2.2.1
Working principle
Bluetooth is a short-range, low-power and open protocol for implementing Wireless Per- sonal Area Networks (WPAN) between mobile devices. It operates on the Industrial, Sci- entific and Medical (ISM) frequency band (2.4 GHz). To minimize potential interference with other technologies using this crowded radio band (WiFi, microwave ovens, etc.), Blue- tooth employs a frequency hopping technique (Bensky, 2007). For technical details about the Bluetooth protocol and the discovery and communication process, the reader is referred to Peterson et al. (2006). In brief, the process can be summarized as follows. To set up a Bluetooth network (‘piconet’) in which all devices follow the same hopping frequency to communicate with each other, a Bluetooth device first needs to be discovered by a master device during an inquiry phase. This master device enters the ‘inquiry’ substate and starts transmitting inquiry packets, which triggers unknown devices within the detection range of the master device that are in the ‘inquiry scan’ substate (‘discoverable’) to respond by trans- mitting an identification message containing their MAC address and class of device (COD) code. In a next phase, called the page phase, the master and slave devices synchronize their internal clocks to align their frequency hopping schemes and start communicating.
All Bluetooth scanners used in this study functioned as master devices, continuously broadcasting inquiry messages and listening for the responses of other discoverable devices that passed within their radio range. This way, mobile devices were detected without the need for an actual connection with the scanner. All further processing was done in the scan- ner. In comparison with other studies that did need a connection (Hay and Harle, 2009), the methodology used in this experiment is better suited for mass events where the participa- tion of the tracked individuals should be minimal. Every time a device was detected, its MAC address, COD code, and the timestamp of the detection were registered. Additionally, the received signal strength intensity (RSSI) of the inquiry response was registered. This in- tensity value is inferred from the received power level with which the response packet was detected by the scanner and is theoretically negatively correlated with the distance between the scanner and the detected device (Hossain and Soh, 2007). Although it is technically pos- sible to also register the ‘friendly name’ of the device (which can be changed by the users), this would significantly slow down the scanning process. Additionally, part of the tracked population might be identified by the inclusion of names or phone numbers. As this would raise serious privacy issues, this information was not registered.
In theory, the RSSI values registered by different scanners can be used to calculate the position of a mobile device through multilateration (Bensky, 2007; Kelly, 2010). Although some studies have reported acceptable accuracies in indoor environments, they have also outlined problems with multipath fading due to obstructions in these environments (Feld- mann et al., 2003; Zhou and Pollard, 2006). Because of the complex environmental setting and the resulting unpredictability of the propagation of Bluetooth signals, positioning in this case study was done through the ‘proximity’ principle, where the position of a detected mobile device is approximated to the position of the sensor by which it is detected (Bensky, 2007). This way, the path of a mobile device is reconstructed by means of a sequence of time intervals spent within the detection ranges of different Bluetooth scanners. The preci- sion of the resulting trajectories ultimately depends on the detection range of the Bluetooth scanners, and on their number and distribution over the study area. In theory, Bluetooth devices are classified into three power classes. Class 1, 2 and 3 devices support theoretical ranges of 100 meters, 10 meters and 1 meter respectively. The observed detection range, however, depends on how many obstructions the signal encounters between the scanner and the mobile device (buildings, furniture, clothing, people, etc.).
2.2.2
Equipment
Figure 2.1 on the following page shows the hardware components used in this Bluetooth tracking experiment. A Bluetooth ‘scanner’, as referred to in the remainder of this chapter, is a combination of a computational unit running the scanning software and processing and storing the results (1), a power source (2), and a USB cable (3) to connect the computational unit with a Bluetooth sensor (4–5). The heart of the computational unit was an ALIX moth- erboard (alix2d2, alix3d2), equipped with a 1 Gb CompactFlash card for storing log files. The operating system was an adapted version of Voyage Linux (based on Debian 5, 2.6.32.15 kernel), and the scanning software, Gyrid1(version 0.3.3), was developed at our research
group. It is a python implementation built around the BlueZ Bluetooth stack (version 4.63). In order to control the detection range, different Bluetooth sensors were used. In this study, we used a combination of class 2 (D-Link DBT-122) and class 1 (Sena Parani UD-100) devices. These are respectively shown as numbers 4 and 5 in figure 2.1 on the next page. At most locations, class 2 devices without an external antenna were used. Where a larger detection range was necessary, a class 1 device with a replaceable antenna was used. Two types of omnidirectional antenna were available with gains of 3 dBi(6) and 5 dBi(7). The
higher the gain of the antenna, the higher the detection range was. Experiments showed that the detection ranges deviated considerably from the theoretical ranges in case of line- of-sight detections (up to 100 meters for the class 2 device, and 300 m for the class 1 device with a 5 dBiexternal antenna).
Figure 2.1:Bluetooth hardware used in the tracking experiment: computational unit (1), power source (2), USB cable (3), class 2 Bluetooth dongle with internal antenna (4), class 1 Bluetooth dongle (5), and
3 dBi(6) and 5 dBi(7) omnidirectional antennas.