FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1

IoTBeholder: A Privacy Snooping Attack on User Habitual Behaviors from Smart Home Wi-Fi Traffic

With the deployment of a growing number of smart home IoT devices, privacy leakage has become a growing concern. Prior work on privacy-invasive device localization, classification, and activity identification have proven the existence of various privacy leakage risks in smart home environments. However, they only demonstrate limited threats in real world due to many impractical assumptions, such as having privileged access to the user's home network. In this paper, we identify a new end-to-end attack surface using IoTBeholder, a system that performs device localization, classification, and user activity identification. IoTBeholder can be easily run and replicated on commercial off-the-shelf (COTS) devices such as mobile phones or personal computers, enabling attackers to infer user's habitual behaviors from smart home Wi-Fi traffic alone. We set up a testbed with 23 IoT devices for evaluation in the real world. The result shows that IoTBeholder has good device classification and device activity identification performance. In addition, IoTBeholder can infer the users' habitual behaviors and automation rules with high accuracy and interpretability. It can even accurately predict the users' future actions, highlighting a significant threat to user privacy that IoT vendors and users should highly concern

Emma: An accurate, efficient, and multi-modality strategy for autonomous vehicle angle prediction

Autonomous driving and self-driving vehicles have become the most popular selection for customers for their convenience. Vehicle angle prediction is one of the most prevalent topics in the autonomous driving industry, that is, realizing real-time vehicle angle prediction. However, existing methods of vehicle angle prediction utilize only single-modal data to achieve model prediction, such as images captured by the camera, which limits the performance and efficiency of the prediction system. In this paper, we present Emma, a novel vehicle angle prediction strategy that achieves multi-modal prediction and is more efficient. Specifically, Emma exploits both images and inertial measurement unit (IMU) signals with a fusion network for multi-modal data fusion and vehicle angle prediction. Moreover, we design and implement a few-shot learning module in Emma for fast domain adaptation to varied scenarios (e.g., different vehicle models). Evaluation results demonstrate that Emma achieves overall 97.5% accuracy in predicting three vehicle angle parameters (yaw, pitch, and roll), which outperforms traditional single-modalities by approximately 16.7%–36.8%. Additionally, the few-shot learning module presents promising adaptive ability and shows overall 79.8% and 88.3% accuracy in 5-shot and 10-shot settings, respectively. Finally, empirical results show that Emma reduces energy consumption by 39.7% when running on the Arduino UNO board

A Miniscule Survey on Blockchain Scalability

With the rise of cryptocurrency and NFTs in the past decade, blockchain technology has been an area of increasing interest to both industry and academic experts. In this paper, we discuss the feasibility of such systems through the lens of scalability. We also briefly dive into the security issues of such systems, as well as some applications, including healthcare, supply chain, and government applications

Passive RFID-based Intelligent Gloves for Alternative and Assistive Communication - A Preliminary Study

We introduce intelligent gloves based on passive ultrahigh frequency (UHF) radio frequency identification (RFID) technology, which comprises of four antenna parts and three RFID integrated circuits (ICs). Each of the ICs (in middle finger, ring finger and small finger) have their unique IDs, which can be activated by gentle touch of thumb, and used to send a specific message, which is displayed on a computer screen. Two users tested the gloves in an office environment with M6 mercury RFID reader and a specially developed software. The achieved success rate in these preliminary tests was 100 %. We consider these results promising first steps for future wearable passive RFID-based augmentative and alternative communication (AAC) solutions.acceptedVersionPeer reviewe

Recent Advances in Wearable Sensing Technologies

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future

Localino T-shirt: The Real-time Indoor Localization in Ambient Assisted Living Applications

In the last decade, smart textiles have become very popular as a concept and have found use in many applications, such as military, electronics, automotive, and medical ones. In the medical area, smart textiles research is focused more on biomonitoring, telemedicine, rehabilitation, sport medicine or home healthcare systems. In this research, the development and localization accuracy measurements of a smart T-shirt are presented, which will be used by elderly people for indoor localization in ambient assisted living applications. The proposed smart T-shirt and the work presented is considered to be applicable in cases of elderly, toddlers or even adults in indoor environments where their continuous real-time localization is critical. This smart T-shirt integrates a localization sensor, namely the Localino sensor, together with a solar panel for energy harvesting when the user is moving outdoors, as well as a battery/power bank that is both connected to the solar panel and the Localino sensor for charging and power supply respectively. Moreover, a mock-up house was deployed, where the Localino platform anchors were deployed at strategic points within the house area. Localino sensor nodes were installed in all the house rooms, from which we obtained the localization accuracy measurements. Furthermore, the localization accuracy was also measured for a selected number of mobile user scenarios, in order to assess the platform accuracy in both static and mobile user cases. Details about the implementation of the T-shirt, the selection and integration of the electronics parts, and the mock-up house, as well as about the localization accuracy measurements results are presented in the paper

From Capture to Display: A Survey on Volumetric Video

Volumetric video, which offers immersive viewing experiences, is gaining increasing prominence. With its six degrees of freedom, it provides viewers with greater immersion and interactivity compared to traditional videos. Despite their potential, volumetric video services poses significant challenges. This survey conducts a comprehensive review of the existing literature on volumetric video. We firstly provide a general framework of volumetric video services, followed by a discussion on prerequisites for volumetric video, encompassing representations, open datasets, and quality assessment metrics. Then we delve into the current methodologies for each stage of the volumetric video service pipeline, detailing capturing, compression, transmission, rendering, and display techniques. Lastly, we explore various applications enabled by this pioneering technology and we present an array of research challenges and opportunities in the domain of volumetric video services. This survey aspires to provide a holistic understanding of this burgeoning field and shed light on potential future research trajectories, aiming to bring the vision of volumetric video to fruition.Comment: Submitte