Showing papers on "Bluetooth published in 2020"
22 Apr 2020
TL;DR: A flexible and fully perspiration-powered integrated electronic skin (PPES) for multiplexed metabolic sensing in situ that delivered a record-breaking power density and displayed a very stable performance during a 60-hour continuous operation.
Abstract: Existing electronic skin (e-skin) sensing platforms are equipped to monitor physical parameters using power from batteries or near-field communication. For e-skins to be applied in the next generation of robotics and medical devices, they must operate wirelessly and be self-powered. However, despite recent efforts to harvest energy from the human body, self-powered e-skin with the ability to perform biosensing with Bluetooth communication are limited because of lack of a continuous energy source and limited power efficiency. Here, we report a flexible and fully perspiration-powered integrated electronic skin (PPES) for multiplexed metabolic sensing in situ. The battery-free e-skin contains multimodal sensors and highly efficient lactate biofuel cells that use a unique integration of zero- to three-dimensional nanomaterials to achieve high power intensity and long-term stability. The PPES delivered a record-breaking power density of 3.5 milliwatt·centimeter−2 for biofuel cells in untreated human body fluids (human sweat) and displayed a very stable performance during a 60-hour continuous operation. It selectively monitored key metabolic analytes (e.g., urea, NH4+, glucose, and pH) and the skin temperature during prolonged physical activities and wirelessly transmitted the data to the user interface using Bluetooth. The PPES was also able to monitor muscle contraction and work as a human-machine interface for human-prosthesis walking.
324 citations
TL;DR: This study surveys the state of the art and key research directions regarding optical wireless hybrid networks, being the first extensive survey dedicated to this topic and outlines important challenges that need to be addressed for successful deployment of optical Wireless hybrid network systems for 5G and IoT paradigms.
Abstract: Optical wireless communication (OWC) is an excellent complementary solution to its radio frequency (RF) counterpart. OWC technologies have been demonstrated to be able to support high traffic generated by massive connectivity of the Internet of Things (IoT) and upcoming 5th generation (5G) wireless communication systems. As the characteristics of OWC and RF are complementary, a combined application is regarded as a promising approach to support 5G and beyond communication systems. Hybrid RF/optical and optical/optical wireless systems offer an excellent solution for recovering the limitations of individual systems as well as for providing positive features of each of the technologies. An RF/optical wireless hybrid system consists both RF and optical-based wireless technologies, whereas an optical/optical wireless hybrid system consists two or more types of OWC technologies. The co-deployment of wireless systems can improve system performance in terms of throughput, reliability, and energy efficiency of individual networks. This study surveys the state of the art and key research directions regarding optical wireless hybrid networks, being the first extensive survey dedicated to this topic. We provide a technology overview of existing literature on optical wireless hybrid networks, such as RF/optical and optical/optical systems. We consider the RF-based macrocell, small cell, wireless fidelity, and Bluetooth, as well as optical-based visible light communication, light fidelity, optical camera communication, and free-space optical communication technologies for different combinations of hybrid systems. Moreover, we consider underwater acoustic communication for hybrid acoustic/optical systems. The opportunities brought by hybrid systems are presented in detail. We outline important challenges that need to be addressed for successful deployment of optical wireless hybrid network systems for 5G and IoT paradigms.
159 citations
TL;DR: The design of a compact wearable sensor patch is presented for measurements of different physiological signals, such as the electrocardiogram, photoplethysmography, and body temperature, and the experimental results demonstrate the feasibility of the overall platform for IoT-connected healthcare applications.
