University of Pittsburgh



INTRODUCTION

The advancement of wireless computing, wireless networks, and mobile devices has spawned a new classification of computing referred to as location-aware computing. Mobile computing devices are both small and wireless, allowing for a range of use while maintaining flexibility. But mobile devises are not “aware” of the context in which they operate; meaning that there is no function in most mobile devices that allows them to make observations about the environment that surrounds them. The device is incapable of displaying or transmitting its current position or recognizing whether or not its user is in motion or stagnant at the time of use.

During the past few years, mobile devices have become more common in society while also becoming more reliable, powerful, and compact. While the mobile evolution has been very encouraging, the progress in location-aware computing has been significantly slower than device development. Wireless network connectivity remains inconsistent, security issues are still potent when talking about wireless communications and mobile elements are severely limited when it comes to energy sources.

While the architecture for multi-layer systems is in existence, algorithms and software to take advantage of the capability of these mobile devises still remains uncovered. GPS (Global Positioning System) remains to be the most widely used location-sensing system. However, with further development, smaller more precise positioning networks could be constructed, and could be tailored to multiple different applications. For example, location-aware networks could be incorporated into a subway setting to guide multiple users to the terminal of the closest and soonest departing train that is going to their destination.

It is clear that the framework for such networks is in place, it just has not been clearly realized in most devices. Though low-cost and low-power wireless communities containing high-bandwidth cannot exist; location-aware computing networks, if useful enough, will be able to overcome most of these harsh realities. Combined with being able to transmit useful data as well as maintain a position-aware device, the possibilities in the filed of location-aware computing are virtually limitless if one can uncover the architecture needed to implement the technology.

SPECIFIC AIMS

The goal of this project is to become familiar with and succeed in implementing a successful location-aware computing network. This position-tracking network will be RSS (Received Signal Strength) based and will be used to determine the precise positions of moving objects in a given space.

Initially, the recreation of a previously established system (see Background and Preliminary Work sections) will be the main priority of the project. The positioning network will be implemented by using wireless access points (APs) and laptop computer. After the signal strength is received and logged in the wireless device, the position of the laptop will be calculated using a triangulating algorithm.

Also, a “radio-map” of the sample space will be constructed using both (x,y) coordinates, and RSS values from three of the APs. After mapping the sample space, the mobile device will also be able to be located via comparison with the database of the various radio-map coordinates. The software will then be calibrated to achieve a certain level of accuracy within a tolerable range.

After the initial goal is realized, the technology will then be implemented onto smaller, more mobile devices such as PDAs and the process will be repeated. The system will then transmit data to a server, and the server will be used to pinpoint the locations of multiple wireless devices in the sample space. If successful, multiple GUIs (Graphical User Interfaces) will be adapted to both the mobile devises and the server in order to make the system as user-friendly as possible. Time permitting, additional features will be added to the system, and the transfer of information between server and mobile nodes will also be implemented to add longevity to the network.

BACKGROUND:

Many efforts to establish an in-building location detection system have been made through the years with varying applications, cost, and technological implementation. The National Science Foundation and NASA have both taken interest in this field for its various applications. We first look into the number of wireless technologies that could implement a location sensing network.

Infrared communication exhibits minimal cost compared to other wireless technology, primarily because TV remote controls have used the technology for years. However, the range of infrared is very limited and must be line of sight. Ultrasonic sensor networks have been developed, but these systems require costly, specialized hardware and accuracy is decreased in complex environments (Patterson). The global positioning system is already in place by using communication over satellite frequency bands, but the key disadvantage to this system is that it does not work indoors.

Finally, we come to Wi-Fi technology. With the rise of Wi-Fi internet, access points are readily available in most office buildings and homes today. While Wi-Fi technology is affected when not in direct line of sight, it is not as critical as with IR and ultrasonic signals. Also, typical Wi-Fi setups involve multiple access points to ensure complete coverage. These coverage areas typically overlap. From this basis, the idea of location-aware computing is revolutionized. Suddenly, the cost of specialized hardware has been

completely eliminated, and development of drivers and software remains the only hurdle to achieve the objectives of other costly, specialized systems.

