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1 تأمین امنیت اینترنت اشیاء: چالشها، تهدیدات و راهکارها
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 30 - تعداد صفحات فایل doc فارسی: 63
چکيده: اينترنت اشيا (IOT) يک جهش تکنولوژيکي بعدي است که باعث بهبود قابل توجهي در جنبه هاي مختلف محيط انسان مانند بهداشت، تجارت و حمل و نقل خواهد شد. با اين حال، با وجود اين واقعيت که ممکن است باعث ايجاد تغييرات اقتصادي و اجتماعي شود، امنيت و حفاظت از حريم خصوصي اشيا و کاربران يک چالش حياتي باقي مي ماند که بايد مورد توجه قرار گيرد. به طور خاص، در حال حاضر، اقدامات امنيتي بايد اقدامات کاربران و اشيا را تحت نظارت و کنترل قرار دهند. با اين حال ماهيت به هم پيوسته و مستقل اشيا علاوه بر قابليت هاي محدود آنها در رابطه با منابع محاسباتي، قابليت کاربرد مکانيزم هاي امنيتي مرسوم را غير ممکن مي سازد. علاوه بر اين، عدم تجانس فن آوري هاي مختلف که اينترنت اشيا را ترکيب مي کند پيچيدگي فرآيندهاي امنيتي را افزايش مي دهد، چرا که هر تکنولوژي با آسيب پذيري هاي مختلف مشخص مي شود. علاوه بر اين، مقادير عظيمي از داده ها که توسط تعاملات چندگانه بين کاربران و اشيا و يا بين اشيا ايجاد مي شود، مديريت آنها و عملکرد سيستم هاي کنترل دسترسي را سخت تر مي کند. در اين زمينه، اين مقاله قصد دارد يک تحليل جامع امنيتي از IoT را با بررسي و ارزيابي تهديدات بالقوه و اقدامات متقابل ارايه دهد. پس از مطالعه و تعيين الزامات امنيتي در زمينه IoT، ما يک آناليز ريسک کمي و کيفي را اجرا کرديم که در حال بررسي تهديدات امنيتي در هر لايه مي باشد. متعاقبا، براساس اين فرآيند ما اقدامات متقابل مناسب و محدوديت هاي آنها را شناسايي کرديم و توجه ويژه اي به پروتکل هاي اينترنت نموديم. در نهايت، دستورالعمل هاي تحقيق براي کار آينده را ارايه مي دهيم.
مقاله ترجمه شده
2 Weaving seams with data: Conceptualizing City APIs as elements of infrastructures
بافتن با داده ها: اندیشه سازی رابط های برنامه های کاربردی (API) شهری به عنوان عناصر زیرساخت-2019
This article addresses the role of application programming interfaces (APIs) for integrating data sources in the context of smart cities and communities. On top of the built infrastructures in cities, application programming interfaces allow to weave new kinds of seams from static and dynamic data sources into the urban fabric. Contributing to debates about ‘‘urban informatics’’ and the governance of urban information infrastructures, this article provides a technically informed and critically grounded approach to evaluating APIs as crucial but often overlooked elements within these infrastructures. The conceptualization of what we term City APIs is informed by three perspectives: In the first part, we review established criticisms of proprietary social media APIs and their crucial function in current web architectures. In the second part, we discuss how the design process of APIs defines conventions of data exchanges that also reflect negotiations between API producers and API consumers about affordances and mental models of the underlying computer systems involved. In the third part, we present recent urban data innovation initiatives, especially CitySDK and OrganiCity, to underline the centrality of API design and governance for new kinds of civic and commercial services developed within and for cities. By bridging the fields of criticism, design, and implementation, we argue that City APIs as elements of infrastructures reveal how urban renewal processes become crucial sites of socio-political contestation between data science, technological development, urban management, and civic participation.
Keywords: Application Programming Interface (API) | infrastructure | Internet of Things (IoT) | interface design | social urban data | smart city
مقاله انگلیسی
3 استخراج خودکار اطلاعات کارشناس برای پایگاه دانش اینترنت اشیا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 14
با توسعه سریع تکنولوژی IOT، نیاز به بازدهی موثر و دقیق دامنه دانش در حال افزایش است. استخراج خودکار اطلاعات کارشناس از صفحات عظیم وب و مدل نمایشی پویا و یکپارچه برای پایگاه دانش مهم است. با این حال، تفاوت های آشکار در ساختار و معناشناسی محتوا از صفحات وب بین هر دو وبسایت نشان می دهد که خزنده وب سنتی، معنای صفحه وب را درک نمی کند و اطلاعات بحرانی کارشناس را استخراج می کند. بنابراین، یک مدل نمایه حرفه ای شش بعدی معرفی شد و سپس یک روش برچسب گذاری توالی با مدل LSTM-CRF برای استخراج اتوماتیک اطلاعات غنی معنادار مبتنی بر ساختار سازمانی، معنی کلمات و ویژگی های متخصصان ارائه شد. نتایج آزمایش بر روی مجموعه داده های آزمایشی نشان داد که نرخ دقیق و فراخوان در مورد تجربه کار و زمینه تحقیق کارشناسان به ترتیب 67.8٪، 66.6٪ و 82.4٪ و 79.6٪ است. علاوه بر این، میانگین F در مورد برخی از ویژگی های مشخص متخصص مانند نام، عنوان، ایمیل، دستاورد و غیره، به 82.5٪ می رسد که بهتر از نتایج الگوریتم های MEMM و LSTM است.