Abstract: The Internet of Things (IoT) is a new communication paradigm that can connect elements from various fields through the Internet. One of the most attractive IoT applications is in the modern healthcare area, as the traditional healthcare system has an increasing demand for social resources, including doctors, nurses, hospital beds, and health monitoring devices. In this article, the design of a compact wearable sensor patch is presented for measurements of different physiological signals, such as the electrocardiogram (ECG), photoplethysmography (PPG), and body temperature. As ECG and PPG sensors are integrated with the same device, the proposed sensor patch can be used to estimate blood pressure (BP) continuously based on the pulse arrival time (PAT) without extra wires and devices. The sensor patch consists of a center board for signal acquisition and processing, a power board for energy supply and charging batteries, and three sensors for vital signs monitoring. All the components are designed in a rigid-flex structure, which can be easily attached to the human body for remote health monitoring applications. The sensors can be detached from the center board for customized measurements of a certain physiological signal (e.g., ECG) to reduce power consumption. Experiments are conducted to validate the performance of the proposed sensor patch by comparison with a commercial reference device. With the integration of a miniaturized Bluetooth low-energy (BLE) module, the proposed sensor system can transmit physiological measurements wirelessly to a gateway. Data encryption is applied on both the sensor patch and gateways to protect data for privacy and security purposes during transmission. Both a mobile gateway (based on smartphones) and a fixed gateway (based on portable computers) are designed as the bridge between the wearable sensor system and the Internet cloud, where health data can be stored and further analyzed. The experimental results demonstrate the feasibility of the overall platform for IoT-connected healthcare applications.
94 citations
TL;DR: DeepWear as discussed by the authors is a deep learning framework for wearable devices to improve the performance and reduce the energy footprint by offloading DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth.
Abstract: Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X execution speedup, as well as 53.5 and 85.5 percent energy saving compared to wearable-only and handheld-only strategies, respectively.
81 citations
TL;DR: In this paper, a hybridized blue energy harvester based on a triboelectric-electromagnetic hybridized generator is developed to extract the abundant energy from water waves.
80 citations
TL;DR: This paper provides a survey of attacks related to the wireless infrastructures of IoT, and to the most used short-range wireless communication technologies in the resource-constrained part of IoT in particular, and provides a taxonomy of these attacks based on a security service-based attack classification.
Abstract: The Internet of Things, abbreviated as IoT, is a new networking paradigm composed of wireless and wired networks, geographically distributed and interconnected by a “secured” backbone, essentially, the Internet. It connects billions of heterogeneous devices, called Things, using different communication technologies and provides end-users, all over the world, with a variety of smart applications. IoT constitutes a new evolution for the Internet in terms of diversity, size, and applications. It also invites cybercriminals who exploit IoT infrastructures to conduct large scale, distributed, and devastating cyberattacks that may have serious consequences. The security of IoT infrastructures strongly depends on the security of its wired and wireless infrastructures. Still, the wireless infrastructures are thought to be the most outspread, important, and vulnerable part of IoT. To achieve the security goals in the wireless infrastructures of IoT, it is crucial to have a comprehensive understanding of IoT attacks, their classification, and security solutions in such infrastructures. In this paper, we provide a survey of attacks related to the wireless infrastructures of IoT in general, and to the most used short-range wireless communication technologies in the resource-constrained part of IoT in particular. Namely, we consider Wi-Fi, Bluetooth, ZigBee, and RFID wireless communication technologies. The paper also provides a taxonomy of these attacks based on a security service-based attack classification and discusses existing security defenses and mechanisms that mitigate certain attacks as well as the limitations of these security mechanisms.
75 citations
TL;DR: In this paper, the Covid-19 contact tracing apps that use Bluetooth Low Energy (LE) to detect proximity within 2m for 15 minutes have been evaluated and it was shown that the received signal strength can be strongly affected by factors including (i) the model of the handset used, (ii) the relative orientation of handsets, (iii) absorption by human bodies, bags etc.
