Path Optimization for Mobile RFID Reader Using Particle Swarm Optimization and Genetic Algorithm

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1 Path Optimization for Mobile RFID Reader Using Particle Swarm Optimization and Genetic Algorithm Mohd Zaki Zakaria 1 and Mohd Yusoff Jamaluddin 2 1 Faculty of Computer and Mathematic University Technology MARA, 40450, Shah Alam Selangor 2 Department of Electrical, Electronic and System Engineering Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia fmzaki@yahoo.co.uk, dryusoff22@yahoo.com ABSTRACT In this paper, we discuss about developing an application using particle swarm optimization (PSO)and genetic algorithm (GA) to get the shortest path for RFID mobile reader to ensure 100% coverage in the given area. For path optimization, the mobile reader will move from one node to another node without going through the obstacles within certain area. The tag reading process is repeated from where the circle stop at the last node and it will immediately move to the first node. Then the process will repeat continuously. Based on this experiment, PSO shows better results in finding minimum path and it also shows better standard deviation compared to GA. Keywords: RFID, Optimization, Nearest Neighbor, PSO, GA I. I TRODUCTIO Optimization for RFID reader is an important issue to reduce the cost of hardware then we need to define the point or location of the RFID reader to ensure that the node will be fully covered by the reader. It is also essential to find the best way to place the nodes in a given area which will guarantee 100% coverage with a minimum number of readers. In recent years, Radio Frequency Identification (RFID) technology has become very popular in many industries such as manufacturing, automotive, services, social retailing and etc. In other words, this technology will influence the society lifestyle. With the ubiquity of RFID deployment, there will be a need to ensure that any object can be traced easily when it is tagged. This can be done with RFID together with a wide variety of objects. Generally, RFID shall be able to tag, trace and check an object whereas RFID tags are attached to objects which a tag has information about the object such as unique ID number, manufactured date and product composition. RFID technology uses radio waves to enable the communication between readers and tags. The RFID tag is a device that can store data such as unique identification number (ID) of the object and transmit the ID to the reader in contactless manner using radio waves. Objects will be identified when tags transmit this information back to the readers within interrogation range. The reader is a device that can read from or write data to compatible RFID tags. RFID readers can be fixed (static) or mobile reader (within certain/given area). Objects will be identified when tags transmit this information back to the readers within interrogation range. The tags can be classified in two types i.e. passive and active. Most of the applications using passive tag because the tag is cheaper and the size is smaller compared to active tags. For passive tag, the range that a reader can detect (interrogation range) is at a maximum 8 meter working distance in the UHF band (Finkenzeller, 2003). In order to provide complete coverage for a given area, we need to arrange RFID readers efficiently. With this, the cost of hardware (RFID reader) reduces. To the best of our knowledge, there is no published work that discuss about mobile RFID reader for asymmetrical and obstacle in a particular area in finding the shortest path to cover the given area. In this paper, we propose a PSO and GA techniques incorporating the nearest neighbor for the movement of RFID mobile reader. In this paper, we intend to find the best way to place the RFID reader (nodes) in a given area that will guarantee 100% coverage with minimum number of reader usage. Then, in the first phase will discuss about developing a prototype of the software simulation to determine how many RFID 532

