Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization

Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This paper presents a self-sustaining and accurate ultra-wideband-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of ultra-wideband transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of $47~nA$ and operates at $1.2~\mu A$ while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of $40~cm$ (2D) in realistic non-line-of-sight conditions, is $10.84~mJ$. In an asset tracking case study within a $200~m^2$ multi-room office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over $95~{\%}$. The system`s long-time self-sustainability has been analyzed over $700~days$ in multiple indoor lighting situations

Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner

Analog Signal Buffering and Reconstruction

Wireless sensor networks (WSNs) are capable of a myriad of tasks, from monitoring critical infrastructure such as bridges to monitoring a person\u27s vital signs in biomedical applications. However, their deployment is impractical for many applications due to their limited power budget. Sleep states are one method used to conserve power in resource-constrained systems, but they necessitate a wake-up circuit for detecting unpredictable events. In conventional wake-up-based systems, all information preceding a wake-up event will be forfeited. To avoid this data loss, it is necessary to include a buffer that can record prelude information without sacrificing the power savings garnered by the active use of sleep states.;Unfortunately, traditional memory buffer systems utilize digital electronics which are costly in terms of power. Instead of operating in the target signal\u27s native analog environment, a digital buffer must first expend a great deal of energy to convert the signal into a digital signal. This issue is further compounded by the use of traditional Nyquist sampling which does not adapt to the characteristics of a dynamically changing signal. These characteristics reveal why a digital buffer is not an appropriate choice for a WSN or other resource-constrained system.;This thesis documents the development of an analog pre-processing block that buffers an incoming signal using a new method of sampling. This method requires sampling only local maxima and minima (both amplitude and time), effectively approximating the instantaneous Nyquist rate throughout a time-varying signal. The use of this sampling method along with ultra-low-power analog electronics enables the entire system to operate in the muW power levels. In addition to these power saving techniques, a reconfigurable architecture will be explored as infrastructure for this system. This reconfigurable architecture will also be leveraged to explore wake-up circuits that can be used in parallel with the buffer system

Ultra-Low-Power Sensors and Receivers for IoT Applications

The combination of ultra-low power analog front-ends and CMOS-compatible transducers enable new applications, such as environmental monitors, household appliances, health trackers, etc. that are seamlessly integrated into our daily lives. Furthermore, wireless connectivity allows many of these sensors to operate both independently and collectively. These techniques collectively fulfil the recent surge of internet-of-things (IoT) applications that have the potential to fundamentally change daily life for millions of people.In this dissertation, the circuit and system design of wireless receivers and sensors is presented that explores the challenges of implementing long lifespan, high accuracy, and large coverage range IoT sensor networks. The first is a wake-up receiver (WuRX), which continuously monitors the RF environment to wake up a higher-power radio upon detection of a predetermined RF signature. This work both improves sensitivity and reduces power over prior art through a multi-faceted design featuring an impedance transformation network with large passive voltage gain, an active envelope detector with high input impedance to facilitate large passive voltage gain, a low-power precision comparator, and a low-leakage digital baseband correlator.Although pushing the prior WuRX performance boundary by orders of magnitude, the first work shows moderate sensitivity, inferior temperature robustness, and large area with external lumped components. Thus, the second work shows a miniaturized WuRX that is temperature-compensated, yet still consumes only nano-watt power and millimeter area while operating at 9 GHz. To further reduce the area, a global common-mode feedback is utilized across the envelope detector and baseband amplifier that eliminates the need for off-chip ac-coupling components. Multiple temperature-compensation techniques are proposed to maintain constant bandwidth of the signal path and constant clock frequency. Both WuRXs operate at 0.4 V supply, consume near-zero power and achieve ~-70 dBm sensitivity.Lastly, the first reported CMOS 2-in-1 relative humidity and temperature sensor is presented. A unified analog front-end interfaces on-chip transducers and converts the inputs into a frequency vis a high-linearity frequency-locked loop. An incomplete-settling switched-capacitor-based Wheatstone bridge is proposed to sense the inputs in a power-efficient fashion

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

IEEE 802.11-Enabled Wake-Up Radio: use cases and applications

IEEE 802.11 is one of the most commonly used radio access technologies, being present in almost all handheld devices with networking capabilities. However, its energy-hungry communication modes are a challenge for the increased battery lifetime of such devices and are an obstacle for its use in battery-constrained devices such as the ones defined by many Internet of Things applications. Wake-up Radio (WuR) systems have appeared as a solution for increasing the energy efficiency of communication technologies by employing a secondary low-power radio interface, which is always in the active state and switches the primary transceiver (used for main data communication) from the energy-saving to the active operation mode. The high market penetration of IEEE 802.11 technology, together with the benefits that WuR systems can bring to this widespread technology, motivates this article’s focus on IEEE 802.11-basedWuR solutions. More specifically, we elaborate on the feasibility of such IEEE 802.11-based WuR solutions, and introduce the latest standardization efforts in this IEEE 802.11-based WuR domain, IEEE 802.11ba, which is a forthcoming IEEE 802.11 amendment, discussing its main features and potential use cases. As a use case consisting of green Wi-Fi application, we provide a proof-of-concept smart plug system implemented by a WuR that is activated remotely using IEEE 802.11 devices, evaluate its monetary and energy savings, and compare it with commercially available smart plug solutions. Finally, we discuss novel applications beyond the wake-up functionality that IEEE 802.11-enabled WuR devices can offer using a secondary radio, as well as applications that have not yet been considered by IEEE 802.11ba. As a result, we argue that the IEEE 802.11-based WuR solution will support a wide range of devices and deployments, for both low-rate and low-power communications, as well as high-rate transmissions.Postprint (author's final draft

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa