Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Standards-based wireless sensor networks for power system condition monitoring

This paper assesses the industrial needs motivating interest in wireless monito ring within the power industry, and reviews applications of WSN technology for substation condition monitoring (Section 2). A key contribution is the identification of a set of technical requirements for substation - based WSNs, focused around security requi rements, robustness to RF noise, and other utility - specific concerns (Section 3). Section 4 comprehensively assesses the suitability of various IWSN protocols for substation environments, using these requirements. A case study implementation of one standar d, ISA100.11a, is reported in Section 5, along with deployment experience. The paper concludes by describing future research challenges for WSN protocols which are specific to this domain

Double smart energy harvesting system for self-powered industrial IoT

312 p. 335 p. (confidencial)Future factories would be based on the Industry 4.0 paradigm. IndustrialInternet of Things (IIoT) represent a part of the solution in this field. Asautonomous systems, powering challenges could be solved using energy harvestingtechnology. The present thesis work combines two alternatives of energy input andmanagement on a single architecture. A mini-reactor and an indoor photovoltaiccell as energy harvesters and a double power manager with AC/DC and DC/DCconverters controlled by a low power single controller. Furthermore, theaforementioned energy management is improved with artificial intelligencetechniques, which allows a smart and optimal energy management. Besides, theharvested energy is going to be stored in a low power supercapacitor. The workconcludes with the integration of these solutions making IIoT self-powered devices.IK4 Teknike

IEEE Access Special Section Editorial: Wearable and Implantable Devices and Systems

© 2013 IEEE. Circuit techniques, sensors, antennas and communications systems are envisioned to help build new technologies over the next several years. Advances in the development and implementation of such technologies have already shown us their unique potential in realizing next-generation sensing systems. Applications include wearable consumer electronics, healthcare monitoring systems, and soft robotics, as well as wireless implants. There have been some interesting developments in the areas of circuits and systems, involving studies related to low-power electronics, wireless sensor networks, wearable circuit behaviour, security, real-time monitoring, connectivity of sensors, and Internet of Things (IoT). The direction for the current technology is electronics systems on large area electronics, integrated implantable systems and wearable sensors. So far, the research in the field has focused on materials, new processing techniques and one-off devices, such as diodes and transistors. However, current technology is not sufficient for future electronics to be useful in new applications; a great demand exists to scale up the research towards circuits and systems. Recent developments indicate that, in addition to fabrication technology, special attention should also be given to design, simulation and modeling of electronics, while keeping sensing system integration, power management, and sensors network under consideration