تقویت میان افزار بر مبنی کاربردهای اینترنت اشیا از طریق مکانیسم مدیریت زمان اجرای قابل جابجایی کیفیت سرویس کاربرد برای یک M2M سازگار با میان افزار IOT
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 22
در سال های اخیر؛ در مخابرات و شبکه های کامپیوتری از طریق مجازی سازی عملکرد شبکه (NFV) و شبکه های تعریف شده نرم افزار (SDN)، مفاهیم و تکنولوژی های جدیدی را شاهد بوده اید. SDN، به برنامه های کاربردی برای کنترل شبکه اجازه می دهد، و NFV، اجازه می دهد تا توزیع توابع شبکه در محیط های مجازی، امکان پذیر شوند، اینها دو نمونه ای هستند که به طور فزاینده ای برای اینترنت اشیا (IoT) استفاده می شود. این اینترنت (IoT) وعده را به ارمغان می آورد که در چند سال آینده میلیاردها دستگاه را به هم متصل کند، و چالش های علمی متعددی را به ویژه در مورد رضایت از کیفیت خدمات (QoS) مورد نیاز برنامه های کاربردی IOT افزایش دهد. به منظور حل این مشکل، ما دو چالش را با توجه به QoS شناسایی کرده ایم: شبکه های متقاطع و نهادهای میانجی که اجازه می دهد تا برنامه با دستگاه های IoT ارتباط برقرار کند. در این مقاله؛ در ابتدا یک چشم انداز نواورانه از یک "عملکرد شبکه" با توجه به محیط توسعه و استقرار آن ارائه می کنیم. سپس، رویکرد کلی از یک راه حل که شامل گسترش پویا، مستقل و یکپارچه از مکانیزم های مدیریت QoS است، را توصیف می کنیم. همچنین مقررات اجرای چنین رویکردی را توصیف می کنیم. در نهایت؛ یک مکانیزم هدایتگر ارائه می کنیم، که به عنوان یک تابع شبکه اجرا می شود، و اجازه کنترل یکپارچه مسیر داده ها از یک ترافیک میان افزار مشخص را می دهد. این مکانیسم از طریق استفاده مربوط به حمل و نقل خودرو ارزیابی می شود.
کلمات کلیدی: اینترنت اشیا | کیفیت سرویس | میان افزار | چارچوب نمونه | گسترش پویا | عملکرد شبکه | محاسبات خودکار.
|مقاله ترجمه شده|
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
محاسبات متن آگاه، یادگیری و داده های بزرگ در اینترنت اشیا: یک مرور-2018
Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers
Index Terms: Big data in Internet of Things (IoT), context awareness, data management and analytics, machine learning in IoT
Advancing distributed data management for the HydroShare hydrologic information system
پیشرفت مدیریت داده های توزیع شده برای سیستم اطلاعات هیدرولوژیکی HydroShare-2018
HydroShare (https://www.hydroshare.org) is an online collaborative system to support the open sharing of hydrologic data, analytical tools, and computer models. Hydrologic data and models are often large, extending to multi-gigabyte or terabyte scale, and as a result, the scalability of centralized data man agement poses challenges for a system such as HydroShare. A distributed data management framework that enables distributed physical data storage and management in multiple locations thus becomes a necessity. We use the iRODS (Integrated Rule-Oriented Data System) data grid middleware as the distributed data storage and management back end in HydroShare. iRODS provides a unified virtual file system for distributed physical storages in multiple locations and enables data federation across geographically dispersed institutions around the world. In this paper, we describe the iRODS-based distributed data management approaches implemented in HydroShare to provide a practical demon stration of a production system for supporting big data in the environmental sciences.
Keywords: Distributed data management ، Big data ، Data sharing ، Hydrologic information systems ، Collaborative environment ، iRODS
MIFIM—Middleware solution for service centric anomaly in future internet models
راه حل MIFIM Middleware برای ناهنجاری سرویس مرکزی در آینده مدل های اینترنتی -2017
Internet is shifting at a rapid pace and evolving into a trend called as ‘‘Future Internet (FI)’’. It can be defined as the union and cooperation of paradigms such as Internet of Things (IoT), Internet of Services (IoS) and Internet of Content (IoC). In these paradigms, the role of Service oriented Computing (SoC) deserves special attention. FI can be defined as an association of web services encompassing innovative services such as converged services, intelligent services and related smart services for overcoming the structural limitations of the current internet. Among many key concerns in the services environment, service discovery and optimal service selection are considered to be vital. Service discovery enables the client to get access to the right service at the right time to complete the requested tasks while service selection determines the feasible service composition that fulfils a set of conditions while maintaining a rich Quality of User Experience (QoUE) and good Quality of Service (QoS). This paper proposes the FI middleware named MIFIM (MIddleware for Future Internet Models) incorporated with Aspect Oriented Module (AOM) for addressing the challenges in particular related to the unknown topology and missing data estimation present in IoT service discovery and optimal service selection routine named Composite Service Selection Module (CSSM) for deriving the best service composition in IoS paradigm. The AOM is evaluated in accordance with MUSIC pervasive computing middleware while CSSM is compared with the other optimality approaches. Experimental results were found encouraging and the proposed components were performing reasonably well when compared to the similar solutions.
