligence (AI) is a branch of computer science, used for computing very complex problems5,6; it behaves like a human intelligence in order to work multiple tasks concurrently. AI has two major branches such as Evolutionary Algorithm (EA) and Swarm Intelligence (SI) and its further developed A Novel Optimal RFID Network Planning by MC-GPSO techniques are as shown in Figure 1. hese techniques are used to ind out the user deined tasks by computer programming as an innovative manner to solve diicult and complex problems3,7. hese techniques works on the basis of nature inspired behavior of biological and of social animals; they are searching their food collectively as selforganized with the systematic approach4,5,7. here is no centralized control to organize the agents to follow the rule to interact with the local agents and environment2. It is a built-in natural property for searching based approach to perform collectively. Each agent interacts with other agents locally according to the environment. he following are the basic advantages of artiicial intelligence: (1) compute in an easy way to perform repeated operations according to the nature based inspiration, (2) highly eicient having capability of exploring most suitable optimal solutions in a very short passage of time, (3) lexible to solve various problems of diferent application areas with slight changes, (4) robust to perform in diicult, harsh and uncertain environmental conditions and (5) able to integrate with other techniques3. Keeping in view of these beneits, the EA and SI could be applicable in various industrial, domestic and commercial applications in the ield of wireless communication networks such as Wireless Sensor Network (WSN), RFID network and cellular radio network7. In this research it was essential to consider the criteria regarding RFID network planning, such as the coordinates of tags and interrogators were ixed as static RNP problem. On the contrary in actual practice during continuous working area RFID tags and readers can be placed at any location. Due to the complexity of the network and cost of RFID system depends on the number of readers, so it was essential to minimize the number of readers at optimum level to achieve the target of cost efective planning of network and the goal of optimum business beneits. In this context many previous researchers were tried to igure-out the optimal network planning in order to operate large-scale network. Keeping in view the facts considering the basic requirement of RFID network planning,  ×  × ( the Multi-Colony Global Particle Swarm Optimization (MC-GPSO) algorithm was used to solve the RNP issues by deploying minimum number of readers to cover all tags with minimum interference between readers. 2. Basic Concept of Particle Swarm Optimization he particle swarm optimization algorithm was irst developed in 1995 by James Kennedy and Russell C. Eberhart. his idea was created with the inspiration of nature based social behavior of ish schooling or bird locking8,9. It is a population based heuristic evolutionary technique used for global optimization. his technique is used for best solution of problems in n-dimensional space by randomly initializing the particle velocity and their interaction with respect to each particle. PSO has two basic equations which are to be implementing to serve the purpose of optimization into the solution space10 and achieve required goal of RNP. hese equations are in mathematical order as follows. ( Vid = ω × Vid + c1 × rand1d × pBest id − x id + c2 × rand 2d × ( gBest id − x id ) ) (1) Xid = Xid + Vid (2) Where, d = 1, 2,……, D (dimension of the search space) i = 1, 2….., N (index of each reader) In this algorithm, each particle individually moves through the problem space with a velocity. he velocity vector having speed and direction, which has to be regulated on the basis of particle’s previous best known (self-cognitive) and the historical best knowledge in its neighborhood (social-inluence)2–4. Inertia weight is an important parameter which controls the convergence and balancing the exploration and exploitation by setting higher value at irst and then gradually reduce toward its lower value for achieving better performance4,7. Inertia part (ω x Vid) is used to avoid sudden changing the velocity of readers in the search space. he cognitive part c1 × rand1d × pBest id − x id + c ×  − x id + c2 × rand 2d × gBest id − x id  is used to represent as an  ) ( ) ( ) external force to drag the particle to move towards better position. he social part c2 × rand2d × (gBest id − x id ) is used Figure 1. Types of Computational Intelligence. 