Abstract: Many countries are deploying Covid-19 contact tracing apps that use Bluetooth Low Energy (LE) to detect proximity within 2m for 15 minutes. However, Bluetooth LE is an unproven technology for this application, raising concerns about the efficacy of these apps. Indeed, measurements indicate that the Bluetooth LE received signal strength can be strongly affected by factors including (i) the model of handset used, (ii) the relative orientation of handsets, (iii) absorption by human bodies, bags etc. and (iv) radio wave reflection from walls, floors, furniture. The impact on received signal strength is comparable with that caused by moving 2m, and so has the potential to seriously affect the reliability of proximity detection. These effects are due the physics of radio propagation and suggest that the development of accurate methods for proximity detection based on Bluetooth LE received signal strength is likely to be challenging. We call for action in three areas. Firstly, measurements are needed that allow the added value of deployed apps within the overall contact tracing system to be evaluated, e.g. data on how many of the people notified by the app would not have been found by manual contact tracing and what fraction of people notified by an app actually test positive for Covid-19. Secondly, the 2m/15 minute proximity limit is only a rough guideline. The real requirement is to use handset sensing to evaluate infection risk and this requires a campaign to collect measurements of both handset sensor data and infection outcomes. Thirdly, a concerted effort is needed to collect controlled Bluetooth LE measurements in a wide range of real-world environments, the data reported here being only a first step in that direction.
74 citations
TL;DR: The proposed model renders an astute way to sort digestible and indigestible waste using a convolutional neural network (CNN), a popular deep learning paradigm, and introduces an architectural design of a smart trash bin that utilizes a microcontroller with multiple sensors.
69 citations
TL;DR: The proposed IoT assisted ECG monitoring framework with secure data transmission has great potential to determine the clinical acceptance of ECG signals to improve the efficiency, accuracy and reliability of an unsupervised diagnostic system.
Abstract: The emerging Internet of Things(IoT) framework allows us to design small devices that are capable of sensing, processing and communicating, allowing sensors, embedding devices and other ' things ' to be created which will help to understand the surroundings. In this paper, the IoT assisted electrocardiogram (ECG) monitoring framework with secure data transmission has been proposed for continuous cardiovascular health monitoring. The development and implementation of a lightweight ECG Signal Strength Analysis has been proposed for automatic classification and realtime implementation, using ECG sensors, Arduino, Android phones, Bluetooth and cloud servers with the proposed IoT-assisted ECG monitoring system. For secure data transmission, the Lightweight Secure IoT (LS-IoT) and Lightweight Access Control (LAC) has been proposed. The ECG signals taken from the MIT-BIH and Physio Net Challenges databases and ECG signals for various physical activities are analyzed and checked in real-time. The proposed IoT assisted ECG monitoring framework has great potential to determine the clinical acceptance of ECG signals to improve the efficiency, accuracy and reliability of an unsupervised diagnostic system.
66 citations
18 May 2020
TL;DR: It is shown that the Bluetooth specification contains vulnerabilities enabling to perform impersonation attacks during secure connection establishment, including the lack of mandatory mutual authentication, overly permissive role switching, and an authentication procedure downgrade.
Abstract: Bluetooth (BR/EDR) is a pervasive technology for wireless communication used by billions of devices. The Bluetooth standard includes a legacy authentication procedure and a secure authentication procedure, allowing devices to authenticate to each other using a long term key. Those procedures are used during pairing and secure connection establishment to prevent impersonation attacks. In this paper, we show that the Bluetooth specification contains vulnerabilities enabling to perform impersonation attacks during secure connection establishment. Such vulnerabilities include the lack of mandatory mutual authentication, overly permissive role switching, and an authentication procedure downgrade. We describe each vulnerability in detail, and we exploit them to design, implement, and evaluate master and slave impersonation attacks on both the legacy authentication procedure and the secure authentication procedure. We refer to our attacks as Bluetooth Impersonation AttackS (BIAS).Our attacks are standard compliant, and are therefore effective against any standard compliant Bluetooth device regardless the Bluetooth version, the security mode (e.g., Secure Connections), the device manufacturer, and the implementation details. Our attacks are stealthy because the Bluetooth standard does not require to notify end users about the outcome of an authentication procedure, or the lack of mutual authentication. To confirm that the BIAS attacks are practical, we successfully conduct them against 31 Bluetooth devices (28 unique Bluetooth chips) from major hardware and software vendors, implementing all the major Bluetooth versions, including Apple, Qualcomm, Intel, Cypress, Broadcom, Samsung, and CSR.
