Jeremy Gummeson

Bio: Jeremy Gummeson is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Wearable computer & Computer science. The author has an hindex of 17, co-authored 53 publications receiving 1058 citations. Previous affiliations of Jeremy Gummeson include Microsoft & Hewlett-Packard.

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Abstract: 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 system's 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 system's 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 system's ability to satisfy its demands compared with existing prediction strategies.

BLINK: a high throughput link layer for backscatter communication

Abstract: Backscatter communication offers an ultra-low power alternative to active radios in urban sensing deployments - communication is powered by a reader, thereby making it virtually "free". While backscatter communication has largely been used for extremely small amounts of data transfer (e.g. a 12 byte EPC identifier from an RFID tag), sensors need to use backscatter for continuous and high-volume sensor data transfer. To address this need, we describe a novel link layer that exploits unique characteristics of backscatter communication to optimize throughput. Our system offers several optimizations including 1) understanding of multi-path self-interference characteristics and link metrics that capture these characteristics, 2) design of novel mobility-aware probing techniques that use backscatter link signatures to determine when to probe the channel, 3) bitrate selection algorithms that use link metrics to determine the optimal bitrate, and 4) channel selection mechanism that optimize throughput while remaining compliant within FCC regulations. Our results show upto 3x increase in goodput over other mechanisms across a wide range of channel conditions, scales, and mobility scenarios.

TypingRing: A Wearable Ring Platform for Text Input

Abstract: This paper presents TypingRing, a wearable ring platform that enables text input into computers of different forms, such as PCs, smartphones, tablets, or even wearables with tiny screens. The basic idea of TypingRing is to have a user wear a ring on his middle finger and let him type on a surface - such as a table, a wall, or his lap. The user types as if a standard QWERTY keyboard is lying underneath his hand but is invisible to him. By using the embedded sensors TypingRing determines what key is pressed by the user. Further, the platform provides visual feedback to the user and communicates with the computing device wirelessly. This paper describes the hardware and software prototype of TypingRing and provides an in-depth evaluation of the platform. Our evaluation shows that TypingRing is capable of detecting and sending key events in real-time with an average accuracy of 98.67%. In a field study, we let seven users type a paragraph with the ring, and we find that TypingRing yields a reasonable typing speed (e.g., 33-50 keys per minute) and their typing speed improves over time.

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

Abstract: 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 microcontroller's 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

Abstract: 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.

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Abstract: Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: (1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and (2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual “more data needs more power and emission of radio waves” status quo, and motivate that future wireless networks necessitate a smart radio environment: a transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as reconfigurable intelligent meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. We will discuss, in particular, two major types of reconfigurable intelligent meta-surfaces applied to wireless networks. The first type of meta-surfaces will be embedded into, e.g., walls, and will be directly controlled by the wireless network operators via a software controller in order to shape the radio waves for, e.g., improving the network coverage. The second type of meta-surfaces will be embedded into objects, e.g., smart t-shirts with sensors for health monitoring, and will backscatter the radio waves generated by cellular base stations in order to report their sensed data to mobile phones. These functionalities will enable wireless network operators to offer new services without the emission of additional radio waves, but by recycling those already existing for other purposes. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of reconfigurable intelligent meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead

Abstract: Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon’s mathematical theory of communication with G. Green’s and J. C. Maxwell’s mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.

Energy harvesting in wireless sensor networks: A comprehensive review

Abstract: Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Micropower energy harvesting

Abstract: 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.