In this work, we present a secure Internet-of-Things framework enabled by UWB, which allows users to wirelessly interact with any public and home appliances in a more secure, seamless and efficient manner. we envision an ecosystem of appliances and mobile devices equipped with UWB transceivers, where the appliances are capable of imposing location-based access control in precise and configurable zones and ensuring security against eavesdropping and hijacking by malicious attackers. We propose a location-based access control algorithm that is robust against range spoofing. Further, we propose the network and application layer protocols that enable one generic user-end application to access the control interface of any appliances and adapt to context.

This paper introduces a novel over-the-air computation method that utilizes low-complexity transmissive intelligent surfaces (TISs) for neural network inference. It is demonstrated that the signal propagation model through TIS closely resembles the fully connected layer of neural networks. And through training, the TIS phase shifts can be determined to perform a specific computation on radio-frequency (RF) signals. Considering the practical constraints of TIS designs with continuous phase shifts in millimeter-wave (mmWave) frequency, we propose a novel discretized complex-valued neural network structure and a training method suitable for low-complexity 1-bit and 2-bit TIS-based neural network layers. It is shown through simulation that the proposed method achieves high accuracy on an image classification task even for 1-bit or 2-bit TISs.

A novel convolutional layer based on transmissive intelligent surfaces (TISs), which operates in the radio-frequency (RF) domain, is introduced for analog over-the-air (OTA) computation in this paper. To be specific, each RF convolutional layer comprises three TISs placed sequentially to perform a 2-D convolution operation. A method for designing TIS transmission coefficients, TIS locations, and TIS element spacing is proposed to execute the 2-D convolution of I ∗ K = O. The transmission coefficients of the second TIS encapsulate information about the kernel K, and the output (O) of the third TIS is the convolutional result of K and the input signal (I), which is the input to the first TIS. To validate the proposed design, a simple neural network featuring a single convolutional layer with one kernel is tested. The simulation results demonstrate that, with a practical size of the proposed design and adequate signal power transmitted to the TISs, the neural network incorporating the proposed TIS-based convolutional layer achieves a good approximation of performance compared to a neural network with the classic complex-valued convolutional layer. This validates the feasibility of the proposed design and the potential for offloading convolution operations from digital processors to the RF domain.

This paper presents UnSpoof, a UWB localization system that can detect and localize distance-spoofing tags with several collaborative passively-receiving anchors. We propose novel formulations that enable passively-receiving anchors to deduce their time-of-arrival (ToA) and time-difference-of-arrival (TDoA) just by overhearing standard two-way ranging (TWR) messages between the tag and one active anchor. Our ToA formulation can be used to precisely localize an honest tag, and to detect a distance-spoofing tag that falsely reports its timestamps. Additionally, our TDoA formulation enables spoof-free localization, which can be used to track down and apprehend a malicious tag Our experimental evaluation shows 30cm $75^{th}$ percentile error for ToA-based honest tag localization, and sub-meter error for TDoA-based localization for spoofing tags. We demonstrate successful detection of distance reduction and enlargement attacks inside the anchors’ convex hull, and graceful degradation outside.

Wireless ranging, where two or more wireless devices determine their relative distance by exchanging messages, is a fundamental primitive in short-range distance measurements and localization. These methods are used by fine-time measurement in WiFi, and ultra-wideband (UWB) radios which is seeing an uptick in the smartphone market. Since ranging depends on accurate timestamps, the clock drifts between devices is an important consideration that affects ranging precision. Several applications have been proposed in the short-range localization context with their own ranging protocols and almost all of them perform clock-drift analysis to assess the quality of the protocol and ranging formulation. Although this is standard practice, there lacks a way to quantify and compare the extent of clock-drift introduced measurement errors. In this work, we introduce a metric based on numerical simulations that enables direct comparisons across schemes, and demonstrate how it helps analyze distance measurement errors.

Enabling reliable indoor localization can facilitate several new applications akin to how outdoor localization systems, such as GPS, have facilitated. Currently, a few key hurdles remain that prevent indoor localization from reaching the same stature. These hurdles include complicated deployment, tight time synchronization requirements from time difference of arrival protocols, and a lack of mechanism to allow a pan-building seamless solution. This work explores ways in which these key hurdles can be overcome to enable a more pervasive use of indoor localization. We propose a novel passive ranging scheme where clients overhear ongoing two-way ranging wireless communication between a few infrastructure nodes, and compute their own relative location without transmitting any signals (preserving user privacy). Our approach of performing two-way ranging between infrastructure nodes removes a crucial timing requirement in traditional time-difference-of-arrival methods thereby relaxing the synchronization requirements imposed by previous techniques. We use ultra-wideband wireless (UWB) radios that can easily penetrate building materials so that spanning an entire floor of a large building with just a few infrastructure nodes is possible. We build working prototypes, including the necessary hardware, and demonstrate the plug-and-play nature of our proposed solution. Our evaluation in three indoor spaces shows 1–2 meter-level localization accuracy with areas as large as 2241 m2. We expect our explorations to re-trigger interest in novel applications for indoor spaces based on fine-grained indoor location knowledge.

This demonstration presents the Location-Specific Public Broadcast system, in which localization and wireless broadcasts are combined to deliver a scalable, privacy preserving, and generic solution to location-based services. Other interactive location-based systems either preload information on the user devices, which are usually bulky, difficult to update and have to be custom-made for each venue, or fetch information from cloud based on location, which sacrifices user privacy. In our system, a wireless access point continuously broadcasts information tagged by locations of interest, and the mobile devices performing passive localization select and display the information pertinent to themselves. In this case, the location-specific information is stored only on the WiFi AP, and the phone app would be ultra lightweight with only the location calculation and information filtering functionalities, which can be used in any space. We envision our solution being adopted in public places, such as museums, aquariums, etc., for location-specific information delivery purposes, like enhancing interactive experience for visitors.

This article reports an automated optically-induced dielectrophoresis (ODEP) system for characterizing the specific membrane capacitance (SMC) of individual cells. The simulation of cell motion is conducted to analyze the electrokinetic forces acting on the cell. A self-developed visual tracking algorithm for multicells is used to realize an automated process for determining the frequency-sweeping range, crossover frequencies, and cell radii. The SMC values of malignant bladder cancer cells (T24 and RT4) and normal urothelial cells (SV-HUC-1) were quantified using the automated system, demonstrating that the system has a measurement speed of ~1 cell/s, an accuracy of 1 kHz for the crossover frequency determination, and an accuracy of 0.2 μm for the cell radius measurement.