66 citations
TL;DR: The results show that the proposed Bluetooth Low Energy based indoor positioning system developed for monitoring the daily living pattern of old people or individuals with disabilities is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the users.
Abstract: In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy.
Posted Content•
TL;DR: It is demonstrated that in real-world scenarios the current GAP design is vulnerable to profiling and possibly de-anonymizing infected persons, and relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system.
Abstract: Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.
TL;DR: This wind-driven self-powered wireless environmental sensing system constructs a platform, which can be widely used in any other wireless remote environmental sensing scene, requiring sustainability and free of maintenance.
Posted Content•
TL;DR: It is found that the Bluetooth LE received signal strength can vary substantially depending on the relative orientation of handsets, on absorption by the human body, reflection/absorption of radio signals in buildings and trains, and so on.
Abstract: We report on measurements of Bluetooth Low Energy (LE) received signal strength taken on mobile handsets in a variety of common, real-world settings. We note that a key difficulty is obtaining the ground truth as to when people are in close proximity to one another. Knowledge of this ground truth is important for accurately evaluating the accuracy with which contact events are detected by Bluetooth LE. We approach this by adopting a scenario-based approach. In summary, we find that the Bluetooth LE received signal strength can vary substantially depending on the relative orientation of handsets, on absorption by the human body, reflection/absorption of radio signals in buildings and trains. Indeed we observe that the received signal strength need not decrease with increasing distance. This suggests that the development of accurate methods for proximity detection based on Bluetooth LE received signal strength is likely to be challenging. Our measurements also suggest that combining use of Bluetooth LE contact tracing apps with adoption of new social protocols may yield benefits but this requires further investigation. For example, placing phones on the table during meetings is likely to simplify proximity detection using received signal strength. Similarly, carrying handbags with phones placed close to the outside surface. In locations where the complexity of signal propagation makes proximity detection using received signal strength problematic entry/exit from the location might instead be logged in an app by e.g. scanning a time-varying QR code or the like.
TL;DR: A wearable heart rate monitoring intelligent sports bracelet monitoring system based on the Internet of things, which is used to monitor the user's changes in the human heart rate during sports.
TL;DR: A miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone, focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations.
Abstract: This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations. To validate the system, it has been tested with different concentrations of NOx including some tests specifically developed to study the behavior of the device in different humidity conditions. Finally, a mobile application has been developed to provide classification services. In this regard, a neural network has been developed, trained, and integrated into a smartphone to process the information retrieved from e-nose devices.
TL;DR: The results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing), and that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak.
Abstract: One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.
TL;DR: UbiFlow is presented, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks and adopts multiple controllers to divide urban-scale SDN into different geographic partitions and achieve distributed control of IoT flows.
Abstract: The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g., WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g., 67.21 percent throughput improvement, 72.99 percent reduced delay, and 69.59 percent jitter improvements, compared with alternative SDN systems.
21 Oct 2020
TL;DR: In this paper, the authors provide a detailed study of the current practice of RSSI-based distance measurements among contact tracing apps by analyzing various factors that can affect the RSSI value and how each app has responded to them.
Abstract: A large number of Bluetooth-based mobile apps have been developed recently to help tracing close contacts of contagious COVID-19 individuals. These apps make decisions based on whether two users are in close proximity (e.g., within 6 ft) according to the distance measured from the received signal strength (RSSI) of Bluetooth. This paper provides a detailed study of the current practice of RSSI-based distance measurements among contact tracing apps by analyzing various factors that can affect the RSSI value and how each app has responded to them. Our analysis shows that configurations for the signal transmission power (TxPower) and broadcasting intervals that affect RSSI vary significantly across different apps and a large portion of apps do not consider these affecting factors at all, or with quite limited tuning.
06 Jul 2020
TL;DR: A set of signal processing and information fusion methods by integration of Nonlinear Least Square (NLS) curve fitting, Kalman Filter (KF), and Gaussian Filter (GF) to boost the accuracy rate of estimated angle are proposed.
