The solid-state lighting is revolutionizing the indoor illumination. Current incandescent and fluorescent lamps are being replaced by the LEDs at a rapid pace. Apart from extremely high energy efficiency, the LEDs have other advantages such as longer lifespan, lower heat generation, and improved color rendering without using harmful chemicals. One additional benefit of LEDs is that they are capable of switching to different light intensity at a very fast rate. This functionality has given rise to a novel communication technology (known as visible light communication—VLC) where LED luminaires can be used for high speed data transfer. This survey provides a technology overview and review of existing literature of visible light communication and sensing. This paper provides a detailed survey of 1) visible light communication system and characteristics of its various components such as transmitter and receiver; 2) physical layer properties of visible light communication channel, modulation methods, and MIMO techniques; 3) medium access techniques; 4) system design and programmable platforms; and 5) visible light sensing and application such as indoor localization, gesture recognition, screen-camera communication, and vehicular networking. We also outline important challenges that need to be addressed in order to design high-speed mobile networks using visible light communication.

Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges

The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

/pdf/internet-of-things-architectures-protocols-and-applications-46rpfbd58i.pdf

Internet of Things: Architectures, Protocols, and Applications

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.

/pdf/studentlife-assessing-mental-health-academic-performance-and-1k0o1twvs5.pdf

StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC-DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power management circuits.

Micropower energy harvesting

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

/pdf/digital-twin-values-challenges-and-enablers-from-a-modeling-3hpoelaogn.pdf

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

How we feel is greatly influenced by how well we sleep Emerging quantified-self apps and wearable devices allow people to measure and keep track of sleep duration, patterns and quality However, these approaches are intrusive, placing a burden on the users to modify their daily sleep related habits in order to gain sleep data; for example, users have to wear cumbersome devices (eg, a headband) or inform the app when they go to sleep and wake up In this paper, we present a radically different approach for measuring sleep duration based on a novel best effort sleep (BES) model BES infers sleep using smartphones in a completely unobtrusive way -- that is, the user is completely removed from the monitoring process and does not interact with the phone beyond normal user behavior A sensor-based inference algorithm predicts sleep duration by exploiting a collection of soft hints that tie sleep duration to various smartphone usage patterns (eg, the time and length of smartphone usage or recharge events) and environmental observations (eg, prolonged silence and darkness) We perform quantitative and qualitative comparisons between two smartphone only approaches that we developed (ie, BES model and a sleep-with-the-phone approach) and two popular commercial wearable systems (ie, the Zeo headband and Jawbone wristband) Results from our one-week 8-person study look very promising and show that the BES model can accurately infer sleep duration (± 42 minutes) using a completely "hands off" approach that can cope with the natural variation in users' sleep routines and environments

/pdf/unobtrusive-sleep-monitoring-using-smartphones-31tfrw4f5j.pdf

Unobtrusive sleep monitoring using smartphones

We present LiSense, the first-of-its-kind system that enables both data communication and fine-grained, real-time human skeleton reconstruction using Visible Light Communication (VLC). LiSense uses shadows created by the human body from blocked light and reconstructs 3D human skeleton postures in real time. We overcome two key challenges to realize shadow-based human sensing. First, multiple lights on the ceiling lead to diminished and complex shadow patterns on the floor. We design light beacons enabled by VLC to separate light rays from different light sources and recover the shadow pattern cast by each individual light. Second, we design an efficient inference algorithm to reconstruct user postures using 2D shadow information with a limited resolution collected by photodiodes embedded in the floor. We build a 3 m x 3 m LiSense testbed using off-the-shelf LEDs and photodiodes. Experiments show that LiSense reconstructs the 3D user skeleton at 60 Hz in real time with 10 degrees mean angular error for five body joints.

/pdf/human-sensing-using-visible-light-communication-3fg5d21gsu.pdf

Human Sensing Using Visible Light Communication

We present StarLight, an infrastructure-based sensing system that reuses light emitted from ceiling LED panels to reconstruct fine-grained user skeleton postures continuously in real time. It relies on only a few (e.g., 20) photodiodes placed at optimized locations to passively capture low-level visual clues (light blockage information), with neither cameras capturing sensitive images, nor on-body devices, nor electromagnetic interference. It then aggregates the blockage information of a large number of light rays from LED panels and identifies best-fit 3D skeleton postures. StarLight greatly advances the prior light-based sensing design by dramatically reducing the number of intrusive sensors, overcoming furniture blockage, and supporting user mobility. We build and deploy StarLight in a 3.6 m x 4.8 m office room, with customized 20 LED panels and 20 photodiodes. Experiments show that StarLight achieves 13.6 degree mean angular error for five body joints and reconstructs a mobile skeleton at a high frame rate (40 FPS). StarLight enables a new unobtrusive sensing paradigm to augment today's mobile sensing for continuous and accurate behavioral monitoring.

https://dl.acm.org/doi/pdf/10.1145/2906388.2906401

Practical Human Sensing in the Light

We present HiLight, a new form of real-time screen-camera communication without showing any coded images (e.g., barcodes) for off-the-shelf smart devices. HiLight encodes data into pixel translucency change atop any screen content, so that camera-equipped devices can fetch the data by turning their cameras to the screen. HiLight leverages the alpha channel, a well-known concept in computer graphics, to encode bits into the pixel translucency change. By removing the need to directly modify pixel RGB values, HiLight overcomes the key bottleneck of existing designs and enables real-time unobtrusive communication while supporting any screen content. We build a HiLight prototype using off-the-shelf smart devices and demonstrate its efficacy and robustness in practical settings. By offering an unobtrusive, flexible, and lightweight communication channel between screens and cameras, HiLight opens up opportunities for new HCI and context-aware applications, e.g., smart glasses communicating with screens to realize augmented reality.

/pdf/demo-real-time-screen-camera-communication-behind-any-scene-eowkzgjpeb.pdf

Tianxing Li

Papers

StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

Unobtrusive sleep monitoring using smartphones

Human Sensing Using Visible Light Communication

Practical Human Sensing in the Light

Demo: Real-Time Screen-Camera Communication Behind Any Scene