IoT Analytics Research Articles

Book review: IoT and Big Data Analytics for Smart Cities: A Global Perspective

Book review: IoT and Big Data Analytics for Smart Cities: A Global Perspective

IoT-based monitoring, analysis and supervisory control of voltage harmonics in a three-phase system using four singles tuned lowpass filter

Total Harmonic Distortion is a vital part of communications, power distribution, and audio. In power distribution, it must be kept as low as possible. Voltage pollution occurs at the point of common coupling, which leads to a higher THD. Higher THD ensues thermal stress to the system where issues in the power system arise. Odd harmonic frequencies, specifically, the 5th, 7th, 11th, and 13th are the primary cause of the increase in THD. Several ways to reduce harmonics are the use of reactors, passive filters, and active front end solutions. This study is focused on minimizing the voltage harmonics by monitoring the voltage coming from a three-phase system by using four single-tuned filters that will tune out the unwanted harmful frequencies. The data will send through the IoT analytics platform which can then be viewed from ThingView, and the filters can be controlled through Arest.io. The system was compared to an oscilloscope then used Root-Mean-Square Error for system reliability test and yielded 0.097696 for data without filter and 0.048485 for data with a filter. Lastly, the system effectively decreased the THD by 32% compared to an unfiltered source.

Smart Agriculture: Integrating IoT and Data Analytics for Improved Farming Practises

Smart Agriculture: Integrating IoT and Data Analytics for Improved Farming Practises

Advanced Critical Care Monitoring with Alert and Auto Intelligence System

Abstract: With great advancements in lifestyle and practices along with pandemic outbreaks such as COVID 19, the number of patients in hospitals is increasing which is a major concern globally. To overcome this situation, an IoT platform likely to provide the scalability required for this challenge is support the continuous monitoring of patients on a global scale. The technical specifications used are Arduino IDE, ThingSpeak Cloud, Max 30100, Temperature sensor, and ESP8266. Arduino kit is an open source platform that has an easy to use software and hardware. It can read inputs from any device. ThingSpeak is an IOT analytics service that allows the collection of data, picturing, and examine live data streams in the cloud. ESP8266 is a WiFi Module that gives the microcontroller access to the Wi-Fi network. LM35 is a series of devices used for measuring temperature with accurate precision. Its output voltage is linearly proportional to the temperature in Centigrade units. Hence, this project paper is focused to analyse the vitals of patients by using wearable, safe, and modern sensors to observe various patients who are affected by cardiovascular disease.

Smart Building Construction Technology, A Digital Innovation for Enhancing Builders Entrepreneurial Sustainability

The building construction industry is currently on the pathway to digitalization in line with the ongoing fourth industrial revolution. However, builders have been quite slow in aligning with the digitalization pathway due to lack of adequate understanding of digital innovative skills. This study aims to develop digital skills needed by builders to become enterprising in the building construction industry. Survey research design was adopted for the study. Two research questions and hypotheses were used to carry out the study. The population for the study comprised 115 male & female practicing builders from some selected building construction industry in South-east states of Nigeria. Structured questionnaire was used to deduce respondent’s perception about digital innovative skills needed for constructing smart buildings. The internal consistency of the questionnaire was determined by the use of Cronbach alpha reliability test which yielded a value of 0.76. Data generated were analyzed using mean to answer research questions. T-test was used to test the hypotheses at probability level of 0.05. findings from the study revealed that BIM, IoT, BSM and data analytics are among the chief digital skills needed by builders to align with the digitalization of building construction industry. The findings further suggested that builders with possession of digital innovative skills promises to stay enterprising in the building construction industry. The study recommended that training opportunities on acquisition of digital skills relevant to the building construction industry should be sought for by all practicing builders in Nigeria to address the evolving dynamics in the built environment.

IoT analytics and smart energy – How I assessed the UK Electric Vehicle charging market opportunity for a leading service monitoring company for connected devices

IoT analytics and smart energy – How I assessed the UK Electric Vehicle charging market opportunity for a leading service monitoring company for connected devices

