OPNET Modeling and Optimization Simulation of ...

Journal of Computational Information Systems 11: 2 (2015) 701–709 Available at http://www.Jofcis.com

OPNET Modeling and Optimization Simulation of Mobile RFID System Guixiong LIU ∗,

Xiaosi CAI, Yuanmao LI

School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China

Abstract A mobile RFID system including network domain, node domain and process domain based on OPNET three-layer modeling mechanism is established to study the effect of anti-collision algorithm and multiple environmental factors on tag recognition rate. Simulation results show that tag recognition rate of CFDSE (Coarse-Fine Double Searching-based tag Estimation) method outperforms other three classical algorithms 3.5%, 5.1%, and 7.2%, respectively. Under the same identification intensity, the effect of environmental factors shows that tag density has greater influence on tag recognition rate than tag speed when tag speed is slow, and vice versa. In practical application, better tag moving speed and density can be selected according to recognition rate curve to ensure the recognition rate. Keywords: Mobile RFID System; Environmental Factors; Anti-collision Algorithm; OPNET

1

Introduction

Unlike RFID tags with fixed position in static environment, tags move through the reader with a certain speed and density in mobile environment. Tag recognition rate is influenced by the moving speed, density and multi-tag anti-collision algorithm. High density and fast speed would result in tags reading leakage. RFID has been widely used in manufacturing, supply chain management and logistics since the 1980s as its costs have been reduced. In traditional study, in order to study the influence of multiple factors on mobile RFID system, lots of repeated experiments [1-2] are required, time-consuming and costly. Therefore it is necessary to study the mobile RFID system modeling and simulation [3-5].

2

Related Works

At present, there are a lot of tools used for the simulation of mobile RFID, including RFID Anywhere, NS, Matlab [6-8], etc. However, because of lacking systematic behavioral description, ∗

Corresponding author. Email address: [email protected] (Guixiong LIU).

1553–9105 / Copyright © 2015 Binary Information Press DOI: 10.12733/jcis13116 January 15, 2015

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G. Liu et al. /Journal of Computational Information Systems 11: 2 (2015) 701–709

it becomes almost impossible to analyze effects of multiple environmental factors on mobile RFID system, which result in a great difference between the simulation model and the true system. Moreover, OPNET allows users to design networks, devices, protocols and applications efficiently. OPNET three-layer modeling mechanism can receive the mapping relationship among RFID communication networks, physical layer, MAC layer parameters and OPNET network domain, node domain and processing domain, and realize building a coherent OPNET model. Javier [9] theoretical analysis a discrete time Markov chain for passive RFID systems and provides a suitable criterion to minimize the probability of losing tags, results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags. Harald [10] shows how to determine the optimal number of read cycles to perform under a given assurance level determining the acceptable rate of missed tags. From the perspective of anti-collision algorithm, Siror [11] and Chemburkar [12] analyzed how multi-tags collision influence the system by establishing the corresponding static RFID OPNET model, and these researches have significant instructive meaning for mobile RFID system model building. Juan [13] established the model with minimum reading leakage rate based on FSA, obtaining the relationship between reading leakage rate and frame length. However, it was limited in the application with fixed tags density, and factors of physical layer were ignored. Francesco M [14] concluded that the tags recognition rate is related to the velocity of tags by comparing with the frame length of static and mobile RFID system, but they only studied one factor and didnt consider the impact of tag density. In this paper, the relationship of influence factors and RFID system evaluation index in mobile environment are deduced in Section 2. Then, a mobile RFID system with the assistant of OPNET platform is created in Section 3 to study the influence rules of multiple environmental factors. In addition, in Section 4, the anti-collision algorithm (CFDSE) which the author has studied before is used to optimize mobile RFID model [15].

3

Information Acquisition Model in Mobile RFID Environment

Fig. 1 shows the information acquisition model in mobile RFID environment. Tags move through reader’s coverage area with velocity v (m/s) and density D (tag/m). The reader must identify tags in (d/v) time, otherwise reading leakage would be happen. How to keep high recognition rate meanwhile improving recognition efficiency is the goal of mobile RFID system. Tag recognition rate is the evaluating index of mobile RFID system, meaning that the number of tags identified in coverage area in proportion to the all tags. The reliable RFID system has higher tag recognition rate and less tag reading leakage. To make the model more versatile, tags running time within the coverage area length d is divided into two parts. The previous N frames are complete and the (N + 1)th frame only has a portion of α(0 6 α 6 1). Therefore, a tag is identified successfully including two cases: i) tag is identified at any of the preceding N complete frames; ii) tag identified at the (N + 1)th frame after preceding N frames. Fig. 2 shows the distribution of tag recognition rate in reader’s coverage area. Tag recognition rate is PN after N frames, and that at the (N + 1)th is αp. Assuming that the time of idle, collision, and success slot is all equal to t, seta series of frame lengths L1 , L2 ....LN ,


