SECURE ROLE BASED HEALTHCARE CLOUD BASED SYSTEM

Localization is a broad scope which includes outdoor localization and indoor localization and this technology was introduced in World War II to identify soldiers in emergency requirements. Global Positioning System (GPS) was introduced during the Vietnam War to get accurate outdoor locations and the same scenario was applied to localize indoor by using microwave Radio frequency and that was not failedas signals ware greatly attenuated inside due to other physical obstacles.

At presentIndoor localization has been widely applied in most areas including in the hospitals to locate the patients, within the logistics department in areas such as supply chain market and stock control, theft protection, among others and also using sales and marketing sector to promote goods which customer required. Its application in the medical sector is increasing over the years. The patients are always at risk of sustaining injuries within time as far as their mobility is concerned. This system has therefore proven to be useful in tracking them especially when they are not being watched physically. There are a number of technologies that have been incorporated within this system. Among which include the Radio Frequency Indication. This is a commonly used technology as it has proven to be cheaper. Using such a devise requires the reference tags, whereby each of the tags will act as the transmitter. The readers are placed within the building ad this will measure the Radio Signal Strength Indicator. The positions of the reference tags whose Radio Signal Strength Indicator is known will be important in estimating the position of the target tag[1].

Since the Radio Signal Strength Indicator displays some disadvantages including being affected the environment, including the scattering, reflection and the refraction of object within the room, the signal is therefore likely to arrive along various paths and thus affecting the localization accuracy. This therefore calls for a better technology. The Wireless Sensor Networks will therefore be introduced to solve the problems caused by the RSSI. This technological network has the capability of acting as both the transmitter and the receiver. In using this approach, the change in signal strength is utilized to identify the potential target area. The next process will involve the selection of the reference tags as the candidate tags to facilitate the calculation of the target’s position. Unlike the Radio Signal Strength Identification system, this technological system is not affected by the environment.

The next part of the paper will look at the problem domain and the research questions. This will be followed by the summary of the background and project objectives and finally it will look into project requirements analysis and specifications.

Despite the usefulness of the Radio Signal Strength Indicator in locating the target object, it has been associated with some setbacks including being affected by the environmental conditions including the reflection, refraction and scattering as well as by the fact that it receives signals from various directions. This makes its level of localization accuracy to be generally low and therefore proving futile in determining the location of the target. This therefore led to the need for better technological tools that would be fundamental in solving the above problems. As a result, the Wireless Sensor Networks therefore comes in play to help in improving the localization accuracy. This network was found to improve the localization accuracy due to its ability to act as both the transmitter and receiver. It is also advantageous as it is not prone to environmental effects.

The project seeks to answer the following research questions:

1. What is the best algorithm that we can implement to minimize attenuation and enhance the accuracy of the indoor localization?

2. How does Radio Signal Strength Indicator ( RSSI) tag relationships help in building the indoor localization with the help of reference tags?

3. What are the advances filters to eliminate distorted indoor localization based on Radio Frequency Identification ( RFID)and how it effect to the process?

The literature review on the study provides an extensive research that clearly brings out the facts on the topic, its implications and applications. For instance, it clearly explains the Radio Frequency Identifications, gives the supporting technological systems that are used in Radio Frequency, and gives an overview on how the technologies work while putting into consideration their roles in contributing to localization, and the advantages and disadvantages of the technological systems.

The Radio Frequency Identification has been a popular information exchange system that has had most applications over time. Several researches have been conducted on the same and its application in several industries including the hospitals, the retail, security, asset management, among others has been paramount. The traditional Radio Frequency Identification is considered the cheapest and therefore widely used. It is made up of two major components including the tag and reader. The reader is concerned with initiating an enquiry or even updating, while the tag receives the command, carries out the order and replies it back to the reader. The tags within the Radio Frequency Identification, has been shown to have two basic components. These include the integrated system and the antenna coil. The integrated circuit is concerned with the execution of the commands and the storage of data. The antenna coil receives and transmits the radio frequency signals [2]. The Radio Frequency Identification has had several technologies with time. For instance, the Radio Signal Strength Indicator played a major role as far as localization was concerned. It is through this technological system that the ability to locate the target was made possible. It however faced some challenges concerning the accuracy relating to localization. The Wireless Local Network has been viewed to having many.[2][3][4][1][5][6]

