Real-time secure communication for Smart City in high-speed Big Data environment
ارتباط امن در زمان واقعی برای شهر هوشمند در محیط داده های بزرگ با سرعت بالا-2018
The recent development in the technology brings the concept of Smart City that is achieved through real-time city related intelligent decisions by analyzing the data harvested from various smart systems in the city using millions of sensors and devices connected over the Internet, termed as Internet of Things (IoT). These devices generate the overwhelming volume of high-speed streaming data, termed as Big Data. However, the generation of city data at a remote location and then transmitting it to central city servers for analysis purpose raises the concerns of security and privacy. On the other hand, providing security to such Big Data streaming requires a high-speed security system that can work in a real-time environment without providing any delay that may slow down the overall performance of the Smart City System. To overthrown these challenges, in this paper, we proposed an efficient and real-time Smart City security system by providing strong intrusion detection at intelligent city building (ICB) and also a security protocol to protect the communication between the remote smart system(RSS)/User and the city analysis building, i.e., ICB. The proposed communication security protocol consists of various phases, i.e., registration phase, session key exchange phase, session key revocation phase, and data transmission phases from RSS to ICB as well as from User to ICB. Vast security analyses are performed to evaluate the credibility of the system. The proposed system is also evaluated on efficiency in terms of computation cost and throughput of overall functions used in the system. The system’s evaluation and the comparative study with existing system show that the prosed system is secure, more efficient, and able to work in a real-time, high-speed Smart City environment.
Keywords: Smart City ، Big Data ، Internet of Things (IoT) ، Communication security ، Cyber security
Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics
اینترنت اشیا و داده های بزرگ برای مراقبت های بهداشتی دقیق: از دستگاه به معماری، برنامه های کاربردی و تجزیه و تحلیل-2018
The technology and healthcare industries have been deeply intertwined for quite some time. New opportunities, however, are now arising as a result of fast-paced expansion in the areas of the Internet of Things (IoT) and Big Data. In addition, as people across the globe have begun to adopt wearable biosensors, new applications for individualized eHealth and mHealth technologies have emerged. The upsides of these technologies are clear: they are highly available, easily accessible, and simple to personalize; additionally they make it easy for providers to deliver individualized content cost-effectively, at scale. At the same time, a number of hurdles currently stand in the way of truly reliable, adaptive, safe and efficient personal healthcare devices. Major technological milestones will need to be reached in order to address and overcome those hurdles; and that will require closer collaboration between hardware and software developers and medical personnel such as physicians, nurses, and healthcare workers. The purpose of this special issue is to analyze the top concerns in IoT technologies that pertain to smart sensors for health care applications; particularly applications targeted at individualized tele-health interventions with the goal of enabling healthier ways of life. These applications include wearable and body sensors, advanced pervasive healthcare systems, and the Big Data analytics required to inform these devices.
Video big data in smart city: Background construction and optimization for surveillance video processing
داده های بزرگ ویدئویی در شهر هوشمند: ساخت و ساز پیش زمینه و بهینه سازی برای پردازش ویدئو نظارتی-2018
Transforming infrastructures, buildings and services with the sensed data from the Internet of Things (IoT) technique has drawn wide attention. Enormous video data from city surveillance cameras poses huge challenges of transmission, storage and analysis, which necessitates new video compression technologies. The fusion of video data generated from smart city could be used to support city management and urban policy. Based on the specific characteristics of surveillance video, which are successive pictures have very strong correlations and each picture can be divided into background and foreground, this work proposes a block-level background modeling (BBM) algorithm to support long-term reference structure for efficient surveillance video coding. A rate–distortion optimization for surveillance source (SRDO) algorithm is also developed to improve the coding performance. Experimental results show that the proposed BBM and SRDO can significantly improve the compression performance, which can effectively support diverse video applications in smart city.
