Wireless Network Visualization and Indoor Empirical ... - CiteSeerX

World Academy of Science, Engineering and Technology 42 2008

Wireless Network Visualization and Indoor Empirical Propagation Model for a Campus WI-FI Network A.R. Sandeep, Y. Shreyas, Shivam Seth, Rajat Agarwal, and G. Sadashivappa

initial test area and then use of the analysis of this test area to update parameters for a predictive model, which can be used to obtain signal strength at different areas and the final coverage area after deployment. Using this optimized predictive model, a network planner can determine the placement and amount of infrastructure required to meet the demands of the network for a deployment of any size. By planning the full deployment using this kind of design methodology, the number of cyclic deploy-and-verify steps can be drastically reduced. Whereas there are various propagation models for outdoor network planning, this paper is meant to extend the same concept to indoor network planning too. We do an extensive survey of the site concerned and obtain signal strength readings at different positions. Using this data with a modified propagation model such as Log Distance Path Loss model, a proper network plan can be obtained well before going for deployment. This can help optimize the Wi-Fi network infrastructure planning without compromising for the Wi-Fi service for the area concerned.

Abstract—With the increasing use of mobile computing devices such as PDAs, laptops, and an expansion of Wireless Local Area Networks (WLAN), there is growing interest in optimizing the WLAN infrastructure so as to increase productivity and efficiency in various colleges and office campuses with carrying out a cost effective infrastructure model. This paper describes an indoor propagation model which can be used to predict the signal strength taking into consideration propagation path losses and a comparison with an existing propagation model is also implemented. Simulation of an optimum ubiquitous Wi-Fi network area plan for R.V.College of Engineering campus in bangalore is also implemented.

Keywords—Wi-Fi,

WLAN,

Indoor

Propagation

Model,

simulation.

I. INTRODUCTION HIS paper outlines the method of simulating a optimal coverage area plan for a campus Wi-fi network with emphasis on minimizing the expenses and time involved. The goal of this work is to develop and present a streamlined, reproducible approach to wireless visualization as well as techniques for proposing a Wi-fi propagation model for prediction of received signal strength. A site survey helps define the contours of RF coverage in a particular facility. It helps us to discover regions where multipath distortion can occur, areas where RF interference is high and find solutions to eliminate such issues. A site survey that determines the RF coverage area in a facility also helps to choose the number of Wireless devices that a campus needs to meet its requirements. If a wireless site survey is not done prior to installation of wireless devices, the wireless LAN’s “clients” will find that the radio frequency signal drops out as they move around the facility, thus becoming disconnected from the host device or other mobile computing devices and their work and potentially causing data loss. In the past, early Wi-Fi coverage has been estimated in largely an ad-hoc manner: infrastructure was placed in a small test area with a basic idea of coverage in mind, using measurement surveys to provide verification and analysis, and then repeating the process for more and more areas until full coverage is achieved. This process can be very costly and timeconsuming. An alternative approach is proposed in this paper that promises lowered cost and lowered time-to-deployment. This method consists of a site-survey and analysis processes on an

T

II. SITE SURVEY A radio frequency (RF) site survey is the first step in the deployment of a Wireless network and the most important step to ensure desired operation. A site survey is a task-by-task process by which the surveyor studies the facility to understand the RF behavior, discovers RF coverage areas, checks for RF interference and determines the appropriate placement of Wireless devices. No matter how comprehensive your computerized and paper analyses may be, they are no substitute for measuring real-world interference and blockage at a site. Only on-site measurements and surveys can give you the complete picture. The goal of an RF site survey is to gather enough information and data to determine the number and placement of access points (APs) that will provide optimal wireless network coverage. And an accurate site survey enables accurate quotes to guide financial decision making. It also serves as a guide for the network design and for installing and verifying the wireless communication infrastructure. The basic requirements for conducting a site survey are: an access point, a laptop with wireless adaptor and a survey utility. A. Surveying Tools For surveying, generally wireless sniffing tools are used to sniff wireless packets from an ad-hoc network setup using an

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World Academy of Science, Engineering and Technology 42 2008

In terms of received power, the same propagation model can be re-written as,

access point. There are varieties of open source wireless sniffing tools available. NetStumbler is an open source tool for Windows that allows you to detect Wireless Local Area Networks (WLANs) using 802.11b, 802.11a and 802.11g. There are several other open and closed source wireless sniffing tools too such as Kismet, miniStumbler etc. which can be downloaded free of cost. There are many commercial wireless surveying tools available too such as Airmagnet Surveyor or Airosneek etc.