2 fixed readers are required to cover a given area based on hexagonal packing which is produces approximately 90% efficiency as shown in Figure 1. The coverage of a wireless sensor network may be approximated by a disk of a prescribed radius (sensing range) which is equivalent to one RFID reader. Coverage can be computed by taking the union of individual coverage areas of all sensors (readers) in the network. This can be done by assuming that each circle is a hexagon which attaches with each other (Fig. 1). We assume all the readers/nodes have the same sensing and transmission range. In the second stage, the location of RFID reader that we calculate before will be used as reference point for mobile reader to visit in periodical of time. We proposed to use the mobile RFID reader due to reducing cost of hardware (reader) and eliminate readers interference. The mobile reader will be moved from one point (node) to another point based on shortest path we calculate using PSO and GA for the given dimension area. The tag reading by mobile RFID reader process is repeated from where the circle stop at the last node and it will immediately move to the first start node. Then the process will repeat continuously. The main contributions of this paper are classified into three (3) aspects. First, the system can be used to eliminate redundant sensors or readers without affecting network coverage so that the mobile RFID reader less node to visit. Second will be an introduction of an algorithm for the RFID mobile reader for path optimization using PSO and GA techniques incorporating the nearest neighbour. Finally, the design of a system that can fit with any design of area whether it is asymmetrical or symmetrical. In addition, the system will enable us to define the position/location of the obstacles which is based on real working environment. II. RELATED WORK The RFID technology also used in help blind people to find the shortest route from his current location to a destination also helps them when they get lost by automatically detecting the lost and recalculate a new route to the same destination (Sokmongkon, 2008), which is the system embeds RFID tags into a footpath that can be read by an RFID reader with a cane antenna. The system also will use for navigation in rescue of hazardous environments where it is difficult to find an emergency exit. The indoor guidance system (Chou, 2004) also using RFID technology to find a guidance path for importance places such as museums, hospital, airport terminal and exhibition halls. The most challenging task is to get the shortest path for a network by minimizing the cost (distance) inside the areas. The shortest path has been used extensively for other purpose such as vehicle routing in the transportation system (Zahn, 1998), path planning inrobotic (Desaulniers, 1995), traffic routing in communication network (Moy, 1994). Anusha proposed an application for complete coverage periodically within a period of time using mobile RFID reader for a symmetrical area (Anusha, 2005). The system proposed by Anusha only catered for symmetrical area and within the area covered by RFID mobile reader and she assumes the areas only locate the proper stacked shelves. The given area is divided by many sectors and one sector catered by one mobile reader. The path shown in Anusha s work, i.e. zig zag type, is similar for every sector. Ammar et al. present the investigation of PSO to solve the shortest path routing problem using a modified priority based indirect encoding incorporating a heuristic operator for reducing the possibility of loop formation in the path construction process (Ammar, 2008).Chvtalal states greedy heuristic technique for solving of set covering problems(chvatal, 1979). Chabini proposed path optimization problems using discrete dynamic networks (Chabini, 1998). Finally, Zhu Xia et al. proposed path optimization for wireless sensor network using particle swarm algorithm (Zhu, 2011). III. PROPOSED ALGORITHM In this section we developed simulation program using PSO and GA techniques to optimized path of RFID mobile reader which is the both techniques incorporating with nearest neighbor. A. Particle Swarm Optimization Particle swarm optimization (PSO) is a population-based stochastic optimization technique developed by Kennedy and Eberhart (Kennedy,1995). This method finds an optimal solution by simulating social behaviour of bird flocking or fish schooling etc. The PSO algorithm flow consist of a population of individuals named particles. Which is every particle is a potential solution to an n-dimensional problem. The group of particles can achieve the solution effectively by using the common information of the group and the information owned by the particle itself. The particles move their state by flying around in an n-dimensional search space based on the velocity 533

