Authenticated key agreement scheme for fog-driven IoT healthcare system

The convergence of cloud computing and Internet of Things (IoT) is partially due to the pragmatic need for delivering extended services to a broader user base in diverse situations. However, cloud computing has its limitation for applications requiring low-latency and high mobility, particularly in adversarial settings (e.g. battlefields). To some extent, such limitations can be mitigated in a fog computing paradigm since the latter bridges the gap between remote cloud data center and the end devices (via some fog nodes). However, fog nodes are often deployed in remote and unprotected places. This necessitates the design of security solutions for a fog-based environment. In this paper, we investigate the fog-driven IoT healthcare system, focusing only on authentication and key agreement. Specifically, we propose a three-party authenticated key agreement protocol from bilinear pairings. We introduce the security model and present the formal security proof, as well as security analysis against common attacks. We then evaluate its performance, in terms of communication and computation costs.

     

电信业新技术新业务网络数据安全分析

随着电信行业技术革新和快速迭代,5G、人工智能、大数据分析、物联网等技术的不断涌现,为电信行业数字化和智能化转型提供了技术前提和基础平台。归纳了电信行业五大重点业务类型及应用前景,以业务支撑系统、大数据平台、云计算业务、物联网业务为典型案例,结合业务特点深度分析业务管理与技术方面的网络数据安全风险,提出数据安全管理工作体系搭建、数据安全技术保障能力配备的对策与建议,为电信企业积极应对新形势、新情况和新问题提供思路。     

电信运营商的云计算与物联网发展策略

以云计算与物联网为代表的新兴IT技术,对传统的电信运营业既带来冲击也带来机遇和发展。在云计算领域,电信运营商通过电信业务能力的开放,聚合电信云和IT云,实现通信资源和计算、存储资源的协同,创新了服务模式;在物联网领域,运营商可以通过构建物联网运营支撑平台和服务体系,在物联网产业链中发挥关键作用。面对新兴IT技术,电信运营商应该坚持合作与开放,并注重标准化,与产业链各方共同努力,开创新的发展空间。     

Middleware technologies for cloud of things: a survey

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 present 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 is discussed.     

Blockchain-Enabled Deep Reinforcement Learning Approach for Performance Optimization on the Internet of Things

Internet of Things (IoT) networks are rapidly expanding, which requires adequate and reliable infrastructure and a large amount of data. The IoT device security and technical confidentiality may benefit from using Blockchain, a decentralised and trustworthy ledger. Increasing transaction throughput and coping with big data transfer situations is critical when dealing with significant volumes of IoT data on the Blockchain. Consequently, this research investigates the Deep Reinforcement Learning (DRL) crucial functioning of the blockchain-enabled IoT structure, wherever transactions are instantaneously expanded and public divisibility is confirmed. It is important to note that DRL and Blockchain are two separate advancements devoted to the reliability and usefulness of system operation. These are both transactional systems. Technology integration into information exchange and research solutions is becoming increasingly critical. As a result of Blockchain, information may be exchanged securely and decentralised. When used in tandem with DRL, it can significantly improve communication efficiency. By combining DRL and Blockchain throughout the IoT, the author first presents a decentralised and efficient communication structure that allows for scalable and trustworthy information allocation and better performance than earlier options. The DRL approach assesses whether to offload and which service to dump to improve performance up to 87.5%. Furthermore, this method focuses on constructing an effective offloading mechanism for Blockchain-based communication systems to boost performance.

     

Survey on data security and privacy-preserving for the research of edge computing

With the rapid development and extensive application of the Internet of things (IoT),big data and 5G network architecture,the massive data generated by the edge equipment of the network and the real-time service requirements are far beyond the capacity if the traditional cloud computing.To solve such dilemma,the edge computing which deploys the cloud services in the edge network has envisioned to be the dominant cloud service paradigm in the era of IoT.Meanwhile,the unique features of edge computing,such as content perception,real-time computing,parallel processing and etc.,has also introduced new security problems especially the data security and privacy issues.Firstly,the background and challenges of data security and privacy-preserving in edge computing were described,and then the research architecture of data security and privacy-preserving was presented.Secondly,the key technologies of data security,access control,identity authentication and privacy-preserving were summarized.Thirdly,the recent research advancements on the data security and privacy issues that may be applied to edge computing were described in detail.Finally,some potential research points of edge computing data security and privacy-preserving were given,and the direction of future research work was pointed out.     

