Museum visits often lack personalized and interactive experiences, limiting visitor engagement with art and historical artifacts. To address this, we present ArtSpeak, a standalone augmented reality (AR) application that transforms traditional art viewing into an interactive storytelling experience. When users point their mobile cameras at an artwork, the system responds to their questions with lifelike, talking-head video narratives generated from historical portraits. However, generating such talking-head videos at runtime is computationally expensive, often requiring over a minute per response. To address this challenge, ArtSpeak introduces two major contributions. First, it employs a collection of frequently asked questions (FAQ) to generate a set of lifelike video responses for various art portraits. Second, it introduces a novel retrieval-based approach that uses GPT-based embeddings and cosine similarity to select the most relevant response. As a result, the system dynamically presents the video reply that best aligns with the user's inquiry, reducing computational overhead and ensuring a real-time, low-latency experience. More precisely, ArtSpeak achieves over 30 x lower latency and reduces energy consumption by approximately 81 % compared to the real-time video generation method. User studies further validate the system's effectiveness, with 85 % of participants rating the retrieved responses as relevant to their queries and 90 % reporting smooth video playback. These results highlight the efficiency and user satisfaction enabled by our retrieval-based approach.

Storing large volumes of traffic video content in cloud storage is an expensive undertaking, given the limited capacity of cloud storage and its inability to store data beyond a few weeks. To address this issue, this article introduces CLOUD-CODEC , a novel video encoding approach tailored specifically for traffic monitoring video. CLOUD-CODEC offers three key advantages: (i) real-time encoding without any delay, (ii) near-perfect video quality upon decoding, and (iii) one-fifth the storage size of traditional encoding methods. CLOUD-CODEC is generally applicable to traffic cameras under various weather and lighting conditions. The encoding algorithm is a lightweight DNN-based object detection and box-shaped segmentation approach. The method can uniquely detect and segment cars, pedestrians, and moving objects with the marginal box-shaped contours. Periodic object detection makes it possible for CLOUD-CODEC to operate in real-time and estimate the movement of objects between predictions. Proof-of-concept evaluations using a massive dataset indicate that CLOUD-CODEC reduces video size by 80%—surpassing AV1 (34.9%), CloudSeg (58.4%), Detection (76.9%), Segmentation (73.1%), and Segm&Sort (69.5%). It achieves a frame rate of 95.8 when encoding and a VMAF score of 72.54 after decoding, with a storage size that is one-fifth of traditional methods. Field-testing of CLOUD-CODEC on metropolitan traffic cameras demonstrates its ability to extend storage time by 74.92%.

This paper presents a uGPS (Underground GPS), which is the first SDR (Software Defined Radio)-based GNSS service to commodity GNSS receivers, including the latest iPhone, Android, and Car navigation. The uGPS is a system consisting of SDRs for GNSS signal generation, a GNSS D.O. for nano-second level timing synchronization's economic selection, fiber optic, and leaky feeder for a minimal environmental effect in tunnel environments. The proposed uGPS provides consistent 8 GPS satellites and 4 GLONASS satellites signal through a 1.5-kilometer-long tunnel. The uGPS equipped tunnel is highly compatible with commodity GNSS/GPS receivers. Therefore, it does not require any APP or special hardware. Our benchmark tested package was on top of the FPGA, in which modifications and upgrades were demonstrated via evaluation on three popular GNSS/GPS devices including Android phones, iPhones, and Car navigation systems. Further extensive evaluations demonstrated that the uGPS consistently achieved average location accuracy of 10 meters in 30~70km/h speed driving tests (without a map-matching algorithm), and a seamless handover between live GNSS and the uGPS system.

This paper presents ABC, the AR(augmented reality), and BLE combined target detector in a crowd of objects. The AR design of BLE analysis is a ready-to-use option for the ever-growing app-based ride market. The carefully designed AR tags and BLE beacons effectively collaborate to identify the target car. Such that a passenger can pinpoint the ride from a far distance. ABC consistently identify the target in a realistic environment through evaluations.