Abstract: With expected widespread implementation of 5G networks and 5G Internet of Things (IoT), indoor localization is expected to become of even further importance. Although Global Positioning System (GPS) ensures efficient outdoor localization, generally speaking, indoor localization systems fail to provide the same level of efficiency. In this regard, there has been recent widespread attention to Angle of Arrival (AoA) with the application on Switch Antenna Array (SAA), as an efficient indoor localization method due to its potential in determining location with low estimation error. The AoA, however, suffers from several issues including being sensitive to multipath effects, noise, fluctuations of received signal, and frequency/phase shifts. To tackle these issues, the paper proposes a set of signal processing and information fusion methods by integration of Nonlinear Least Square (NLS) curve fitting, Kalman Filter (KF), and Gaussian Filter (GF) to boost the accuracy rate of estimated angle. The proposed fusion framework is evaluated based on a real Bluetooth Low Energy (BLE) dataset and results illustrate significant potentials in terms of improving overall BLE-based achievable accuracy in angle detection.
TL;DR: Results prove that scalability is especially challenging for Bluetooth mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments, and introduce randomization in these timing parameters, as well as varying the duration of the Advertising Events.
Abstract: This article evaluates the quality-of-service performance and scalability of the recently released Bluetooth mesh protocol and provides general guidelines on its use and configuration. Through extensive simulations, we analyze the impact of the configuration of all the different protocol’s parameters on the end-to-end reliability, delay, and scalability. In particular, we focus on the structure of the packet broadcast process, which takes place in time intervals known as Advertising Events and Scanning Events . Results indicate a high degree of interdependence among all the different timing parameters involved in both the scanning and the advertising processes and show that the correct operation of the protocol greatly depends on the compatibility between their configurations. We also demonstrate that introducing randomization in these timing parameters, as well as varying the duration of the Advertising Events , reduces the drawbacks of the flooding propagation mechanism implemented by the protocol. Using data collected from a real office environment, we also study the behavior of the protocol in the presence of WLAN interference. It is shown that Bluetooth mesh is vulnerable to external interference, even when implementing the standardized limitation of using only 3 out of the 40 Bluetooth low-energy frequency channels. We observe that the achievable average delay is relatively low, of around 250 ms for over 10 hops under the worst simulated network conditions. However, results prove that scalability is especially challenging for Bluetooth mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments.
Proceedings Article•
01 Jan 2020TL;DR: The potential of Frankenstein, a fuzzing framework based on advanced firmware emulation, is demonstrated by finding three zero-click vulnerabilities in the Broadcom and Cypress Bluetooth stack, which is used in most Apple devices, many Samsung smartphones, the Raspberry Pis, and many others.
Abstract: Wireless communication standards and implementations have a troubled history regarding security. Since most implementations and firmwares are closed-source, fuzzing remains one of the main methods to uncover Remote Code Execution (RCE) vulnerabilities in deployed systems. Generic over-the-air fuzzing suffers from several shortcomings, such as constrained speed, limited repeatability, and restricted ability to debug. In this paper, we present Frankenstein, a fuzzing framework based on advanced firmware emulation, which addresses these shortcomings. Frankenstein brings firmware dumps "back to life", and provides fuzzed input to the chip's virtual modem. The speed-up of our new fuzzing method is sufficient to maintain interoperability with the attached operating system, hence triggering realistic full-stack behavior. We demonstrate the potential of Frankenstein by finding three zero-click vulnerabilities in the Broadcom and Cypress Bluetooth stack, which is used in most Apple devices, many Samsung smartphones, the Raspberry Pis, and many others. Given RCE on a Bluetooth chip, attackers may escalate their privileges beyond the chip's boundary. We uncover a Wi-Fi/Bluetooth coexistence issue that crashes multiple operating system kernels and a design flaw in the Bluetooth 5.2 specification that allows link key extraction from the host. Turning off Bluetooth will not fully disable the chip, making it hard to defend against RCE attacks. Moreover, when testing our chip-based vulnerabilities on those devices, we find BlueFrag, a chip-independent Android RCE.