Keywords: Future Internet (FI) | Middleware | Internet of Things (IoT) | QoS (Quality of Service) | QoUE (Quality of User Experience) | PSO (Particle Swarm Optimization)
Optimized task allocation on private cloud for hybrid simulation of large-scale critical systems
تخصیص کار بهینه شده در ابر خصوصی برای شبیه سازی ترکیبی از سیستم های بحرانی در مقیاس بزرگ-2017
Simulation represents a powerful technique for the analysis of dependability and performance aspects of distributed systems. For large-scale critical systems, simulation demands complex experimentation environments and the integration of different tools, in turn requiring sophisticated modeling skills. Moreover, the criticality of the involved systems implies the set-up of expensive testbeds on private infrastructures. This paper presents a middleware for performing hybrid simulation of large-scale critical systems. The services offered by the middleware allow the integration and interoperability of simulated and emulated subsystems, compliant with the reference interoperability standards, which can provide greater realism of the scenario under test. The hybrid simulation of complex critical systems is a research challenge due to the interoperability issues of emulated and simulated subsystems and to the cost associated with the scenarios to set up, which involve a large number of entities and expensive long running simulations. Therefore, a multi-objective optimization approach is proposed to optimize the simulation task allocation on a private cloud.
Keywords: Large-scale critical systems | Hybrid simulation | Resource optimization | Middleware | Cloud computing
Improving the gossiping effectiveness with distributed strategic learning (Invited paper)
بهبود اثربخشی شایعات با استفاده از استراتژی یادگیری توزیع شده (مقاله دعوت شده)-2017
Gossiping is a widely known and successful approach to reliable communications, tolerating packet losses and link crashes. It has been extensively used in several middleware kinds, such as event notification services and application domains, like infrastructures for air traffic management, power grid control, health information exchange, just to cite some of them. Despite achieving a high loss-tolerance and scalability degrees, gossiping is affected by degraded performances and heavy traffic loads on the network. For this reason, it may be not optimal in applications where reliability must be provided jointly with timeliness and/or in congestion-prone networks. The crucial aspect for improving a gossiping scheme is deciding which nodes should receive a gossiping message, and our driving idea is to adopt a distributed strategic learning logic to determine such nodes in an efficient manner. This is able to resolve gossiping’s weakness points and to achieve better performance and reduced traffic loads. This paper describes how to introduced strategic learning in a gossip scheme so as to determine the best set of nodes that can be used to send gossip messages and to optimize their utility. Such a solution has been experimentally assessed through a set of simulations demonstrating the effectiveness of the proposal.
Keywords: Event-based communications | Gossiping | Reliable multicasting | Strategic learning | Game theory | Reinforcement learning
MidHDC: Advanced topics on middleware services for heterogeneous distributed computing: Part 2✩
MidHDC: Advanced topics on middleware services for heterogeneous distributed computing: Part 2-2017
Currently distributes systems support different computing paradigms like Cluster Computing, Grid Computing, Peer-to-Peer Computing, and Cloud Computing all involving elements of heterogeneity. These computing distributed systems are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use. The second part of this special issue presents advances in allocation algorithms, service selection, VM consolidation and mobility policies, scheduling multiple virtual environments and scientific workflows, optimization in scheduling process, energy-aware scheduling models, failure Recovery in shared Big Data processing systems, distributed transaction processing middleware, data storage, trust evaluation, information diffusion, mobile systems, integration of robots in Cloud systems.
Keywords: Middleware services | Resource management | Mobile computing | Cloud computing | HPC | Heterogeneous distributed systems
Middleware technologies for cloud of things-a survey
تکنولوژی های میان افزار برای ابرهای اشیاء - یک مرور -2017
The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things (CoT), which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable “Middleware” which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we presents several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares are discussed.
Keywords: CoT | IoT | Middleware | Fog computing | Cloud
Description and classification for facilitating interoperability of heterogeneous data/events/services in the Internet of Things
شرح و طبقه بندی برای تسهیل قابلیت همکاری داده های ناهمگن / رویدادها / خدمات در اینترنت اشیاء-2017
The Internet of Things (IoT) refers to an infrastructure that integrates things over standard wired/wireless networks and allows them to exchange information with each other. The IoT is a very complex hetero geneous network, enabling seamless integration of these things is a huge challenge. A publish/subscribe method of integration can be formulated to solve the problems of interconnecting billions of heteroge neous things. In our work, an IoT framework that uses an abstraction layer that decouples an application from the service calls and network interfaces is required to send and receive messages on a particular thing. This paper provides definitions and classifications for heterogeneous data/events/services accord ing to the properties of the things in order to integrate them into a framework for description. Based on these definitions and classifications, heterogeneous data/events/services in the IoT were integrated via topic description through the Data Distribution Service (DDS) middleware standard for real-time pub lish/subscribe. This paper also concludes with general remarks and a discussion of future work.
Keywords: Internet of Things (IoT) | Data Distribution Service (DDS) | Topic | Description | Interoperability
Security towards the edge: Sticky policy enforcement for networked smart objects
امنیت به سمت لبه: اجرای سیاست های مهم برای اشیاء هوشمند شبکه-2017
Article history:Received 13 June 2017Revised 24 July 2017Accepted 25 July 2017Available online 25 July 2017Keywords: Internet of Things SecuritySticky policy Enforcement Middleware PrototypeOne of the hottest topics in the Internet of Things (IoT) domain relates to the ability of enabling com- putation and storage at the edges of the network. This is becoming a key feature in order to ensure the ability of managing in a scalable way service requests with low response times. This means being able to acquire, store, and process IoT-generated data closer to the data producers and data consumers. In this scenario, also security and privacy solutions must be applied in a capillary way at the edges of the network. In particular, a control on access to data generated by IoT devices is necessary for guaranteeing proper levels of security and privacy as well as for preventing violation attempts, while allowing data owners to monitor and control their information. In this paper, a sticky policy approach is proposed as a strategy for eﬃciently managing the access to IoT resources within an existing distributed middleware architecture. As demonstrated in the experimental evaluation, sticky policies represent a promising and eﬃcient technique to increase the robustness (in a security perspective) of the IoT system.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Internet of Things | Security | Sticky policy | Enforcement | Middleware Prototype