Keywords: Smart cities ، Internet of Things ، Video big data ، Data fusion ، Surveillance video processing
Water utility decision support through the semantic web of things
پشتیبانی تصمیم گیری ابزار آب از طریق وب معنایی اشیا-2018
Urban environments are urgently required to become smarter. However, building advanced applications on the Internet of Things requires seamless interoperability. This paper proposes a water knowledge management platform which extends the Internet of Things towards a Semantic Web of Things, by leveraging the semantic web to address the heterogeneity of web resources. Proof of concept is demonstrated through a decision support tool which leverages both the data-driven and knowledge based programming interfaces of the platform. The solution is grounded in a comprehensive ontology and rule base developed with industry experts. This is instantiated from GIS, sensor, and EPANET data for a Welsh pilot. The web service provides dis coverability, context, and meaning for the sensor readings stored in a scalable database. An interface displays sensor data and fault inference notifications, leveraging the complementary nature of serving coherent lower and higher-order knowledge.
Keywords: Water management ، Decision support tool ، Interoperability ، Big data ، Ontology ، Semantic web ، Internet of things ، Smart water networks
A hybrid model of Internet of Things and cloud computing to manage big data in health services applications
یک مدل ترکیبی از اینترنت اشیا و محاسبات ابری برای مدیریت داده های بزرگ در برنامه های خدمات بهداشتی-2018
Over the last decade, there has been an increasing interest in big data research, especially for health services applications. The adoption of the cloud computing and the Internet of Things (IoT) paradigm in the healthcare field can bring several opportunities to medical IT, and experts believe that it can significantly improve healthcare services and contribute to its continuous and systematic innovation in a big data environment such as Industry 4.0 applications. However, the required resources to manage such data in a cloud-IoT environment are still a big challenge. Accordingly, this paper proposes a new model to optimize virtual machines selection (VMs) in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0. Industry 4.0 applications require to process and analyze big data, which come from different sources such as sensor data, without human intervention. The proposed model aims to enhance the performance of the healthcare systems by reducing the stakeholders’ request execution time, optimizing the required storage of patients’ big data and providing a real-time data retrieval mechanism for those applications. The architecture of the proposed hybrid cloud-IoT consists of four main components: stakeholders’ devices, stakeholders’ requests (tasks), cloud broker and network administrator. To optimize the VMs selection, three different well-known optimizers (Genetic Algorithm (GA), Particle swarm optimizer (PSO) and Parallel Particle swarm optimization (PPSO) are used to build the proposed model. To calculate the execution time of stakeholders’ requests, the proposed fitness function is a composition of three important criteria which are CPU utilization, turn-around time and waiting time. A set of experiments were conducted to provide a comparative study between those three optimizers regarding the execution time, the data processing speed, and the system efficiency. The proposed model is tested against the state-of-the-art method to evaluate its effectiveness. The results show that the proposed model outperforms on the state-of-the-art models in total execution time the rate of 50%. Also, the system efficiency regarding real-time data retrieve is significantly improved by 5.2%.
Keywords: Big data ، Industry 4.0 ، Cloud computing ، Internet of Things ، Health services ، Genetic Algorithm ، Particle swarm optimization
A parallel metaheuristic data clustering framework for cloud
یک چارچوب خوشه بندی داده های متا مکاشفه ای موازی برای ابر-2018
A high performance data analytics for internet of things (IoT) has been a promising research subject in recent years because traditional data mining algorithms may not be applicable to big data of IoT. One of the main reasons is that the data that need to be analyzed may exceed the storage size of a single machine. The computation cost of data analysis tasks that is too high for a single computer system is another critical problem we have to confront when analyzing data from an IoT system. That is why an efficient data clustering framework for metaheuristic algorithm on a cloud computing environment is presented in this paper for data analytics, which explains how to divide mining tasks of a mining algorithm into different nodes (i.e., the Map process) and then aggregate the mining results from these nodes (i.e., Reduce process). We further attempted to use the proposed framework to implement data clustering algorithms (e.g., k-means, genetic k-means, and particle swarm optimization) on a standalone system and Spark. The experimental results show that the performance of the proposed framework makes it useful to develop data clustering algorithms on a cloud computing environment.