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Maximum tag recognition rate

Fig. 1: Information acquisition model in mobile RFID environment

PN +1

PN

tag recognition rate P

α

P2 P1

Fig. 2: Information acquisition model in mobile RFID environment LN =1 0 < N < +∞, according to the DFSA method to estimate the number of unidentified tags, ∑ The average frame length of N preceding complete frames is L = ( Li )/N , and the ratio of the (N + 1)th frame length is nf = vDLt. In the principle of maximum channel utilization, the number of tags unidentified in reader’s coverage area is n. Therefore, the system throughout can be calculated from Eq. (1): n 1 S = (1 − )n−1 (1) L L Tags recognition rate in a frame is: p=

E(S) 1 = (1 − )n−1 n L

(2)

The average number of tags in a frame is nf = vDLt and the total number of tags unidentified in reader’s coverage area is: n=

N ∑

nf (1 − p)i−1 + αnf (1 − p)N

(3)

i=1

Tag recognition rate can be calculated as: P = 1 − (1 − p)N (1 − αp)

(4)

Take Eqs. (1), (2), (3) into (4), it can be concluded as: P =

S vDt

(5)

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According to Eq. (5), when the velocity and the density of tags are constant, tag recognition rate P is proportional to the system throughout S. The higher throughout S is, the greater P will be, and vice verse. So the tag recognition rate can be improved by optimizing multi-tag anti-collision algorithm. And the tag identification rate P is inversely proportional to velocity v, density D and the length of slots t. It means that the higher the density of tags and the faster the velocity are, the lower the tag recognition rate will be, and more tag reading leakage will occur. Therefore, controlling the velocity and density of tags, helps to reduce tag reading leakage and improve tag recognition rate.

4

Mobile RFID System Modeling on OPNET

Eq. (5) can numerically obtain tag recognition rate within the set of feasible values. To further research the effect of multi-tag anti-collision algorithm and multiple environmental factors on the mobile RFID system, we establish an OPNET three-layer modeling mechanism including network domain, node domain and process domain correspondence with a mobile RFID system networks, MAC layer, and an application layer. The most notable feature of OPNET Modeler is that it provides a modeling platform for users to customize, and create models in different hierarchical levels. As such, OPNET Modeler is one of the most appropriate simulators for RFID research and development. Gen-2 RFID developed by EPC global and defines the requirements for a passive-backscatter, reader-talk-first (RTF), slotted random anti-collision, and radio-frequency identification (RFID) system operating in the (860C960) MHz frequency range (UHF). Fig. 3 shows the mobile RFID system flow chart based on OPNET three-layer modeling mechanism.

4.1

A mobile RFID system network domain on OPNET

A mobile RFID system network domain consists of the reader, tags and trajectory. Tags distribute in one side of readers. Due to the tags be numerous, controlling the trajectories of tags is complex. According to relative motion we can control the trajectory of the reader. Use OPNET toolbar to define trajectory to control readers moving velocity, initial position, the vertical distance and belt to simulate the actual mobile RFID system on OPNET. Information feedback by node domain and process domain can locate the reader and antenna position change. Fig. 4 conceptualizes this scenario and depicts a mobile RFID system network domain.

4.2

A mobile RFID system node domain on OPNET

A mobile RFID node domain includes reader node and tag node (shown in Fig. 5). RFID reader node domain consists of reader proc, reader tx and reader rx. Module reader proc is used to setup MAC layer multi-tag anti-collision algorithm and send messages to wireless physical layer reader tx. Receiving message module reader rx receive messages from tag and sends to module reader proc. Tag process module Point send location message to reader such as tag’s latitude, longitude, and altitude. The transceiver and receiver modules is the core of node domain, the communication process consists of 14 wireless pipeline stages, most of the pipeline stages must be executed on a


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Network domain topological structure Network domain Setup reader moving trajectory

Setup reader node and tag node Node domain

Establish radio channel transceiver & antenna

Multi-tag anti-collision algorithm module Process domain Frame length dynamic estimation module

Fig. 3: A mobile RFID system flow chart based on Fig. 4: A mobile RFID system network domain OPNET three-layer modeling mechanism

(a) Reader node domain

(b) Tag node domain

Fig. 5: A mobile RFID system node domain per-receiver basis whenever a transmission occurs. Antennas are only used for modeling radio transmission, its primary purpose is to allow modeling of directional gain. The antenna of RFID reader is directional, flared shape, depending on its angle to activate the tag in coverage area. The antenna of tag is isotropic.

