Oscar Botero
email: oscar.botero@it-sudparis.eu

Hakima Chaouchi
email: hakima.chaouchi@it-sudparis.eu

Peer-to-Peer framework for RFID/non-RFID reader-enabled users

Keywords: Peer-to-peer, Radio Frequency Identification, RFID reading proxy, WLAN I

Radio Frequency Identification (RFID) technology can be used for a wide range of applications such as tracking and tracing objects and individuals, localization, collaborative platforms and the like. For portable devices with non-RFID capabilities there is no possibility to allow them to benefit from this technology if no integration platforms are implemented. In this paper we propose an P2P-based architectural framework in order to enable RFID applications for devices that have non-RFID reading capabilities but a wireless interface. We also propose an application layer time-division-based scheme to allow RFID-enabled devices to scan for RFID tags, preventing readerto-reader interference and avoiding reading collisions. We depict the system architecture and we perform simulations to compare transmission delay metrics and numerical results are provided.

INTRODUCTION

Radio Frequency Identification (RFID) systems are becoming more popular due to reduced components costs [START_REF]RFID tag pricing guide[END_REF] but also because of their versatility. Among the myriad of RFID applications we can find [START_REF] Wiebking | A Roadmap for RFID Applications and Technologies[END_REF]: identification and tracking of objects and individuals, localization, citizen's documents (passports, driving licenses), collaborative architectures, public transport access. RFID is also a key technology for the Internet of Things. These systems are composed of readers, tags and middleware. Basically the tags provide an identification number linked to persons, animals or items that is retrieved by readers and processed by the middleware.

If we consider current portable devices like mobile phones, laptops, pads, etc we observe a lack of RFID embedded hardware implying that is not feasible for them to interact with such systems; however, we can suggest a network architecture that allows integration of non-RFID enabled but wirelessinterface-enabled devices and provide RFID-based applications to them. In this paper we propose a Peer-to-Peer (P2P)-based RFID architecture for users with non-RFID capabilities in order to allow them to take advantages of this technology.

Peer to peer architectures are flexible solutions when computational resources and data sharing are involved. The nodes in such approaches can perform as clients or servers depending on the tasks required. The advantage of P2P against classical client-server approaches lies in the distributed deployment of the services as well as the scalability, flexibility and fault-tolerance of the network.

For this work we based our analysis on IEEE 802.11. These are the most popular short-distance wireless transport protocols [START_REF] Ding | Application of RTLS in Warehouse Management Based on RFID and Wi-Fi, Wireless Communications, Networking and Mobile Computing[END_REF] that allow us to implement affordable, flexible and scalable communication platforms although other wireless technologies can be used to establish the network backbone.

Based on previous research [START_REF] Botero | Platform and experimentation of secure service location with P2P/Client-Server over ad hoc networks[END_REF] we integrated a proxy-based security solution to perform network management and AAA functions over a P2P overlay.

Regarding the RFID reader-to-reader interference different anti-collision protocols have been proposed [5, 6, 7, and 8]. In our work we suggest a time-division-based scheduling mechanism allowing RFID readers to operate in a sequenced manner and more precisely permitting heterogeneous readers (using different lower layers protocols and either sharing or not the same RFID operation frequency) to be part of the network. The mechanism is controlled by P2P overlay messages.

We used NS2 to simulate the data exchange between nodes and obtain the associated transmission delay. We evaluated the case of 20 simultaneous nodes being served.

For this work we rely on the following assumptions:

• Readers use the EPC global Generation-2(Gen2) protocol to scan tag's IDs. • We consider RFID passive tags to be deployed not changing their position frequently. • Tags' antenna is never at 90° with respect to the reader thus tags can be always detected. • All the nodes have a wireless interface and can run the P2P protocols required.

The rest of the paper is structured as following. In section II the related work is mentioned. In section III we describe the system architecture. In section IV the anti-collision mechanism is depicted. In section V we present the system delay analysis. In section VI an application example is shown and finally we conclude the paper in section VII.

II. RELATED WORK

P2P-RFID collaborative networks have been proposed in previous research work. In [START_REF] Zhou | Interconnected RFID Reader Collision Model and its Application in Reader Anti-collision[END_REF] a P2P scheme to deal with the reader-to-reader collision problem is presented. In [START_REF] Peng | A P2P Collaborative RFID Data Cleaning Model[END_REF] a P2P Collaborative RFID Data Cleaning Model is described. A P2P data resolver is implemented in [START_REF] Decker | A Peer-To-Peer Approach for Resolving RFIDs[END_REF]. We observed also research on 802.11 and RFID integration in [START_REF] Ding | Application of RTLS in Warehouse Management Based on RFID and Wi-Fi, Wireless Communications, Networking and Mobile Computing[END_REF] for localization services. In [START_REF] Pradip | Ubiquitous Architectural Framework and Protocol for Object Tracking using RFID Tags[END_REF] an architectural RFID system for localization is described. It presents a framework based in edge and intermediate nodes, in a distributed manner. In these papers we could not find neither how to integrate users with non-FID capabilities nor heterogeneous readers to a RFID network.

The novelties we suggest are three: First, the integration of non-RFID devices in the RFID network via the P2P framework to benefit from available information related to the surrounding RFID tags and providing all the nodes in the system with an extended virtual range. This virtual reading range allows users to obtain information from RFID network deployments overcoming physical tags' scanning limitations. Second, the prevention of reader-to-reader interference by a time-divisionbased mechanism that allows the inclusion of heterogeneous mobile readers, and third, our Service Authentication Proxy already developed is proposed to be part of the platform. This proxy node runs over a P2P framework and performs authentication of all joining nodes among other features. The next section of this paper will describe the System Architecture depicting all the entities and features.

III. SYSTEM'S ARCHITECTURE

A. Network Architecture

The proposed Framework allows portable devices to obtain RFID tags' IDs by using a P2P overlay. Basically there are RFID proxies that will manage the requests that come from both non-RFID-enabled users and mobile readers, providing them with the requested information. The system also offers: service authentication, AAA functions (authentication, authorization, and accounting), and reader-to-reader interference control. Our approach presents a generic platform that can be set up to satisfy different kind of requirements. It consists of the following elements: Multi-Mode-Nodes (MMN), Mobile Readers (MR), users with non-RFID capabilities or Virtual Readers Nodes (VRN), Management and Authenticator Nodes (MAN) and passive RFID tags (Fig. 1). These components are described as follows:

B. MMN 1) Definition

A Multi-Mode-Node (MMN) is the integration of a wireless (e.g. Wi-Fi) and RFID interfaces bind to a processing module. It is basically a RFID proxy that manages users' requests related to the RFID reading service.

