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Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020
Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing
A model for big spatial rural data infrastructure in Turkey: Sensor-driven and integrative approach
یک مدل برای زیرساخت های داده های بزرگ فضایی روستایی در ترکیه: رویکرد حسگر محور و یکپارچه-2020
A Spatial Data Infrastructure (SDI) enables the effective spatial data flow between providers and users for their prospective land use analyses. The need for an SDI providing soil and land use inventories is crucial in order to optimize sustainable management of agricultural, meadow and forest lands. In an SDI where datasets are static, it is not possible to make quick decisions about land use. Therefore, SDIs must be enhanced with online data flow and the capabilities to store big volumes of data. This necessity brings the concepts of the Internet of Things (IoT) and Big Data (BD) into the discussion. Turkey needs to establish an SDI to monitor and manage its rural lands. Even though Turkish decision-makers and scientists have constructed a solid national SDI standardization, a conceptual model for rural areas has not been developed yet. In accordance with the international agreements, this model should adopt the INSPIRE Directive and Land Parcel Identification System (LPIS) standards. In order to manage rural lands in Turkey, there are several legislations which characterize the land use planning, land classification and restrictions. Especially, the Soil Protection and Land Use Law (SPLUL) enforces to use a lot and a variety of land use parameters that should be available in a big rural SDI. Moreover, this model should be enhanced with IoT, which enables to use of smart sensors to collect data for monitoring natural occurrences and other parameters that may help to classify lands. This study focuses on a conceptual model of a Turkish big rural SDI design that combines the sensor usage and attribute datasets for all sorts of rural lands. The article initially reviews Turkish rural reforms, current enterprises to a national SDI and sensor-driven agricultural monitoring. The suggested model integrates rural land use types, such as agricultural lands, meadowlands and forest lands. During the design process, available data sets and current legislation for Turkish rural lands are taken into consideration. This schema is associated with food security databases (organic and good farming practices), non-agricultural land use applications and local/ European subsidies in order to monitor the agricultural parcels on which these practices are implemented. To provide a standard visualization of this conceptual schema, the Unified Modeling Language (UML) class diagrams are used and a supplementary data dictionary is prepared to make clear definitions of the attributes, data types and code lists used in the model. This conceptual model supports the LPIS, ISO 19156 International Standard (Geographic Information: Observations and Measurements) catalogue and INSPIRE data theme specifications due to the fact that Turkey is negotiating the accession to EU; however, it also provides a local understanding that enables to manage rural lands holistically for sustainable development goals. It suggests an expansion for the sensor variety of Turkish agricultural monitoring project (TARBIL) and it specifies a rural theme for Turkish National SDI (TUCBS).
Keywords: Spatial data infrastructures | Big data | Internet of things | Rural land use | INSPIRE | LPIS
Data mining of customer choice behavior in internet of things within relationship network
داده کاوی رفتار انتخاب مشتری در اینترنت اشیایی که در شبکه ارتباطی قرار دارند-2020
Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customers consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.
Keywords: Internet of things | Customer relationship network | Decision making | Recommendation | Fusion algorithm
Intelligent decision-making of online shopping behavior based on internet of things
تصمیم گیری هوشمندانه از رفتار خرید آنلاین مبتنی بر اینترنت اشیا-2020
The development of big data and Internet of things (IoT) have brought big changes to e-commerce. Different kinds of information sources have improved the consumers’ online shopping performance and make it possible to realize the business intelligence. Grip force and eye-tracking sensors are applied to consumers online reviews search behavior by relating them to the research approaches in IoT. To begin with, public cognition of human contact degrees of recycled water reuses with grip force test was measured. According to the human contact degrees, 9 recycled water reuses presented by the experiment are classified into 4 categories. Based on the conclusion drawn from grip force test, purified recycled water and fresh vegetable irrigated with recycled water are regarded as the drinking for high-level human contact degree and the irrigation of food crops for low-level human contact degree respectively. Several pictures are designed for eye-tracking test by simulating an on-line shopping web page on Taobao (the most popular online shopping platform in China). By comparing the fixation time participants spent on the areas of interest (AOIs), we justify that consumers online reviews search behavior is substantially affected by human contact degrees of recycled products. It was found that consumers rely on safety perception reviews when buying high contact goods.