云计算分布式缓存技术及其在物联网中的应用

物联网是信息技术发展到一定阶段的产物,而云计算平台是物联网应用的基础。文章从当前云计算应用所面临的问题和缺陷出发,介绍了云计算分布式缓存的部署方式、功能架构及关键技术,并说明了分布式缓存高性能、高吞吐、高可靠性、高扩展性等优势和特性。文章解决了物联网应用普遍面临的数据可靠性、大容量内存共享、多模块数据一致保障、线性扩容等难题,为物联网平台云化架构的底层支撑奠定了基础。     

Authentication-based Access Control and Data Exchanging Mechanism of IoT Devices in Fog Computing Environment

The emergence of fog computing has witnessed a big role in initiating secure communication amongst users. Fog computing poses the ability to perform analysis, processing, and storage for a set of Internet of Things (IoT) devices. Several IoT solutions are devised by utilizing the fog nodes to alleviate IoT devices from complex computation and heavy processing. This paper proposes an authentication scheme using fog nodes to manage IoT devices by providing security without considering a trusted third party. The proposed authentication scheme employed the benefits of fog node deployment. The authentication scheme using fog node offers reliable verification between the data owners and the requester without depending on the third party users. The proposed authentication scheme using fog nodes effectively solved the problems of a single point of failure in the storage system and offers many benefits by increasing the throughput and reducing the cost. The proposed scheme considers several entities, like end-users, IoT devices, fog nodes, and smart contracts, which help to administrate the authentication using access policies. The proposed authentication scheme using fog node provided superior results than other methods with minimal memory value of 4009.083 KB, minimal time of 76.915 s, and maximal Packet delivery ratio (PDR) of 76.

     

Edge Computing Offloading Strategy Based on Dynamic Non-cooperative Games in D-IoT

With the vigorous development of Internet of Things technology, the current distribution network is developing towards the information-based and intelligent distribution Internet of Things (D-IoT). D-IoT adopts the mode of the cloud computing center and the edge cloud network working together. The edge cloud network has a large number of intelligent terminals, which can well adapt to the current sharply expanding power data scale. In order to further improve the ability of the edge network in D-IoT to process data in real time, and to maximize the quality of user experience (QoE) while minimizing energy consumption when performing computing offload, this paper proposes a dynamic non-cooperative game based edge Computing task offloading strategy, considering the dynamic nature of task generation, designed a distributed iterative optimization algorithm, which decomposes computing offloading into a series of sub-problems to solve. The results of simulation experiments prove that the calculation offloading mechanism proposed in this paper can greatly improve D -Compute efficiency of IoT system.

     

An Efficient Hybrid Computing Environment to Develop a Confidential and Authenticated IoT Service Model

Wireless Personal Communications - The applications and scope of the Internet of Things (IoT) goes on increasing when cloud computing combines with IoT. Cloud enriches the capacity of IoT in...     

Survey of Testing Methods and Testbed Development Concerning Internet of Things

The concept of Internet of Things (IoT) was designed to change everyday lives of people via multiple forms of computing and easy deployment of applications. In recent years, the increasing complexity of IoT-ready devices and processes has led to potential risks related to system reliability. Therefore, the comprehensive testing of IoT technology has attracted the attention of many researchers, which promotes the extensive development of IoT testing methods and infrastructure. However, the current research on IoT testing methods and testbeds mainly focuses on specific application scenarios, lacking systematic review and analysis of many applications from different points of view. This paper systematically summarizes the latest testing methods covering different IoT fields and discusses the development status of the existing Internet of things testbed. Findings of this review demonstrate that IoT testing is moving toward larger scale and intelligent testing, and that in near future, IoT test architecture is set to become more standardized and universally applicable with multi-technology convergence—i.e., a combination of big data, cloud computing, and artificial intelligence—being the prime focus of IoT testing.

     

云计算环境下基于大数据的物联网信息化建设

云计算以及大数据技术的支持,能够为物联网的信息化建设奠定基础。云计算技术的应用优势较为显著,其廉价、安全且科学的计算与存储能力,能够突出技术应用优势。依靠大数据与云计算的相互协调,能够建立起更优质的物联网信息管理系统,分析计算物联网网络环境,调整分布式技术应用方案,展现大数据的属性。在构建信息化框架期间,建立起大数据感知层做好重点环节的设计,能够更好地满足物联网信息化的发展需要,完成系统功能与实体模块的配合。技术融合的效率增强,能够有效提升物联网信息化建设水平,文中将结合实践深入探索其工作要点。     