In most cases indoor obstacles such as physical devices may attenuate and create noises for the RF signal so, as a result of this accuracy of location can be vary. The best example is hospital environment and there are various obstacles can interfere radio frequency and in order to solve this issue Wireless Sensor Network can be implemented. Establishing platform for benchmark RF- Based Localization Solution can help to automate evaluation and also to compare different solutions. Factors such as Hardware and Human presence can be influence to Receiving Signal Strength analyzing the WLAN with RSS can deliver better idea about various distortions last but not least tracking the devices that connected to the system by using Wi-Fi Assisted Particle Filter that requires RSS finger print database is another best method for indoor localization.[7][8][9][10][11][12]

Reflection and diffraction are two major causes that can distort Radio frequency and the solutions for that can be reduce conductor media such as metal and reduce the sharp edges in the environment that Radio Frequency is using. Using decoding procedure to reduce noise level when using ZigBee, which is powerful yet cheap solution is also a solution that we can consider to implement while planning Indoor localization. Finally Empirical and Parametric evaluation can be perfumed in order to understand the environment.[13][14][15][16][17]

Indoor localization is one of the popular application areas like medical and emergency care. Patient tracking can be done with the help of indoor localization techniques, where the exact location of the patients can be traced during emergency conditions. Indoor localization can be applied using different types of technologies and among them; RFID is quite popular due to its feasible configuration and cost. RFID works with the help of multiple reference tags, where they are deployed in advance to the target and the required transmission is done via these tags and the corresponding information is read using RSSI (Radio Signal Strength Indicator). With this information, exact position of the target tag can be tracked using the RSSI information which is closed to the target tag.

Accuracy of this information depends on many external factors like signal reflection, signal refraction and object scattering within a room. Number of paths taken by the signals to reach the receiver will be drastically increased due to these signal level issues and in general this process is known as multi-path phenomenon. Due to this phenomenon, performance of the RSSI based target estimation will be degraded in a very large indoor area and in few cases; information from different RSSI tags might be same.

There is scope to improve the accuracy of the information gathered from the sensors via the RSSI tags in critical medical applications and this can be overcome using the advanced information gathering techniques and devices of networking like the Wireless Sensor Networks. Building the radio map also has some limitations due to the environmental factors, where it should be rebuilt if there are any changes to the environment.[18][19][20][21][22][23]

RFID is one of the popular and old forms of information gathering and monitoring techniques, where the radio frequency based communication, data transmission and receiving are done with this technology. There are many types of applications supported by RFID technology and this is mostly used for location tracking, position tracking and monitoring. Both the internal and external location tracking and the object tracking can be done with the help of RFID tags, where coordinate based tagging and location identification are implemented by this technology.

Always, the nearest tag coordinates are used for the position and location tracking with the RFID technology and the information is gathered using the RSSI (Radio Signal Strength Indicator). Readers and Transmitters of RFID will utilize this information from the RSSI to get the accurate location of the objects or the. Position of the target tags and the reference tags are used by the RSSI module to the get the position of the objects and here the accuracy is defined by considering the nearest tag information. Various factors will contribute to the performance or the quality of information sourced from the RF tags and these include both the environment and internal technological factors.

With the standards of 802.11, information accuracy can be improved using the multiple access points to a specific target object and this accuracy can be further improved if a typical radio map can be built. Distance among the sensors can be measured and embedded with the RSSI information and this value may be deviated due to the multi-path phenomenon and external environmental factors and the final value will be completely different either less or more to the actual distance value.

Object tracking for those without any RF devices can also be done using the Transceiver-free object tracking, where multiple numbers of wireless sensor nodes are used in this case. These sensor nodes are deployed and the required target object location is gathered using the RSSI dynamics. Environmental changes might affect the accuracy and robustness of the sensor nodes and finally causes lot of communication overhead and interference among the sensor nodes. Control information gathered from the RSSI or the wireless sensor nodes deployed can be increased, but this approach need lot of hardware configuration requirements. Environmental factors like snow, fog, paint and ice affects the information quality of the RFID tags and for such cases, GPS (Global Positioning System) can be used.

This is completely as satellite based navigation and communication system and mostly used for outdoor location tracking. Even, GPS has some limitations due its dependency on the satellites and can’t accurately detect the inside or indoor objects. Using the existing infrastructure and detecting the indoor objects with the RFID technology is challenging, where some of the other options also include 802.11, infrared and ultrasonic and the typical RFID based communication.

