Jeremy Gummeson

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a systems demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a systems ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each systems ability to satisfy its demands compared with existing prediction strategies.

On the limits of effective hybrid micro-energy harvesting on mobile CRFID sensors

Mobile sensing is difficult without power. Emerging Computational RFIDs (CRFIDs) provide both sensing and general-purpose computation without batteries--instead relying on small capacitors charged by energy harvesting. CRFIDs have small form factors and consume less energy than traditional sensor motes. However, CRFIDs have yet to see widespread use because of limited autonomy and the propensity for frequent power loss as a result of the necessarily small capacitors that serve as a microcontrollers power supply. Our results show that hybrid harvesting CRFIDs, which use an ambient energy micro-harvester, can complete a variety of useful workloads--even in an environment with little ambient energy available. Our contributions include (1) benchmarks demonstrating that micro-harvesting from ambient energy sources enables greater range and read rate, as well as autonomous operation by hybrid CRFIDs, (2) a measurement study that stresses the limits of effective ambient energy harvesting for diverse workloads, (3) application studies that demonstrate the benefits of hybrid CRFIDs, and (4) a trace-driven simulator to model and evaluate the expected behavior of a CRFID with different capacitor sizes and operating under varying conditions of mobility and solar energy harvesting. Our results show that ambient harvesting can triple the effective communication range of a CRFID, quadruple the read rate, and achieve 95% uptime in RAM retention mode despite long periods of low light.

Flit: a bulk transmission protocol for RFID-scale sensors

RFID-scale sensors present a new frontier for distributed sensing. In contrast to existing sensor deployments that rely on battery-powered sensors, RFID-scale sensors rely solely on harvested energy. These devices sense and store data when not in contact with a reader, and use backscatter communication to upload data when a reader is in range. Unlike conventional RFID tags that only transmit identifiers, RFID sensors need to transfer potentially large amounts of data to a reader during each contact event. In this paper, we propose several optimizations to the RFID network stack to support efficient bulk transfer while remaining compatible with existing Gen 2 readers. Our key contribution is the design of a coordinated bulk transfer protocol for RFID-scale sensors that maximizes channel utilization and minimizes energy lost due to idle listening while also minimizing collisions. We present an implementation of the protocol for the Intel WISP, and describe several parameters that are tuned using empirical measurements that characterize the wireless channel. Our results show that the burst protocol improves goodput in comparison to vanilla EPC Gen 2 tags, improves energy-efficiency, allows multiple RFID sensors to share the channel, and also coexists with passive, non-sensor tags.

An energy harvesting wearable ring platform for gestureinput on surfaces

This paper presents a remote gesture input solution for interacting indirectly with user interfaces on mobile and wearable devices. The proposed solution uses a wearable ring platform worn on users index finger. The ring detects and interprets various gestures performed on any available surface, and wirelessly transmits the gestures to the remote device. The ring opportunistically harvests energy from an NFC-enabled phone for perpetual operation without explicit charging. We use a finger-tendon pressure-based solution to detect touch, and a light-weight audio based solution for detecting finger motion on a surface. The two level energy efficient classification algorithms identify 23 unique gestures that include tapping, swipes, scrolling, and strokes for hand written text entry. The classification algorithms have an average accuracy of 73% with no explicit user training. Our implementation supports 10 hours of interactions on a surface at 2 Hz gesture frequency. The prototype was built with off-the-shelf components has a size similar to a large ring.

Wirelessly powered bistable display tags

Paper displays have a number of attractive properties, in particular the ability to present visual information perpetually with no power source. However, they are not digitally updatable or re-usable. Bistable display materials, such as e-paper, promise to enable displays with the best properties of both paper and electronic displays. However, rewriting a pixelated bistable display requires substantial energy, both for communication and for setting the pixel states. This paper describes a bistable display tag that, from an energy standpoint, is capable of perpetual operation. A commercial off-the-shelf NFC-enabled phone generates RF signals carrying both the information and energy necessary to update the display. After the update is complete, the display continues to present the information with no further power input. We present one example implementation, a companion display for a mobile phone that can be used to capture and preserve a screenshot. We also discuss other potential applications of energy neutral bistable display tags.

An Adaptive Link Layer for Range Diversity in Multi-Radio Mobile Sensor Networks

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogeneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system.

An adaptive link layer for heterogeneous multi-radio mobile sensor networks

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distanceYC while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range and interference diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system while handling node mobility. Our results also show that our system can handle short, medium and long-term wireless interference in such environments.

SRCP: Simple Remote Control for Perpetual High-Power Sensor Networks

Remote management is essential for wireless sensor networks (WSNs) designed to run perpetually using harvested energy. A natural division of function for managing WSNs is to employ both an in-band data plane to sense, store, process, and forward data, and an out-of-band management plane to remotely control each node and its sensors. This paper presents SRCP , a Simple Remote Control Protocol that forms the core of an out-of-band management plane for WSNs. SRCP is motivated by our target environment: a perpetual deployment of high-power, aggressively duty-cycled nodes capable of handling high-bandwidth sensor data from multiple sensors. The protocol runs on low-power always-on control processors using harvested energy, distills an essential set of primitives, and uses them to control a suite of existing management functions on more powerful main nodes. We demonstrate SRCPs utility by presenting a case study that (i) uses it to control a broad spectrum of management functions and (ii) quantifies its efficacy and performance.

Demo: NFC-based sensor data caching

Near Field Communications (NFC) is an emerging technology that conveniently establishes radio communication by bringing two entities in close proximity of one another. Many use cases for devices equipped with this technology have been proposed ranging from payment systems to convenient data exchange. In this demo, we implement a prototype NFC-based sensor device that periodically writes sensor data into a non-volatile memory; this data may be read out at a later time with an NFC-equipped smartphone or other device. The sensor features an ambient energy harvesting unit that allows sensing operations while disconnected from the phone; the phone is used as a supplemental harvesting source in addition to a way to offload collected sensor data.

Fabric as a Sensor: Towards Unobtrusive Sensing of Human Behavior with Triboelectric Textiles

Smart apparel with embedded sensors have the potential to revolutionize human behavior sensing by leveraging everyday clothing as the sensing substrate. However, existing textile-based sensing techniques rely on tight-fitting garments to obtain sufficient signal to noise, making it uncomfortable to wear and limiting the technology to niche applications like athletic performance monitoring. Our solution leverages functionalized fabric to measure the triboelectric charges induced by folding and compression of the textile itself, making it a more natural fit for everyday clothing. However, the large sensing surface of a functionalized textile also increases body-coupled noise and motion artifacts, and introduces new challenges in how we suppress noise to detect the weak triboelectric signal. We address these challenges using a combination of textile, electronics, and signal analysis-based innovations, and robustly sense joint motions by improving SNR and extracting highly discriminative features from the signal. Additionally, we demonstrate how the same sensor can be used to measure relative changes in skin moisture levels induced by sweating. Our design uses a simple-to-manufacture layered architecture that can be incorporated into any conventional, loosely worn textile. We show that the sensor has high performance in natural conditions by benchmarking the accuracy of sensing several kinematic metrics as well as sweat level. Additionally, we provide real-world performance evaluations across three application case studies including activity classification, perspiration measurements during exercise, and comfort level detection for HVAC systems.