Zero-Trust Model for Smart Manufacturing Industry

Traditional security architectures use a perimeter-based security model where everything internal to the corporate network is trusted by default. This type of architecture was designed to protect static servers and endpoints; however, we need to adapt to emerging technologies where serverless applications are running on containers, mobile endpoints, IoT, and cyber-physical systems. Since the beginning of the fourth industrial revolution (Industry 4.0), there has been a massive investment in smart manufacturing which responds in real-time to the supply chain and connects the digital and physical environments using IoT, cloud computing, and data analytics. The zero-trust security model is a concept of implementing cybersecurity techniques considering all networks and hosts to be hostile irrespective of their location. Over the past few years, this model has proven to be a remarkably effective security solution in conventional networks and devices. In this paper, the zero-trust approach will be fully explored and documented explaining its principles, architecture, and implementation procedure. It will also include a background of the smart manufacturing industry and a review of the existing cyber security solutions followed by a proposed design of the zero-trust model along with all the enabling factors for on-premises and cloud-hosted infrastructure. Various security solutions such as micro-segmentation of the industrial network, device discovery, and compliance management tools that are essential in achieving complete zero-trust security are considered in the proposed architecture.

Editorial: Evolution from AI, IoT and Big Data Analytics to Metaverse

Time flies. Since I took over the Editor-in-Chief position from this journal's founding Editor-in-Chief, Professor Fei-Yue Wang in 2018, five years has past just like a second. Looking back from today, as a team, we, including editorial board members, editorial staff members, our early career advisory board members, all contributing authors and all reviewers, should be proud of what this journal has achieved. This journal is the first of its kind of journals, resulting from IEEE’ s collaboration with Chinese Association of Automation-an outside-USA professional organization/institution. We have overcome many barriers and difficulties, and well proven that we, united and working together, can accomplish a challenging mission. We indeed set a great example for IEEE and its many societies to pursue more and more collaboration with other publishers, organizations and institutions from not only China but also such countries as India, Brazil, and Japan.

Real-time surrogate-assisted preprocessing of streaming sensor data

A new dynamic programming based algorithm for real-time simultaneous segmentation, compression, and C2-smoothing of potentially infinite data streams – studied in the context of streaming sensor data – is shown to be both effective and (energy) efficient. Its three key elements – the cubic splinelet based adaptive search space, the objective function (combining two measures of approximation errors), and the two-phase search space reduction technique (combining segment length based pruning and surrogate based pruning) – are presented in detail. The output quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show that the new algorithm outperforms both reference algorithms for all test streams, in some cases—significantly (up to 32% smaller approximation errors and up to 41% higher compression ratios). Due to the effective search space auto-adaptation and special pruning techniques, in typical cases, this is obtained with significantly lower computational cost. The proposed algorithm can be applied to various domains including online compression and smoothing of streaming data coming from IoT devices, sensor networks, and sensors located in autonomous vehicles (cars, drones) and robots. The possible application areas also include real-time IoT analytics and embedded time-series databases.

Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics

The integration of the huge data streams produced by the Industrial Internet of Things (IIoT) can provide invaluable knowledge in the context of Industry 4.0, and is also an open research issue. The present paper proposes a semantic approach to this issue, centred around the notion of process as the backbone. We build an ontology describing the fundamental elements involved in IIoT and their relations, and discuss the construction of the Process-aware IIoT Knowledge Graph, where raw sensor data are enriched with information about process activities and the physical production environment. We also propose a framework for querying the Knowledge Graph, and we demonstrate its capabilities by considering the production of metal accessories as case study.

A Reference Architecture for IoT-Enabled Smart Buildings

The management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.

Mapping Top Strategic E-commerce Technologies in the Digital Marketing Literature

The increasing use of e-commerce technologies has been studied in several fields and from different perspectives: technological, economic, organizational and social: hence, the need for a literature review that provides a map of top strategic e-commerce technologies from a managerial perspective and, more specifically, for digital marketing research. This paper aims to provide researchers with a comprehensive overview of the range of e-commerce technologies that have had a significant role in shaping digital marketing strategies. Based on a thematic analysis, e-commerce technologies were classified through eleven categories. The objective is to reveal how each set of technologies affected the different digital marketing strategies. Both descriptive and clustering analyses show that the most evoked technologies in the digital marketing literature are Information and Communication Technologies and platforms. Results outline the growing interest in artificial intelligence technologies. Moreover, this literature review reveals how digital marketing research has focused on technology-enabled segmentation and targeting strategies, along with the use of social platforms and the development of new products and services. The scarcity of marketing papers studying the impact of cloud technologies, IoT, blockchain and data analytics orient researchers towards exploring further the potential of these technologies for digital strategies.

