Intermittent Computing: Challenges and Opportunities

The maturation of energy-harvesting technology and ultra-low-power computer systems has led to the advent of intermittently-powered, batteryless devices that operate entirely using energy extracted from their environment. Intermittently operating devices present a rich vein of programming languages research challenges and the purpose of this paper is to illustrate these challenges to the PL research community. To provide depth, this paper includes a survey of the hardware and software design space of intermittent computing platforms. On the foundation of these research challenges and the state of the art in intermittent hardware and software, this paper describes several future PL research directions, emphasizing a connection between intermittence, distributed computing, energy-aware programming and compilation, and approximate computing. We illustrate these connections with a discussion of our ongoing work on programming for intermittence, and on building and simulating intermittent distributed systems

Energy managed reporting for wireless sensor networks

In this paper, we propose a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its individual network involvement based on its own energy resources and the information contained in each packet. The information content is ascertained through a system of rules describing prospective events in the sensed environment, and how important such events are. While the packets deemed most important are propagated by all sensor nodes, low importance packets are handled by only the nodes with high energy reserves. Results obtained from simulations depicting a wireless sensor network used to monitor pump temperature in an industrial environment have shown that a considerable increase in the network lifetime and network connectivity can be obtained. The results also show that when coupled with a form of energy harvesting, our technique can enable perpetual network operatio

Optimal Checkpointing for Secure Intermittently-Powered IoT Devices

Energy harvesting is a promising solution to power Internet of Things (IoT) devices. Due to the intermittent nature of these energy sources, one cannot guarantee forward progress of program execution. Prior work has advocated for checkpointing the intermediate state to off-chip non-volatile memory (NVM). Encrypting checkpoints addresses the security concern, but significantly increases the checkpointing overheads. In this paper, we propose a new online checkpointing policy that judiciously determines when to checkpoint so as to minimize application time to completion while guaranteeing security. Compared to state-of-the-art checkpointing schemes that do not account for the overheads of encrypted checkpoints we improve execution time up to 1.4x.Comment: ICCAD 201

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

Accurate Power Consumption Evaluation forPeripherals in Ultra Low-Power embedded systems

International audienceWe propose a methodology to measure, model and simulate power consumption of peripheral devices of a lowpower embedded micro-controller, while keeping a reasonable development cost. This methodology is experimented against the low-power MSP-EXP430FR5739 platform that includes nonvolatile RAM for intermittent computing purposes and a handful of peripherals. The experimental measurements enable the characterization of the consumption of the peripherals, while many existing comparable studies do not provide power consumption for peripherals. These measurements are integrated into a simulator that targets low-power peripheral-intensive applications, as are most of IoT embedded programs. The accuracy of the power consumption estimation is within a 5% error on intermittent embedded computing using peripherals

ETAP: Energy-Aware Timing Analysis of Intermittent Programs

Energy harvesting battery-free embedded devices rely only on ambient energy harvesting that enables stand-alone and sustainable IoT applications. These devices execute programs when the harvested ambient energy in their energy reservoir is sufficient to operate and stop execution abruptly (and start charging) otherwise. These intermittent programs have varying timing behavior under different energy conditions, hardware configurations, and program structures. This article presents Energy-aware Timing Analysis of intermittent Programs (ETAP), a probabilistic symbolic execution approach that analyzes the timing and energy behavior of intermittent programs at compile time. ETAP symbolically executes the given program while taking time and energy cost models for ambient energy and dynamic energy consumption into account. We evaluate ETAP by comparing the compile-time analysis results of our benchmark codes and real-world application with the results of their executions on real hardware. Our evaluation shows that ETAP’s prediction error rate is between 0.0076% and 10.8%, and it speeds up the timing analysis by at least two orders of magnitude compared to manual testing.acceptedVersio

Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling. However, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service. In this paper, we present Eco, a hardware-software co-design enabling generic energy management on IoT nodes. Eco is tailored to devices with limited resources and thus targets most of the upcoming IoT scenarios. The proposed measurement module combines commodity components with a common system interfaces to achieve easy, flexible integration with various hardware platforms and the RIOT IoT operating system. We thoroughly evaluate and compare accuracy and overhead. Our findings indicate that our commodity design competes well with highly optimized solutions, while being significantly more versatile. We employ Eco for energy management on RIOT and validate its readiness for deployment in a five-week field trial integrated with energy harvesting

Energy-efficiency improvements for optical access

This article discusses novel approaches to improve energy efficiency of different optical access technologies, including time division multiplexing passive optical network (TDM-PON), time and wavelength division multiplexing PON (TWDM-PON), point-to-point (PTP) access network, wavelength division multiplexing PON (WDM-PON), and orthogonal frequency division multiple access PON (OFDMA-PON). These approaches include cyclic sleep mode, energy-efficient bit interleaving protocol, power reduction at component level, or frequency band selection. Depending on the target optical access technology, one or a combination of different approaches can be applied

Energy neutral operation of vibration energy-harvesting sensor networks for bridge applications

greatly benefit from the use of wireless sensor networks (WSNs), however energy harvesting for the operation of the network remains a challenge in this setting. While solar and wind power are possible and credible solutions to energy generation, the need for positioning sensor nodes in shaded and sheltered locations, e.g., under a bridge deck, is also often precluding their adoption in real-world deployments. In some scenarios vibration energy harvesting has been shown as an effective solution, instead. This paper presents a multihop vibration energy-harvesting WSN system for bridge applications. The system relies on an ultra-low power wireless sensor node, driven by a novel vibration based energy-harvesting technology. We use a receiver-initiated routing protocol to enable energy-efficient and reliable connectivity between nodes with different energy charging capabilities. By combining real vibration data with an experimentally validated model of the vibration energy harvester, a hardware model, and the COOJA simulator, we develop a framework to conduct realistic and repeatable experiments to evaluate the system before on-site deployment. Simulation results show that the system is able to maintain energy neutral operation, preserving energy with careful management of sleep and communication times. We also validate the system through a laboratory experiment on real hardware against real vibration data collected from a bridge. Besides providing general guidelines and considerations for the development of vibration energy-harvesting systems for bridge applications, this work highlights the limitations of the energy budget made available by traffic-induced vibrations, which clearly shrink the applicability of vibration energy-harvesting technology for WSNs to applications that do not generate an overwhelming amounts of data

Practical opportunistic data collection in wireless sensor networks with mobile sinks

Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost of deployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly in practice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on lowdelay and high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobile sinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused by both packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP and the IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETXbased routing protocols for static WSNs can be easily extended to WSN-MSs with minimal modification by using CA-ETX. Further, by combing CA-ETX with the dynamic backpressure routing, we present a throughput-optimal scheme Opportunistic Backpressure Collection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bed experiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms of packet transmission delay, communication overhead, storage overheads, reliability, and scalability