Time to forge ahead: The Internet of Things for healthcare

Situated at the intersection of technology and medicine, the Internet of Things (IoT) holds the promise of addressing some of healthcare's most pressing challenges, from medical error, to chronic drug shortages, to overburdened hospital systems, to dealing with the COVID-19 pandemic. However, despite considerable recent technological advances, the pace of successful implementation of promising IoT healthcare initiatives has been slow. To inspire more productive collaboration, we present here a simple—but surprisingly underrated—problem-oriented approach to developing healthcare technologies. To further assist in this effort, we reviewed the various commercial, regulatory, social/cultural, and technological factors in the development of the IoT. We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem. To this end, we explore the key enabling technologies that underpin the fog architecture, from the sensing layer all the way up to the cloud. It is our hope that ongoing advances in sensing, communications, cryptography, storage, machine learning, and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.     

人工智能在网络安全运维服务中的应用

近年来,国内外网络安全形势趋于复杂,关系到国民经济命脉的关键信息基础设施在传统模式下得不到有效保护。网络安全运维服务以“专业保安”身份着力打造关键信息基础设施的整体防御能力,但随着人工智能、大数据、云计算、5G、物联网以及边缘计算等新技术发展的应用,大量关键信息隐藏在海量数据中很难被发现并有效利用。因此,以人工智能为抓手,研究人工智能赋能网络安全运维服务,打造智慧运营新思路,解决实际运维服务过程中智能化、自动化等问题。     

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.     

Energy Efficient Multitier Random DEC Routing Protocols for WSN: In Agricultural

Wireless Personal Communications - Fog computing is an emerging paradigm that provides confluence facilities between Internet of Things (IoT) devices and cloud. The fog nodes process the...     

CIRUS: an elastic cloud-based framework for Ubilytics

The Internet of Things (IoT) has become a reality with the availability of chatty embedded devices. The huge amount of data generated by things must be analysed with models and technologies of the “Big Data Analytics”, deployed on cloud platforms. The CIRUS project aims to deliver a generic and elastic cloud-based framework for Ubilytics (ubiquitous big data analytics). The CIRUS framework collects and analyses IoT data for Machine to Machine services using Component-off-the-Shelves (COTS) such as IoT gateways, Message brokers or Message-as-a-Service providers and big data analytics platforms deployed and reconfigured dynamically with Roboconf. In this paper, we demonstrate and evaluate the genericity and elasticity of CIRUS with the deployment of a Ubilytics use case using a real dataset based on records originating from a practical source.     

移动互联网中的云计算安全架构

移动互联网(Mobile Internet)是以移动设备为核心,融合了互联网和移动通信技术的一种计算机技术,是当今信息科技发展的热点之一。而云计算(Cloud Computing)作为一种高效、灵活、节能的计算模式,是当前信息化建设的重要技术手段之一。移动互联网与云计算的结合,可以为用户提供更多、更丰富的服务,提高信息处理的效率和质量。然而,移动互联网中的云计算也面临着安全问题,如何构建安全、可靠的云计算安全架构,成了一个亟待解决的问题。     

A framework for harmonizing internet of things (IoT) in cloud: analyses and implementation

Internet of Things (IoT) refers to uniquely identifiable entities. Its vision is the world of connected objects. Due to its connected nature the data produced by IoT is being used for different purposes. Since IoT generates huge amount of data, we need some scalable storage to store and compute the data sensed from the sensors. To overcome this issue, we need the integration of cloud and IoT, so that the data might be stored and computed in a scalable environment. Harmonization of IoT in Cloud might be a novel solution in this regard. IoT devices will interact with each other using Constrained Application Protocol (CoAP). In this paper, we have implemented harmonizing IoT in Cloud. We have used CoAP to get things connected to each other through the Internet. For the implementation we have used two sensors, fire detector and the sensor attached with the door which is responsible for opening it. Thus our implementation will be storing and retrieving the sensed data from the cloud. We have also compared our implementation with different parameters. The comparison shows that our implementation significantly improves the performance compared to the existing system.

     

Cloud Computing: Concept, Model, and Key Technologies

Cloud computing is a new network computing paradigm based on IP architecture, and its potential lies in new ICT business applications. For the majority of operators and enterprises, the main task associated with cloud computing is next generation data center transformation. This will ensure cloud computing becomes more widespread among enterprises, institutions, organizations, and operators. Cloud computing not only provides traditional IT resource usage and application services, but also supports full resource usage and application services such as IT, communications, video, mobile, and Internet of Things using a converged network infrastructure. Key cloud computing technologies include unified fabric, unified virtualization, and unified computing system. The formation of an open industry alliance and promotion of open technology standards will be critical for the future development of cloud computing.