Effectiveness of the RFID object tracking mainly depends on the factors like

· Power availability at the tag reader

· Power availability at the tag responder

· High frequency structure issues and environmental conditions

Regions and sub-regions used to determine the RF tag based object location is possible if the antenna design is available as per the standards of distance ranges. Obstructions and absorptions will impact the signal field strength and thus also reduces the distance range accuracy. So, the signal strength information from of the RF tags plays the crucial role in analyzing the accuracy of the indoor localization and with the current traditional approaches, accuracy is widely impacted due to the environments, space and technical limitations.

From the project the types of hardware and software that are required for the project are the RFID Reader, the integrated RFID system, The RFID Antenna, the RFID software for the processes the RFID Printer, the RFID tags that will need to be used for tracking, the RFID labels the RFID inlay and the optional RFID wrist band to increase the mobility of the research team. Since this is an auto identification and data capture system that uses radio frequency for data transfers scanners and hand held readers will be required for the research study.

A box will be required to place the tagged objects a connection to the internet, the RFIS application software which is packaged retail software has to be bought, The main circuit of the RFID reader besides the inlay has to be developed or bought, the hardware for the communication interface will need to be set up, the central control t collect the radio frequency signal readings also has to be set up, the Wi-Fi connection will be sued because the positioning accuracy will be studied in the indoor and the outdoor environments of tracking assets and objects. Computer machines will be required in order to display data and communicate with the components of the RFID technology[22]

Table 1

Table 2

[24]

[25] Figure 1

Figure 2

( Start Calculate Original Radio Signal from sender node Apply Filtering Algorithm if RSSI is < Threshold Yes Distance estimation No End Send the location to the sender )

[18][8] Figure 3

Figure 4.Kalman Filter Algorithm

The Kalman filter is useful for smoothing noisy data by taking a sequence of noisy values and estimating the value of the underlying variables more reliably. Additionally this filter ideal for continuously changing nodes. Also, this filters mainly working with probability and matrices. Above flowchart shows to calculate location with motion of user joints so, it is very useful as it can be monitored falling of user.

RMS is Root Mean Square it can be calculated using

Where are the out put of the tri axel gyroscope at waist. If RMS value is close to zero means the user is in static state.[26]

Figure 5 SA-LANDMARK Algorithm flow chart

It has two phases: sensor assisted phase and localization phase. both phases uses sensor information to determine a subarea where the target object is located, so that it can conclude subarea selection phase .As a result it is able exclude that reference RFID tags they all are away from target object. In localization phase the information of RFID tags inside the subarea is used to locate the object.

In first phase we implemented two algorithms to deal subarea selection: maximal algorithm, intersectional algorithm and in second phase, it uses RSSI vector for Euclidean distance algorithm.

maximal dynamic algorithm is used to calculate subarea of interest by differentiating summation of the dynamic of RSSI for each subarea and intersection algorithm determine target object by using intersection links of sensor that has subarea , it may contain target object.

Figure6 COCKTAIL Algorithm Flow chart

In cocktail algorithm, it has two phases: sensor assisted phase and support vector regression, in sensor assisted phase it implements same as SA-LANMARC algorithm that is to determine subarea where the target object is located, apart from this in support vector regression phase its aim to determine hyper-plane which can accurately predict the training data

Known as Sequential Monte Carlo Method and this filter is based on simulations also this filter aims to estimate the sequence of hidden parameters.

Table 3

Table 4

Figure 7

Figure 8

The Project is Radio Frequency based advanced filter for Indoor localization, so, we use Received Signal StrengthIndicator (RSSI) to locate the object.We will gather project requirement according to MN691 research regarding RF based Filter and we finalized and proposed COCTAIL filter for filtration for received RSSI signal to get accurate location.

Basically we need to use at least three reference nodes and one target node for this project .Three Reference nodes called anchor nodes which will fixed in indoor location and coordinates will be measured manually and it use as a reference coordinates .Target node will be placing unknown location within the range of three anchor nodes.

The ZigBee communication Technology will use to implement this project as there are many advantages comparing with other technologies such as Bluetooth, Wi-Fi and so on. Advantages are low power consumption and thesignalrange.

Three anchor nodes received signals which transmit by the target node then anchor node will determine the RSSI strength and calculate the location. This main process may receive accurate location but some instance it anchor nodes receive several RSSI signals because of surround obstacles due to these issues the accuracy level may decrease. To overcome this issue we are going to propose a filter to smooth the signal strengthso, we hope to use Trilateration method and COCTAIL filter to minimize the disturbance and get accurate location.