2 Vol 8 (17) | August 2015 | www.indjst.org to closely interact with each swarm and predict to plan the number of better positions in the search space3. c1 and Indian Journal of Science and Technology d Khalid Hasnan, Aftab Ahmed, Badrul-aisham, Qadir Bakhsh, Kashif Hussain and Kamran Latif c2 (acceleration coeicients) are the learning rates which specify the proportional importance of self-cognitive and social-inluence. he rand1 and rand2 are random numbers uniformly distributed in (0,1). In this way, the particle can move towards a promising area in the global search. PSO algorithm has advantages such as easy to apply, fast convergence, highly eicient and irm validity. Due to these advantages the algorithm has been used in wide range of application in various systems in recent years and also has provision for further improvements in future applications3,4. In PSO population, each particle randomly initialize in the search space has a position and a velocity. In irst step each particles has to search its own optimal best position in the space with respect to its previous position that is called personnel best “pBest” and is recorded in its memory. In second step its position is compared to the neighbor particles in their group and set their positions best ittest possible that is called local best “lBest”. Finally their position is compared to the global search among all particles existing in the search space and set their position, that position is called global best “gBest”. his is the iteration based techniques followed by each particle in each iteration step and updates velocity and optimal best positions until meets the termination conditions11. In this context readers are particles which have to be randomly initialized the velocity and position irst. In the next step it evaluates the itness of each reader followed by objective functions according to precedence and inds their “pBest” and “gBest” of each reader until termination conditions meets. If termination conditions on priority basis meets the requirement of objective functions, then reader is deployed at its proper place to achieve the business beneits of enterprise in supply chain cycle, otherwise update velocity and positions of each reader until meets the objective function at optimum level. In this research particle swarm optimization algorithm was used innovatively for solving RNP issues by MultiColony Global Particle Swarm Optimization (MC-GPSO) algorithm. 3. Methodology of RFID Network Planning In this research the methodology of solving objective functions of RNP problems such as tags coverage, minimum interference and deploying minimum number of readers described as follows. Vol 8 (17) | August 2015 | www.indjst.org 3.1 Tags Coverage he very important and basic objective function of all RFID systems is the entire tags coverage in speciied area. It has been achieved by placing the RFID readers at the centre of each cluster of the tags in the working area. If the signal power received at the tags higher than the minimum required power level (threshold power) is –10 dBm, the contact between reader and tags has to be established. In order to activate the tag with required power, it responses back to the reader through backscatter signal. he objective function, tags coverage has been formulated as the sum of the diference between the actual powers received by each tag to the required power. he mathematical equation of tags coverage is described as follows. Coverage = ∑ (P NT i =1 tagi − Preq ) (3) Ptagi= Actual received power at each tag, Preq= required threshold power = –10dBm NT=Number of tags in working area By Friis transmission equation power at each tag can calculate by the following equation4. Ptag = Preader × Gtag × Greader Coverage = ∑ NT i =1  λ  ×  4 πd  2 (4) [Preaderi + Gtagi + Greaderi + 20 log10 ( 0.026 ( xi − ai ) + ( yi − bi ) 2 2 + 10]) (5) 3.2 Interference he interference between RFID readers took place at the area of thick populated reader environment where the interrogation range of each reader overlap to the other reader interrogation range, in this scenario each reader attempt to read the similar tags at the same time, as a consequence unafordable level of misinterpret to be happened3. Due to interference many number of tags cannot be identiied by the readers in the working area. It efects to reduce the tags coverage which is the most important objective function in RNP. he interference can be solved by separating the readers interrogation ranges and varying the radiated power of readers. Due to changing the positions of readers away from each other and variation of radiated power, the interference reduced accordingly. his objective function can be solved by the following mathematical equation7. Indian Journal of Science and Technology 3 A Novel Optimal RFID Network Planning by MC-GPSO Interference = ∑ N max −1 i =1 ∑ N max ( ) ( ) d R , R − r + r  (6) i j i j  j = i +1  Nmax is the total available number of readers, “d” is distance between readers, Ri is the position of ith reader, Rj is the position of jth reader, ri is the interrogation range of ith reader and rj is the interrogation range of jth reader. According to the above formula the interference condition can be occurred when the sum of interrogation ranges of two readers (ri + rj) is greater than the center to center distance “d” between the same readers [(ri + rj) > d]. 