Pr (dB) = Pr (d0) - 10nlog (d/d0)

The log distance path loss model is mainly an outdoor model and neglects the effect due to the surrounding object. Hence, it is not applicable for an indoor wireless network. IV. EXTENDING OUTDOOR PATH LOSS MODEL TO INDOOR PLANNING To achieve more accurate result and to represent more realistic indoor propagation model, the surrounding environment has to be considered. Walls, floors, partitions are made up of materials that reflect the electromagnetic signal. As a result, the measured signal strength will be less than that predicted by the log-distance path loss model. There are three factors affecting the propagation, which play much more serious role in deciding indoor coverage footprint than in an outdoor environment. These are shadowing, wall attenuation factor and floor attenuation. A receiver in a Wi-Fi network is said to be in the shadow region when there is an obstacle blocking its line-of-sight to the access point. Different materials produce varying amount of attenuation in the shadow region. The amount of attenuation is also frequency dependent. Table 1 shows the attenuation produced due to some commonly used materials.

III. INTRODUCTION TO PROPAGATION MODEL The wireless channel places fundamental limitations on the communication systems because of its dynamic nature. The transmission path between the transmitter and the receiver can vary from simple line of sight to one that is obstructed by trees, walls, floors etc... This can cause the signal strength of two points that are equidistant from the access point to be entirely different. Propagation models focus on predicting the average signal strength that may be received at a particular distance from a transmitter. Thus, it is important to determine the propagation model for the indoor wireless network by taking shadowing into account. A. Free Space Loss Model [4] Free space propagation model is used to predict the signal strength at a distance from the receiver when there is no obstruction between the transmitter and the receiver. It is the foundation for all other models. It is derived from Friis’s free space equation given by:Pr (d) = (PtGtGrO ) / (4–) d L 2

2 2

TABLE I ATTENUATION DUE TO DIFFERENT MATERIALS

(1) Material

Where, Pr (d) = power received at a distance‘d’ from transmitter. Pt = transmitted power. Gt,Gr = transmitter and receiver antenna gains respectively. L = largest antennae dimension.

loss at 2.4 GHz loss at 5.2 GHz

The path-loss represents the attenuation the signal undergoes as it propagates through the medium. It is given by, PL (db) = 10log (Pt/ Pr)

Brick -4Db

12mm Ply board -0.5Db

18mm Plywood -1.9dB

Glass -0.5dB

-14.6dB

-0.7dB

-1.8dB

-1.7dB

The obstruction caused due to walls is indicated using the Wall Effect Factor (WEF) or Wall Attenuation Factor (WAF). Walls reflect the electromagnetic radiation falling on it producing a shadow region behind it. The attenuation produced depends on the material and thickness of the wall. The losses in the wireless signal observed when the receiver is in another floor as the access point is called Floor Attenuation factor. FAF depends on type of the material separating the floors and is frequency dependent.

(2)

= -10log (GtGrO2/ (4–)2d2L) B. Log Distance Path Loss Model The free space propagation model is a theoretical model; not applicable to real life situations. Log distance path loss model is a practical path loss model which is based on the fact that the received power decreases logarithmically with distance. The average path loss for an arbitrary Tx-Rx separation is given by,

A. How to calculate WAF WAF is calculated by finding the difference between the signal strength values for two sets of points, equidistant from the access point but one set having the LOS and the other having a wall blocking the access point. Graphs are drawn as shown in Fig. 1, showing points with LOS and points without LOS separately. WAF is calculated by finding the difference in the curves and averaging them.