The possibilities for computers suddenly grow immensely and expand into a world that has only been conceptualized. Location aware computing is only in its infancy, but some of the applications could transform the world. One interesting concept is Cyber-Foraging. This is where “a computer can extend its resources by pointing to remote resources that are found opportunistically” (Patterson). An example of this would be a business man at an airport needing to send emails out before he gets on his flight. Say the bandwidth at his particular airport terminal is so low that the system knows he cannot send out his emails, but at a nearby terminal, sufficient bandwidth is readily available. The cyber foraging system would then notify the user of the terminal that has the bandwidth available so the man could send his emails in time to catch his flight.

PRELIMINARY WORK:

Existing research on the subjects of ubiquitous sensor networks and received signal strength triangulation is relatively new due to the recent drop in wireless component and integration costs, as well as the prevalence of UbiCare-oriented research organizations. Advances in circuit miniaturization, open-source operating systems and software solutions, and communication ability have led to the development of several design approaches. Research was done to determine what requirements our design needed to fulfill and exactly how those solutions would be accomplished; references to appropriate materials are found at the end of the paper.

In addition to the literature review we did on our own, Dr. Znati gave us a list of six technical papers pertaining to location-aware computing. The first paper, “Challenges in Location-Aware Computing,” was a treatise on the current state of the field, and also a discussion of the key obstacles that need to be overcome in the field before this technology’s full potential can be realized (Patterson 1). According to the paper, “chief among these [obstacles] are developments in: viable infrastructure for location-sensing; … adaptive methods and mechanisms for mobile clients; viable support for privacy protection; and appropriate technologies for representing and displaying geospatial information ‘everywhere,’ even when communication and display capabilities are severely limited” (Patterson 11).

“A Relative Positioning System for Co-located Mobile Devices” is a technical report on the Relate system for device self-location (Krohn 177). The system avoids relying on a known infrastructure (immobile reference beacons) by using peer-to-peer sensing to deduce the position of the device in reference to other mobile devices implementing Relate. The group used a USB dongle with ultrasound support to communicate between devices. Using a solution like this allowed the system to not only sense the location of the device but also the orientation.

The other three papers were written by two researchers at Microsoft Research. “User Location and Tracking in an In-Building Radio Network” sets the groundwork for “RADAR: An In-Building RF-based User Location and Tracking System,” in which the experiment they laid out in the first paper is modified to tackle some additional issues. “A Software System for Locating Mobile Users: Design, Evaluation, and Lessons” identifies several shortcomings and key limitations of the RADAR system after a yearlong testing period, and proposes ways in which to triple the accuracy of their system (Bahl 1).

The RADAR project is the basis upon which we chose to build our location-aware implementation. The Microsoft researchers decided to use existing radio-frequency wireless network technology to build their proof-of-concept system. They processed real-time received signal strength information from multiple Lucent WaveLAN access points and combined that information with a database of empirical measurements at different positions to infer the position of the device (a laptop, in their case). In the last of the three papers, the group details how they developed an open-source hardware-agnostic library called WiLIB in order to allow application-level processes access to RSS values from the wireless network interface device. They were initially able to successfully achieve an error distance on the order of a few meters, which was substantially improved in the later papers.

ALTERNATIVES CONSIDERED:

There were a great many design choices to make regarding how we intended to carry out our project. Firstly we needed to choose a wireless technology to use, having many choices available ranging from 802.11b to Bluetooth to ultrasound to RF. After careful research and consideration, we chose to work with 802.11b because it is easily available, cheap, and oftentimes already implemented in the infrastructure of a building that would require location-aware computing, such as a hospital. Another choice was what devices to implement different components of the system on – we chose to use Palm handhelds for the moving “patient” units, a WiFi-enabled laptop as the server, and wireless access points as the immobile reference beacons. The Palms were suggested by Dr. Znati in order to more closely and easily simulate the situation of a moving user in an untethered environment.