With the next generation immersive video applications, network capacity is becoming a growing bottleneck to deliver a high quality video to end-users. Recent advances to tackle this challenge introduced super-resolution (SR) for video quality enhancement through neural computations by leveraging client-side compute capacity. However, the existing SR models are bulky, compute-, and memory-expensive, which makes it difficult to deploy them in practice. In this work, we present dcSR, a lightweight data-centric SR approach that enables a practical neural quality enhancement for videos. On the server-side, dcSR constructs micro SR models trained on a few selected frames from each video through a data-centric paradigm by employing a long term video scene understanding mechanism. On the client-side, dcSR integrates the micro SR models into the regular video decoder and enhances the video quality in real-time without compromising on quality enhancement. We evaluate dcSR and show its benefits by comparing it with previous methods.

Page Load Time (PLT) is critical in measuring web page load performance. However, the existing PLT metrics are designed to measure the Web page load performance on desktops/laptops and do not consider user interactions on mobile browsers. As a result, they are ill-suited to measure mobile page load performance from the perspective of the user. In this work, we present the Mobile User-Centered Page Load Time Estimator (muPLTest), a model that estimates the PLT of users on Web pages for mobile browsers. We show that traditional methods to measure user PLT for desktops are unsuited to mobiles because they only consider the initial viewport, which is the part of the screen that is in the user’s view when they first begin to load the page. However, mobile users view multiple viewports during the page load process since they start to scroll even before the page is loaded. We thus construct the muPLTest to account for page load activities across viewports. We train our model with crowdsourced scrolling behavior from live users. We show that muPLTest predicts ground truth user-centered PLT, or the muPLT, obtained from live users with an error of 10-15% across 50 Web pages. Comparatively, traditional PLT metrics perform within 44-90% of the muPLT. Finally, we show how developers can use the muPLTest to scalably estimate changes in user experience when applying different Web optimizations.

In accordance with the recent enhancement of display technology, users demand a higher quality of streaming service, which escalates the bandwidth requirement. Considering the recent advent of high FPS (frame per second) 4K and 8K resolution 360° videos, such bandwidth concern further intensifies in 360° Virtual Reality (VR) content streaming even at a larger scale. However, the currently available bandwidth in most of the developed countries can hardly support the bandwidth required to stream such a scale of content. To address the mismatch between the demand on higher quality of streaming service and the saturated network improvement, we propose SALI360 that practically solves the mismatch by utilizing the characteristics of the human vision system (HVS). By pre-rendering a set of regions - where viewers are expected to fixate - on 360° VR content in higher quality than the other regions, SALI360 improves viewers' quality of perception (QoP) while reducing content size with geometry-based 360° content encoding. In our user experiment, we compare the performance of SALI360 to the existing 360° content-encoding techniques based on 20 viewers' head movement and eye gaze traces. To evaluate viewers' QoP, we propose FoL (field of look) that captures viewers' quality perception area in the visual focal field (8°) rather than a wide (around 90°) field of view (FoV). Results of our experimental 360° VR video streaming show that SALI360 achieves 53.3% of PSNR improvement in FoL, while gaining 9.3% of PSNR improvement in FoV. In addition, our subjective study on 93 participants verifies that SALI360 improves viewers' QoP in the 360° VR streaming service.

This paper presents GateScatter, the first backscatter-based gateway connecting commodity IoT to WiFi. The backscatter design of GateScatter is an economic option towards pervasive Internet connectivity for ever-growing IoT. The carefully designed tag optimally reshapes ZigBee IoT packets with an arbitrary payload into an 802.11b WiFi packet over the air, such that the payload can be reliably retrieved at the WiFi receiver (hence a gateway). Gate-Scatter is highly compatible - it works with a wide range of IEEE 802.15.4-compliant systems, is agnostic to upper layer proprietary protocols, and does not require any modification to the commodity IoT platforms. GateScatter is extended to BLE IoT for generality. We prototype GateScatter hardware on FPGA where the wide applicability is demonstrated through evaluations on five popular IoT devices including Samsung SmartThings sensor, Philips smart bulb, and Amazon Echo Plus. Further extensive evaluations show that GateScatter consistently achieves throughput above 200 kbps and range of over 27 m under diverse practical scenarios including a corridor, dormitory room, and under user mobility.