Trilateration method is a concept that uses only distance to estimate the location of the target node by using two dimensional plane. To measure distance we use three anchor nodes which has antennas send signals and the intersection of three circumference will determine the location of the target node.

[27] Figure 9 Trilateration Method

Comparing with other algorithms The COCKTAIL Algorithm is much better for use in dynamic environment and also it is more comprehensive indoor localizationalgorithm which has two sub categories The first category is Sensor Assisted phase called SA phase which use sensor information to determine the target node and it has anability to remove other reference RFID tags which are locate much distance from the target node.

The second category called SVR stand for Support Vector Regression .The SVR phase using information from all reference tags which are located in a building to locate the target node. So it is very accurate algorithm than other algorithms as it gets all information from all reachable nodes in the building. However this concept is much complex than other algorithms.

We hope to use these two concepts to determine accurate target node and also we hope to develop this method introducing our own algorithm by modifying these concepts.

Initially we have discussed few idea regarding the project, but we have decided theproceed with the RF based indoor localization filter. In this project we are expecting to increase the accuracy of the reading which we are collecting from the dedicated ZigBee network by using algorithm we hope to introduce in the next phase of this project. During the initial weeks we have gathered much information by reading various journal articles and have studied how such algorithms are used in similar scenarios. Having proper understand using those algorithms we designed our project by using various tools such Gantt Charts. Kalman Filter, SA-Landmark and cocktail algorithms are the best solutions we consider to be fit in our project. However, finally we decided to go with the Trilateration method and cocktail algorithms. Those phases are sensor assisted and localization phase which gives two way approaches to identify the location with minimal noise.

Reference [1] G.-Y. Jin, X.-y. LU and M.-S. Park, "An Indoor Localization Mechanism Using Active RFID Tag," IEEE Xplore Digital Library, 2006. [2] R. Bunker and A. Elsherberni, "A Modular intergration RFID System for Inventory Control Applications," Electronics, 2016. [3] Z. Dian, L. Kezhong and M. Rui, "A Precise R4FID Indoor Localization System with Sensor Network Assistance," China Communications, 2015. [4] V. C. Gungor, Industrial Wireless Sensor Networks, CRC Press-Taylor & Francis Group, 2013. [5] P. Kulakowski, "Wireless Sensor Networks: Technology, Protocols, and Aplplications," IEEE Communications Magazine , p. 42, 2008. [6] W.-Y. Lee, K. Hur, T. Kim, D.-S. E. Eom and J.-O. Kim, "Large Scale Indoor Localization System Based on Wireless Sensor Networks for Ubiquitous Computing," Wireless Personal Communication, pp. 241-260, 2012. [7] E. D. P. F. L. V. H. N. W. T. V. Tom Van Haute, "Platform for Benchmarking of RF-Based Indoor Localization Solutions," IEEE communication Magazine, pp. 126-133, september 2015. [8] L. K. M. R. Zhang Dian, "A precise RFID indoor localization system with sensor network assistance," TELECOMMUNICATIONS FOR REMOTE MEDICINE, pp. 13-22, April 2015. [9] P. M. S. J. a. A. S. Yogita Chapre, "Received Signal Strength Indicator and Its Analysis in a Typical WLAN System," in 38th Annual IEEE conference, Sydney, 2013. [10] Y. Z. Z. Z. M. W. Y. F. a. Z. G. Feng Hong, "Indoor Localization and Tracking Using WiFi-Assisted Particle Filter," in 39th Annual IEEE Conference, Canada, 2014. [11] A. D. A. B. P. C. P. Anastasis C. Polycarpou, "On the Design, Installation, and Evaluation of a Radio-Frequency Identifi cation System for Healthcare Applications," IEEE Antennas and Propagation Magazine, vol. 54, no. 4, pp. 257-271, 2012. [12] W. N. a. I. B. Collings, "Indoor Wireless Networks of the Future Adaptive Network Architecture," IEEE Communications Magazine, pp. 131-137, March 2012. [13] K. K. H. Charlie, Indoor Radio Channel of Bluetooth Technology, Western Australia, 2001. [14] G. &. F. Morrison, "Super-resolution modeling of the indoor radio propagation channel," Ieee Transactions on Vehicular Technology, vol. 47, no. 2, p. 649, 1998. [15] M. J. Jia J., "Impulsive noise rejection for zigBee communication systems using error-Balanced wavelet filtering," AEU - International Journal of Electronics and Communications, vol. 70, no. 5, pp. 558-567, 2016. [16] H. Hashemi, "The indoor radio propagation channel," Proceedings of the IEEE, vol. 81, no. 7, pp. 943-968, 1993. [17] X. L. J. M. K. Pahlavan, "Indoor geolocation science and technology," IEEE Communications Magazine, vol. 40, no. 2, pp. 112-118, 2002. [18] C. l. Z Yang, "Adaptive Fitting Reference Frame for 2-D Indoor Localization Based on RFID," International Journal of Engineering and Technology, pp. 114-118, 2014. [19] A. .. Singh .S, "Survey on Localization Techniques of RFID for IOT," International Journal of Computer Applications, vol. 137, no. 12, pp. 23-27, 2016. [20] O. a. J. C. Luo.X, "Comparative evaluation of Recieved Signal Strength Index based indoor localization technique for construction jobsites.," Advance Engineering Informatics, vol. 25, no. 2, pp. 355-363, 2011. [21] B. J, "Improving Indoor Positioning Accuracy with Dense,Cooperating Beacons," Procedia Computer Science, vol. 40, pp. 1-8, 2014. [22] H. S. Sunhong P, "Autonomous Mobile Robot Navigation Using Passive RFID in Indoor Environment," IEEE Transactions on Industrial Electronics, vol. 56, no. 7, pp. 2366-2373, 2009. [23] J. U, "TDOA Based Localization Algorithms for RFID systems using Benchmark Tags," Korean Managment Science Review , vol. 29, no. 3, pp. 1-11, 2012. [24] Bisk, "Project Team Roles and Responsibilities," Villanova University, 2017. [25] D. Linman, "The Role Of Business Analyst In Project Management," MyManagementGuide.com, 2013. [26] X. Y. Y. W. a. X. X. Jian Huang, "An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization," Sensors, 2016. [27] "INTECH," [Online]. Available: https://www.intechopen.com/books/radio-frequency-identification-from-system-to-applications/localizing-with-passive-uhf-rfid-tags-using-wideband-signals. [28] L. K. M. R. ZHANG Dian, "A Precise RFID Indoor Localization System with Sensor Network Assistance," TELECOMMUNICATIONS FOR REMOTE MEDICINE, pp. 13-22, 2015.