We chose the laptop as a server because we already had one available which minimizes cost; also, wireless access points are relatively inexpensive and easier to set up and leave in fixed positions than entire computers with wireless interfaces. Finally, we needed to decide what language to implement the software components in. We considered coding each software component completely in Matlab, since we are all very fluent in its use, as a proof-of-concept and then later trying to rewrite it for the Palm handheld. However, after learning that there exists a Palm OS library that makes it very easy to write C code for the handheld, we decided to use C.

In terms of replicating the Microsoft experiment, we didn’t see any alternative approach. They have already laid a great deal of the fundamental research in this specific field, and it would be a waste of time to “reinvent the wheel” by trying to start from scratch. Above all, we want to break new ground and do things that haven’t been done before, so it makes sense to incorporate all of the knowledge that is already developed on the subject and build upon it.

Another design option we considered was for all five wireless access points to be physically connected to the same local area network as the server, and have the patient devices connect to the closest access point to transmit information to the server. Our other choice is to have the server have its own dedicated wireless internet connection. We are still evaluating these options and have not yet decided how we will implement the server communication.

DESIGN APPROACH:

We have chosen a design approach that will expand upon what has already been accomplished in the field of location-aware computing. We first intend to replicate the works of the Microsoft project. They have “built a software system, RADAR, to locate mobile users to an in-building radio-frequency (RF) wireless LAN” (Bahl 1). We are taking on the same design approach as they did by using an 802.11b infrastructure using five access points strategically located throughout the floor of an office building. We are then going to construct a radio map; a database consisting of all the received signal strengths and (x,y) coordinates of the floor. We intend to then use this radio map as one of the ways of location detection by taking the five received signal strengths given from the access points and looking for (x,y) coordinate pair that matches with those strength readings.

To elaborate, we intend to place the access points in the orientation shown in the figure below, with the test bed being the fifth floor of the Sennot Square building. Although the specific brand of access points are to be determined (not yet gathered by our advisor), the test computer will be a Lenovo Thinkpad X60 Tablet PC with an Atheros 802.11a/b/g/n Wireless LAN Mini-PCI Express Adapter network interface card. The software application we have yet to develop will be in Visual C++ using Wireless Researcher’s Application Programming Interface (WRAPI) to extract the received signal strength (RSS) values from the NIC. We are using WRAPI because it is non-hardware specific, and thus we have the freedom to perform this on any mobile device with an 802.11b compatible wireless NIC.

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Figure 1. Strategic positioning of the five access points(black dots)

Once this experiment is carried out successfully, the software used on the laptop will then be translated over to a PALM mobile device. This will allow for a more “real system” involving a true mobile device that is in use in today’s society. If successful, we will expand on this simple design and create a user-friendly GUI for the applications on both the mobile device and the server. We will also develop, compare, and combine other methods of location detection in order to make the application as accurate as possible. The algorithm will be tuned to produce the most accurate results possible, and additional features to the system (including data transfer) will be added if time permits the work.

COST ESTIMATE:

In order to carry out our experiments and testing, we have decided the following items are needed:

Five 802.11b compatible access points $80 ea. (5) = $400

Laptop w/ Windows XP Already acquired

Microsoft Visual Studio Free through Pitt

Windows Driver Development Kit Free through Microsoft

Palm OS Developer’s Suite Free through Access(Developer’s Network Website)

Palm Tx $300 (2) = $600

Server Desktop Computer Free through Pitt

Although all the development software is free, the networking hardware will be supplied through our advisor in the Telecommunications Department, Dr. Znati. The two Palm Tx we have were supplied through the Bioengineering department, which has generously allocated a total of $1500 towards our project, $600 of which has been spent on the Palm Tx models.

MILESTONES & SCHEDULE:

There are many objectives that we hope to accomplish next semester. We have laid out a timeline of what needs to be completed and a schedule of how long we expect each step to take.

