Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.

An application-specific image processing array based on WSe2 transistors with electrically switchable logic functions

With the rapid development of artificial intelligence, parallel image processing is becoming an increasingly important ability of computing hardware. To meet the requirements of various image processing tasks, the basic pixel processing unit contains multiple functional logic gates and a multiplexer, which leads to notable circuit redundancy. The pixel processing unit retains a large optimizing space to solve the area redundancy issues in parallel computing. Here, we demonstrate a pixel processing unit based on a single WSe2 transistor that has multiple logic functions (AND and XNOR) that are electrically switchable. We further integrate these pixel processing units into a low transistor-consumption image processing array, where both image intersection and image comparison tasks can be performed. Owing to the same image processing power, the consumption of transistors in our image processing unit is less than 16% of traditional circuits.

Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

A Rabin Cryptosystem-Based Lightweight Authentication Protocol and Session Key-Generation Scheme for IoT Deployment

Handling unpredictable attack vulnerabilities in self-proclaiming secure algorithms in WSNs is an issue. Vulnerabilities provide loop holes for adversary to barge in the privacy of the network. Attacks performed by the attacker can be active or passive. Adversary may listen to the sensitive information and exploit its confidentiality which is passive, or adversary may modify sensitive information being transferred over a WSN in case of active attacks. As Internet of things has basically three layers, middle-ware layer, Application layer, perceptron layer, most of the attacks are observed to happen at the perceptron layer in case of both wireless sensor network and RFID Tag implication Layer. Both are a major part of the perceptron layer that consist a small part of the IoT. Some of the major attack vulnerabilities are exploited by executing the attacks through certain flaws in the protocol that are difficult to identify and almost complex to identify in complicated bigger protocols. As most of the sensors are resource constrained in terms of memory, battery power, processing power, bandwidth and due to which implementation of complex cryptosystem to keep the data being transferred secure is a challenging phase. The three main objectives studied in this scenario are setting up the system, registering user and the sensors via multiple gateways. Generating a common key which can be used for a particular interaction session among user, gateway and the sensor network. In this paper, we address one or more of these objectives for some of the fundamental problems in authentication and mutual authentication phase of the WSN in IoT deployment. We prevent the leakage of sensitive information using the rabin cryptosystem to avoid attacks like Man-in-the-middle attack, sensor session key leakage, all session hi-jacking attack and sniffing attacks in which data is analyzed maliciously by the adversary. We also compare and prove the security of our protocol using proverif protocol verifier tool.

Ontology-Based Modelling of State Machines for Production Robots in Smart Manufacturing Systems

The integration of modern IT technologies in production equipment does not only enable them to acquire information from different sources and provide it to others but also to make decisions depending on the situation. Due to the limited processing power of such equipment, usage of state machine to describe and program it is considered a promising direction. However, the necessity of intensive interaction of the equipment units causes problems related to interoperability, which are usually solved with the usage of ontologies. The objective of the presented research is to model state machines of production robots via ontologies. The results are demonstrated on the example of a fragment of an automated production line.

Image Feature Analysis and Dynamic Measurement of Plantar Pressure Based on Fusion Feature Extraction

Footprint recognition and parameter measurement are widely used in fields like medicine, sports, and criminal investigation. Some results have been achieved in the analysis of plantar pressure image features based on image processing. But the common algorithms of image feature extraction often depend on computer processing power and massive datasets. Focusing on the auxiliary diagnosis and treatment of foot rehabilitation of foot laceration patients, this paper explores the image feature analysis and dynamic measurement of plantar pressure based on fusion feature extraction. Firstly, the authors detailed the idea of extracting image features with a fusion algorithm, which integrates wavelet transform and histogram of oriented gradients (HOG) descriptor. Next, the plantar parameters were calculated based on plantar pressure images, and the measurement steps of plantar parameters were given. Finally, the feature extraction effect of the proposed algorithm was verified, and the measured results on plantar parameters were obtained through experiments.

Enabling Processing Power Scalability with Internet of Things (IoT) Clusters

Internet of things (IoT) devices play a crucial role in the design of state-of-the-art infrastructures, with an increasing demand to support more complex services and applications. However, IoT devices are known for having limited computational capacities. Traditional approaches used to offload applications to the cloud to ease the burden on end-user devices, at the expense of a greater latency and increased network traffic. Our goal is to optimize the use of IoT devices, particularly those being underutilized. In this paper, we propose a pragmatic solution, built upon the Erlang programming language, that allows a group of IoT devices to collectively execute services, using their spare resources with minimal interference, and achieving a level of performance that otherwise would not be met by individual execution.

Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion

It is envisioned that healthcare systems of the future will be revolutionized with the development and integration of body-centric networks into future generations of communication systems, giving rise to the so-called “Internet of Bio-nano things”. Molecular communications (MC) emerge as the most promising way of transmitting information for in-body communications. One of the biggest challenges is how to minimize the effects of environmental noise and reduce the inter-symbol interference (ISI) which in an MC via diffusion scenario can be very high. To address this problem, channel coding is one of the most promising techniques. In this paper, we study the effects of different channel codes integrated into MC systems. We provide a study of Tomlinson, Cercas, Hughes (TCH) codes as a new attractive approach for the MC environment due to the codeword properties which enable simplified detection. Simulation results show that TCH codes are more effective for these scenarios when compared to other existing alternatives, without introducing too much complexity or processing power into the system. Furthermore, an experimental proof-of-concept macroscale test bed is described, which uses pH as the information carrier, and which demonstrates that the proposed TCH codes can improve the reliability in this type of communication channel.

Development on advanced technologies – design and development of cloud computing model

Big Data has been created from virtually everything around us at all times. Every digital media interaction generates data, from computer browsing and online retail to iTunes shopping and Facebook likes. This data is captured from multiple sources, with terrifying speed, volume and variety. But in order to extract substantial value from them, one must possess the optimal processing power, the appropriate analysis tools and, of course, the corresponding skills. The range of data collected by businesses today is almost unreal. According to IBM, more than 2.5 times four million data bytes generated per year, while the amount of data generated increases at such an astonishing rate that 90 % of it has been generated in just the last two years. Big Data have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. This paper presents a view of the BD challenges and methods to help to understand the significance of using the Big Data Technologies. This article based on a bibliographic review, on texts published in scientific journals, on relevant research dealing with the big data that have exploded in recent years, as they are increasingly linked to technology

Spectral Properties of Foams and Emulsions

The optical and spectral properties of foams and emulsions provide information about their micro-/nanostructures, chemical and time stability and molecular data of their components. Foams and emulsions are collections of different kinds of bubbles or drops with particular properties. A summary of various surfactant and emulsifier types is performed here, as well as an overview of methods for producing foams and emulsions. Absorption, reflectance, and vibrational spectroscopy (Fourier Transform Infrared spectroscopy-FTIR, Raman spectroscopy) studies are detailed in connection with the spectral characterization techniques of colloidal systems. Diffusing Wave Spectroscopy (DWS) data for foams and emulsions are likewise introduced. The utility of spectroscopic approaches has grown as processing power and analysis capabilities have improved. In addition, lasers offer advantages due to the specific properties of the emitted beams which allow focusing on very small volumes and enable accurate, fast, and high spatial resolution sample characterization. Emulsions and foams provide exceptional sensitive bases for measuring low concentrations of molecules down to the level of traces using spectroscopy techniques, thus opening new horizons in microfluidics.