PL (dB) v (d/d0) n Or PL (dB) = PL (d0) + 10nlog (d/d0)

(4)

(3)

Where, n = path loss exponent. PL (d0) = power received at ‘d0’.

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B. How to calculate FAF? The Floor Attenuation Factor is calculated by finding the difference between the signal strength values for two sets of survey points, equidistant from the access point but one set lying in the same floor as the access point and the other lying in a different one. Graphs are drawn as shown in Fig. 2, showing points in the same floor as the access point and points lying in a different floor, separately. FAF is calculated by finding the difference in the curves and averaging them. The average FAF is calculated by converting the signal strength values from db to volts and finding their mean. This mean value is then converted back to db.

Fig. 1 Showing the method of taking survey readings

V. INDOOR EMPIRICAL PROPAGATION MODEL (IEPM) Based on the survey data collected, an empirical model can be coined for the indoor Wi-Fi network of the campus. This involves the calculation of WAF and FAF from the graphs of the signal strength for each access point. Based on the values obtained for WAF and FAF above, the final Indoor Empirical Wi-Fi Propagation Model for the campus can be obtained, Pr (dB) = Pr (d0) - 10nlog (d/d0) – 9.25 dB

(5)

Where, WAF = attenuation due to a wall blocking the line of sight = -9.25 db Pr (dB) = Pr (d0) - 10nlog (d/d0) – 10.77 dB

(6)

Where, FAF = attenuation due to floor partitions = -10.77 db Fig. 2 Graph to calculate WAF From graph WAF for telecom2 = 7.5 db

VI. COMPARISON OF LOG DISTANCE PATH LOSS MODEL AND IEPM Log distance path loss model is basically an outdoor model. It predicts the signal strength without taking the surrounding environment into account. Thus, it becomes inaccurate in cases where there is no line of sight between the observed point and the access point, making it unsuitable for use with indoor Wi-Fi networks. The Indoor Empirical Propagation Model takes care of the attenuation in an indoor environment by including WAF and FAF. As a result, the signal strength predictions are accurate in both LOS and non-LOS cases. The difference between the predicted signal strength by Log distance path loss model and that by IEPM is as much as 9 db in case of non-LOS cases. As a result, the accuracy of the IEPM is much higher with a variance of ±10% as compared to log distance path loss model with a higher variance of about ±20% between the predicted signal and actual measurement in indoor environment.

Similar graphs are obtained for various blocks or buildings and their corresponding WAF is calculated. The average WAF is calculated by converting the signal strength values from db to volts for all the blocks and then finding their mean. This mean value is then converted back to db to get the final average WAF.

VII. SIMULATION OF COVERAGE AREA MAPS The section shows the various coverage area maps simulated for R. V. College of engineering campus, Bangalore.

Fig. 3 Graph to calculate FAF. From graph, Floor Attenuation Factor between first and second floor for access point TELECOM1 =10.8 db

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VIII. CONCLUSION AND FUTURE WORK The IEPM can be used to predict the signal strength of an indoor Wi-Fi network. Thus, the coverage of the access points can be determined on paper, without actually performing any surveying. This helps in optimizing the Wi-Fi network and reduces the cost of implementation. Further improvements can be made to the IEPM by performing more extensive surveying covering more number of blocks. The variance of the predicted signals can be improved from ±10%. IEPM can be implemented as a code which takes the distance data as the input and gives the predicted signal strength as the output, making it even easier to determine the network coverage. Data collection can be enhanced by interpolation methods. Fig. 4 Google earth image of coverage area of the RVCE campus

ACKNOWLEDGMENT

The simulation is implemented on Radio-mobile, which is a tool for plotting RF patterns and predicting the performance of radio systems. Initially, the location of access points in various departments has been determined and simulation parameters for each access point are given.