2) Operation

At startup the MMN will perform a security control process against the MAN in order to be authenticated, then a setup stage will allow it to be configured obtaining the applications and settings required to operate. Later the MMN will be ready to manage users' requests. A user's incoming request can be of three kinds: authorization, scanning (RFID reading) and time slot assignation. The MMN will forward the authentication request to the MAN performing the security control to access the network. At this step the user can also obtain the applications and settings required in the network. The scanning process can be triggered by the user, the MAN and the MMN itself and once the tags list is obtained it will be propagated into the network via the P2P overlay (Share process). The RFID reader-to-reader interference is controlled by using a time-division scheduling mechanism. The MMN will scan its environment for RFID tags' IDs on a time slot basis. These time slots will be defined at the Setup step. Each MMN will obtain from the MAN a group of time slots in which the node will be able to scan for tags and also to assign them to requesting users. The basic processes of a MMN are presented in the next figure (Fig. 2).

C. MAN 1) Definition

The Management and Authenticator node (MAN) provides service access control, AAA, and network management features (statistics, network state, etc). A MAN is based on a proxy solution with a distributed AAA server. We developed a practical implementation on Java SE by using a set of P2P protocols developed by Sun [3, 12, 13, 14, and 15] and we included encryption and MAC (Message authentication code) in the message information exchange to secure the communications.

2) Operation

The MAN's authentication mechanism is depicted in Fig. 3. It behaves as a proxy that communicates with the AAA server on behalf of the peer in order to authenticate the user. This node will grant that only authorized users will join and use the network services.

D. VRN 1) Definition

Virtual Reader Nodes (VRN) are portable devices (i.e. mobile phones, laptops, pads, and so on) with non-RFID capabilities but with a wireless interface. They will obtain tags' IDs through a transferred list from the MMN by using the network overlay. The virtual range is defined as a feature that allows the node to obtain RFID IDs present in the system gathered in a collaborative way among all the nodes and consequently overcoming physical range scanning limitations. By exchanging the discovered tags' IDs the network will build a database representing the RFID deployment.

2) Operation

The basic operation chart flow of VRNs is shown in the next figure (Fig. 4). The VRN will perform a security control in order to be authenticated. The MMN and the MAN will be responsible to provide or deny the access to the overlay. At this step the VRN can also obtain the applications and settings required to operate. Then the VRN will obtain through the wireless interface the list of scanned tags. The user will have three more choices: to share this information with other authenticated nodes, to request again for tags' IDs or to disconnect from the network.

E. MRN 1) Definition

Mobile readers are devices that have RFID scanning capabilities. They can be homogeneous or heterogeneous regarding the technology and operation frequency.

2) Operation

MRN follows the same principle that VRN differing only on the scheduling and time slot assigned to scan RFID tags' IDs.

If a MR requires tags' scanning, it will be synchronized with the MMN in order to obtain a valid time slot that will be used to obtain the tags' IDs. The MAN will assign a group of time slots to each MMN and they can be increased or reduced related to the network capacity and service delay.

IV. ANTI-COLLISION MECHANISM

We suggest a time-division-based mechanism to prevent the interference of the MMNs while scanning RFID tags' IDs. MMNs will perform a sequenced scanning coordinated by the MAN. MAN will provide to each MMN a set of time slots available that will be used for the node itself and the MRNs that request reading slots. On this way it is also possible to integrate heterogeneous readers into the network because it is a technology-independent mechanism. Only non-interfering readers are allowed to scan tags but if a node is far enough not to cause interference to a MMN, or if a mobile node located in the MMN's cell which scanning power is low in relation to the MMN's then it is possible to allow it to read tags simultaneously. Network planning should be performed in order to define the interfering zones and also each device should have a profile description to let the system know its power capabilities.

The mechanism works in this way: the system will schedule the RFID tags' scanning process following a time slot division scheme. The MAN will assign to each registered MMN a pull of time slots that will allow them to schedule a certain number of mobile readers in each MMN cell. This process can be run dynamically to extend or reduce the number of assigned time slots based on performance metrics. The MMN will coordinate the scanning by using two messages in the network overlay: SCN and ENDSCN. SCN will notify that the node is reading tags' IDs. Once it is finished it will send an ENDSCN message to inform that the node ended the process. TSCN is the time needed for the node to obtain all the tags' IDs located on the readers' range. Each node will reply to these messages with an ACK. If the nodes will not interfere due to distant location, the message will not be propagated to them and they can perform parallel scanning. This will be determined based on the network planning. The message exchange for 4 nodes (MMN/MRN) is shown in the following figure (Fig. 5)

Fig. 5. Overlay Messages for anti-collision mechanism

If there is an increasing number of MRNs requesting for time slots, the Service Delay will increase depending on variables like power, number of tags to read, tag identification speed among others. On the other hand the mechanism prevents the interference because only one reader will be scanning at a time.

V. FRAMEWORK DELAY ANALYSIS

A. Simulation Setup

We set up a simple NS2 scenario based on 802.11g in order to evaluate the transmission delay of the MMNs. We used the Two-ray ground reflection model and a frequency operation of 2.4 GHz on Direct Sequence Spread Spectrum (DSSS). It was set by a 30x30 meters flat grid and we defined three configurations:

• A MMN in the middle of the grid and a single fixed user node at (0, 0). • A MMN in the middle of the grid and a single user node at the (0, 0) location performing random movements. • A MMN in the middle of the grid and 20 user nodes at random fixed positions inside the grid.

The nodes were modeled following the scheme depicted in Fig. 6. In order to measure the transmission delay we created a list of tags' IDs to be sent. We obtained the file size associated to a specific number of tags. We computed random files (by using a bash shell script) with unique tags' IDs with 96 bits extension as the EPC standard stands [START_REF]EPCglobal Tag Data Standards Version 1[END_REF]. These files were transmitted from a modeled MMN to a user node and the data rate of the channel varied in these steps: 1, 6, 18, 36, and 54 Mbps. We performed the evaluation of the transmission of gzip compressed files and we measured the associated delay. In the following table (Table I) we present the mean values for compressed and non-compressed files that represent the list of scanned tags to be transmitted from the MMNs to the users.

B. RFID Tag Identification Speed (TIS)

Based on the results obtained in [17, 18, and 19] we considered the EPC-Gen2 protocol to calculate the TIS. For a data rate of 62.5 Kbytes between tags and readers we obtain a constant rate of approximately 280 tags per second. In Fig. 7 we observe the time required to detect n tags (from 0 to 10k tags).