Autonomous Rover for Groundwork Lawn Mowing

The objective of this paper is to design and implement a lawnmower robot that can be used to mow grass from lawns and playgrounds remotely, online, or autonomously. The robot follows a rectangle zigzag trajectory through the lawn without any human interference. A set of concurrently running behaviors are defined to perform mowing operation. Sonar ranging is used to detect and avoid obstacles continuously throughout the route. The micro-controller connects to an ethernet board and uploads the robot's functions to a web server. Through the web server, users can monitor a 3D model of the moving rover and data from the lawnmower sensors. In addition, the robot's behaviors and connections are uploaded to IoT analytics platform to aid the performance evaluation and feature development.

Value Orientation and Significant Insights on Blending IOT and Big Data Analytics in Current Context

The internet is constantly growing and evolving. The Internet of Things (IoT), which uses machine-to-machine learning, might be seen as the future of Internet applications (M2M). Big Data, real-time analytics, and IoT can be used to create actionable intelligence. IoT and big data can be seen as two sides of the same coin. Benefits of IoT can be easily realized with the connection between Big Data and the things on the Internet. IoT applications are used in a wide range of industries, including disaster management, retail management, and health care. Despite the advantages of combining IoT and Big Data analytics, there are inherent complications and obstacles that must be addressed. The integration of Big Data analytics and IoT is the main topic of this study.

IoT Analytics and ERP Interoperability in Automotive SCM

Abstract Objective – The objective of this paper is to understand the potential of Interoperability between ERP and IOT Analytics in enabling the agile performance in Automotive supply chain by exploring the influence between Interoperability, SC Visibility and SCM agile performance and propose design for decision making system using Artificial neural network integration with fuzzy logic technique. Design/methodology/approach – TOE view was used to develop theoretical framework in addition to the elaborate literature review. Empirical analysis on the collected data from professionals in the automotive industry used to conclude on the findings. Findings – The IOT-Analytics and ERP interoperability identified as an enabler of SCM agile performance. Originality/value – The research article provides theoretical and empirical evidence over the IOT analytics and ERP interoperability potential impact in the Automotive SCM with novel approach towards designing effective decision support system using artificial neural network-fuzzy logic integration technique.

Complete Integrated Automation of the Electrochemical Corrosion Protection System of Pipelines Based on IoT and Big Data Analytics

This article is devoted to the issues of creating an adaptive intelligent system, for monitoring and controlling the technological process for electrochemical corrosion protection of main pipelines (MP), which has been designed for remote control of electrochemical protection (EChP) parameters and their optimization as well as adaptive control of the parameters of cathodic protection stations while taking into account changes in external conditions. The multi-objective problem of optimizing the operating modes of cathodic protection stations (CPS) is considered because optimization is carried out according to both the criterion of optimal distribution of the protective potential (uniform distribution of the protective total (pipe-to-soil) potential along the length of the pipeline) and to the criterion of the minimum total protective current of stations. The structure of the distributed electrochemical protection system is described in the article. A more complete picture of the protection of the pipeline and solving the problems of optimizing the electrochemical protection modes in real time is possible due to remote monitoring of control and measuring points (CMP) in the middle of the pipeline between neighboring cathodic protection stations, as well as in all corrosion and hazardous zones where they are also installed. In addition to the often-used GSM/GPRS networks in electrochemical protection systems, an energy-efficient LPWAN (Low-Power Wide-Area Network) data transmission network is also used and data collection is carried out using a cloud IoT platform. The functionality of the system is described, web application screens are shown in various operating modes for remote monitoring and control of the protective parameters of cathodic protection stations is reported. Analytical data processing for the tasks assessing the protection of objects in the pipeline system against corrosion are also shown. The system ensures that the electrochemical protection process is maintained at an optimal level between the destructive zones of “underprotection” and “overprotection”, taking into account monitoring data, geological conditions at the pipeline site, climatic or seasonal changes and other factors. In general, this system provides an increase in the reliability of the electrochemical protection system as a whole and, accordingly, it prevents possible emergency situations on the pipeline system while also reducing the cost of pipeline maintenance due to the reliability and continuity of protection.

