We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Composing Energy Services in a Crowdsourced IoT Environment

Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~2-3 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what can be instantaneously harvested. We provide experimental validation, using both careful measurement studies as well as a controlled study with human participants, to show the viability of a custom-built WiWear-based wearable device, at least in office environments.

WiWear: Wearable Sensing via Directional WiFi Energy Harvesting

Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.

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Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that can handle multiple (even 15) concurrently exercising users; (b) develop a suite of statistical models to detect several commonplace repetition-level mistakes; and (c) experimentally study the efficacy of multiple in-situ corrective feedback strategies. Via an end-to-end evaluation of ERICA with 33 participants naturally performing 3 dumbbell-based exercises, we show that (a) ERICA identifies over 94% of mistakes during the first 5 repetitions of a set, (b) the resulting feedback is viewed favorably by 78% of users, and (c) the feedback is effective, reducing mistakes by 10+% during subsequent repetitions.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6897&context=sis_research

ERICA: enabling real-time mistake detection & corrective feedback for free-weights exercises

Smart devices contain sensitive information that has to be guarded against unauthorized access through authentication. Existing authentication methods become obsolete as they are designed either for logging-in one device at a time or are ineffective in a multi-user multi-device environment. This paper presents TwistIn, a simple gesture that takes a smartwatch as an authentication token for fast access and control of other smart devices. Our mechanism is particularly useful for devices such as smartphones, smart glasses, and small IoT objects. To log in a device, one simply need to pick it up and twist it a few times. Then, by co-analyzing the motion data from the device and the watch, our method can extend the user authentication on the watch to the device. This is a simple and tangible interaction that takes only one to two seconds to perform. Furthermore, to account for user variation in wrist bending, we decompose wrist and forearm rotations via an optimization to improve the method accuracy. We implemented TwistIn, collected thousands of gesture samples, and conducted various experiments to evaluate our prototype system and show that it achieved over 95% detection accuracy.

TwistIn: Tangible Authentication of Smart Devices via Motion Co-analysis with a Smartwatch

Ultra-low power communication is critical for supporting the next generation of battery-operated or energy harvesting battery-less Internet of Things (IoT) devices. Duty cycling protocols and wake-up receiver (WuRx) technologies, and their combinations, have been investigated as energy-efficient mechanisms to support selective, event-driven activation of devices. In this paper, we go one step further and show how WuRx can be used for an efficient and multi-purpose low power downlink (LPD) communication channel. We demonstrate how to (a) extend the wake-up signal to support low-power flexible and extensible unicast, multicast, and broadcast downlink communication and (b) utilize the WuRx-based LPD to also improve the energy efficiency of uplink data transfer. In addition, we show how the non-negligible energy overhead of conventional microcontroller based decoding of LPD communication can be substantially reduced by using the low-power universal asynchronous receiver/transmitter (LPUART) module of modern microcontrollers. Via experimental studies, involving both a functioning prototype and larger-scale simulations, we show that our proposed approach is compatible with conventional WLAN and offers a two-orders-of-magnitude improvement in uplink throughput and energy overheads over a competitive, IEEE 802.11 PSM-based baseline. This new LPD capability can also be used to improve the RF-based energy harvesting efficiency of battery-less IoT devices.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6968&context=sis_research

Low-Power Downlink for the Internet of Things using IEEE 802.11-compliant Wake-Up Receivers

We describe our vision of a multiple mobile or wearable device environment and share our initial exploration of our vision in multi-wrist gesture recognition. We explore how multi-device input and output might look, giving four scenarios of everyday multi-device use that show the technical challenges that need to be addressed. We describe our system which allows for recognition to be distributed between multiple devices, fusing recognition streams on a resource-rich device (e.g., mobile phone). An Interactor layer recognises common gestures from the fusion engine, and provides abstract input streams (e.g., scrolling and zooming) to user interface components called Midgets. These take advantage of multi-device input and output, and are designed to simplify the process of implementing multi-device gestural applications. Our initial exploration of multi-device gestures led us to design a modified pipelined HMM with early elimination of candidate gestures that can recognize gestures in almost 0.2 milliseconds and scales well to large numbers of gestures. Finally, we discuss the open problems in multi-device interaction and our research directions.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4289&context=sis_research

MAGI: Enabling multi-device gestural applications

The paper introduces a novel, holistic approach for robust Screen-Camera Communication (SCC), where video content on a screen is visually encoded in a human-imperceptible fashion and decoded by a camera capturing images of such screen content. We first show that state-of-the-art SCC techniques have two key limitations for in-the-wild deployment: (a) the decoding accuracy drops rapidly under even modest screen extraction errors from the captured images, and (b) they generate perceptible flickers on common refresh rate screens even with minimal modulation of pixel intensity. To overcome these challenges, we introduce DeepLight, a system that incorporates machine learning (ML) models in the decoding pipeline to achieve humanly-imperceptible, moderately high SCC rates under diverse real-world conditions. Deep-Light's key innovation is the design of a Deep Neural Network (DNN) based decoder that collectively decodes all the bits spatially encoded in a display frame, without attempting to precisely isolate the pixels associated with each encoded bit. In addition, DeepLight supports imperceptible encoding by selectively modulating the intensity of only the Blue channel, and provides reasonably accurate screen extraction (IoU values >= 83%) by using state-of-the-art object detection DNN pipelines. We show that a fully functional DeepLight system is able to robustly achieve high decoding accuracy (frame error rate =0.95Kbps) using a human-held smartphone camera, even over larger screen-camera distances (approx =2m).

DeepLight: Robust & Unobtrusive Real-time Screen-Camera Communication for Real-World Displays

We tackle the problem of developing a wearable device that operates indoors on harvested RF energy, but can support gesture tracking applications that require relatively energy-intensive inertial sensors. We propose an infrastructure-assisted energy harvesting paradigm, where a ubiquitously-deployed WiFi infrastructure helps to significantly improve the harvesting power, by employing state-of-the-art techniques for device-free user localization and beamformed, directional packet transmission. Preliminary experimental results suggest that these techniques might be able to support an intelligent, triggered mode of gesture recognition. The results also point to several practical challenges that such an opportunistically beamformed mode of WiFi-based energy harvesting must address.

Vu H. Tran

Papers

WiWear: Wearable Sensing via Directional WiFi Energy Harvesting

Low-Power Downlink for the Internet of Things using IEEE 802.11-compliant Wake-Up Receivers

MAGI: Enabling multi-device gestural applications

DeepLight: Robust & Unobtrusive Real-time Screen-Camera Communication for Real-World Displays

Can WiFi Beamforming Support an Energy-Harvesting Wearable?