Working on this topic and to come up with this paper was not something which we could have done all on our own. Many people helped us through our work and we want to take this opportunity to acknowledge their contribution. First, we will like to express our sincere gratitude towards our Vice-Principal and Head, Department of Telecommunication and P.G. Studies, Prof. K.N. Raja Rao, for providing all the facilities and support to us for this work. We would also like to thank all the faculty members of our department for providing their help and encouragement. We would like to thank all the HODs of various departments in our college for permitting us to do survey in each department and also for providing us with necessary information as and when required. Finally, we would like to express our gratitude towards our friends who gave their valuable suggestions and support. REFERENCES [1]

R Chris Lentz “802.11b Wireless Network Visualization and Radio wave Propagation Modeling” Dartmouth Computer Science Technical Report TR2003-451, June 2003. [2] Lorne C. Liechty MS Thesis “Path Loss Measurement and Model analysis of a 2.4 GHz wireless network in an outdoor environment”. [3] A. Domazetovic, L. J. Greenstein, N. B. Mandayam, and I. Seskar “Propagation Model for short range wireless channels with predictable path geometries” IEEE Transactions on Communications, vol. 53, No. 7, pp. 1123-1126, July 2005. [4] Theodore S. Rappaport “Wireless Communications: Principle and Practice” Prentice Hall, 3rd Edition. [5] “Surveying Wireless Networks” Ukerna Technical Guide. [6] Simon Byers and Dave Kormann 802.11b access point mapping, communications of ACM. Pages 41-46, may 2003. [7] Y.Wang,X.Jia,H.K.Lee “An Indoor positioning system based on wireless local area network infrastructure” , 6th international symposium, SATNAV 2003. [8] Schneider, F. lambrechet, A. Baier. Enhancement of the okamura hata propagation model using detailed morphological and building data. IEEE publication,1996. [9] Guoqiang mao, Brain D. O. Anderson, Baris Fidan “Path loss exponent for wireless sensor network localization”. [10] http://www.cplus.org/rmw/english1.html [11] http://www.ibm.com/developerworks/visualizingdata withGNUplot [12] http://www.Maps.google.com

Fig. 5 Existing coverage area plotted on a digital terrain elevation map of RVCE campus. The figure shows the placing of access points in each department in the campus

Fig. 6 Suggested optimal coverage area plan of the RVCE campus on a digital terrain elevation map, with insertion of new access points

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A. R. Sandeep has completed his Bachelor of Engineering in Telecommunication Engineering from R V College of Engineering, Bangalore that is affiliated to Visveshvaraya Technological University, Belgaum, Karnataka, India in the year 2008.The author is presently working with NOKIA-SIEMENS NETWORKS and has also interned at Control and Automation Group, Electronics Corporation of India Limited in the year 2006. Shreyas Y. has completed his Bachelor of Engineering in Telecommunication Engineering from R V College of Engineering, Bangalore, India that is affiliated to Visveshvaraya Technological University, Belgaum, Karnataka, in the year 2008.The author is presently working with NOKIA-SIEMENS NETWORKS. Shivam Seth has completed his Bachelor of Engineering in Telecommunication Engineering from R V College of Engineering, Bangalore, that is affiliated to Visveshvaraya Technological University, Belgaum, Karnataka, India in the year 2008. The author is currently working in ERICSSON India. Rajat Agarwal has completed his Bachelor of Engineering in Telecommunication Engineering from R V College of Engineering, Bangalore, that is affiliated to Visveshvaraya Technological University, Belgaum, Karnataka, India in the year 2008. The author is presently working in Agilant Technologies. G. Sadashivappa, Asst. Professor of Telecommunication & P G Studies, is pursuing his doctoral programme at VTU, Belgaum,INDIA. He obtained his Master degree in Industrial Electronics from KREC (NIT), Suratkal, INDIA and BE in Electronics & Communication from SIT, Tumkur,INDIA. His research interests are Image & Video Coding, Signal Processing and Fiber Optics. He has guided more than 40 undergraduate and more than 3 post graduate projects. Currently he is teaching courses on Optical Fiber Communication, Advanced Digital Communication, Analog Communication, optical networking and Electronic Circuits. He has presented papers in national and international conferences. He has also published Books on Electronic circuits and Power Electronics. Currently, he is pursuing his Phd in Computer networking.

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