Literature review of Radio Frequency based Advance Filter for Indoor Localization.

Indoor localization is a new tracking system which is widely using in many industries by using RF (Radio Frequency).It is true that every new technologies have pros and Cons so, this solutions also have some many drawbacks such as accuracy of location detection, radio frequency signal strength issues and noisy of radio frequency signals are few negative effects.so, to mitigate such issues we can suggest many solutions. However our major concern is to implement a filter to minimize such issues and maximize the accuracy of indoor localization.

A Precise RFID Indoor Localization System with Sensor Network Assistance

According to the journal Indoor localization is a very common technology which use to tack the location of people. In this paper discussed about indoor localization in a hospital which use to tack patients within the premises by using traditional Radio frequency technology. The author further discussed about Radio Frequency is cheaper than other technology but obstacles mislead the tracking location and also create noise as a result of that minimize the accuracy of the location.

To solve this solution he has implemented and suggests using Wireless Sensor Networks (WSNs) with few nodes and the work as transmitters and receivers and the location decided by using Radio Signal Strength Indicator (RSSI). The introduction of WSN may increase the accuracy of the location. Further the author has suggested two algorithms SA-LANDMARC and COCKTAIL to check the accuracy of the location.

Platform for Benchmarking of RF-Based Indoor Localization Solutions

The Journal Paper elaborates and implemented new benchmarking platform to evaluate indoor localization solutions. It is true that indoor localization tracking system has been using in many industries and different fields. However this Author has identified a major issue of indoor localisation: lack of comparability of existing localization systems also majority of systems are not comparable but individually evaluated indeed not repeatable. To overcome these issues the author has introduced benchmarking platform called EVARILOS. This benchmarking platform can be used to automate evaluation and also use to compare different solutions in different environments which using multiple evaluation metrics.

Received Signal Strength Indicator and Its Analysis in a Typical WLAN System

This paper is based on Received Signal Strength (RSS) and how to implement indoor localization using fingerprints. Further this paper has describe critical factors which effect for the indoor localization such as Spatial ,temporal ,environmental ,hardware and human presence. Indeed these factors may effect to the receiving signal strength of the indoor localization. Also, this paper has further discussed about the reliability of the indoor localization tracking data by using RSS method.