Keywords: Online reviews search behavior | Recycled products | Grip force sensor | Eye-tracking sensor | Internet of Things (IoT)
Text mining of industry 4:0 job advertisements
استخراج متن آگهی های شغلی صنعت 4:0-2020
Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.
Keywords: Human resource management | Text mining | Job profiles | Big data analytics | Industry 4.0 | Education | Smart factory
Internet of Things: Evolution and technologies from a security perspective
اینترنت اشیاء: تکامل و فناوری ها از دیدگاه امنیتی-2020
In recent years, IoT has developed into many areas of life including smart homes, smart cities, agriculture, offices, and workplaces. Everyday physical items such as lights, locks and industrial machineries can now be part of the IoT ecosystem. IoT has redefined the management of critical and non-critical systems with the aim of making our lives more safe, efficient and comfortable. As a result, IoT technology is having a huge positive impact on our lives. However, in addition to these positives, IoT systems have also attracted negative attention from malicious users who aim to infiltrate weaknesses within IoT systems for their own gain, referred to as cyber security attacks. By creating an introduction to IoT, this paper seeks to highlight IoT cyber security vulnerabilities and mitigation techniques to the reader. The paper is suitable for developers, practitioners, and academics, particularly from fields such as computer networking, information or communication technology or electronics. The paper begins by introducing IoT as the culmination of two hundred years of evolution within communication technologies. Around 2014, IoT reached consumers, early products were mostly small closed IoT networks, followed by large networks such as smart cities, and continuing to evolve into Next Generation Internet; internet systems which incorporate human values. Following this evolutionary introduction, IoT architectures are compared and some of the technologies that are part of each architectural layer are introduced. Security threats within each architectural layer and some mitigation strategies are discussed, finally, the paper concludes with some future developments.
Keywords: IoT | Internet of Things | Security | Cyber security | Secure by Design | Next Generation Internet | Smart city | Sustainable city | Energy reduction | Building Energy Management Systems
Agent negotiation in an IoT-Fog based power distribution system for demand reduction
عامل مذاکره در سیستم توزیع برق مبتنی بر IoT-Fog برای کاهش تقاضا-2020
Growing energy demand is calling for an effective energy management. In smart homes all devices are connected to Internet by means of Internet of Things. There is a possible means of studying the consumer usage pattern and accordingly forecast their energy demand. Multi Agents has been used in computer science for a long time and applied for lot of applications for replicating the job of human. So towards monitoring and controlling the cyber physical systems, these multi agent system has been applied in smart transportation, smart cities, Smart Grid and so. This paper proposes a Multi-agent System (MAS) for smart energy management in an IoT based system. Inspired by the competition in human societies for accepting best proposals: this work proposes an Agent Negotiation system for demand reduction. The Agents in IoT system negotiate with the meter agent for accepting a proposal which will reduce the peak hour usage. The negotiation agent also negotiates with the meter agent for using energy when the availability of renewables are surplus. This negotiation is done with hundreds and thousands of homes thus helping Utilities to meet the supply-demand effectively. Consumers get the best pricing based on the accepted policies.
Keywords: Internet of Things | Multi-agent system | Negotiation | Distribution automation | Smart grid
Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?
اعمال اینترنت اشیاء و ابتکارات تحلیلی داده های بزرگ در شرکت های اروپایی و آمریکایی: آیا کیفیت داده راهی برای استخراج ارزش تجارت است؟-2020
Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.
Keywords: Big data analytics | Internet of things | Strategic management | Knowledge-based theory | Dynamics capability theory
A method of NC machine tools intelligent monitoring system in smart factories
روش ابزار و ماشین آلات NC سیستم نظارت هوشمند در کارخانه های هوشمند-2020
The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories..
Keywords: CNC | Monitoring system | Data analysis | Machine tool | Smart factory
Manufacturing big data ecosystem: A systematic literature review
ساخت اکوسیستم داده های بزرگ: مروری بر ادبیات سیستماتیک-2020
Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL