Detecting Non-Discoverable Bluetooth Devices

Cross, D.N.; Hoeckle, Justin; Lavine, Michael K.; Rubin, Jason; Snow, Kevin Z.

doi:10.1007/978-0-387-75462-8_20

“…In addition to privacy concerns, Bluetooth technology also has some security concerns (Munro, 2008;Wong and Stajano, 2005;Cross et al, 2007). Wong and Stajano (2005) reported that the MAC address in Bluetooth is closely tied into the access procedure of the device.…”

Section: The Security Of a Bluetooth Devicementioning

confidence: 99%

“…Wong and Stajano (2005) reported that the MAC address in Bluetooth is closely tied into the access procedure of the device. Cross et al (2007) showed that even if a Bluetooth device is not in discovery mode, algorithms can be designed to gain access into the device in less than 24 hours.…”

Section: The Security Of a Bluetooth Devicementioning

confidence: 99%

Wireless Data Collection System for Real-Time Arterial Travel Time Estimates: Final Report

Porter¹,

Kim²,

Magaña³

2011

3

0

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Sponsoring Agency Code Supplementary Notes AbstractThis project pursued several objectives conducive to the implementation and testing of a Bluetooth (BT) based system to collect travel time data, including the deployment of a BT-based travel time data collection system to perform comprehensive testing on all the components. Two different BT-based travel time data collection systems were installed. The first system, composed of two DCUs, was installed on a corridor located in Salem, OR. Extensive testing was done on this system, including the collection of travel time samples. A second system composed of five DCUs was installed along 99W in the city of Tigard, OR. Very limited data collection was done on 99W due to the lack of network connectivity.Six different antenna types were characterized using the two DCU BT-based travel time data collection system. The result of the antenna characterization tests showed that vertically polarized antennas with gains between 9 and 12 dBi are good candidates to support a BT-based travel time data collection system. Antennas with circular polarization do not seem to improve the performance, despite the lack of control regarding the orientation of BTenabled devices in most applications. Travel time samples were also collected with this system. The results indicate that a trade-off exist between the number of samples obtained and the accuracy of these travel time samples. This trade-off is most likely the result of differences in road coverage areas provided by the different antenna types.17.

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“…The variation of the detection rate indicates that this can be a localisation parameter. In addition, due to security reasons, some Bluetooth devices change to non-discoverable mode if they are not in use [99] for a period of time. Not all Bluetooth devices reply to inquiry packets when they are paired and connected to another Bluetooth device [4].…”

Section: Only Detect a Proportion Of Passing Vehiclesmentioning

confidence: 99%

“…Therefore, the actual number of vehicles is much lower in my collected data, and this small proportion of co-detections do not affect the estimated traffic volume and traffic characteristics. However as explained in Chapter 3, Section 3.3 due to security reasons, some Bluetooth devices change to non-discoverable mode if they are not in use [99] and not all Bluetooth devices reply to inquiry packets when they are paired and connected to another Bluetooth device [4]. Therefore, the chance of multi-tenancy detection can be very low in the collected data as this is the case in the collected data.…”

Section: Outputs Of Mtdibtmentioning

confidence: 99%

Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

Sichani¹

0

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<p>A comprehensive traffic monitoring system can assist authorities in identifying parts of a road transportation network that exhibit poor performance. In addition to monitoring, it is essential to develop a localized and efficient analytical transportation model that reflects various network scenarios and conditions. A comprehensive transportation model must consider various components such as vehicles and their different mechanical characteristics, human and their diverse behaviours, urban layouts and structures, and communication and transportation infrastructure and their limitations. Development of such a system requires a bringing together of ideas, tools, and techniques from multiple overlapping disciplines such as traffic and computer engineers, statistics, urban planning, and behavioural modelling. In addition to modelling of the urban traffic for a typical day, development of a large-scale emergency evacuation modelling is a critical task for an urban area as this assists traffic operation teams and local authorities to identify the limitations of traffic infrastructure during an evacuation process through examining various parameters such as evacuation time. In an evacuation, there may be severe and unpredictable damage to the infrastructure of a city such as the loss of power, telecommunications and transportation links. Traffic modelling of a large-scale evacuation is more challenging than modelling the traffic for a typical day as historical data is usually available for typical days, whereas each disaster and evacuation are typically one-off or rare events. Damage due to a disaster, combined with a sudden increase of demand due to the evacuation of people will likely result in increased pressure on the remaining, potentially fragmented, infrastructure. The lessons learnt from evacuation modelling can assist traffic operation teams and local authorities to provide safer and more efficient planning. The development of pervasive personal digital devices such as phones, watches, and headphones which can be interconnected with technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor traffic flows within their cities. Moreover, modern vehicles and navigation systems can interconnect to the personal devices of drivers and passengers primarily via Bluetooth technologies. By continuously monitoring such devices when they are discoverable and in range, traffic patterns can be estimated based on, not only the volume of detection, but also other characteristics of the devices that can be used to give more refined estimates of the real underlying traffic flows. This thesis examines Bluetooth traffic data collected from Bluetooth Traffic Monitoring Systems (BTMS) for modelling and monitoring the urban traffic. BTMS can monitor and track individual detected vehicles through a city. Installation, processing, data transmission, and maintenance of BTMS are easier, quicker and cheaper than existing standard monitoring systems such as CCTV cameras and inductive loops. Inductive loops are typically point-wise traffic monitoring systems that are installed in the roads and can measure the traffic flow. However, the use of BTMS devices presents several challenges: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This thesis enumerates and investigates these challenges through statistical modelling, various protocols for cleaning and data preparation, dynamic estimation of the detection rate, and simulation through the case study of the city of Wellington, New Zealand. The city of Wellington experienced damage from the 2016 Kaikoura earthquake (a magnitude 7.8 earthquake), which led to road closures and other infrastructure damage. As part of modelling, performance evaluation, and identifying impacted routes by the 2016 Kaikoura earthquake, this thesis analyses three weeks of BTMS data from the periods before and after the earthquake. Furthermore, this thesis proposes a multi-disciplinary dynamic traffic modelling (TFDA2M) framework and evaluates the performance of TFDA2M on various large-scale evacuation scenarios. These scenarios cover a wide range of real-world use cases which may occur during a disaster such as power failure, an abrupt increase in demand, and damage to the main transportation infrastructure. The findings of this thesis highlight an immediate need for preparations of a large-scale evacuation planning for Wellington to mitigate the consequences of a large-scale evacuation due to a future disaster. Moreover, TFDA2M can assist traffic operation managers and authorities in making smarter decisions (both quantitative and spatially) through the simulation process. Since TFDA2M has a flexible schema, it can be set to monitor, assess, and manage the traffic flow on a daily basis and disaster occasions.</p>

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“…The variation of the detection rate indicates that this can be a localisation parameter. In addition, due to security reasons, some Bluetooth devices change to non-discoverable mode if they are not in use [99] for a period of time. Not all Bluetooth devices reply to inquiry packets when they are paired and connected to another Bluetooth device [4].…”

Section: Only Detect a Proportion Of Passing Vehiclesmentioning

confidence: 99%

Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

Sichani¹

1

0

View full text Add to dashboard Cite

<p>A comprehensive traffic monitoring system can assist authorities in identifying parts of a road transportation network that exhibit poor performance. In addition to monitoring, it is essential to develop a localized and efficient analytical transportation model that reflects various network scenarios and conditions. A comprehensive transportation model must consider various components such as vehicles and their different mechanical characteristics, human and their diverse behaviours, urban layouts and structures, and communication and transportation infrastructure and their limitations. Development of such a system requires a bringing together of ideas, tools, and techniques from multiple overlapping disciplines such as traffic and computer engineers, statistics, urban planning, and behavioural modelling. In addition to modelling of the urban traffic for a typical day, development of a large-scale emergency evacuation modelling is a critical task for an urban area as this assists traffic operation teams and local authorities to identify the limitations of traffic infrastructure during an evacuation process through examining various parameters such as evacuation time. In an evacuation, there may be severe and unpredictable damage to the infrastructure of a city such as the loss of power, telecommunications and transportation links. Traffic modelling of a large-scale evacuation is more challenging than modelling the traffic for a typical day as historical data is usually available for typical days, whereas each disaster and evacuation are typically one-off or rare events. Damage due to a disaster, combined with a sudden increase of demand due to the evacuation of people will likely result in increased pressure on the remaining, potentially fragmented, infrastructure. The lessons learnt from evacuation modelling can assist traffic operation teams and local authorities to provide safer and more efficient planning. The development of pervasive personal digital devices such as phones, watches, and headphones which can be interconnected with technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor traffic flows within their cities. Moreover, modern vehicles and navigation systems can interconnect to the personal devices of drivers and passengers primarily via Bluetooth technologies. By continuously monitoring such devices when they are discoverable and in range, traffic patterns can be estimated based on, not only the volume of detection, but also other characteristics of the devices that can be used to give more refined estimates of the real underlying traffic flows. This thesis examines Bluetooth traffic data collected from Bluetooth Traffic Monitoring Systems (BTMS) for modelling and monitoring the urban traffic. BTMS can monitor and track individual detected vehicles through a city. Installation, processing, data transmission, and maintenance of BTMS are easier, quicker and cheaper than existing standard monitoring systems such as CCTV cameras and inductive loops. Inductive loops are typically point-wise traffic monitoring systems that are installed in the roads and can measure the traffic flow. However, the use of BTMS devices presents several challenges: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This thesis enumerates and investigates these challenges through statistical modelling, various protocols for cleaning and data preparation, dynamic estimation of the detection rate, and simulation through the case study of the city of Wellington, New Zealand. The city of Wellington experienced damage from the 2016 Kaikoura earthquake (a magnitude 7.8 earthquake), which led to road closures and other infrastructure damage. As part of modelling, performance evaluation, and identifying impacted routes by the 2016 Kaikoura earthquake, this thesis analyses three weeks of BTMS data from the periods before and after the earthquake. Furthermore, this thesis proposes a multi-disciplinary dynamic traffic modelling (TFDA2M) framework and evaluates the performance of TFDA2M on various large-scale evacuation scenarios. These scenarios cover a wide range of real-world use cases which may occur during a disaster such as power failure, an abrupt increase in demand, and damage to the main transportation infrastructure. The findings of this thesis highlight an immediate need for preparations of a large-scale evacuation planning for Wellington to mitigate the consequences of a large-scale evacuation due to a future disaster. Moreover, TFDA2M can assist traffic operation managers and authorities in making smarter decisions (both quantitative and spatially) through the simulation process. Since TFDA2M has a flexible schema, it can be set to monitor, assess, and manage the traffic flow on a daily basis and disaster occasions.</p>

show abstract

Detecting Non-Discoverable Bluetooth Devices

Cited by 8 publications

References 2 publications

Wireless Data Collection System for Real-Time Arterial Travel Time Estimates: Final Report

Wireless Data Collection System for Real-Time Arterial Travel Time Estimates: Final Report

Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

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