به سمت لبه هوشمند: ارتباطات بی سیم به یادگیری ماشین میرسد
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 31
احیای هوش مصنوعی در اواخر (AI) تقریباً در هر شاخهای از علم و فناوری، انقلابی ایجاد کرده است. با توجه به گجتهای تلفن همراه هوشمند و همه جا حاضر و دستگاههای اینترنت اشیا (IoT)، انتظار میرود که اکثر برنامههای هوشمند را بتوان در لبهی شبکههای بی سیم استقرار داد. این روند باعث شده است، تمایل قوی برای تحقق «لبه هوشمند» ایجاد شود تا از برنامههای کاربردی مجهز به AI در دستگاههای لبه مختلف استفاده شود. بر این اساس، یک حوزهی پژوهشی جدید به نام یادگیری لبه به ظهور رسیده است که از دو رشته عبور میکند و انقلابی در آنها ایجاد میکند: ارتباطات بی سیم و یادگیری ماشین. یک موضوع اصلی در یادگیری لبه غلبه بر قدرت محاسباتی محدود و همچنین دادههای محدود در هر دستگاه لبه است. این امر با استفاده از پلت فرم محاسبات لبه تلفن همراه (MEC) و استخراج دادههای عظیم توزیع شده در تعداد زیادی دستگاه لبه محقق شده است. در چنین سیستمهایی، یادگیری از داده توزیع شده و برقراری ارتباط بین سرور لبه و دستگاهها دو جنبهی حیاتی و مهم است و همجوشی آنها، چالشهای پژوهشی جدید و زیادی را به همراه دارد. این مقاله از یک مجموعه جدید از اصول طراحی برای ارتباطات بی سیم در یادگیری لبه پشتیبانی میکند که در مجموع ارتباطات یادگیری محور نامیده میشوند. مثالهای گویایی ارائه شدند تا اثربخشی این اصول طراحی مشخص شوند و برای این منظور فرصتهای تحقیقاتی منحصر به فردی شناسایی شدند.
کلمات کلیدی: سرورها | مدل سازی جوی | هوش مصنوعی | پایگاه های داده توزیع شده | ارتباطات بی سیم | یادگیری ماشین | مدل سازی محاسباتی
|مقاله ترجمه شده|
Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement
احراز هویت سریع و مجوز پیشرو در اینترنت اشیا با مقیاس بزرگ: نحوه استفاده از هوش مصنوعی برای تقویت امنیت-2020
Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles in supporting diverse vertical applications by connecting heterogenous devices, machines, and industry processes. Conventional authentication and authorization schemes are insufficient to overcome the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security levels. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection leads to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI)-enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the base station to identify the prearranged access time sequences or frequency bands or codes used in IoT devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for achieving adaptive access control. These new AI-enabled approaches establish the connections between transceivers quickly and enhance security progressively so that communication latency can be reduced and security risks are well controlled in large-scale IoT systems. Finally, we outline several areas for AI-enabled security provisioning for future research.
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
Digital transformation: Five recommendations for the digitally conscious firm
تحول دیجیتال: پنج توصیه برای شرکت آگاه دیجیتالی-2020
Digital transformation is one of the key challenges facing contemporary businesses. The need to leverage digital technology to develop and implement new business models forces firms to reevaluate existing capabilities, structures, and culture in order to identify what technologies are relevant and how they will be enacted in organizational processes and business offerings. More often than not, these profound changes require firms to revisit old truths as they develop strategies that thread the needle between beneficial innovation and harmful disruption. This article uses the Internet of Things (IoT) as a backdrop to demonstrate the concerns associated with transformative technologies and offers five recommendations as to how firms can develop the strategies needed for digital transformation and become digitally conscious: (1) Start small and build on firsthand benefits; (2) team up and create competitive advantage from brand recognition; (3) engage in standardization efforts; (4) take responsibility for data ownership and ethics; and (5) own the change and ensure organization-wide commitment. As such, this article shows that digital transformation should be a top management priority and a defining trait of corporate business strategy, and that by becoming digitally conscious, firms may get a head start on their transformation journey.
KEYWORDS: Digital transformation | Digitization | Digitalization | Internet of Things | Digital consciousness
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)
AI and IoT Based Monitoring System for Increasing the Yield in Crop Production
سیستم مانیتورینگ مبتنی بر هوش مصنوعی و اینترنت اشیا برای افزایش عملکرد در محصولات زراعی-2020
Artificial Intelligence (AI) and Internet of things (IoT) based monitoring systems are in great demand and gives a precise extraction and analysis of data. In this paper, the research is performed on a marigold plant to detect the most suitable conditions for plant growth. The philosophy behinds this work is to reduce the risks in agriculture and to promote smart farming practices. The effect of physical conditions like humidity, temperature, soil temperature and moisture and light intensity on the plant growth, is monitored using IoT based monitoring system. The data responsible for the plant growth is obtained using different sensors units like DHT11, LDR, DS18B20, Soil Moisture sensors, Noir camera, singleboard microcontrollers and Application Programming Interfaces (APIs). The variation of plant growth rate w.r.t. the intensity of sunlight was observed within the range of 1000 lx- 1200 lx, category-2 (best). The further analysis of the extracted parameters is done using different Machine Learning (ML) algorithms. Logistic Regression, Gradient Boosting Classifier and Linear Support Vector Classifier (SVC) algorithms are found best for analysis of physical parameters responsible for the marigold plant growth.
Keywords: Machine Learning | Internet of Things | Smart farming | Agriculture | Artificial Intelligence | OpenCV | Python | Thingspeak
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
Blockchain, AI and Smart Grids: The Three Musketeers to a Decentralized EV Charging Infrastructure
بلاکچین ،هوش مصنوعی و شبکه های هوشمند: سه تفنگدار به زیرساخت شارژ EV غیرمتمرکز-2020
The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.