C. Transmission Delay

In the first simulation case (A MMN in the middle of the grid and a single fixed user node at (0, 0)) we obtained the results shown in Fig. 8. We observe the transmission delay of the different gzip compressed files (tags' IDs) for different data rates. For the highest data rate (54 Mbps) the transmission delay for a file representing 10k tags (202,606 Kbytes) is about 1.33 seconds contrasted with 3.24 seconds for 1 Mbps data rate. From 0 to 100 tags the delay remains almost the same for all data rates (about 1.12 to 1. 32 seconds) and then increases linearly.

Considering the second simulation case (A MMN in the middle of the grid and a single user node at the (0, 0) location performing random movements) the plot is presented in the Fig. 9. We observe that the delay increases slightly compared to the fixed node due to the node's movements and the power variations. For a 54 Mbps data rate and 10k tags the delay is approximately 1.84 seconds. For 1 Mbps we obtained 3.74 seconds. For the third simulation case, we placed 20 user nodes and we transmit the tag's files in parallel. In the following figure we present the delay experienced (Fig. 10). From 0 to 100 tags, the transmission delay remains almost constant for all the different data rates evaluated (1.9 seconds). For a 10k tags' list, we obtain a delay of 9.27 seconds for 54Mbps and 45.37 seconds at 1Mbps data rate.

D. User Service Delay

The first time a node will be served it will experience a Service Delay (USD) based on the following parameters [START_REF]RFID tag pricing guide[END_REF] User Service Delay = T Auth + T Setup + T Req + T scnDelay + T Tx [START_REF]RFID tag pricing guide[END_REF] Where:

• T Auth = Delay of node authorization • T Setup = Time to download required applications and configuration • T Req = Time to send a request to the MMN • T scnDelay = MMN scanning delay • T Tx = Tags' Ids transmission delay For the following requests the Service Delay is reduced to (2) USD = T Req + T scnDelay + T Tx [START_REF] Wiebking | A Roadmap for RFID Applications and Technologies[END_REF] If the MMN had scanned its environment before a user request the USD will be (3)

USD = T Req + T Tx (3)
To estimate a rough value for USD we can assume:

• TSetup = TTx ≈ 2 s that represents the time to download a file of 200 k bytes @ 54 MBps for a fixed node. This value will increase depending on the size of the application to be downloaded. • TReq and TAuth are in the milliseconds order (60 ms), as we obtained in [START_REF] Botero | Platform and experimentation of secure service location with P2P/Client-Server over ad hoc networks[END_REF] thus we can neglect them (not significant compared with seconds order). • TscnDelay = 35 s (10K tags using Gen2 protocol) Thus, USD = 37 s for a single fixed node served at 54Mbps data rate obtaining a list of 10k tags. If we assume that the MMN had already read the tags and has the list prepared by the time the request was made then: USD ≈ 2 s. For 20 nodes we can obtain: USD = 35 s + 20 x 2 s = 75 s at 54Mbps data rate obtaining a list of 10k tags. We observe that the USD will increase linearly with the number of requesting nodes.

VI. EXAMPLE APPLICATION

Suppose a scenario inside a museum. Instead of simple audio guides, users can use their Wi-Fi enabled mobile phones to download the Museum's guide application. The system will also automatically keep track of the works in exhibition by using RFID passive tags attached or close to them. Readers can be activated to perform surveillance tasks as well. By obtaining the tags' list with the associated information description, the museum can publish exhibition's websites and keep an inventory database (see Fig. 11).

VII. CONCLUSION AND FUTURE WORK

This paper describes a P2P-RFID-WiFi framework that allows non-RFID-reader enabled devices to take advantages of RFID network deployments. We provide a virtual reading range to both non-enabled RFID devices and mobile readers. We also suggested an application layer time-division-based scheduling mechanism to allow RFID readers to scan for tags in a sequenced way and to integrate heterogeneous readers to the network. We plan to implement the framework and to set up a testbed to obtain real platform's performance metrics.

Fig. 1 .

 1 Fig. 1. Network architecture.

Fig. 2 .

 2 Fig. 2. Multi-Mode-Node processes.

Fig. 3 .

 3 Fig. 3. Management and authenticator Node (MAN).

Fig. 4 .

 4 Fig. 4. Virtual Reader Node (VRN) operation chart flow.

Fig. 6 .

 6 Fig. 6. NS2 Modeled nodes.

Fig. 7 .

 7 Fig. 7. Tag identification delay for Gen2 protocol based on 280 tags/second.

Fig. 8 .

 8 Fig. 8. File transmission delay representing tags' Ids from a MMN to a fixed VRN.

Fig. 9 .

 9 Fig. 9. File transmission delay representing tags' IDs from a MMN to a random moving VRN.

Fig. 10 .

 10 Fig. 10. Parallel File transmission delay representing tags' IDs from a MMN to 20 fixed users.

Fig. 11 .

 11 Fig. 11. Museum Application.

TABLE I .

 I File size representing tags' IDs.

	Number of	Non-compressed	Mean compressed	Sample Variance	Confidence Interval 95%
	Tags	file size (bytes)	file size (bytes)	Compressed files
	1	192	71.633	2.68244	71.633 +/-0.16626
	10	1920	274.444	3.88277	274.444 +/-0.240657
	100	19200	2105.14	10.9783	2105.14 +/-0.680445
	1000	192000	20335.9	33.7461	20335.9 +/-2.0916
	2000	384000	40577.3	44.9006	40577.3 +/-2.78297
	3000	576000	60850.1	49.6987	60850.1 +/-3.08036
	4000	768000	81094.1	55.6748	81094.1 +/-3.45076
	5000	960000	101333	58.3231	101333 +/-3.6149
	6000	1152000	121610	59.6907	121610 +/-3.69967
	7000	1344000	141850	59.442	141850 +/-3.68425
	8000	1536000	162092	61.6346	162092 +/-3.82015
	9000	1728000	182365	60.572	182365 +/-3.75429
	10000	19200000	202606	63.5281	202606 +/-3.93751


Oscar Botero
email: oscar.botero@it-sudparis.eu

Hakima Chaouchi
email: hakima.chaouchi@it-sudparis.eu

RFID network topology design based on Genetic Algorithms

Keywords: Genetic Algorithms, Radio Frequency Identification, Network Planning, Network Topology Design. I

Radio Frequency Identification (RFID) is a well known technology that has entered successfully in the realm of innumerable applications and is considered as one of the principal building blocks for the realization of the Internet of Things concept. However, the majority of RFID applications require the utilization of multiple RFID readers and therefore effective and efficient planning of their networks is a major concern. Network planning is a complex process that involves different steps, one of which is its topology design. In this paper, we focus on the topology design process of a RFID network following an optimization-based approach. More precisely, we propose a multi-objective cost function that is used to evaluate candidate solutions for the position and power levels of the set of RFID readers to be deployed. In order to obtain optimal solutions we applied Genetic Algorithms, a well known heuristic technique. Finally, a developed software tool to assist in the topology design is also presented and processing delay measurements are performed.

