713 research outputs found
Methods and Tools for Battery-free Wireless Networks
Embedding small wireless sensors into the environment allows for monitoring physical processes with high spatio-temporal resolutions. Today, these devices are equipped with a battery to supply them with power. Despite technological advances, the high maintenance cost and environmental impact of batteries prevent the widespread adoption of wireless sensors. Battery-free devices that store energy harvested from light, vibrations, and other ambient sources in a capacitor promise to overcome the drawbacks of (rechargeable) batteries, such as bulkiness, wear-out and toxicity. Because of low energy input and low storage capacity, battery-free devices operate intermittently; they are forced to remain inactive for most of the time charging their capacitor before being able to operate for a short time. While it is known how to deal with intermittency on a single device, the coordination and communication among groups of multiple battery-free devices remain largely unexplored. For the first time, the present thesis addresses this problem by proposing new methods and tools to investigate and overcome several fundamental challenges
Routing for Intermittently-Powered Sensing Systems
Recently, intermittent computing (IC) has received tremendous attention due
to its high potential in perpetual sensing for Internet-of-Things (IoT). By
harvesting ambient energy, battery-free devices can perform sensing
intermittently without maintenance, thus significantly improving IoT
sustainability. To build a practical intermittently-powered sensing system,
efficient routing across battery-free devices for data delivery is essential.
However, the intermittency of these devices brings new challenges, rendering
existing routing protocols inapplicable.
In this paper, we propose RICS, the first-of-its-kind routing scheme tailored
for intermittently-powered sensing systems. RICS features two major designs,
with the goal of achieving low-latency data delivery on a network built with
battery-free devices. First, RICS incorporates a fast topology construction
protocol for each IC node to establish a path towards the sink node with the
least hop count. Second, RICS employs a low-latency message forwarding
protocol, which incorporates an efficient synchronization mechanism and a novel
technique called pendulum-sync to avoid the time-consuming repeated node
synchronization. Our evaluation based on an implementation in OMNeT++ and
comprehensive experiments with varying system settings show that RICS can
achieve orders of magnitude latency reduction in data delivery compared with
the baselines
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
Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks
The battery-less Internet of Things (IoT) devices are a key element in the
sustainable green initiative for the next-generation wireless networks. These
battery-free devices use the ambient energy, harvested from the environment.
The energy harvesting environment is dynamic and causes intermittent task
execution. The harvested energy is stored in small capacitors and it is
challenging to assure the application task execution. The main goal is to
provide a mechanism to aggregate the sensor data and provide a sustainable
application support in the distributed battery-less IoT network. We model the
distributed IoT network system consisting of many battery-free IoT sensor
hardware modules and heterogeneous IoT applications that are being supported in
the device-edge-cloud continuum. The applications require sensor data from a
distributed set of battery-less hardware modules and there is provision of
joint control over the module actuators. We propose an application-aware task
and energy manager (ATEM) for the IoT devices and a vector-synchronization
based data aggregator (VSDA). The ATEM is supported by device-level federated
energy harvesting and system-level energy-aware heterogeneous application
management. In our proposed framework the data aggregator forecasts the
available power from the ambient energy harvester using long-short-term-memory
(LSTM) model and sets the device profile as well as the application task rates
accordingly. Our proposed scheme meets the heterogeneous application
requirements with negligible overhead; reduces the data loss and packet delay;
increases the hardware component availability; and makes the components
available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure
HoPP: Robust and Resilient Publish-Subscribe for an Information-Centric Internet of Things
This paper revisits NDN deployment in the IoT with a special focus on the
interaction of sensors and actuators. Such scenarios require high
responsiveness and limited control state at the constrained nodes. We argue
that the NDN request-response pattern which prevents data push is vital for IoT
networks. We contribute HoP-and-Pull (HoPP), a robust publish-subscribe scheme
for typical IoT scenarios that targets IoT networks consisting of hundreds of
resource constrained devices at intermittent connectivity. Our approach limits
the FIB tables to a minimum and naturally supports mobility, temporary network
partitioning, data aggregation and near real-time reactivity. We experimentally
evaluate the protocol in a real-world deployment using the IoT-Lab testbed with
varying numbers of constrained devices, each wirelessly interconnected via IEEE
802.15.4 LowPANs. Implementations are built on CCN-lite with RIOT and support
experiments using various single- and multi-hop scenarios
Sensor function virtualization to support distributed intelligence in the internet of things
It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it
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
Hardware Architectures for Low-power In-Situ Monitoring of Wireless Embedded Systems
As wireless embedded systems transition from lab-scale research prototypes to large-scale commercial deployments, providing reliable and dependable system operation becomes absolutely crucial to ensure successful adoption. However, the untethered nature of wireless embedded systems severely limits the ability to access, debug, and control device operation after deployment—post-deployment or in-situ visibility. It is intuitive that the more information we have about a system’s operation after deployment, the better/faster we can respond upon the detection of anomalous behavior. Therefore, post-deployment visibility is a foundation upon which other runtime reliability techniques can be built. However, visibility into system operation diminishes significantly once the devices are remotely deployed, and we refer to this problem as a lack of post-deployment visibility
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