We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this technique, we basically explore the backscatter channel by reflecting the incident RF signal with different changes in the phase. We define a measure of the backscatter channel, which we call backscatter channel state information (BCSI). The BCSI is composed of backscatter channel phase, backscatter amplitude, and change in baseline excitation level. When acquired over time, this measure provides rich RF analytics that can be used to extract various types of information from the environment of the tags by signal processing/machine learning methods. We show in the paper that this analytics is invariant w.r.t. to some variables including the deployment environment. We provide results from experiments with our tags that demonstrate the invariance of the BCSI.

Video streaming service has been essential to the Internet ecosystem since a majority of Internet contents is consumed via streaming ser-vices, such as Netflix and Youtube [3]. Moreover, contents providers started to upload 4K and even 8K videos on streaming service to satisfy users' demand for higher quality of streaming service in accordance with the recent refinement on display technology.

While GPS (Global Positioning System) navigation and GPS based autonomous car driving techniques are mature, the security of GPS signal has not been a primary system concern. In fact, it has been shown that an ordinary GPS can be easily spoofed by a low-cost, open-source based software defined radio (SDR) system such as bladeRF and HackRF [3, 4] which can cause serious complications to the navigation system of the car especially for self-driving cars which are driven based on the information from sensors, cameras, and GPS. In this work, we design a new GPS authentication system called lightweight authentication GPS (AuthGPS) to authenticate GPS signal against GPS spoofing and LTE base station broadcast message spoofing. In the past, there have been many successful attempts on GPS spoofing which resulted in shifting the destination of the car to the spoofer's desired location. In a case where the car is connected to the Internet via LTE network, the navigation system can find out if the GPS is spoofed by acquiring the correct GPS satellite information from LTE base stations' location information. However, a skillful attacker can spoof the LTE base station signals [7] as well using another SDR which will produce spoofed LTE base station information. Hence giving wrong information about GPSinformation according to the will of the attacker. Currently, there are defense methods against the GPS spoofing [6]. One of them is signal-processing-based methods. By monitoring unusual or unreasonable signal changes at GPS receivers, GPS spoofing attack can be detected. Received Power Monitoring (RPM) looks at all the received amplitude and automatic gain control (AGC) setpoint [1]. The receiver will sense drastic power jump if a spoofing attack occurs. However, an overly powerful spoofing attack with noise is not detectable in the case of this mechanism. Another way to detect spoofing is the symmetric-key encryption mechanism of GPS signals. According to previous research [2], encrypted precision code for anti-spoofing referred to as P(Y) code, might not be able to be spoofed. A spoofing attack can be detected by calculating cross-correlation between spoofed coarse/acquisition code, referred to as C/A code, and P(Y) code. Unfortunately, this P(Y) code is only for military purpose while the C/A code can be publicly accessible. This method requires knowledge of specific key which is not revealed to the public to decrypt P(Y) code. Monitoring the direction of arrival of the signals can be one of the methods [6]. A GPS receiver measures the direction-of-arrival vector with more than 3 antennas. Even though this signal-geometrybased system with multiple antennas makes GPS robust, an attacker can spoof signals from multiple directions, which is more difficult to detect the spoofing. The idea here is to develop a system that can discriminate the spoofed signal without complex computation or heavy message exchange. An important component of AuthGPS is a verification of valid GPS satellite information via LTE base stations' location information. We propose 6-digit one-time password-based authentication system in this paper.