INTRODUCTION

RFID Technology is one of the leading enabling building blocks of the Internet of Things and it is widely used for tracking and tracing items in production chains and stores as well as for different innovative applications such as citizen's documents, surveillance, public transportation, and building access control [START_REF] Wiebking | A Roadmap for RFID Applications and Technologies[END_REF]. For certain applications i.e. warehouse items tracking, interactive museums, tracing products in supermarkets, among others; it is required to deploy a network of RFID readers in a cost-effective way. This can be achieved by following a network planning procedure. Network planning is an iterative process that involves several steps such as topological design, network synthesis and network realization [START_REF] Penttinen | Chapter 10 -Network Planning and Dimensioning[END_REF]. This process can be based on different objectives such as coverage area, interference reduction, number of tags detected and low deployment cost. Apparently, for optimally satisfying all these -often opposed -objectives, a non-linear optimization problem with a large search space needs to be solved [START_REF] Yang | A RFID Network Planning Method Based on Genetic Algorithm[END_REF].

In this paper we focused on the topological design of an RFID network in order to obtain the optimal placement and power levels of the RFID readers that are to be deployed. Due to the nature of the problem: a search space with a large number of candidate solutions and different valid local optima, this problem suits to be solved by a well know heuristic technique, Genetic Algorithms (GAs) [START_REF] Mitchell | An Introduction to Genetic Algorithms[END_REF]. GA method is a search technique applied when dealing with huge search spaces and when multiple objectives should be achieved in parallel. GAs are used to obtain optimal solutions for a broad number of problems and are inspired in biological observations linked to the evolution of the organisms. Consequently, in this work a GAs-based RFID network topology design is proposed. It allows us to obtain RFID readers' location and power levels relying on a multi-objective cost or fitness function that evaluates and rates the solutions provided. In addition, in order to add design flexibility capabilities, we developed a software tool that permits us to vary different parameters such that solutions for diverse test scenarios can be easily proposed.

The rest of the paper is organized as follows. In section II related work is given. Section III provides a brief background on RFID and GA. In section IV the anatomy of the network topology design process is depicted. In section V we describe the software tool developed to assist the topology design. In section VI numerical results related to processing delay measurements are provided and finally the paper concludes in section VII.

II. RELATED WORK

A variety of RFID network issues have been proposed to be solved by using evolutionary techniques including GA. In [START_REF] Seo | A New GA-Based Resource Allocation Scheme for a Reader-to-Reader Interference Problem in RFID Systems[END_REF] a new technique for RFID resources allocation based on GA was proposed to deal with the reader-to-reader interference problem. In [5, 6 and 7] GA and binary particle swarm optimization are used to solve multi RFID networks scheduling problem to optimize channels allocation in the system. More related to our work are [START_REF] Yang | A RFID Network Planning Method Based on Genetic Algorithm[END_REF] and [START_REF] Chen | RFID networks planning using a multi-swarm optimizer[END_REF] where GA are used to propose RFID planning methods.

In our approach the candidate solutions for the required topology design are evaluated by using a linear weighted multiobjective function that considers six parameters: overlapping of the RFID reading area, number of useless readers deployed, number of tags covered, number of readers deployed out of the area to be covered, number of redundant readers deployed and number of tags located inside overlapped reading areas. We also developed a flexible software tool to assist in the topology design process that provides customizable GA parameters, functions to import and export scenarios and parameters settings, a scenario editor and visualization of the solving process and solutions obtained.

III. BACKGROUND

This section provides a brief overview regarding RFID networks and GA.

A. RFID Technology

RFID stands for radio-frequency identification and it is mainly used for tracking and tracing objects, animals or persons. Its major advantage over its predecessor; the barcode is that the identification is stored electronically and it can be retrieved wirelessly via an interrogator or reader with no line of sight requirement [START_REF] Finkenzeller | RFID handbook -Second Edition[END_REF]. The basic hardware implementation of this technology (Fig. 1) is built upon three elements: transponders or tags that store an identifier (ID) related to a specific object, readers or interrogators who obtain the ID stored in the tags, and the RFID middleware that performs processing of collected data following a certain purpose. The most used frequencies are 860-960 MHz named Ultra High Frequency band (UHF) used also used by the Electronic Product Code (EPC) Gen II/ISO 18000-6c standards and 13.56 MHz named High Frequency band (HF) used by ISO 18000-3 standard [START_REF] Hakima | The Internet of Things: Connecting Objects[END_REF]. A RFID network requires the interaction of multiple readers and a planning strategy should be followed in order to reduce interference problems, maximize the number of tags covered, reduce the number of deployed readers and reduce the cost of the network.

B. Genetic Algorithms (GAs)

GAs were initially proposed in the 60's by John Holland [START_REF] Mitchell | An Introduction to Genetic Algorithms[END_REF] and at the beginning they were applied to study natural adaptation and how to bring them into computer models. In fact, they try to emulate as an abstraction what happens in nature regarding how organisms reproduce and survive based on evolution. GAs process populations constituted of several individuals. An individual is conformed of one or more chromosomes. The chromosome is the set of genes that encode particular properties of the individual. The genes can have different possible values called alleles. The genes are located in a particular locus or position inside the chromosome. In GAs the chromosome refers to a candidate solution to a problem. Generally, a chromosome is represented as a binary string that encodes it. The genes are bits whose alleles can be either one or zero. However, there are different representations for chromosomes that include real numbers, alphabet characters, etc.

GAs operators mimic what nature does regarding organisms' evolution. The main operators are: Selection, Crossover and Mutation. Selection obtains the fittest individuals (best performing solution) in order to make them exchange their genetic information to produce a new offspring. Crossover performs the combination of the selected individuals to produce new solutions and finally, mutation flips alleles to add variety and reduce local optima convergence. GAs search through a huge number of possible solutions where every chromosome represents one point in the space search. The solutions are evaluated based on a fitness function that provides a measurement which indicates how well the individual solves the current problem. A simple GAs workflow is as follows:

1. A random population is generated.

2. The population is evaluated by using a fitness function.

3. Through Selection, Crossover and Mutation a new population is created which is called next generation that is actually a next iteration.

