There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Deep learning for smart manufacturing: Methods and applications

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

Blockchain for Internet of Things: A Survey

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

The Role of Advanced Sensing in Smart Cities

This paper presents a technique to determine the occupancy and indoor environment quality (IEQ) in buildings by enhancing physical measurements from a distributed sensor network with a statistical estimation method. The research is motivated by the increasing demand for improving energy efficiency while maintaining healthy and comfortable environment in buildings. Features representing the occupancy level and the relative changes are extracted from a suite of sensors: passive infra-red (PIR) sensors, Carbon Dioxide (CO 2 ) concentration sensors, and relative humidity (RH) sensors, which are networked and installed in a laboratory. An Autoregressive Hidden Markov Model (ARHMM) has been developed to model the occupancy pattern, based on the measurements, given its ability to establish correlations among the observed variables. The result is compared with that obtained from the classical Hidden Markov Model (HMM) and Support Vector Machines (SVM), which indicates that the ARHMM estimation method performed better than the other two methods, with an average estimation accuracy of 80.78%.

Occupancy and indoor environment quality sensing for smart buildings

Cloud Computing for Cloud Manufacturing: Benefits and Limitations

One of the primary energy consumers in buildings are the Heating, Ventilation, and Air-Conditioning (HVAC) systems, which usually operate on a fixed schedule, i.e., running from early morning until late evening during the weekdays. This fixed operation schedule does not take the dynamics of occupancy level in the building into consideration, therefore may lead to waste of energy. An estimate of the number of occupants in the building with time can contribute to improving the control policy of the building's HVAC system by reducing energy consumption. In this paper, the auto-regressive hidden Markov model (ARHMM), is investigated to estimate the number of occupants in a research laboratory in a building using a wireless sensor network deployed. The network is composed of stand-alone sensing nodes with wireless data transmission capability, a base station that collects data from the sensing nodes, and a server to analyze the data from the base station. Experimental results and numerical simulation demonstrate that the ARHMM is more effective in estimating the number of occupants in the laboratory than the HMM algorithm, especially when the occupancy level fluctuates frequently.

Occupancy estimation for smart buildings by an auto-regressive hidden Markov model

The increasing complexity of manufacturing machines and the continued demand for high productivity have led to growing applications of sensor networks to enable more reliable, timely, and comprehensive information gathering from the machines being monitored. An effective and efficient utilization of sensor networks requires new sensor designs that enable adaptive event-driven information gathering based on the condition of the machines, as well as a coordinated information distribution adjusted to the available communication bandwidth of the network. This paper investigates several fundamental aspects regarding the architectural design of a sensory node controller (SNOC). The SNOC is the key element in a large-scale sensor network that coordinates the operation of individual sensors and the communication among various sensing clusters to realize distributed intelligent sensing. A parametric SNOC design that dynamically adjusts the power supply and the data-acquisition procedure to reduce the overall energy consumption of the sensor network is presented. Considerations on both the hardware and software aspects of the design to achieve energy efficiency are described, and analytical formulations are derived. Simulation results for a sensor network consisting of 40 SNOCs, each coordinating eight physical sensors, have shown that the design is able to reduce the energy consumption by about 43%, as compared to traditional techniques. A prototype SNOC was designed and implemented, based on the platform of a commercially available microcontroller, and experimentally tested for its ability to dynamically adjust the power consumption. The study has provided a concrete input to the design optimization and experimental realization of an SNOC-based sensor network for machine-system monitoring.

Architectural design of a sensory node controller for optimized energy utilization in sensor networks

Disclosed herein is a novel sensing technique, termed Multiple Excitation Capacitance Polling (MECaP), that improves the efficiency of Electrical Capacitance Tomography (ECT). Unlike traditional alternating current techniques, where excitation signal is applied to an electrode one at a time, MECaP involves simultaneously applying multiple excitation signals, in a progressively increasing fashion, to multiple electrodes on an ECT sensor. The received signals are filtered or otherwise decomposed (e.g., Fourier transformed) into different components, and the individual components are used to generate an image of the article or substance disposed between the electrodes. Because multiple capacitances can be simultaneously measured as a consequence, scanning with MECaP can significantly increase the image scanning speed. For example, scanning with MECaP may enable frames rates of tens of kHz for imaging dynamic processes such as engine combustion.

Zhaoyan Fan

Papers

Occupancy and indoor environment quality sensing for smart buildings

Cloud Computing for Cloud Manufacturing: Benefits and Limitations

Occupancy estimation for smart buildings by an auto-regressive hidden Markov model

Architectural design of a sensory node controller for optimized energy utilization in sensor networks

Multiple excitation capacitance polling for enhanced electronic capacitance tomography