کلمات کلیدی: اینترنت اشیا | مدل مشخصات کارشناس | یادگیری عمیق | برچسب زدن تکراری
مقاله ترجمه شده
4 Toward modeling and optimization of features selection in Big Data based social Internet of Things
به سوی مدل سازی و بهینه سازی انتخاب ویژگی ها در داده های بزرگ مبتنی بر اینترنت اشیا اجتماعی-2018
The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
Keywords: SIoT ، Big Data ، ABC algorithm، Feature selection
مقاله انگلیسی
5 A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
معماری جدید اینترنت اشیاء و اکوسیستم داده های بزرگ برای نظارت بر سیستم مراقبت سلامت هوشمند و سیستم هشدار دهنده امن-2018
Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.
Keywords: Wireless sensor networks ، Internet of Things ، Big data analytics ، Cloud computing and health car
مقاله انگلیسی
6 A Review of Policies concerning development of Big Data Industry in Pakistan
بررسی سیاست های مربوط به توسعه صنعت داده های بزرگ در پاکستان-2018
This In the present globalized smart ecosystem, various suggestions of using data as a new tool for the development of the economy are still going on to be presented. Hence, developed countries are trying to pursue different policy measures to develop the big data industry, including promoting big data R&D sector and investment in human resources to retain the pace of this global trend. The government of Pakistan has supported liberal policies to activate the IT its applications such as big data, Internet of Things (IOT) and electronic government (e-government). We used the Analytic Network Process (ANP) model to prioritize policy measures and find out its implications for Pakistan. This study will convey an important lesson for developing countries and particularly for South Asian countries to establish policies for developing big data as a new tool for economic growth in the context of smart ecosystem environment.
Keywords: Big data; Internet of things; IOT; Analytic network process; Ecosystem
مقاله انگلیسی
7 Power-aware gateway connectivity in battery-powered dynamic IoT networks
اتصال دروازه ای توان - آگاه در شبکه های پویای اینترنت اشیای کار کننده با باتری-2018
The paradigm of Internet of Things (IoT) is on rapid rise in today’s world of communication. Every networking device is being connected to the Internet to develop specific and dedicated applications. Data from these devices, called as IoT devices, is transmitted to the Internet through IoT Gateways (IGWs). IGWs support all the technologies in an IoT network. In order to reduce the cost involved with the deployment of IGWs, specialized low-cost devices called Solution Specific Gateways (SSGWs) are also employed alongside IGWs. These SSGWs are similar to IGWs except they support a subset of technologies supported by IGWs. A large number of applications are being designed which require IGWs and SSGWs to be deployed in remote areas. More often than not, gateways in such areas have to be run on battery power. Hence, power needs to be conserved in such networks for extending network life along with maintaining total connectivity. In this paper, we propose a dynamic spanning tree based algorithm for power-aware connectivity called SpanIoTPower-Connect which determines (near) optimal power consumption in battery-powered IoT networks. SpanIoTPower-Connect computes the spanning tree in the network in a greedy manner in order to minimize the power consumption and achieve total connectivity. Additionally, we propose an algorithm to conserve power in dynamic IoT networks where the connectivity demand changes with time. Our simulation results show that our algorithm performs better than Static Spanning Tree based algorithm for power-aware connectivity (Static ST) and a naive connectivity algorithm where two neighboring SSGWs are connected through every available technology. To the best of our knowledge, our work is the first attempt at achieving power-aware connectivity in battery-powered dynamic IoT networks.
keywords: Internet of Things| IoT gateway| IoT network| Power-aware| Performance evaluation
مقاله انگلیسی
8 Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers
پتانسیل ها، گرایشات و چشم اندازها در روشهای لبه ای: مراکز داده ای مات، تکه ابر، لبه ای سیار و میکرو-2018
Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service providers data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.
keywords: Edge computing| Fog computing| Internet of Things
مقاله انگلیسی
9 High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT
الگوریتم های c-means احتمالی اولویت بالا بر اساس تجزیه تانسور برای داده های بزرگ در اینترنت اشیا-2018
Internet of Things (IoT) connects the physical world and the cyber world to offer intelligent services by data mining for big data. Each big data sample typically involves a large number of attributes, posing a remarkable challenge on the high-order possibilistic c-means algorithm (HOPCM). Specially, HOPCM requires high-performance servers with a large-scale memory and a powerful computing unit, to cluster big samples, limiting its applicability in IoT systems with low-end devices such as portable computing units and embedded devises which have only limited memory space and computing power. In this paper, we propose two high-order possibilistic c-means algorithms based on the canonical polyadic decomposition (CP-HOPCM) and the tensor-train network (TT-HOPCM) for clustering big data. In detail, we use the canonical polyadic decomposition and the tensor-train network to compress the attributes of each big data sample. To evaluate the performance of our algorithms, we conduct the experiments on two representative big data datasets, i.e., NUS-WIDE-14 and SNAE2, by comparison with the conventional highorder possibilistic c-means algorithm in terms of attributes reduction, execution time, memory usage and clustering accuracy. Results imply that CP-HOPCM and TT-HOPCM are potential for big data clustering in IoT systems with low-end devices since they can achieve a high compression rate for heterogeneous samples to save the memory space significantly without a significant clustering accuracy drop.
Keywords: Big data ، IoT ، Possibilistic c-means clustering ، Canonical polyadic decomposition ، Tensor-train network
مقاله انگلیسی
10 Big data for internet of things: A survey
داده های بزرگ برای اینترنت اشیا: یک مرور-2018
With the rapid development of the Internet of Things (IoT), Big Data technolo gies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is ex tensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data ap proaches in each domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.
Keywords: Big Data, data analytics, Internet of Things, healthcare, energy, transportation, building automation, Smart Cities
مقاله انگلیسی
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