We present the vision of BARNET (Backscattering Activity Recognition NEtwork of Tags), a network of passive RF tags that use RF backscatter for tag-to-tag communication. BARNET not only provides identification of tagged objects but also can serve as a 'device-free' activity recognition system. BARNET's key innovation is the concept of backscatter channel state information (BCSI) which can be measured via systematic multiphase probing of the backscatter tag-to-tag channel using innovative processing on the passive tags. So far such measurements were only possible using active radio receivers that consume much higher power. Changes in BCSI provide signatures for different activities in the environment that can be learned using suitable machine learning tools. We develop the BARNET tag architecture which shows that an ASIC implementation can run on harvested RF power. We develop a printed circuit board (PCB) prototype using discrete components to evaluate activity recognition performance. We show that the prototype can recognize human daily activities with an average error around 6%. Overall, BARNET uses passive tags to achieve the same level of performance as systems that use powered, active radios.

Radio frequency (RF)-powered backscatter communication between passive tags holds tremendous potential as an enabling technology for a ubiquitous “Internet of Things.” We develop a backscattering tag-to-tag network (BTTN), comprised of passive tags capable of large-scale, passive, and multihop communication with each other via backscatter modulation of an external RF excitation signal. The low sensitivity and lack of active demodulator on passive tags present significant challenges to the communication, including a unique phase cancellation problem, which significantly affects the range and robustness of a passive tag-to-tag link. We overcome these challenges using innovative tag architecture and also develop a novel multiphase backscatter modulation technique with a learning mechanism that overcomes the phase cancellation problem. This improves the link performance bringing passive tag-to-tag communication closer to practical use. The additional hardware compared to the conventional radio frequency identification tag architecture includes one more terminating impedance in the modulator. The data rate is reduced due to backscatter at two different phases while the increase in the power consumption is negligible. We develop prototype BTTN tag hardware and firmware and evaluate its performance. The prototype achieves link ranges of up to 3 m at 5 kb/s with an excitation power level of only -20 dBm while successfully overcoming phase cancellation. We further extend BTTN operation to a multihop network where we demonstrate a four hop link capable of communicating over 12 m under similar conditions.

A primary concern of many elders is `solitary death,' being found as a rancid corpse long after they had passed away. In numerous cases, bodies are left unattended for days, months, or even years. These unfortunate cases have increased every year and have become a major social problem in many nations. Current warning systems utilize sensors or smartwatches, which are often costly, ineffective, and uncomfortable. This paper proposes WiSDom, a model-driven solitary death prevention system based on WiFi signals and real-time supervised training. The proposed methodology utilizes WiFi's Channel State Information (CSI) for the primary activity identification estimation, represents the system by a discrete event state transition model, maps the estimated activities into the external events of the model, validates its estimation with the forthcoming events, and labels the validated samples for the supervised training of its clustering algorithm in real-time closed-loop. Through the experimental results, we show that the system effectively warns emergency cases and swiftly detects fatal situations.

Humans see only a tiny region at the center of their visual field with the highest visual acuity, a behavior known as foveation. Visual acuity reduces drastically towards the visual periphery. 'Foveated' video coding/compression techniques exploit this non-uniformity to gain significant efficiency by compressing more in the periphery and less in the center. We propose a practical and scalable method to use such a technique for video streaming service over the Internet. The essential idea is to use a commodity webcam on the user side to provide real-time gaze feedback to the server with the server sending appropriately coded video to the client player. We develop a multi-resolution video coding approach that is scalable in that it is possible to pre-code the video in a small number of copies for a given set of resolutions. The coding approach is designed to match the error performance of an eye tracker built using commodity webcams. We demonstrate that the technique is energy efficient and thus usable in mobile devices. We develop a methodology for performance evaluation of such a system when network budgets may vary and video quality may fluctuate. Finally, we present a comprehensive user study that demonstrates a bandwidth reduction of a factor of 2 for the same user satisfaction.