4. Repeat from step 2 until a solution is obtained or other limitations are reached (execution time, memory capacity, number of generations, etc.).

Other search methods like Hill Climbing, Simulated Annealing and tabu search as well as GAs share the same principle that is they generate a set of candidate solutions, evaluate them according to a fitness function, discard or keep solutions based on fitness and produce variants of the solutions. What differentiates GAs from the rest is that they use stochastic selection, crossover and mutation at the same time.

There is no rigorous answer to the question: why and when to use GAs over other search methods. Intuitively, the applicability of GAs suits when the search space is "large" and the problem does not require a global optimum to be obtained. GAs are also prone to noisy fitness evaluation (involving error measurement of real world processes) because they accumulate fitness statistics over many generations and behave robustly in the presence of small amounts of noise. The success of GAs also depends on the way the problem is coded, the operator functions, the parameters and the evaluation function.

IV. RFID NETWORK TOPOLOGY MODELING

A. Overview

As we stated previously, we aim to obtain the optimal position and power level of the RFID readers to be deployed by following a certain criteria. The main goal of the topology design solution is that it needs to minimize the interference effect, maximize coverage that will be directly translated into a maximum number of tags detected as well as to reduce network cost by discarding redundancy and useless readers' deployment. In order to rate candidate solutions, we define a multi-objective fitness function that considers different factors that impact the RFID network topology design and that are described in Section B. Furthermore, we chose a twodimension squared area as our test region and model the RFID reader coverage as a circular area with the reader located at the center. We assume RFID passive tags to be deployed. In the following section the multi-objective fitness function used to evaluate the candidate solutions is described.

B. Multi-objective fitness function

A linear weighted fitness function (f T ) to evaluate each possible solution is defined (Equation ( 1)). It provides a measureable indication on how well each evaluated individual performs at solving the current problem. The coefficients (w i ) can be set empirically, stressing different simulation requirements for giving more importance to some objectives than others. This function is composed of the following six objectives:

(1) f T = w i *f i 6 i=1
, where:

w i =1 6 i=1
Tag Reader Middleware

1) Overlapping of the reading area

The tags located inside the reading area will be normally detected but if any other reader interferes there might be collisions (Fig. 2). Thus, in order select only solutions with reduced reading area interference, we measured the overlapped area caused by all the deployed readers and we defined an acceptance threshold. Equations ( 2) to [START_REF] Guan | Genetic Approach for Network Planning in the RFID Systems[END_REF] show the definition of our first objective function (f 1 ).

The Coverage ratio (CRatio) specifies the amount of overlapping allowed on each reader. We observe that values from 0.10 to 0.25 require less iterations in order to obtain a suitable solution. If Cratio is equal to zero, the GAs will take more time to obtain a total no-overlapping deployment layout. This implies that the number of readers required will increase since enough readers need to be packed inside a square perimeter to cover the entire area [START_REF] Lubachevsky | Minimum perimeter rectangles that enclose congruent non-overlapping circles[END_REF].

2) Number of useless readers

It is to notice that even if a layout complies with the overlapping parameter it is possible to obtain readers that cover no tags and they need to be discarded from the optimal solutions (Fig. 3). Therefore, we measure and minimize the number of useless readers (Equations ( 6) to ( 8)).

3) Number of tags covered

One of the main objectives of a RFID deployment is to be able to detect and obtain the IDs of the totality of the deployed tags. We defined a target of 100% of tags detected (Equations ( 9) to [START_REF] Hakima | The Internet of Things: Connecting Objects[END_REF]).

We assume that if the tags are covered they will be detected but in practical applications it will not always hold, thus we provide an error reference value determined by defining three cases. In the first case, we associated a uniform probability distribution to the event of a tag being detected. The second and third cases define concentric circular areas inside the reading region with variable probabilities of detecting tags. For the two regions case we defined 90% and 10% as the probability detection rates, whereas 90%, 70% and 10% for the three regions case (Fig. 4). Finally, the parameter obtained is a reference value that indicates the number of tags that might not be detected.

4) Number of readers located out of the deployment area

Since we are allowing a huge number of solutions to be evaluated we need to restrict as valid ones all of those whose readers are only located inside the area to be covered. For that purpose we measure the number of readers located out of the established bounds and we eliminate such solutions from the valid ones (Equations ( 12) to ( 14)).

5) Number of redundant readers

Some solutions might include redundant readers (Fig. 5), which happens when two or more readers cover the same or a subset of tags. Such solutions must be discarded because they imply interference, no useful operation and additional cost. In order to measure the redundant readers, we observe the set of tags that each reader might detect and we increase the count each time we find a redundant one. The target is set to zero redundant readers (Equations ( 15) to (17)).

6) Number of tags located in overlapped reading areas

Finally, we observe that for certain geometrical considerations readers will tend to overlap in some areas. Additionally, it is possible to obtain solutions that comply with all the previous five objectives but that may present tags located inside overlapped areas (Fig. 6). Consequently, reading collisions might occur because several readers can be performing a reading process at the same time. Hence, we defined our last objective function to take into account and prevent this problem (Equations ( 18) to (20)). The target is set to obtain zero tags in the overlapped areas.

C. Problem Coding

The topology design problem is coded by using traditional binary coding. A reader's position and its power level are represented with 21 bits. The binary string is divided as shown in Fig. 7. The first 3 bits provide a 7 power step parameter. The following power steps are used: 0 (reader OFF), 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, and 1 Watt (maximum power). Then the 2D position of the reader is defined by a X-Y Cartesian coordinate system with a 10 cm resolution. By allocating 5 bits for the integer part of the position we can obtain 31 different values and by using 4 bits for the decimal part we code values from 0 to 9. Readers are placed between the range of X [0, 31.9] and Y [0,31.9]. For bigger areas the string needs to be modified in order to allow more values. If we define a maximum of 10 readers to be deployed, each candidate solution is composed by 10 x 21 = 210 bits. This gives us a huge search space of 2 210 possibilities where GAs perform successfully in order to obtain an optimal solution.

D. Propagation Models

We used the Friis equation [START_REF] Ahson | RFID handbook : applications, technology, security, and privacy[END_REF] in order to estimate the coverage area of each reader as a reference value (Equation ( 21

E. Genetic Algorithm Implementation

The GA method proposed has the following operators implemented:

1) Selection

Three selection operators are implemented: Roulette Wheel, Tournament and Random Selection. In the first method, each individual is assigned a slice of a "roulette wheel" where the size of each division is related to the fitness they have. The roulette will spin as many times as the size of the new required population. The higher the fitness value, the higher the probabilities for an individual to be selected. In the Tournament method we select "k" individuals from the population. "k" is known as the tournament size. The individual that has the highest fitness among the rest is the one to be selected. Finally Random Selection chooses an individual among the total population randomly. We also implemented Elitism that keeps the best solution obtained through all the iterations performed.

