Wi-Fi Based Tracking Systems 665 in .NET Insert QR in .NET Wi-Fi Based Tracking Systems 665

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Wi-Fi Based Tracking Systems 665 using .net framework toproduce qr code on web,windows application GS1 DataBar bar codes 31.5 Positioning Alternatives Ekahau uses the received sign al strength indicator (RSSI) as the basis for positioning and a probabilistic framework for estimating the location of the tracked item. Because RSSI data is a standard in all Wi-Fi networks, regardless of infrastructure vendor, the Ekahau positioning and tracking system can be used without any changes to an existing Wi-Fi network infrastructure. Another benefit is that signal strength values change relatively smoothly with respect to changes in location, which means that the RSSI method is not as sensitive to measuring errors as the timing-based or AOA approaches.

When using the RSSI signals, two alternatives are possible: one can either measure base station (access point) transmission strength from the perspective of the mobile device, or one can measure the transmission strength of the mobile device from the perspective of the base station. Ekahau has chosen to use the first approach because signal measurement from the client perspective provides the most accurate, comprehensive measurement of the WLAN, including Wi-Fi characteristics such as multi-path. In addition, signals transmitted from the WLAN infrastructure tend to be stronger and more consistent than signals transmitted from mobile devices.

31.5.1 Location estimation Once the location-dependent variable has been chosen as the basis for positioning, the next question is to ask how to derive reliable location estimates from transmitted data.

More precisely, the goal is to build a predictive mathematical model, which receives the chosen type of signal information (RSSI in this case) as an input, and outputs the estimated of location of the mobile device in question in meaningful coordinates. Location variable can be discrete or nominal (like room B226 or lobby ), or continuous (x, y, and z measured in pixels or distance units). 31.

5.2 The Cell ID approach The simplest solution to this problem is the so-called Cell-ID positioning. In this approach the current location is assumed to be somewhere within the coverage area of the base station to which the mobile terminal is currently associated (the base station from where the current feed of data is arriving over the wireless radio way).

As the associated base station is often the same as the station with the strongest detected signal at the current location, and as the strongest signal comes often from the nearest base station, this location estimate makes intuitively some sense. However, it is obvious that the accuracy can even in optimal conditions only be relative to the distances between the base stations. In Wi-Fi environments this can be dozens of meters and the associated base station is not necessarily the station with the strongest signal.

Also the strongest signal may not come from the nearest base station. Consequently, the Cell-ID approach is generally quite inaccurate and unreliable and thus not feasible for practical applications, unless the requirements for the positioning accuracy is extremely low. These problems with the Cell-ID approach can be overcome to some degree by decreasing the transmitting power of the signal sources so that the signal can be detected.

666 Wi-Fi Based Tracking Systems only at the immediate vicinit y of the transmitter. This is the idea behind the radio frequency identification (RFID) tags and similar devices. However, in order to enable accurate positioning everywhere, a huge number of RFID readers should be placed around the environment so that their individual coverage areas together would cover the whole area.

On the other hand, if positioning is required only at designated spots (like for example only at the doorways), then this type of an approach can be feasible, and has been adopted in many warehousing applications and similar environments (with the cost of purchasing the dedicated RFID hardware and software). 31.5.

3 Triangulation-based approaches Another commonly used approach for solving the positioning problem is based on the idea of triangulation. In this approach, one constructs a function that outputs the distance to each base station, given the measured signals. If the distance to at least three base stations can be determined, then the intersection of the three circles around the base stations at the estimated distances gives the current location of the mobile terminal.

However, when applied in practice, the triangulation-type approaches face severe problems. The main problem is that the signal measurements are inherently noisy due the multipath problem and other factors, as discussed in the beginning of the section. If one or more of the distance estimates are of poor quality, then the circles drawn around the base stations may not intersect at all, in which case the method does not produce a location estimate at all.

The approach also breaks down completely if signals from less than three base stations are observable. This is a known problem with utilizing the GPS approach in places with high surrounding obstacles. Furthermore, the locations of the base stations needs to be known, otherwise positioning becomes totally impossible.

31.5.4 Ekahau Estimation Technology In order to overcome the above problems, Ekahau has developed a probabilistic positioning framework, where the world is taken to be probabilistic, not deterministic, and one accepts the fact that the measured signals are inherently noisy.

A probabilistic model assigns a probability for each possible location (L) given observations (O) consisting of the RSSI of each channel: P(L. O) = P(O L)P(L)/P(O) where P (O L) is the conditional probabi .NET QR lity of obtaining observations O at location L. P (L) is the prior probability of location L.

P (O) is a normalizing constant. The formula above is an example of an application of a mathematical theorem known as the Bayes rule. Based on probability theory, this Bayes theorem gives a formal way to quantify uncertainty, and it defines a rule for refining a hypothesis by factoring in additional evidence and background information, and leads to a number representing the degree of probability that the hypothesis (in our case, location estimate) is true.

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