In this work, we investigate the problem of localizing RFID tags using a ranging method used in frequency-modulated radars. The idea is to exploit the phase change of the tag response due to frequency changes that normally happen as the RFID reader frequency hops. We demonstrate the general feasibility of this technique in ranging standard RFID tags using commodity readers. We then use it for a localization application - localizing shopping carts in supermarket aisles. We show that the ranging and localization accuracies are very good (median errors 5cm and 10cm respectively) even at distances over 4m making the technique competitive with existing techniques that require more complex set up.

Even though people of all social standings use current mobile devices in the wide spectrum of purpose from entertainment tools to communication means, some issues with real-time video streaming in hostile wireless environment still exist. In this article, we introduce CoSA , a link-aware real-time video streaming system for mobile devices. The proposed system utilizes a 3D camera to distinguish the region of importance (ROI) and non-ROI region within the video frame. Based on the link-state feedback from the receiver, the proposed system allocates a higher bandwidth for the region that is classified as ROI and a lower bandwidth for non-ROI in the video stream by reducing the video's bit rate. We implemented CoSA in a real test-bed where the IEEE 802.11 is employed as a medium for wireless networking. Furthermore, we verified the effectiveness of the proposed system by conducting a thorough empirical study. The results indicate that the proposed system enables real-time video streaming while maintaining a consistent visual quality by dynamically reconfiguring video coding parameters according to the link quality.

Programmable wireless devices which can perceive the current radio environment, decide available spectra, and dynamically change the radio access method, and networking protocols have been proposed to improve spectrum usage, interference mitigation, and connectivity. However, these transitions are currently lacking at upper layers. In this paper, we propose an upper layer cognitive system beyond channel sensing effectiveness and spectrum utilization achieved in adjusting PHY and MAC parameter settings. The proposed cognitive video streaming system achieves end-to-end goals at an upper video layer: quality of experience, service continuity, and survivability. Based on the link state of the receiver, the proposed system identifies areas of importance in the image stream, allocates higher bandwidth for the important areas compared to the other areas which is adjusted to use the less bandwidth with CoSA encoder and decoder, and receive the link state feedback from the receiver. To further understand the CoSA's benefit, we implemented two real test-beds for the cognitive video streaming system where the one uses conventional IEEE 802.11 networking technologies and the other employs a software defined radio platform, and performed a performance evaluation study in order to see the effectiveness of the proposed scheme. The results indicate that the proposed system can dynamically change actual data rates according to SINR feedback, which results in at least 180% improvement of transmission time compared to conventional method, while maintaining PSNR range between 30 to 40 dB with eminently reduced data size.

Location-based services (LBSs) are often based on an area or place as opposed to an accurate determination of the precise location. However, current mobile software frameworks are geared towards using specific hardware devices (e.g., GPS or 3G or WiFi interfaces) for as precise localization as possible using that device, often at the cost of a significant energy drain. Further, often the location information is not returned promptly enough. To address this problem, we design a framework for mobile devices, called Geo-fencing. The proposed framework is based on the observation that users move from one place to another and then stay at that place for a while. These places can be, for example, airports, shopping centers, home, offices and so on. Geo-fencing defines such places as geographic areas bounded by polygons. It assumes people simply move from fence to fence and stay inside fences for a while. The framework is coordinated with available communication chips and sensors based on their energy usage and accuracy provided. The essential goal is to determine when users check in or out of fences in an energy effiecient fashion so that appropriate LBS can be triggered. Windows based smartphones are used to prototype Geo-fencing. Validations are conducted with the resulting traces of outdoor and indoor activities of several users for several months. The results show that Geo-fencing provides an effective framework for use with LBSs with a significant energy saving for mobile devices.

In this paper, we propose a distributed, infrastructure-free algorithm for supporting self-localization and location-tracking of portable devices in home networks that do not rely on any positioning infrastructure, such as GPS (Global Positioning System). The proposed algorithm employs the received signal strength (RSS) to estimate the current position of each portable device and then elaborates the position with the box-based sequential Monte Carlo (BSMC) method. Simulation results indicate that the proposed algorithm is superior to the well-received Centroid algorithm in terms of the distance estimation error.