3.3 Number of Readers Ater solution of the most important problem tags coverage by deploying the number of readers in the working area of RF network, it needs to reduce the number of readers because its cost is too high, if greater the number of readers higher is the overall cost of the system. So it was necessary to igure-out how much extra readers were deployed at initial stage which were no more to be useful and must be removed from the network using MultiColony Global Particle Swarm Optimization (MC-GPOS) algorithm. his objective function can be solved by the following formula3. Nreq = Nmax – Nextra (7) Nreq is the number of required readers, Nmax is the number of available readers and Nextra is the number of extra readers he equations of each objective function such as tags coverage, minimum interference and required number of readers were put in to the multi-colony global PSO algorithm. he MC-GPSO was calculated the optimum level of network planning according to the priority of objective function. groups. he 3 number of swarms were generated in the solution space. Each swarm having number of RFID readers which can interact and share information with each other. It builds a fully connected mesh topology within the swarm as well as neighboring swarms as shown in Figure 2, which were initialized in the working area. It evaluates their itness best positions such as local best position “lBest” and the global best “gBest” position by sharing knowledge within the group and among the adjacent groups. 3.5 Coding of RFID Reader Representation he coding translates the variable parameters of reader representation. It indicates the number of readers deployed in the network and their locations and radiated power of each reader. In this research each reader is represented as D = 3Nmax, multidimensional real number vector. Nmax is maximum number of available readers which was deployed in the network at initial stage. By the representation of readers coding as the vector 2Nmax ( r r is denoted the coordinates xi , yi ) of each ith reader ( ) and the vector 1 Nmax denoted as radiated power pir of each ith reader which decide the interrogation range of each reader. To consider the above vector notations then the entire ith reader notation in the whole swarm is denoted as Xi =  xi1 , yi1 , pi1 , xi2 , yi2 , pi2 , ……………..xiN max , yiN max , piN max  (8) (x , y ) r i r i are the coordinates and (p ) r i is radiated power of each reader “r” Where [r = 1, 2,……..,Nmax] 3.4 Setting Topology of Search Space he topology of the search space was set as an architectural structure of particles positions and their interaction between each other. he particles were divided into groups as multi-colony to make easy and an eicient working environment. hese groups have been deployed for the implementation of a speciic task in a large scale network. As a result the overall target was easily be achieved within short passage of time with less efort. In the proposed algorithm the whole swarm divided into 3 groups. he interaction of each particle of multi-colony approach share information within the same group as well as other 4 Vol 8 (17) | August 2015 | www.indjst.org Figure 2. Topology diagram. Indian Journal of Science and Technology Khalid Hasnan, Aftab Ahmed, Badrul-aisham, Qadir Bakhsh, Kashif Hussain and Kamran Latif 3.6 Setting Parameters he parameter was set as the Nmax =10 Number of RFID reader initially deployed, the radiated power range of reader at each tag (Ptag) is 0.1 to 2 watt (20 to 33 dBm), this variation was directly proportional to the circular range of radiated power of reader from its center. he minimum threshold power of tags (Ttag) is -10 dBm. he minimum threshold power of readers (Treader) is – 70 dBm. he power gain of tag antenna (Gtag) is 3.9 dBi and the power gain of reader antenna (Greader) is 7.3 dBi. Inertia weight (ω) is 0.9 to 0.4 and acceleration coeicients were set as c1 = c2 = 2.0. Operating frequency of reader was set as UHF 915 MHz and the number of iteration was set as 15000. he working area was set as 50m ×฀50m. 3.7 Application of MC-GPSO At the start of the MC-GPSO algorithm, each individual reader in the swarm initializes its velocity irst, within the limit of [Vmin, Vmax] and positions were uniformly distributed in random numbers as [Xmin, Xmax], where Xmin is the lower bound and Xmax is the upper bound of the readers position. he PSO population comprised on “N” number of reader in “D” dimensional search space, so each reader has velocity vector Vi = [Vi1, Vi2, ……, ViD], position vector Xi = [Xi1, Xi2, ……., XiD] and pervious best position vectors pBesti = [pBesti1, pBesti2,…,pBestiD] and global best position vector gBest = [gBesti1, gBesti2,…,gBestiD]. In this process the velocity and position of each reader is randomly initialized in the search space according to the coordinates (x , y ) . ( p ) is radiated power within the transmisr i r i r i sion range of each reader (r = 1, 2,…….., Nmax). At irst instance all readers “Nmax” were deployed in the working area for RFID network planning. At the initial stage the availability of all readers in the network represented by a vector as switch on position (1, 1,…….., 1). hose readers meets the objective functions is remain switched on, otherwise it is switched of. he switched of readers are represented by the vector (0, 0,….0). 3.8 Operating Procedure of MC-GPSO he step by step operating procedure of Multi-Colony Particle Swarm Optimization (MC-GPSO) is described as follows. Step1. Randomly initialize the velocity and position of each reader. Vol 8 (17) | August 2015 | www.indjst.org Step2. Evaluate the itness of each reader according to the objective functions by using equation (5), (6) and (7) in each swarm and then record the “pBest” of each reader. hen calculate the “gBest” in each swarm. Step3. Update the position and velocity of all readers by using equations (1) and (2). If the updated position and velocity of each reader is better than the previous best, then new best position ixed as current best position, otherwise kept as the previous best. Step4. Evaluate the itness value of each reader and compare with the previous best itness position and velocity on the basis of recorded knowledge and then update each reader position according to the global position in each group. Step5. If the itness value achieved so far as global best and maximum iteration has to be completed and global best position achieved, then go to the next step as stop operation else go to step 3. 3.9 Simulation Result he scenario of the working area was set as 50m × 50m as shown in Figure 3 (a). he 100 numbers of tags are randomly distributed in working space as shown in Figure 3 (b) Ater plotting of tags (as blue star “∗”), the 10 number of RFID readers was initialized and distributed in order to cover all the tags. he coordinates of readers is shown as red plus sign (+) and their interrogation range as red dotted line circle. he 6 number of RFID readers was covered all the tags in the deined space as shown in Figure 3 (c). he unnecessary number of readers was removed from the space by skip reader operator used in MC-GPSO algorithm. hose readers which covers all the tags, its interrogation zone was overlapped and interrogates the same tags concurrently, due to the overlapping of interrogation zone between each reader; the interference occurred that efects the misread of tags which decreases the QoS in the network. he interference was reduced by adjusting the distance between readers as to take away from each other also regulates their interrogation power with consideration of the tags coverage until minimum or no interference occurred. As a result the minimum interference was achieved as shown in Figure 3 (d). Indian Journal of Science and Technology 5 A Novel Optimal RFID Network Planning by MC-GPSO 4. Conclusion (a) he innovative application of MC-GPSO algorithm gives optimal result of objective functions of RNP including full coverage of tags in 50m × 50m square working area using minimum number of readers with minimum interference. he MC-GPSO algorithm reduces the overall cost of the RFID network planning on the basis of calculating optimal number of readers and its coordinates for best placement. he overall cost of RNP is less as compared to existing state-of-the-art ones due to minimum number of RFID readers calculated. his algorithm can be used for diferent size and shapes of indoor working areas and is reliable on the basis of simulations results before physical implementation. 5. Acknowledgement (b) his research supported by the Postgraduate Incentive Research Grant under Vot. No.1156. he authors would like to thank the University Tun Hussein Onn Malaysia (UTHM) for inancially supporting of this research. 6. References (c) (d) Figure 3. Deployment of Tags and Readers in Working Space (a) (b) (c) and (d). 6 Vol 8 (17) | August 2015 | www.indjst.org 1. Ahmed A, Hasnan K, Aisham B, Bakhsh Q. Impact of RFID and Xbee Communication Network on Supply Chain Management. Applied Mechanics and Materials. 2014; 660:983–7. 2. Chen H, Zhu Y, Hu K, Ku T. RFID network planning using a multi-swarm optimizer. Journal of Network and Computer Applications. 2011; 34(3):888–901. 3. Gong Y, Shen M, Zhang J. Optimizing RFID Network Planning by using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination. IEEE Transactions on Industrial Informatics. 2012; 8(4):900–12. 4. Feng H, Qi J. 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Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the Congress on Evolutionary Computation. 2000; 1(7):84–8. 9. Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A. Inertia Weight Strategies in Particle Swarm. hird World Congress on Nature and Biologically Inspired Computing; 2011; p. 633–40. Vol 8 (17) | August 2015 | www.indjst.org 10. Antonio S. Algorithm and Swarm Intelligence. Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetic; 2009. p. 1201–8. 11. Bhattacharya I, Roy UK. Optimal Placement of Readers in an RFID Network Using Particle Swarm Optimization. International Journal of Computer Networks & Communications. 2010; 2(6):225–34. Indian Journal of Science and Technology 7