23 Nov 2020
TL;DR: It is shown for the first time that the backscatter tag can identify various excitation signals in an ultra-low-power way, including WiFi, Bluetooth, and ZigBee, and it is demonstrated that it can leverage excitation diversity to provide uninterrupted communication and greater throughput gains, whereas the single-protocol tag being idle when target carriers are not available.
Abstract: We present multiscatter, a novel backscatter design that can simultaneously work with multiple excitation signals for personal IoT sensors. Specifically, we show for the first time that the backscatter tag can identify various excitation signals in an ultra-low-power way, including WiFi, Bluetooth, and ZigBee. Further, we employ a new modulation approach, overlay modulation, that can leverage those excitation signals to convey tag data on top of productive data, which makes decoding both data possible with only a single personal radio. Since 2.4 GHz signals and personal radios are everywhere, multiscatter is readily deployable in our everyday IoT applications. We prototype multiscatter using an FPGA and various commodity radios. Extensive experiments show that for mixed 802.11b&n, Bluetooth and ZigBee signals, the average identification accuracy of four protocols is more than 93%. The maximal aggregate throughput of both productive and tag data is 278.4 kbps with a single Bluetooth radio, and the maximal backscatter ranges are 28 m, 22 m, and 20 m for WiFi, Bluetooth, and ZigBee, respectively. We also demonstrate that it can leverage excitation diversity to provide uninterrupted communication and greater throughput gains, whereas the single-protocol tag being idle when target carriers are not available.
01 Feb 2020
TL;DR: In this article, the authors comprehensively survey state-of-the-art applications built with BLE, obstacles to adoption of BLE in new application areas, and current solutions from academia and industry that further expand the capabilities of Bluetooth Low Energy (BLE).
Abstract: As an emerging technology with exceptional low energy consumption and low-latency data transmissions, Bluetooth Low Energy (BLE) has gained significant momentum in various application domains, such as Indoor Positioning, Home Automation, and Wireless Personal Area Network (WPAN) communications. With various novel protocol stack features, BLE is finding use on resource-constrained sensor nodes as well as more powerful gateway devices. Particularly proximity detection using BLE beacons has been a popular usage scenario ever since the release of Bluetooth 4.0, primarily due to the beacons’ energy efficiency and ease of deployment. However, with the rapid rise of the Internet of Things (IoT), BLE is likely to be a significant component in many other applications with widely varying performance and Quality-of-Service (QoS) requirements and there is a need for a consolidated view of the role that BLE will play in applications beyond beaconing. This paper comprehensively surveys state-of-the-art applications built with BLE, obstacles to adoption of BLE in new application areas, and current solutions from academia and industry that further expand the capabilities of BLE.
TL;DR: A Faded Memory Kalman Filter (FMKF) is applied by considering more weights for new measurements to overcome the issue of inaccuracy in the prediction model and to predict the traffic flow with more resolution to compensate for the prediction error originating from modelling error.
17 Jun 2020
TL;DR: This work demonstrates that the key negotiation protocols of Bluetooth and BLE are vulnerable to standard-compliant entropy downgrade attacks, and shows how an attacker can downgrade the entropy of any Bluetooth session key to 1 byte, and of any BLE long-term key and sessionKey to 7 bytes.
Abstract: Bluetooth (BR/EDR) and Bluetooth Low Energy (BLE) are pervasive wireless technologies specified in the Bluetooth standard. The standard includes key negotiation protocols used to generate long-term keys (during pairing) and session keys (during secure connection establishment). In this work, we demonstrate that the key negotiation protocols of Bluetooth and BLE are vulnerable to standard-compliant entropy downgrade attacks. In particular, we show how an attacker can downgrade the entropy of any Bluetooth session key to 1 byte, and of any BLE long-term key and session key to 7 bytes. Such low entropy values enable the attacker to brute-force Bluetooth long-term keys and BLE long-term and session keys, and to break all the security guarantees promised by Bluetooth and BLE. As a result of our attacks, an attacker can decrypt all the ciphertext and inject valid ciphertext in any Bluetooth and BLE network. Our key negotiation downgrade attacks are conducted remotely, do not require access to the victims’ devices, and are stealthy to the victims. As the attacks are standard-compliant, they are effective regardless of the usage of the strongest Bluetooth and BLE security modes (including Secure Connections), the Bluetooth version, and the implementation details of the devices used by the victims. We successfully attack 38 Bluetooth devices (32 unique Bluetooth chips) and 19 BLE devices from different vendors, using all the major versions of the Bluetooth standard. Finally, we present effective legacy compliant and non-legacy compliant countermeasures to mitigate our key negotiation downgrade attacks.