2) Crossover

We implemented two types of crossover: one point crossover with two parents, and two points' crossover with three parents [START_REF] Eiben | Parallel Problem Solving from Nature PPSN III[END_REF]. The idea of the crossover is to combine genes from the selected parents to obtain a new solution that theoretically should perform better. The cutting points are selected randomly and then the genes are combined to form new individuals. Once the new individuals are generated, the algorithm chooses the best solution (highest fitness) among the new individuals as the new offspring. 3) Mutation Three mutation operators were implemented. The simplest one flips one randomly selected gen from 1 to 0 or vice versa. The other two operators are: Cataclysmic mutation and Migration [START_REF] Whitley | A Genetic Algorithm Tutorial[END_REF]. The first performs intensive bits flips randomly in the whole population (except on the best solution kept if Elitism is used). The second method introduces randomly generated individuals. Mutation augments the diversity and reduces convergence to local optima.

V. SOFTWARE TOOL DESIGN

The purpose of developing a software tool is to provide a flexible framework that permits the experimentation with several parameters variation but also for observing the solutions generated and providing a graphical interface for specifying scenario problems. JAVA SE [16] was used to develop the application on the Netbeans 6.9 IDE [17]. The tool provides several customizable parameters that can be easily saved and recalled. The block diagram of the application is represented in the following figure (Fig. 8).

1) Customizable Parameters

The tool has a Graphical user Interface (GUI) that allows setting and modifying a variety of parameters. For the GA we can define the following parameters: Population size, number of genes per individual, mutation and crossover rates, cataclysmic and migration counters, type of crossover, type of selection and Elitism. The set of RFID parameters that can be modified are: Frequency operation, number of deployed tags, number of readers, transmission antenna gain, tag antenna gain and the power threshold to detect tags. An option for testing that allows us to generate random deployed tags scenarios is also included. Finally, the set of weights of the cost function the size of the area to be covered and the interference ratio can be tuned. The screenshots are presented in Fig. 9.

2) Import-Export and other functions

The tool provides saving and loading functionalities of all the parameters involved. Additionally, for testing purposes a function to generate, save and load random scenarios was included as well as a Scenario Editor for defining deployment problems (Fig. 10 right side). The GA can be executed in a step-by-step fashion to observe the evolution of the solutions. The results and the execution of the GA can be seen in a graphical way (Fig. 10 left side). Moreover, we can register the time and the number of generations used to find a solution into a text file which can be utilized for statistics processing. We can also generate and export the solution report into pdf format. The button shortcuts of all the functions are shown in Fig. 11.

VI. EXPERIMENT AND RESULTS

The objective of our experiment is to measure the time and the number of generations that were employed for different cases. We rely on the assumptions that the current or approximate position of the RFID tags can be obtained and RFID Tags' antennae are never at 90° with respect to the readers, thus they can always be detected.

A. Processing Time and number of iterations

We measured the number of iterations and the time required to solve a scenario composed of 30 tags into a square area of 20 m x 20 m. The coefficients for the objective function were set empirically to 0.2 for w 1 to w 4 and to 0.1 for w 5 and w 6 . We allowed an interference ratio of 0.25. The GA was set to run with a three individual crossover, roulette wheel selection and elitism activated. We ran 100 times each experiment. The complete list of parameters is presented in the Table I The first measurement involved the comparison of Friis and ITU propagation model in terms of number of iterations and processing time. The results are expressed in Fig. 12. We observe that the ITU model presents less coverage range than the Friis, consequently, it implies that more iterations and processing time are required for the ITU in order to obtain an optimal solution in contrast with the Friis model.

Secondly, we compared the variation of the crossover choice in the GA. We used two and three individual methods for both ITU and Friis models. The results are presented in Fig. 13. We observe that three individual crossover allows the convergence of the solution in less number of iterations though more processing time per iteration is required. This is due to the complexity of the three-individual crossover function where more candidate solutions need to be evaluated in order to obtain the final individual. However, this increase in processing time is compensated by the smaller number of iterations required to obtain a solution with fitness equals to one.

VII. CONCLUSION AND FUTURE WORK

In this paper, we presented a RFID network topology design based on Genetic Algorithms. This approach produces flexible RFID reader deployment layouts that optimize the location and power level of the readers to be installed. It takes into account factors such as interference, number of tags to be detected and reader redundancy. We also developed a customizable software tool to assist in the design and we performed measurements that enable the performance comparison of all the processes involved in the topology design. As future work, we are planning to study other optimization methods for more dynamic network configurations in order to improve the processing time.

Fig. 1 .

 1 Fig. 1. Basic RFID architecture.

Fig. 2 .

 2 Fig. 2. Overlapping of the reading area.

Fig. 3 .

 3 Fig. 3. Useless Reader.

Fig. 4 .

 4 Fig. 4. Zones with different tag detection probabilities.

Fig. 5 .

 5 Fig. 5. Redundant Reader.

Fig. 6 .

 6 Fig. 6. Tags located in the overlapped area.

Fig. 7 .

 7 Fig. 7. Genes representing a RFID Reader.

  power, Pt: Transmitted power, Gr: Receiving antenna gain Gt: Transmitting antenna gain, λ: wavelength of the frequency used r: reading range For representing indoor scenarios, we use the ITU indoor propagation model. The ITU indoor path loss model is expressed in equation (22). Where: L = the total path loss (dB). f = Frequency of transmission (MHz). d = Distance (m). N = the distance power loss coefficient. n = Number of floors between the transmitter and receiver. P f (n) = the floor loss penetration factor.

Fig. 8 .

 8 Fig. 8. RFID Topology design Tool modular structure.

Fig. 9 .

 9 Fig. 9. Topology design tool customizable parameters.

Fig. 10 .

 10 Fig. 10. Solutions Pane (Left) and Scenario Editor (Right).

Fig. 11 .

 11 Fig. 11. Tool functions menu shortcuts.

Fig. 12 .

 12 Fig. 12. Number of generations vs. fitness and vs. processing time for Friis and ITU propagation models.

Fig. 13 .

 13 Fig. 13. Number of generations vs. fitness and vs. processing time for Friis and ITU propagation models using 2 and 3 individual crossover.

Genetics Module Scenario Editor Solutions Visualiser Import Export and General Functions Topology Design Tool Propagation models Module

  .

