Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy.Comment: This paper has been accepted by IEEE Internet of Things Journal, Special Issue on Artificial Intelligence Powered Edge Computing for Internet of Thing

Exploiting LoRaWAN for efficient and resilient IoT networks

It is estimated that, by 2025, there will be more than 21 billion Internet of Things (IoT) devices deployed in various domains. These massive IoT devices will be interconnected by numerous IoT networks with the Internet as the backbone. The IoT networks will be primarily wireless, ranging from cellular networks, Wi-Fi infrastructures, low-power multi-hop wireless networks (e.g., Zigbee and Bluetooth personal area networks), and the recently emerging low-power wide-area networks. The greatly increased pervasive connectivity owing to the deployment of these IoT networks will foster the next-generation Internet-based innovations. This thesis focuses on exploiting LoRaWAN, a representative low-power wide-area networking technology, to build efficient and resilient low-power wireless IoT networks. Given the increasingly crowded radio frequency (RF) spectrum, the efficiency of utilizing the finite wireless bandwidth is a primary goal of designing and operating IoT networks. Moreover, the networks' resilience, i.e., their abilities to recover and maintain connectivity and efficiency despite external disturbances such as interference from neighbor RF technologies and even cyber-attacks, is also important to the IoT applications. This thesis aims at studying how the low-power long-range communication capability of LoRaWAN can be exploited to address some of the efficiency and resilience issues in IoT networks. This thesis studies the following two main problems. First, it studies how to use the one-hop LoRaWAN to build out-of-band control planes for the low-power multi-hop wireless networks to improve their efficiency and resilience. Specifically, it exploits the simplicity of LoRaWAN to manage the complexity of multi-hop wireless networks. Second, given LoRaWAN's communication throughput limitation due to the narrow bandwidth and low duty cycle defined in LoRaWAN specification, this thesis studies how to efficiently maintain the common notion of time among all LoRaWAN end devices. In addition, it investigates the potential attacks that aim at disrupting the common notion of time and develops countermeasures for resilience. The details of the two main problems and this thesis' solutions are as follows. The first part of this thesis addresses the Separation of Control and Data Planes (SCDP) for low-power multi-hop wireless networks using LoRaWAN. SCDP is a desirable paradigm for low-power multi-hop wireless networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. For example, when a node loses connections with its neighbors, the controller cannot reach the node anymore. Recently, multi-radio platforms (e.g., TI CC1350 and OpenMote B) are increasingly available, which make the physical SCDP possible. To advance the network architecture design, this thesis leverages on the LoRaWAN to form one-hop out-of-band control planes called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control need to be dealt with to achieve high efficiency and good resilience. To address these challenges, a TDMA-based multi-channel transmission control is designed, which features an urgent channel and negative acknowledgment. On a testbed of 16 nodes, LoRaCP is applied to physically separate the control-plane network of the Collection Tree Protocol (CTP) from its Zigbee-based data-plane network. Extensive experiments show that LoRaCP increases CTP's packet delivery ratio from 65% to 80% in the presence of external interference, while consuming little per-node average radio power. LoRaWAN is promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data with a common notion of time is a critical system function. The second part of this thesis considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN's one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices' clocks at all times. However, this thesis shows that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. In the attack, the attacker records the signal sent by the transmitter and sends a colliding frame to jam the receiver. Then, it replays the recorded signal after an intended delay. Real experiments show that the attack can affect the end devices in large areas up to about 50,000 square meters. In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, this thesis proposes a LoRa TimeStamping (LoRaTS) gateway design that integrates a commodity LoRaWAN gateway and a listen-only low-power software-defined radio to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa's chirp spread spectrum modulation, this thesis develops signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa's default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. Our approach supports the bandwidth-efficient sync-free time stamping and requires no modifications on the LoRaWAN end devices. Extensive real-world experiments based on a testbed deployed in a university campus show the effectiveness of the proposed approach. The availability of multiple RF technologies and the mixed use of them in IoT networks create both opportunities and also challenges. The approach designs presented in this thesis demonstrate the exploitation of the new unique features of LoRaWAN in addressing the efficiency and resilience issues of the legacy Zigbee networks and LoRaWAN networks themselves. The author's future work will also follow the same methodology to improve IoT networks.Doctor of Philosoph

Theoretical and Numerical Investigations on Static Characteristics of Aerostatic Porous Journal Bearings

To investigate the static characteristics of aerostatic journal bearings with porous bushing, the flow model—in which the compressibility of lubricating gas is considered—is established based on the Reynolds lubrication equation, Darcy equation for porous material, and continuity equation. With the finite difference method, difference schemes for non-uniform grids, relaxation method, and virtual node method, the numerical method for the governing equations of compressible flow in porous journal bearings is proposed. The effects of nominal clearance of bearings and compressibility of gas on the static characteristics are analyzed. Under the same minimum film thickness and the same gas compressibility, as the nominal clearance widens, the load capacity, mass flow rate, and power consumption increase. Under the same minimum film thickness and the same nominal clearance, with the increase in gas polytropic index, the load capacity strengthens, while the mass flow rate and power consumption decline. This study could provide a reference for the design of porous journal bearings

Analytical methodology and pharmacokinetic study of elagolix in plasma of rats using a newly developed UPLC-MS/MS assay

Elagolix, as a competitive gonadotropin-releasing hormone (GnRH) receptor antagonist, has been recently approved by the US FDA for the management of moderate to severe pain due to endometriosis in women. In this study, we developed and verified an analysis assay to detect the concentration level of elagolix in plasma from rats after sample preparation based on a newly validated ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) technique in this study. The process of sample preparation used acetonitrile for a quick and easy protein precipitation method and diazepam was engaged as the internal standard (IS). Then, gradient elution was used to elute elagolix and IS. The mobile phase used in the present experiment was consisted of solvent A (acetonitrile) and solvent B (water having formic acid with the volume ratio of 0.1%), and the type of the C18 column used was named Acquity UPLC BEH C18 column with the specification of 2.1 mm × 100 mm, 1.7 μm. Multiple reaction monitoring (MRM) in positive ion mode for the experiment was engaged to detect the level of elagolix with electrospray ionization (ESI) source by m/z 632.4 → 529.5 transition for quantification and m/z 632.4 → 177.1 transition for qualification. It was found that the method in the scope of 1–2000 ng/mL indicated excellent linearity (r2 > 0.9983). The precision of this assay for intra-day was between 3.5 and 5.5%, and for inter-day was between 9.4 and 12.7%, respectively; the accuracy was 1.2–13.9% for the intra- and inter-day. The stability, extraction recovery, and matrix effect of the method were all in accordance with the rules of assay validation in biological medium proposed by FDA, whose application was also successfully used to determine the concentration of plasma elagolix from an experiment on pharmacokinetic investigation after oral administration of 15 mg/kg elagolix