3 updated until a relatively unchanging state has been encountered, or until computational limitations are exceeded. To date, PSO seems to be a viable and successful solution as it has been applied to solve many optimization problems such as data classification, pattern recognition and image processing, power system design, decision making for stock market, robotic applications and identification of emergent systems. With reference to the original PSO, each particle knows its best value so far (pbest), velocity, and position. Additionally, each particle knows the best value in its neighbourhood (gbest). A particle modifies its position based on its current velocity and position. For all iteration, any particle is updated by following two "best" values. The first one is the best solution (fitness) it has achieved so far this value is called pbest and the fitness value is also stored. Another "best" value that is tracked by the particle swarm optimizer is the best value obtained so far by any particle in the population, which is called a global best (gbest). The velocity of each particle is calculated using = + + ( ) (1) = 1 < 0 otherwise The flow chart for this problem as below Start Initialize parameter c1, c2 and ω Initialize random position and velocities Terminati on criterion no Evaluate fitness function Update velocities and position based on nearest neighbour constrain Update pbest Update gbest yes End where is the velocity vector, is a modified velocity vector and is a positioning vectorof particle i at generation k. Whereas for is the best position found by particle i and is the best position found by particle s neighbourhood of the entire swarm. and are the cognitive and social coefficients which are used to bias the search of a particle toward its own best experience (pbest) and the best experience of the entire swarm (gbest). ω is the inertia weight which is employed to control the impact of the previous history of velocities on the current velocity of each particle. Large values of ω facilitate exploration and searching new areas, while small values of ω navigate the particles to more refined search. The velocity equation includes two different random parameters, represented by a variable and to ensure good exploration of the search space and to avoid entrapment in local optima. The sigmoid function for the updating velocity = (2) The modified position vector is obtained using B. Genetic Algorithm GA represent one type of heuristic that researchers have applied to optimized path such as in travelling salesman problem, scheduling of job on a production line or solving problem related to optimization. GA work by generating a population of chromosomes. The components within a chromosome are called genes. Each chromosome represents a possible solution to a problem. New chromosomes are created by crossover or mutation. A number of different crossover methods have been proposed in the literature and most commonly used operators such as order crossover (Goldberg, 1989), partial map crossover (Starkweather, 1991), cycle crossover (Oliver, 1987) or simple crossover (Knosala, 2001). Whereas mutation provides randomness within chromosomes to increase coverage of the search space and help to prevent premature convergence on a local optimum. In this case, we are using GA to optimize the path for RFID mobile reader. The chromosomes are evaluated according to a fitness (objective) function which is the fittest will survive and the 534

4 less fit will be eliminated. The result is a gene pool that evolves over time to produce better results to a problem. The searching process to get a good result continues until no significant improvement occurs in the population for a given number of iterations. The flow chart as below: start Initialize population Evaluate fitness function Criterion termination no Applies mutation yes Choose the best solution as parent Applies crossover and create a new population Applies nearest neighbour constrain end Figure 1: A typical 76 nodes (number of RFID reader) with interrogation range is 4 meter fit for the given area. The size of a hexagon is based on which is equivalent to an interrogation of a reader. To address this problem, the software simulation that is developed can define the read range of RFID reader and obstacles inside the area covered based on user requirement. The In Figure 1, RFID mobile reader will skip the node which is the obstacle appeared at that point.the system we develop will determine the coordinate of each reader in an area to optimize the reader usage and its coverage. IV. SIMULATIO SETUP A D RESULTS A. Simulation Setup A lot of research activities have been proposed in localization and positioning applications of RFID reader and tags (Bahl, 2000). In general, one of the most fundamental issues in wireless sensor network is reader coverage. That means every point of selected area must be within the sensing range of at least one sensor network. In literature, this problem has been proposed in many different ways, for example using Mobile Reader (Fagih, 2012). The coverage of a wireless sensor network may be approximates by a disk of a prescribed radius (sensing range). Coverage can be computed by taking the union of individual coverage areas of all sensors (readers) in the network. Figure 2: A typical 36 nodes (RFID reader) with interrogation range is 5 meters with 3 obstacles in the given area using PSO 535