					Save
	Save Settings	Open Settings	Step Mode	Reset	results (Image)	Scenario Editor	View GA running
	Save current scenario Start GA Stop GA	View Results	Export pdf report	Tags generator	Save time and number of generations

TABLE I .

 I Simulation Parameters.


Oscar Botero
email: oscar.botero@it-sudparis.eu

Hakima Chaouchi
email: hakima.chaouchi@it-sudparis.eu

RFID service for non-RFID enabled devices Embedded hardware implementation

Keywords: Embedded hardware, Radio Frequency Identification, WLAN. I

Radio Frequency Identification (RFID) applications require the use of specialized hardware in order to obtain the IDs that are provided by the tags. This condition limits the extension of the information to devices with non-RFID capabilities. In this paper we present the design and implementation of a RFID service conceived for devices with non-RFID hardware but a WLAN interface. More specifically, we utilized embedded hardware to build an affordable and practical device that allows obtaining lists of tag IDs by using a common WLAN interface. Additionally, we performed delay metrics experiments and measurements are provided.

INTRODUCTION

RFID technology provides an information platform that allows tracking and tracing persons, animals and items extending the limitations of the barcode due to the non-line of sight detection requirements as well as the general reduced device size. The basic architecture is constituted by Interrogators or Readers, Tags and Middleware. The tags provide an Identification number (ID) that is linked to the object of interest. The Readers perform the ID collection from the deployed tags and transmit that information to the Middleware in order to accomplish certain purpose or application [1, 2 and 3].

RFID systems required specialized hardware in order to access to the information they provide. However, this condition limits the extensibility of applications to devices that do not have available RFID hardware. To overcome this barrier integration platforms can be implemented [START_REF] Botero | Peer-to-Peer Framework for RFID/Non-RFID Reader-Enabled Users[END_REF].

In order to propose an integration platform we need to consider common points that will allow the information flow.

In this paper we propose the design and implementation of a system that provides the tag IDs gathered by a RFID reader to any device that has a common wireless interface, overriding the necessity of having a RFID-enabled device at the user's side.

We focused on the implementation of the solution by using embedded hardware due to the reduced costs, flexibility and versatility.

There are different technologies that allow wireless communications like ZigBee, Bluetooth, Wimax, etc. However, we selected Wi-Fi as the common denominator due to the popularity of those protocols, the huge number of portable devices Wi-Fi enabled [START_REF] Smith | Wi-Fi enabled mobile phone handsets in the US[END_REF] and the coverage range, thus a service based on RFID information can be extended to common devices like mobile phones, pads and laptops. On the other hand, the hardware used it is open to add other wireless devices that uses the aforementioned technologies.

There are some related work oriented to the implementation of RFID systems by using embedded hardware to provide different services and applications. In [START_REF] Jian | Location Aware Public/Personal Diversity of Information Services based on embedded RFID Platform[END_REF] a Location Aware service is implemented based on an embedded RFID platform. In [START_REF] Li | Design of a Hybrid RFID/GPS-Based Terminal System in Vehicular Communications[END_REF] a system to perform vehicular monitoring is implemented by combining RFID, GPS and GSM modules with a microcontroller as the coordinator and processor unit. In this way a tracking system can be put into practice for vehicular uses. The integration of RFID and Wireless Sensor Networks (WSN) by using embedded hardware can be seen in [START_REF] Xiaoguang | The research of network architecture in warehouse management system based on RFID and WSN integration[END_REF]. Basically, in that proposal a RFID and a RF transceiver are bind to a microcontroller in order to provide warehouse management services. In [START_REF]An Embedded System and an Architecture for Access Control and Access Management[END_REF] an architecture and hardware implementation of an Access Control system is presented. It uses embedded hardware and shows a modular architecture approach. In [START_REF]Verification and Validation Communication Layer of Embedded Smart Card System[END_REF] a verification and validation system is developed based on a microprocessor in order to reduce cost and provide easy maintaining.

Our contribution proposes the extension of the information provided by the RFID tags to users that have no RFID hardware available but a WLAN (Wireless Local Area Network) interface. We orchestrate the control and information delivery by using a microcontroller as the core element and different modules that provide RFID detection and wireless connectivity. Additionally, we measured delay metrics and we provide application scenarios of the system developed.

The rest of the paper is organized as follows. Section II describes the generic system architecture followed by section III where the hardware and software implementation is depicted. In section IV we provide the results of the measurement performed. In section V two application examples are presented and finally the paper concludes in section VI.

II. SYSTEM ARCHITECTURE

A. Overview

Our solution can be defined as a proxy that transmits the RFID tag IDs to users connected by the WLAN interface. On this way they do not need to have embedded RFID hardware to obtain the information provided by the RFID deployment but a WLAN interface, or any other interface commonly agreed.

Regarding the implementation, we rely on a microcontroller as the center of the system and different hardware modules that perform specific tasks. In Fig. 1 we present the basic modular structure of the system and in section B we explain the generic components.

B. Modules

There are four basic modules that constitute the system. These are described as follows.

1) Microcontroller (MC)

The MC provides the control and coordination of the different modules in the system. This device should provide enough memory space to hold the controlling algorithm as well as enough ports to branch the different modules. They will perform specific functions like RFID reading, external memory and the wireless interface among others.

2

) RFID Reader

It performs the scanning or detection of the RFID tag IDs that are going to be transmitted to the users that request them via the wireless interface.

3

) External Memory

Microcontrollers are limited by the memory they normally provide. For this reason we require external storage space in order to record the tag IDs detected by the RFID module.

4) Wireless Interface

This module is the common bridge to the users with non-RFID embedded hardware and the service that will provide the list of RFID tags detected.

III. IMPLEMENTATION

A. Overview

We implemented the proxy solution by using commercially available modules (Fig. 2). A microcontroller is the core of the system and it provides enough ports, memory and processing power required to this implementation. We added three modules that perform the RFID detection, external memory space and WLAN interface respectively. The description of all these elements is provided as follows.

B. Modules 1) Microcontroller (MC)

We base our solution on the affordable and versatile Atmel 1280 microcontroller [START_REF]Verification and Validation Communication Layer of Embedded Smart Card System[END_REF]. It provides an 8-bits-16MHz core with 128KBytes of flash memory, 8 Kb SRAM, 4 Kb EEPROM, 54 digital ports, 16 analog ports, I2C, SPI, Serial ports and operates between 2.7 and 5.5 volts. A commercial board provided by Arduino [12] provides an USB programming interface and several open source libraries in Clike code. We remark that any other microcontroller or embedded board that provides enough resources to implement the system can be used.