WaP: Indoor Localization and Tracking Using Wi-Fi-Assisted Particle Filter

This paper is about Indoor localization and usage of Wi-Fi –Assisted Particle (WaP) to track the location also, smart phone tracking applications which using radio based solution. For this task it was mandatory to maintain a RSS fingerprint database and examined three different observations turn verifying, room distinguishing and entrance discovering.so, based on these factor they designed a filter called Wi-Fi-Assisted Particle filter further they use RSS and floor plan of the building to identify the accurate location. Finally the experiment results show that localization error is less than 0.71m for more than 100 paths for 8 people.

On the Design, Installation, and Evaluation of a Radio-Frequency Identification System for Healthcare Applications

This paper has used RFID (Radio Frequency Identification) system in health care industry as a health care application. This application has used RFID and Ultra High Frequency (UHF) to develop this location system .The final product of this system has installed in Cyprus hospital to get feedback about the system. This system covered three main areas :

1) Patient identification system by using RFID enables card and wristbands.

2) Real time location tracking system of medical assets and expensive equipment which are located inside of the hospital.

3) Maintain quick drug inventory management system by using smart labels.

Further, they have concerned about other factors such as user-friendliness, privacy of patients data, reliability of the system and also hospital information security.

Indoor Wireless Networks of the Future: Adaptive Network Architecture

This paper describe about indoor wireless network traffic because of increasing of users and to reduce this issue they propose a new network architecture and reconfiguring existing topologies and frequency bands. This may lead to minimize indoor traffic demands. The main aspect of this paper is introduce a main fixed antenna which is inside of the building and this antenna can maintain different frequencies and this invention is more efficient and cost effective. This architecture can be implemented other networks like GSM and WLAN. According to the simulation the traffic demand can be minimized by 75% and the best thing is that it more convenient and very cost effective.

RFID INDOOR LOCALISATION SYSTEM WITH SENSOR AND FILTERS ASSISTANCE

The literature review on the study provides an extensive research that clearly brings out the facts on the topic, its implications and applications. For instance, it clearly explains the Radio Frequency Identifications, gives the supporting technological systems that are used in Radio Frequency, and gives an overview on how the technologies work while putting into consideration their roles in contributing to localization, and the advantages and disadvantages of the technological systems.

The Radio Frequency Identification has been a popular information exchange system that has had most applications over time. Several researches have been conducted on the same and its application in several industries including the hospitals, the retail, security, asset management, among others has been paramount. The traditional Radio Frequency Identification is considered the cheapest and therefore widely used. It is made up of two major components including the tag and reader. The reader is concerned with initiating an enquiry or even updating, while the tag receives the command, carries out the order and replies it back to the reader. The tags within the Radio Frequency Identification, has been shown to have two basic components. These include the integrated system and the antenna coil. The integrated circuit is concerned with the execution of the commands and the storage of data. The antenna coil receives and transmits the radio frequency signals[2]. The Radio Frequency Identification has had several technologies with time. For instance, the Radio Signal Strength Indicator played a major role as far as localization was concerned. It is through this technological system that the ability to locate the target was made possible. It however faced some challenges concerning the accuracy relating to localization. The Wireless Local Network has been viewed to having many advantages when compared to the Radio Signal Strength Indicator.

Indoor localization is one of the popular application areas like medical and emergency care. Patient tracking can be done with the help of indoor localization techniques, where the exact location of the patients can be traced during emergency conditions. Indoor localization can be applied using different types of technologies and among them; RFID is quite popular due to its feasible configuration and cost. RFID works with the help of multiple reference tags, where they are deployed in advance to the target and the required transmission is done via these tags and the corresponding information is read using RSSI (Radio Signal Strength Indicator). With this information, exact position of the target tag can be tracked using the RSSI information which is closed to the target tag.

Accuracy of this information depends on many external factors like signal reflection, signal refraction and object scattering within a room. Number of paths taken by the signals to reach the receiver will be drastically increased due to these signal level issues and in general this process is known as multi-path phenomenon. Due to this phenomenon, performance of the RSSI based target estimation will be degraded in a very large indoor area and in few cases; information from different RSSI tags might be same. There is scope to improve the accuracy of the information gathered from the sensors via the RSSI tags in critical medical applications and this can be overcome using the advanced information gathering techniques and devices of networking like the Wireless Sensor Networks. Building the radio map also has some limitations due to the environmental factors, where it should be rebuilt if there are any changes to the environment.

5