6,029 research outputs found
Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things
The number of connected sensors and devices is expected to increase to billions in the near
future. However, centralised cloud-computing data centres present various challenges to meet the
requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput
and bandwidth constraints. Edge computing is becoming the standard computing paradigm for
latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related
to centralised cloud-computing models. Such a paradigm relies on bringing computation close to
the source of data, which presents serious operational challenges for large-scale cloud-computing
providers. In this work, we present an architecture composed of low-cost Single-Board-Computer
clusters near to data sources, and centralised cloud-computing data centres. The proposed
cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT
workload requirements while keeping scalability. We include an extensive empirical analysis to
assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data
centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud
architectures, and evaluate them through extensive simulation. We finally show that acquisition costs
can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209
Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments
The increased penetration of uncertain and variable renewable energy presents
various resource and operational electric grid challenges. Micro-level
(household and small commercial) demand-side grid flexibility could be a
cost-effective strategy to integrate high penetrations of wind and solar
energy, but literature and field deployments exploring the necessary
information and communication technologies (ICTs) are scant. This paper
presents an exploratory framework for enabling information driven grid
flexibility through the Internet of Things (IoT), and a proof-of-concept
wireless sensor gateway (FlexBox) to collect the necessary parameters for
adequately monitoring and actuating the micro-level demand-side. In the summer
of 2015, thirty sensor gateways were deployed in the city of Managua
(Nicaragua) to develop a baseline for a near future small-scale demand response
pilot implementation. FlexBox field data has begun shedding light on
relationships between ambient temperature and load energy consumption, load and
building envelope energy efficiency challenges, latency communication network
challenges, and opportunities to engage existing demand-side user behavioral
patterns. Information driven grid flexibility strategies present great
opportunity to develop new technologies, system architectures, and
implementation approaches that can easily scale across regions, incomes, and
levels of development
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Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings
Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions
The Deformable Mirror Demonstration Mission (DeMi) CubeSat: optomechanical design validation and laboratory calibration
Coronagraphs on future space telescopes will require precise wavefront
correction to detect Earth-like exoplanets near their host stars. High-actuator
count microelectromechanical system (MEMS) deformable mirrors provide wavefront
control with low size, weight, and power. The Deformable Mirror Demonstration
Mission (DeMi) payload will demonstrate a 140 actuator MEMS deformable mirror
(DM) with \SI{5.5}{\micro\meter} maximum stroke. We present the flight
optomechanical design, lab tests of the flight wavefront sensor and wavefront
reconstructor, and simulations of closed-loop control of wavefront aberrations.
We also present the compact flight DM controller, capable of driving up to 192
actuator channels at 0-250V with 14-bit resolution. Two embedded Raspberry Pi 3
compute modules are used for task management and wavefront reconstruction. The
spacecraft is a 6U CubeSat (30 cm x 20 cm x 10 cm) and launch is planned for
2019.Comment: 15 pages, 10 figues. Presented at SPIE Astronomical Telescopes +
Instrumentation, Austin, Texas, US
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