• Continue to learn how to program Palm OS handhelds using C++ net libraries

• Obtain Palm OS-based handhelds for use as reference locations, moving “patients”, and a server (minimum five units)

Estimated Deadline: Friday January 26

• Create the immobile reference location program backend, which runs on at least three units at a time and transmits an 802.11 (WiFi) signal that patient units can pick up and deduce their location from

Estimated Deadline: Friday February 2

• Create the patient program backend, which runs on a patient handheld unit and receives broadcasts from reference location units, deduces its own location, and transmits that location to one central server unit

Estimated Deadline: Friday February 16

• Create the server program backend, which runs on one handheld unit and collects location information from all active patient units

Estimated Deadline: Friday February 23

• Create robust GUIs for each of these three programs. The server GUI must be especially well-developed because it is responsible for displaying the output generated by the system (the map of patient unit locations)

Estimated Deadline: Friday March 9

• Troubleshooting and refinement

Estimated Deadline: Friday March 30

EXPECTED PROBLEMS:

Communication between hardware and software will most likely be the biggest hurdle in our project. Once we can establish communication between our application and we are able to extract the data we need from the NIC, the algorithm and software development will not be difficult due to the large library of resources available for Visual Studio (MSDN). This may be quite a challenge because our hardware level network understanding is not to level of a computer science majors. To help with this problem, our advisor Dr. Znati has made some of his graduate level CS students available for reference.

Another potential problem is during the later stage of the project when we want to transfer what we have done onto Palm’s. In this case, we would have to find a way to extract RSS values from a wireless chip embedded in the Palm. This could prove to be difficult, as a thorough inspection of the Palm OS Developer’s Suite has shown no method of extraction.

REFERENCES:

Bahl, Paramvir, and Venkata N. Padmanabhan. A Software System for Locating Mobile Users: Design, Evaluation, and Lessons. Microsoft Research. 26 Jan. 2007 .

Bahl, Paramvir, and Venkata N. Padmanabhan. RADAR: an in-Building RF-Based User Location and Tracking System. Microsoft Research. 26 Jan. 2007 .

Bahl, Paramvir, and Venkata N. Padmanabhan. User Location and Tracking in an in-Building Radio Network. Microsoft Research. 1999. 26 Jan. 2007 .

Foster, Lonnon R. Palm OS Programming Bible, Second Edition. Indianapolis: Wiley

Publishing Inc, 2002.

Kamol Kaemarungsi, Prashant Krishnamurthy, "Properties of Indoor Received Signal Strength for WLAN Location Fingerprinting," Mobiquitous, pp. 14-23, First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04), 2004.

Key Technical Challenges and Current Implementations of Body Sensor Networks. Department of Computing, Imperial College London, UK. 14 Dec. 2006 .

Krohn, Albert. A Relative Positioning System for Co-Located Mobile Devices. TecO, University of Karlsruhe, Germany. 177-190. 26 Jan. 2007 .

Lo, Benny, Surapa Thiemjarus, Rachel King, and Guang-Zhong Yang. "Body Sensor Network – a Wireless Sensor Platform For." Imperial College of Science, Technology, and Medicine. 14 Dec. 2006 .

Patterson, Cynthia A., Richard R. Muntz, and Cherri M. Pancake. Challenges in Location-Aware Computing. 26 Jan. 2007 .

"UbiCare 2006." UbiCare. 2006. University of Pittsburgh. 14 Dec. 2006 .

" - Ubiquitous Computing for Healthcare in the Community." UbiCare. Jan. 2003. The Distributed Software Engineering Group at the Department of Computing, Imperial College London. 14 Dec. 2006 .

"UbiMon." UbiMon - Ubiquitous Monitoring Environment for Wearable and Implantable Sensors. Imperial College, London. 14 Dec. 2006 .

Yasuhisa Takizawa, Peter Davis, Makoto Kawai, Hisato Iwai, Akira Yamaguchi, and

Sadao Obana. Self-Organizing Location Estimation Method Using Received

Signal Strength. IEICE Trans Commun E89-B: 2687-2695.

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