2) RFID Reader

The RFID module is a Low Frequency (LF) Wiegand interface device [13]. Wiegand uses 26 bits format where 24 bits are user data (Tag ID) and 2 bit are used as parity.

The decoding time is less than 100 ms with a maximum effective distance up to 150 mm. It can read passive RFID tags that comply with the EM4100 standard. It uses two digital ports connection in the MC in order to transmit the detected tag ID. For this implementation the tag IDs are limited to 24 bits due to the limitations of the module however other RFID readers can be used to implement different standards and have longer IDs.

3) External Memory

If the MC is powered off the information that is not saved in the ROM will be lost. Due to the reduced amount of memory available (4Kb) we included an external storage module. It is basically a SD (Secure Digital) card unit that allows us to store the tag IDs that had been obtained through the RFID reader module. We can provide up to 16GBytes of external storage by using a standard SD card. This module is connected to the MC by using a SPI (Serial Peripheral Interface Bus) interface. There are available FAT (File Allocation Table) libraries to read, write and edit the log files recorded.

4) Wireless Interface

We employed a 2.4 GHz, low power 802.11 a/b compliant module [14]. It provides WLAN connectivity up to 2Mbps. It can be setup in Infrastructure or Ad hoc modes and supports open, WEP (Wired Equivalent Privacy), WPA and WPA2 (Wi-Fi Protected Access II) methods. This unit also uses the SPI port of the MC In order to provide the list of tags to the users we create a basic web server that provides via HTML the list of gathered tags by the RFID readers. In this way users with a WLAN interface can connect to the ad hoc network set up by the system and obtain the list of available tags. (Fig. 3) Fig. 3. Web server.

5) RFID Tags

We used Standard EM4100 compliant RFID passive tags (Fig. 4). Due to the characteristics of the RFID reader we employed it can detect 24 bits of the tag ID.

C. Flow Diagram

The proposed system performs the following processes in order to provide the tag IDS to users. At first, the system executes the Initialization phase where all the modules run certain routines to make their resources and functionalities available. The WLAN module is activated and the initial page is loaded thus it is ready to be displayed when the requests arrive. After that, the SD module is initialized providing access to the card in order to read and write information. Later, the routine that starts the RFID reader runs. At this step the port interruption is assigned in the MC in order to detect the pulses transitions that identify the bits in the RFID tag.

Once the Initialization process ends, the system is ready to deal with two types of events, either RFID tag detection or a WLAN user request.

If a tag is detected the MC sends the ID to the SD card and updates the information that will be published in the web site that the web server manages.

If a user connects to the server, it will send a webpage that includes the list of detected tags that are stored into the SD card. On this way the user can get the IDs of the tags without requiring a RFID reader but a WLAN interface. The server runs in ad hoc mode and different security methods can be implemented (open, WEP, WPA). The processes flow chart is presented in the following figure (Fig. 5)

IV. EXPERIMENT AND RESULTS

We measured the delay incurred while initializing the different modules. In this way we can obtain the total time require for the system to perform this phase. Secondly, we measured the transmission delay of the tag IDs to a user connected via the WLAN interface.

A. Initialization Delay

In this section we present the initialization delay of the modules used.

1) WLAN Module

In table I we show the average time required for the WLAN module to be fully functional.

2) SD Module

In table II we present the average time required for the SD module in order to be ready to read and write files into the SD memory card.

3) RFID Module

Finally, in table III we show the average time required for the RFID module to be functional and ready to detect tag IDs. The Total Initialization Delay (TID) can be calculated by using linear Equation (1).

TID = WLANDelay + SDDelay + RFIDDelay

(1)

The average TID is about 21.47 seconds where the WLAN module is the biggest contributor due to the complexity of the module and the setting up of different mechanisms that it requires in order to operate.

B. RFID Tag IDs Transmission Delay

We conducted a test by using the software protocol analyzer Wireshark [15] in order to measure the time a user needs to wait in order to get the list of available tag IDs.

The results are presented in the next figure (Fig. 6) and in the Table IV. We measured from 1 to 200 tag IDs and for the maximum value we obtain a delay of approximately 1.6 seconds.

V. APPLICATIONS

In this section we present two applications based on the implemented system.

A. Building Access Log

We can implement a Building Access Register by detecting the tag IDs of the persons who access the building. Additionally, we can add an external clock reference provided for example by the DS1307 from Maxim [16] in order to track the date and time of the access. We used an I2C (Inter-Integrated Circuit) module that allows us to obtain an external clock reference driven by a crystal and battery backup (Fig. 7). The log file is available via the WLAN connection. The block diagram is shown in Fig. 8. The way it works is similar to the basic configuration stated before but what changes is that every time a tag is registered by the RFID reader, it is saved into the SD card adding up the current date. The Web Server will be updated afterwards thus the current tag list is available to the users that require that information.

B. RFID-WSN (Wireless Sensor Networks)

The basic configuration of the system can be expanded by adding other modules to provide connectivity for a WSN. In this manner data from sensors and RFID can be combined to provide an information platform and that does not require, at the user side, the use of specialized hardware to obtain the data. A commercial XBee module can be included to allow ZigBee connectivity [17] (Fig. 9) In Fig. 10 we present a general block diagram where data collected from sensors, GPS and other modules can be combined to provide an information and integration platform.

VI. CONCLUSION AND FUTURE WORK

In this paper we provide the design and implementation of a RFID information service that provide tag IDs to users connected via a wireless interface. For certain applications this system provides an integration platform into the RFID networks for devices that have no suitable hardware (RFID Reader). Due to the modularity of the system more features and capabilities can be implemented to extend it use to diverse applications and requirements, i.e. Wireless Sensor Networks. A future work path would include experiments with a network of MC to explore limitations and capabilities as well as other devices like micro-computers.

Fig. 1 .

 1 Fig. 1. Block Diagram of the System.

Fig. 2 .

 2 Fig. 2. Hardware Modules used.

Fig. 4 .

 4 Fig. 4. RFID passive tags.

Fig. 5 .

 5 Fig. 5. Processes.

Fig. 6 .

 6 Fig. 6. Tag IDs transmission Delay.

Fig. 7 .

 7 Fig. 7. External Clock Reference Module.

Fig. 8 .

 8 Fig. 8. Block Diagram of the System.

Fig. 9 .

 9 Fig. 9. ZigBee Module.

Fig. 10 .

 10 Fig. 10. Block Diagram of the System.

TABLE I .

 I WLAN Initialization Delay.

TABLE II .

 II SD Initialization Delay.

TABLE III .

 III RFID Initialization Delay.

TABLE IV .

 IV Tag IDs transmission Delay.