AWS IoT analytics platform for microgrid operation management

Microgrid (MG) represents a promising opportunity for integrating renewable energy systems with the electric power grid. However, numerous complexities need to be addressed in the process. The electrical grid is complex, vulnerable, and centralized. Thus, the integration is challenging owing to the stochastic nature of renewable energy generation, which affects the possibility of reliable forecasting. The wastage due to poor estimation of clean energy generation discourages new investments in this area. However, recent advancements in big data technologies enable processing a large amount of data captured from multiple sources in real-time. It opens the possibility of improving the operational optimization of MGs and the performance of forecasting models. The overall MG problem is formulated using a two-stage stochastic mixed-integer linear programming problem with recourse. Amazon Web Services (AWS) IoT analytics platform inputs data in real-time and runs a sophisticated wind generation forecast analysis. The stochastic model is solved using the Sample Average Approximation (SAA) algorithm. The innovative methodology leads to significant improvements in the total average operating cost by integrating AWS IoT Analytics compared to traditional methods that use historical data. Computations are performed for different power-grid settings, including a power-outage, and different power generators units capacities with total operation average cost savings of 7.6% and 5.9%, respectively. Sensitivity analysis showed that the SSA algorithm could solve all the instances by providing high-quality solutions. The AWS IoT strategy outperformed at 7.7 % and 3.6% for optimality gap and CPU time, respectively. We examined an actual case in Peru for an agricultural application to assess the performance of a stochastic optimization model with a real-time IoT wind generation forecast strategy. The results revealed the following capabilities of our novel framework: (1) it can realize higher cost savings from the MG operating systems; (2) improve real-time renewable energy forecasting; (3) facilitate robust decision-making under conditions of uncertainty.

A Method to Estimate Water Flow Rate Using Sound of Flowing Water

With the population increasing exponentially, there has been a shortage of water in various countries. To help people manage water resources in an appropriate way, a water monitoring and management system needed to be built. Several invasive flow meters are present to cater to the problem but there exist several overhead expenses and issues with it. A good alternative for the above, a non-invasive flow meter, would be a more efficient solution. Though, some non-invasive flow meters exist, they’re expensive and unsuitable for use in some critical scenarios. So, a novel method and system, to calculate the rate of flowing water using audio-processing of the sound of flowing water, was developed. The system would be clamped on the surface of the pipe, unlike other systems which operate from inside the pipe. The flow meter developed using this method would only require a microphone on the sensor’s part, which will make it cost-effective. The process involves an algorithm that processes the sound of water using a microcontroller unit and then computes a single value which then depicts the range of flow rate. Further, using IOT analytics, future predictions and other important metrics would be displayed on a simple yet detailed user interface.

Osmotic Message-Oriented Middleware for Internet of Things

IoT is a trending computational concept that converts almost everything in modern life into a smart thing in various innovational and outstanding approaches. Smart homes, connected cities, autonomous vehicles, industrial automation, and smart healthcare that allows doctors to perform a patient examination and enable executing a remote surgery are now applicable through the smart connected things. Moreover, the recent IoT analytics report expects the universal number of connected IoT things to grow by 9%, to 12.3 billion operating terminals. It is expected that there will be more than 27 billion IoT live connections by 2025. In this paper, we present osmotic message-oriented middleware, introducing an end-to-end IoT platform to federate the dynamic orchestration process of resources across different heterogeneous types of devices belonging to physical and virtual infrastructures (e.g., edge, fog, and cloud layers); the orchestration process follows the osmotic computing concepts represented as the self-adaptive MAPE-K model, which maintains/adopts itself on the runtime through feedback loops from the provisioning engine, which collects the node’s hardware and software performance matrices. Accordingly, the orchestration process utilizes the optimized dynamic Hungarian algorithm to solve the MELs’ assignment problem based on the vibrant runtime provisioning data. The implemented middleware prototype is tested on both simulated and real-life environments to validate the architecture hypothesis of running an efficient, robust, elastic, and cost-efficient end-to-end osmotic IoT ecosystem, which unlocks a new implementation model for the IoT numerous domains.

Industry 4.0 enabling manufacturing flexibility: technology contributions to individual resource and shop floor flexibility

PurposeThis paper focuses on understanding the contribution of Industry 4.0 technologies to manufacturing flexibility.Design/methodology/approachA multiple-case study was conducted through interviews and complementary data from 12 adopters of Industry 4.0 technologies from the industrial sector. To enable a broad perspective, cases from 5 industry sectors with different technological intensity levels were studied.FindingsThe findings show that Industry 4.0 technologies are mostly used to improve machine flexibility since there is a major focus on technological approaches rather than on wider flexibility. The results also showed that cloud services, IoT, and data analytics provide the basis for flexible operation, and collaborative robots, ERP/MES/PLM, AGVs, and traceability devices are the most commonly implemented technologies for flexibility. However, inherent contingency factors such as production complexity and product life cycle need to be considered.Originality/valueThis article expands the research on manufacturing flexibility, considering new capabilities introduced by Industry 4.0.