Face-to-Face Proximity Estimation Using Bluetooth On Smartphones android project | Face-to-Face Proximity Estimation Using Bluetooth On Smartphones android project free download
Face-to-Face Proximity Estimation Using Bluetooth On Smartphones
ABSTRACT:
The availability of “always-on” communications has tremendous implications for how people interact socially. In particular, sociologists are interested in the question if such pervasive access increases or decreases face-to-face interactions. Unlike triangulation which seeks to precisely define position, the question of face-to-face interaction reduces to one of proximity, i.e., are the individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5 m) and can cover a wide variety of environments. Existing approaches such as GPS and Wi-Fi triangulation are insufficient to meet the requirements of accuracy and flexibility. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We propose a proximity estimation model to determine the distance based on the RSSI values of Bluetooth and light sensor data in different environments. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption.
EXISTING SYSTEM:
In recent years, the presence of portable devices ranging from the traditional laptop to fully fledged smartphones has introduced low-cost, always-on network connectivity to significant swaths of society. Network applications designed for communication and connectivity provide the facility for people to reach anywhere at any time in the mobile network fabric. Digital communication, such as texting and social networking, connect individuals and communities with ever expanding information flows, all the while becoming increasingly more interwoven. There are compelling research questions whether such digital social interactions are modifying the nature and frequency of human social interactions. A key metric for sociologists is whether these networks facilitate face-to-face interactions or whether these networks impede face-to-face interactions.
DISADVANTAGES OF EXISTING SYSTEM:
] Where subjects are asked about their social interaction proximity, is unreliable.
] Interactions are not limited to any particular area and can take place at a wide variety of locations
PROPOSED SYSTEM:
We demonstrate the viability of using Bluetooth for the purposes of face-to-face proximity estimation and propose a proximity estimation model with appropriate smoothing and consideration of a wide variety of typical environments. We study the relationship between the value of Bluetooth RSSI and distance based on empirical measurements and compares the results with the theoretical results using the radio propagation model.
We explore the energy efficiency and accuracy of Bluetooth compared with Wi-Fi and GPS via real-life measurements. We deploy an application “PhoneMonitor” which collects data such as Bluetooth RSSI values on 196 Android-based phones. Based on the data collection platform, we are able to use the proximity estimation model across several real-world cases to provide high accurate determination of face-to-face interaction distance
ADVANTAGES OF PROPOSED SYSTEM:
ü It provides adequate accuracy for detecting something like buddy proximity (e.g., median accuracy of 20-30 meters),
ü Different from the above proximity detection method, our work is a fine grain Bluetooth-based proximity detection method which can provide adequate accuracy for face-to-face proximity estimation without environment limitations.
MODULES:
1. Data Collection System
2. Power Comparison
3. Bluetooth RSSI versus Distance
4. Proximity Estimation
MODULES DESCRIPTION:
Data Collection System:
In this module, our system collects Bluetooth data including the detailed values of RSSI, MAC address, and Bluetooth identifier (BTID). The data is recorded once the phone detects other Bluetooth devices around. In addition to Bluetooth, data points from a variety of other subsystems (light sensor, battery level and etc.) are gathered in order to compare and improve the proximity estimation. Separate threads are employed to compensate for the variety of speeds at which the respective subsystems offer relevant data. We also record the location data reported by both GPS and network providers (either Wi-Fi or cell network). In order to deter-mine whether the phone is sheltered (e.g., inside a backpack or in hand) and the surroundings (e.g., inside or outside buildings) during the daytime, we keep track of the light sensor data. The battery usage percentage is recorded for the energy consumption comparison. The Android platform was selected for its customization capabilities through normal API or rooted customized interfaces with respect to hardware-level inter-actions. We keep the data records in a local SQLite database on the phone and upload them to MySQL database on the servers periodically with security for backup and analysis. With current Android APIs, each kind of data is invoked through the corresponding function calls. The default sensing granularity in terms of updating time interval for Bluetooth is 30 seconds. Intuitively, larger time intervals can help save energy; hence we also enable the changing of such sensing interval in order to explore its impact on the energy consumption.
Power Comparison:
Energy is one of the most important considerations for applications on smartphones. Compared to a PC, the energy of mobile phones is quite limited. Therefore it is essential to utilize an energy saving method in the system. Before we reveal the relationship between Bluetooth RSSI values and the distance, we compare the energy consumption of Blue-tooth, Wi-Fi and GPS in order to ensure that Bluetooth is suitable for proximity estimation on smartphones. There are three ways to measure the energy consumption on the smartphone. One is to use a model introduced in Android 2.0 to check the battery each application is taking. However, the numbers are normalized and it does not pro-vide the detailed power measurement. Another way is battery simulator such as Monsoon. Such expensive way measures the accurate power usage but it goes far beyond our requirement. The third way to measure energy consumption is to write an app to log the battery level and export the log to computer for analysis. It is widely used in both Symbian and iOS energy analysis. We used this method shows that such method is good enough for comparison of the energy consumption among different wireless technologies. The experiments were run on the same phone within several days. For each type of technologies, the application collects the signal strength data and the default update interval is 30 seconds. The application starts to run when the phone is fully charged and stops when the phone is out of battery. It is the only application running on the phone and collects the data of one technology at once. The battery level was recorded periodically (every half an hour) in order to obtain the results. The log shows that Bluetooth clearly having the best capability for energy saving. The phone running Bluetooth almost has twice the battery life than the one with Wi-Fi logging. Moreover, when the time granularity of Bluetooth update becomes larger, the battery can even last longer.
Bluetooth RSSI versus Distance:
The relationship between RSSI and distance becomes more complicated. Our challenge was to assess how much impact these environmental factors have on Bluetooth RSSI values. Therefore, we carried out several experiments to understand how the Bluetooth indicators fade with distance under these environmental influences. Indoor experiments were conducted in a noisy hallway
(around seven other Bluetooth devices detected) in the cam-pus engineering building. Outdoor experiments were con-ducted in the open area outside the building. In the measurement there were no obstacles between the two phones and the antennas of the phones were aligned towards each other. In such a way, we tried to build up a relatively simple and “ideal” environment where the possible impact factors are reflection and noise only. We repeated the measurements over the period of an hour with the distance being increased by 0.5 meters between each round. The initial fluctuations of indoor RSSI results with different distances. Although the data varies significantly even within the same distance, there is a noticeable gap exists between different distances. Such results further shed light on the viability of using Bluetooth RSSI to indicate the face-to-face proximity. Based on these indoor and outdoor results, there are two main environmental factors that may affect the RSSI values: inside/outside building and inside/outside a backpack. Besides those factors, it is also necessary to take multiple-phones scenario into consideration since phones with Bluetooth around may have interference on Bluetooth RSSI values.
Proximity Estimation:
As mentioned in the beginning, the objective of the paper is to provide accurate proximity estimation for face-to-face communication. This raises a question: what is the face-to-face communication distance? In this section, we first define the face-to-face distance and then use the indoor results as a threshold to do the estimation in real world scenarios. Since the error rate of using a simple threshold is relatively high, we explore the possible reasons and propose a proximity estimation model with the introduction of light sensor values. Distance of face-to-face communication. When we have dinner with our friends sitting at the same table, the conversation among us is called face-to-face communication; or when we talk with someone side by side, the distance between us is also called face-to-face communication. In other words, face-to-face communication happens when people are close enough to have conversations in a convenient manner. People typically have such communication when they are sitting or walking together. Thus, we calculate the distance for this kind of communication by measuring distances across the campus (such as diagonal of desk in dining hall, distance between desks in classrooms and etc.) and the average value is equal to 1.52 m.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System : Pentium IV 2.4 GHz.
Ø Hard Disk : 40 GB.
Ø Floppy Drive : 1.44 Mb.
Ø Monitor : 15 VGA Colour.
Ø Mouse : Logitech.
Ø Ram : 512 Mb.
Ø MOBILE : ANDROID
SOFTWARE REQUIREMENTS:
Ø Operating system : Windows XP/7.
Ø Coding Language : Java 1.7
Ø Tool Kit : Android 2.3 ABOVE
Ø IDE : Eclipse
VIDEO OUTPUT
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