4.3

A mobile RFID process domain on OPNET

Process domain is the core of mobile RFID system which is modeled with a Finite State Machine (FSM), the reader process domain includes both multi-tag anti-collision algorithm module and frame length mobile estimation module two parts. Fig. 6 shows the mobile RFID reader process domain. The process model anti-collision algorithm includes Power up, Query, Query Ajust, QueryRep and other mandatory instructions. Count on collision, idle and correct slots by self-interrupt on the basis of the spastics adjust the next frame length dynamically. Power up module activates tags in coverage area, and set the transceiver frequency, data transfer rate, bit error rate, and modulation mode. Query Adjust module setup next frame length and then wait for tags reply. QueryRep module determines whether the slot is correct, when multiple tags or no tag answering, the reader resend the command to prompt tag counter decrease and the tag re-identify in the

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Fig. 6: A mobile RFID system reader process domain next round. According to slot counts, End module judge whether the first slot is in a frame. When slot-count ̸= 0, the slot is not the first and then use OPNET time-out interrupt function op intrpt schedule self (transmit time, code) to judge whether tags response in this slot. T ES module mobile estimates the number of unidentified tags. Terminating of a mobile RFID system is whether the reader has finished running trajectory. RFID tag consists of coupling components and chips. The reader can identify the objects with tags paste on. On the basis of energy supply, tags include active tag, passive tag and semi-active tag. Passive tag without batteries and extracts the energy from reader emits. Due to low power dissipation, low cost and high reliability, passive tag is widely used in checkout procedure of production flow. Fig. 7 shows the mobile RFID tag process domain which is applied to varieties of multi-tag anti-collision algorithm and environment. According to the message of frame lengths from reader, tag generates a random integer between (1, N ) as the slot to transmit, make use of OPNET self-contained function simulation time (op sim time) to receive current frame number, the slot count add, then call the function if slot shot() to send the message to the reader when slot count equal to the random integer of self slot, the tag transmit its unique ID information by self-interrupt flow when the next frame starts. DFSA PACKET (shown in Fig. 8) including packet types, frame length, tag ID information, Command instruction. Packet-Type means data flow comes from reader or tag. Frame length setup on the basis of tag frame length mobile estimation module. Unique tag ID is composed of EPC code which indicates its entity identity. Command instruction is used for setup different state transition.

5 5.1

Experiments Application and analysis of mobile RFID system

Modeling and optimizing the simulation of mobile RFID system on OPNET to study how multitag anti-collision algorithm and environmental factors affect mobile RFID systems. Simulation model is shown in Fig. 1, the belt length is 30m and width is 1m. The vertical distance between


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Fig. 7: A mobile RFID system tag process domain

Fig. 8: DFSA PACKET reader and belt is 2m. The reader transmitter power is 3V, which can activate all tags in reader’s coverage area, and the reader directional antenna beam width is 34 degrees. The antenna of tags is isotropic. In anti-collision algorithm, the interval time of frame slot is 0.005s, and the initial frame length is 8. With the same density and velocity of tags, independent experiments of each algorithm were done 100 times. Fig. 9 illustrates the effects of the density and velocity on tag recognition rate. The recognition rate decreases with the increasing of the velocity and density.

Fig. 9: Curves of relationship between tag recognition rate and velocity, density In order to compare the performance of the classical multi-tags anti-collision algorithm (lowbound, Schout, Khandelwal) and CFDSE algorithm, one of the variables would be keep unchanged

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(v=1m/s). The simulation results were shown in Fig. 10. It shows the relationship between tag recognition rate in each anti-collision algorithm and tag density. The tag recognition rate decreases with the increasing of the tag density, and CFDSE (our algorithm which is studied before in static RFID) anti-collision algorithm outperforms other three algorithms. The tag recognition rate was increased 11.7%, 7.7%, and 3.8%, respectively.

Fig. 10: Simulated evolution of tag recognition

Fig. 11: Graphical analysis of tag recog-

rate in each anti-collision algorithm and tag density

nition rate and velocity in different identification intensity

In the same identification intensity, when the velocity is low, the effect of tag density on identification rate is greater, and vice versa. Therefore, the relationship between velocity and density must be reasonable controlled to reduce tag reading leakage. At this simulation condition, the appropriate system parameters to keep higher identification rate and system throughout are RI=60tag/s and v=2m/s. So in practice application, just substituting the real parameters into the simulation model, we can obtain optimal settings from Fig. 11 to ensure the identification rate.

6

Conclusions

In the same identification intensity, when the velocity is low, the effect of tag density on identification rate is greater, and vice versa. Therefore, the relationship between velocity and density must be reasonable controlled to reduce tag reading leakage. At this simulation condition, the appropriate system parameters to keep higher identification rate and system throughout are RI=60tag/s and v=2m/s. So in practice application, just substituting the real parameters into the simulation model, we can obtain optimal settings from Fig. 12 to ensure the identification rate.

Acknowledgement This work was partially supported by the New Century Excellent Researcher Award Program from Ministry of Education of China (No. NCET-08-0211), middle-end and high-end integrated service systems and intelligent vehicle product development and industrialization (No. 2012A090200005).


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