Keywords: Metaheuristic algorithm ، Internet of things ، Data clustering problem
Improving Quality of Experience in multimedia Internet of Things leveraging machine learning on big data
بهبود کیفیت تجربه در اینترنت اشیا چندرسانه ای یادگیری ماشین بر روی داده های بزرگ-2018
With rapid evolution of the Internet of Things (IoT) applications on multimedia, there is an urgent need to enhance the satisfaction level of Multimedia IoT (MIoT) network users. An important and unsolved prob lem is automatic optimization of Quality of Experience (QoE) through collecting/managing/processing various data from MIoT network. In this paper, we propose an MIoT QoE optimization mechanism leveraging data fusion technology, called QoE optimization via Data Fusion (QoEDF). QoEDF consists of two steps. Firstly, a multimodal data fusion approach is proposed to build a QoE mapping between the uncontrollable user data with the controllable network-related system data. Secondly, an automatic QoE optimization model is built taking fused results, which is different from the traditional way. QoEDF is able to adjust network-related system data automatically so as to achieve optimized user satisfaction. Simulation results show that QoEDF will lead to significant improvements in QoE level as well as be adaptable to dynamic network changes.
Keywords: Data fusion ، Multimedia Internet of Things ، Big data ، Quality of Experience ، Machine learning ، Neural network
In-Mapper combiner based MapReduce algorithm for processing of big climate data
الگوریتم MapReduce مبتنی بر ترکیب Mapper در پردازش داده های آب و هوایی بزرگ -2018
Big data refers to a collection of massive volume of data that cannot be processed by conventional data processing tools and technologies. In recent years, the data production sources are enlarged noticeably, such as high-end streaming devices, wireless sensor networks, satellite, wearable Internet of Things (IoT) devices. These data generation sources generate a massive volume of data in a continuous manner. The large volume of climate data is collected from the IoT weather sensor devices and NCEP. In this paper, the big data processing framework is proposed to integrate climate and health data and to find the correlation between the climate parameters and incidence of dengue. This framework is demonstrated with the help of MapReduce programming model, Hive, HBase and ArcGIS in a Hadoop Distributed File System (HDFS) environment. The following weather parameters such as minimum temperature, maximum temperature, wind, precipitation, solar and relative humidity are collected for the study are Tamil Nadu with the help of IoT weather sensor devices and NCEP. Proposed framework focuses only on climate data for 32 districts of Tamil Nadu where each district contains 1,57,680 rows and so there are 50,45,760 rows in total. Batch view precomputation for the monthly mean of various climate parameters would require 50,45,760 rows. Hence, this would create more latency in query processing. In order to overcome this issue, batch views can precompute for a smaller number of records and involve more computation to be done at query time. The In-Mapper based MapReduce framework is used to compute the monthly mean of climate parameter for each latitude and longitude. The experimental results prove the effectiveness of the response time for the In-Mapper based combiner algorithm is less when compared with the existing MapReduce algorithm.
Keywords: Big data ، Internet of Things ، Weather sensor devices ، MapReduce programming ،Model ، Hadoop distributed file system
Big Data Analytics in Industrial IoT Using a Concentric Computing Model
تجزیه و تحلیل داده های بزرگ در اینترنت اشیا صنعتی با استفاده از یک مدل محاسباتی مرکزی-2018
The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).
Keywords: Big Data, data analysis, Internet of Things,learning (artificial intelligence)
A Big Data Analytics Architecture for the Internet of Small Things
معماری تحلیل داده های بزرگ برای اینترنت اشیا کوچک-2018
The SK Telecom Company of South Korea recently introduced the concept of IoST to its business model. The company deployed IoST, which constantly generates data via the LoRa wireless platform. The increase in data rates generated by IoST is escalating exponentially. After attempting to analyze and store the massive volume of IoST data using existing tools and technologies, the South Korean company realized the shortcomings immediately. The current article addresses some of the issues and presents a big data analytics architecture for its IoST. A system developed using the proposed architecture will be able to analyze and store IoST data efficiently while enabling better decisions. The proposed architecture is composed of four layers, namely the small things layer, infrastructure layer, platform layer, and application layer. Finally, a detailed analysis of a big data implementation of the IoST used to track humidity and temperature via Hadoop is presented as a proof of concept.
Keywords: Big Data, data analysis, Internet of Things, parallel programming