TL;DR: An energy model of the BLE protocol, which allows the computation of a device’s power consumption in all possible operating modes and is not only one of the most accurate ones known so far (because it accounts for all protocol parameters), but it is also the only one that models all the operating modes of BLE.
Abstract: Bluetooth Low Energy (BLE) is a wireless protocol optimized for low-power communication. To design energy-efficient devices, the protocol provides a number of parameters that need to be optimized within an energy, latency, and throughput design space. Therefore, an energy model that can predict the energy consumption of a BLE-based wireless device for different parameter value settings is needed. As BLE differs from the well-known Bluetooth Basic Rate (BR) significantly, models for Bluetooth BR cannot be easily applied to the BLE protocol. In past years, there have been a couple of proposals on energy models for BLE. However, none of them can model all the operating modes of the protocol. This article presents an energy model of the BLE protocol, which allows the computation of a device’s power consumption in all possible operating modes. To the best of our knowledge, our proposed model is not only one of the most accurate ones known so far (because it accounts for all protocol parameters), but it is also the only one that models all the operating modes of BLE. Based on this model, guidelines for system designers are presented that help choose the right parameters for optimizing the energy consumption. The model is publicly available as a software library for download.
TL;DR: In this article, a parking system based on Bluetooth Low Energy (BLE) beacons and particle filtering is proposed for both indoor and outdoor parking spaces, which is able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon.
Abstract: Urban centers and dense populations are expanding, hence, there is a growing demand for novel applications to aid in planning and optimization. In this article, a smart parking system that operates both indoor and outdoor is introduced. The system is based on Bluetooth low energy (BLE) beacons and uses particle filtering to improve its accuracy. Through simple BLE connectivity with smartphones, an intuitive parking system is designed and deployed. The proposed system pairs each spot with a unique BLE beacon, providing users with guidance to free parking spaces and a secure and automated payment scheme based on real-time usage of the parking space. Three sets of experiments were conducted to examine different aspects of the system. A particle filter is implemented in order to increase the system performance and improve the credence of the results. Through extensive experimentation in both indoor and outdoor parking spaces, the system was able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon.
07 Jul 2020
TL;DR: An efficient and cost-effective indoor navigation system for driving people inside large smart buildings that identifies the user position according to information sent by Beacons, processes the best path for indoor navigation at the edge computing infrastructure, and provides it to the user through the smartphone.
Abstract: The emergency we are experiencing due to the coronavirus infection is changing the role of technologies in our daily life. In particular, movements of persons need to be monitored or driven for avoiding gathering of people, especially in small environments. In this paper, we present an efficient and cost-effective indoor navigation system for driving people inside large smart buildings. Our solution takes advantage of an emerging short-range wireless communication technology – IoT-based Bluetooth Low Energy (BLE), and exploits BLE Beacons across the environment to provide mobile users equipped with a smartphone hints on how to arrive at the destination. The main scientific contribution of our work is a new proximity-based navigation system that identifies the user position according to information sent by Beacons, processes the best path for indoor navigation at the edge computing infrastructure, and provides it to the user through the smartphone. We provide some experimental results to test the communication system considering both the Received Signal Strength Indicator (RSSI) and the Mean Opinion Score (MOS).
TL;DR: A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications.
Abstract: A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO2) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a -10 dB impedance bandwidth of 36 MHz, a peak realized gain of -4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.