5 number of readers based on the area. Using PSO and GA techniques, the RFID mobile reader will scan every single node inside the area as Figures 2 and 3 above. PSO and GA will find a path to complete a circle, in which the minimum path (distance) is chosen as the best path for the RFID mobile reader. Figure 2 and Figure 3 show the number of readers for a given area which have 3 obstacles that the mobile reader cannot go through. The mobile reader can start at any nodes by random. Figure 3: A typical 36 nodes (RFID reader) with interrogation range is 5 meters with 3 obstacles in a given area using GA We start the algorithm with an initialization process to determine the number of readers to be used in a given area and the system will optimize V. RESULT A D DISCUSSIO We used the following configuration the PSO and GA algorithm. For PSO number of particle is 10, cognition factor c 1 and social factor c 2 is 1.4 and inertia weight w is 0.4 to 0.9. Whereas for GA number of population is 10 and mutation ratio is 0.3. All the algorithms were run 100 and 500 times as shown in table 1 and 2. Table 1: The result of optimization path using PSO No of Iteration No of Running Max Path Min Path Average Path Standard Deviation Min Tour path Table 2: The result of optimization path using GA No of Iteration No of Running Max Path Min Path Average Path Standard Deviation

6 Min Tour path The results of the two algorithms show that the PSO is significantly better than the GA algorithm which is the minimum path (best value) is 3126 for the 500 running and iterations. The value of standard deviation for PSO is lower than GA. This means the performance of the proposed approach that derived from calculating the margin of error in each path sequence for PSO is better than GA. VI. CO CLUSIO A D FUTURE WORK In this paper, we design a simulation for RFID mobile reader to optimize the path to cover the given area using PSO and GA method. The both techniques we incorporate with nearest neighbor to get better result. The experimental results show that the PSO technique has competitive potential for solving discrete optimization problems like path optimization compare to GA technique. Further research can be done using hybrid optimization for GA and PSO to detect indoor tag scanning for passive RFID localization. The system will compute the coordinates of the tracking tags (items) based on the data available from each group. G. Desaulniers, F. Soumis, An efficient algorithm to find a shortest path for a car-like robot, IEEE Trans. Robot. Automat. 11 (6) (1995) Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA. J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, 1995, pp Knosala, R., Wal, T., A production scheduling problem using genetic algorithm. Journal of Materials Processing Technology 109 (1 2), Moy, J., Open Shortest Path First Version 2. RFQ 1583, Internet Engineering Task Force. Oliver, I., Smith, D., Holland, J., A study of permutation crossover operators on the traveling salesman problem. In: Proceedings of the Second International Conference on Genetic Algorithms, London, pp P.Bahl, V.N. Padmanabhan, RADAR: an in-building RF-based user location and tracking systems, in IEEE INFOCOM, pg S. Anusha & Sridhar Iyer, A Coverage Planning Tool for RFID Networks With Mobile Readers: EUC workshops, LNCS 3823 Sakmongkon Chumkamon, Peranitti Tuvaphanthaphiphat, Phongsak Keeratiwintakorn, Proceedings of ECTI-CON 2008 Starkweather, T., Whitley, D., Whitley, C., Mathial, K., A comparison of genetic sequencing operators. In: Proceedings of the Fourth International Conference on Genetic Algorithms, Los Altos, CA, pp V.Chvatal. A greedy heuristic for the set covering problem, Mathematics of Operational Research, INFORMS. 4(3): , 1979 Zhu X. Zhang Y., Wireless sensor network path optimization based on particle swarm algorithm, in IEEE 2011 REFERE CES Ammar W.Mohemmed et al. Solving shortest path problem using particle swarm optimization. Applied Soft Computing 8(2008) Chabini i., Discrete dynamic shortest path problems in transportation applications: Transportation Research Record, , 1998 Chou L.D., Wu C.H., Ho S.P., Lee C.C. and Chen J.M. (2004) Requirement Analysis andimplementation of Palm-based MultimediaMuseum Guide Systems. IEEE 18th International F.B. Zahn, C.E. Noon, Shortest path algorithms: An evaluation using real road networks, Transport. Sci. 32 (1998) Fagih A.E. et al.,2012.coverage-based Placement in RFID Networks: An Overview in Mobile, Ubiquitous, and Intelligent Computing (MUSIC) Finkenzeller, K. : RFID Handbook, Fundamental and applications in contactless smart cards and identificationdritte edn. Chichester : John Wiley