Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.     

IOTSim: A simulator for analysing IoT applications

A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.     

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.     

物联网环境下数据转发模型研究

随着5G移动通信技术、软件定义网络、命名数据网、移动边缘计算或雾计算等新兴技术或方法的出现及深入研究,物联网应用得到进一步升华。在这种应用场景多样化、服务质量高要求、参与对象普及化的环境下,隶属物联网子范畴的传统无线传感器网络数据转发模型已经不能完全适应这种时代需求,更加适合物联网应用的数据转发模型成为物联网连续性服务保障的基础性问题及研究热点。本文首先对物联网架构及其应用环境下的数据转发关键问题进行了分析;其次,对目前有代表性的物联网数据转发相关研究成果进行分类总结;然后,选取不同物联网场景下典型的数据转发模型及其使用的数学方法进行评述、分析和对比;最后,指出目前研究中存在的问题及相应解决方案,并对未来的发展方向进行了展望。研究表明,5G等新兴技术的出现为物联网环境下数据转发模型研究带来了新的机遇和挑战,今后的工作重点是对物联网环境下数据转发的节能模型和方法进行攻关,为实际应用提供坚实的理论基础。     

Hierarchical fuzzy-based Quality of Experience (QoE)-aware application placement in fog nodes

Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in-time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE-aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic. The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.44%, 37.83% improvement in RG, and 28.46%, 37.5%, 23.07% improvement in PTRR, when compared with QoE-aware, randomized, FIFO algorithms, respectively.     

A secure fog-based platform for SCADA-based IoT critical infrastructure

The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low-latency and enhanced security for mission-critical data. However, fog security and privacy for SCADA-based IoT critical infrastructures remains an under-researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA-based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic-based access approach to the cloud services using identity-based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog-based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. These results can pave the way toward the development of a more secure and trusted SCADA-based IoT critical infrastructure, which is essential to counter cyber threats against next-generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog-based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.     

雾计算中支持外包与撤销的属性基加密方案

雾计算将云计算的计算能力、数据分析应用等扩展到网络边缘,可满足物联网设备的低时延、移动性等要求,但同时也存在数据安全和隐私保护问题。传统云计算中的属性基加密技术不适用于雾环境中计算资源有限的物联网设备,并且难以管理属性变更。为此,提出一种支持加解密外包和撤销的属性基加密方案,构建“云-雾-终端”的三层系统模型,通过引入属性组密钥的技术,实现动态密钥更新,满足雾计算中属性即时撤销的要求。在此基础上,将终端设备中部分复杂的加解密运算外包给雾节点,以提高计算效率。实验结果表明,与KeyGen、Enc等方案相比,该方案具有更优的计算高效性和可靠性。     

An evolutionary game approach to IoT task offloading in fog-cloud computing

The Journal of Supercomputing - Developing the edge and fog computing has been the result of the fast growth of cloud-based IoT applications. These new paradigms define new resource management...     

物联网安全测评技术综述

近年来,物联网大规模应用于智能制造、智能家居、智慧医疗等产业,物联网的安全问题日益突出,给物联网的发展带来了前所未有的挑战。安全测评技术是保障物联网安全的重要手段,在物联网应用的整个开发生命周期都需要进行安全测评工作,以保证物联网服务的安全性和健壮性。物联网节点面临计算能力、体积和功耗受限等挑战,智慧城市等应用场景提出了大规模泛在异构连接和复杂跨域的需求。本文首先总结了目前物联网中常用的安全测评方法和风险管理技术;然后从绿色、智能和开放三个方面分析物联网安全技术的发展现状和存在的安全问题,并总结了物联网安全测评面临的挑战以及未来的研究方向。     

IoT‐fog‐cloud based architecture for smart systems: Prototypes of autism and COVID‐19 monitoring systems

Making resources closer to the user might facilitate the integration of new technologies such as edge, fog, cloud computing, and big data. However, this brings many challenges shall be overridden when distributing a real‐time stream processing, executing multiapplication in a safe multitenant environment, and orchestrating and managing the services and resources into a hybrid fog/cloud federation. In this article, first, we propose a business process model and notation (BPMN) extension to enable the Internet of Things (IoT)‐aware business process (BP) modeling. The proposed extension takes into consideration the heterogeneous IoT and non‐IoT resources, resource capacities, quality of service constraints, and so forth. Second, we present a new IoT‐fog‐cloud based architecture, which (i) supports the distributed inter and intralayer communication as well as the real‐time stream processing in order to treat immediately IoT data and improve the entire system reliability, (ii) enables the multiapplication execution within a multitenancy architecture using the single sign‐on technique to guarantee the data integrity within a multitenancy environment, and (iii) relies on the orchestration and federation management services for deploying BP into the appropriate fog and/or cloud resources. Third, we model, by using the proposed BPMN 2.0 extension, smart autistic child and coronavirus disease 2019 monitoring systems. Then we propose the prototypes for these two smart systems in order to carry out a set of extensive experiments illustrating the efficiency and effectiveness of our work.     

Design,code generation and simulation of IoT environments with mobility devices by using model-driven development: SimulateIoT-Mobile

Systems based on the Internet of Things (IoT) are continuously growing in many areas such as smart cities, home environments, buildings, agriculture, industry, etc. Device mobility is one of the key aspects of these IoT systems, but managing it could be a challenge. Mobility exposes the IoT environment or Industrial IoT (IIoT) to situations such as packet loss, increased delay or jitter, dynamism in the network topology, new security threats, etc. In addition, there is no standard for mobility management for the most commonly used IoT protocols, such as MQTT or CoAP. Consequently, managing IoT mobility is a hard, error-prone and tedious task. However, increasing the abstraction level from which the IoT systems are designed helps to tackle the underlying technology complexity. In this regard, Model-driven development approaches can help to both reduce the IoT application time to market and tackle the technological complexity to develop IoT applications. In this paper, a Domain-Specific Language based on SimulateIoT is proposed for the design, code generation and simulation of IoT systems with mobility management for the MQTT protocol. The IoT systems generated integrate the sensors, actuators, fog nodes, cloud nodes and the architecture that supports mobility, which are deployed as microservices on Docker containers and composed suitability. Finally, two case studies focused on animal tracking and a Personal mobility device (PMD) based on bicycles IoT systems are presented to show the IoT solutions deployed.     

NVM Storage in IoT Devices: Opportunities and Challenges

Edge storage stores the data directly at the data collection point, and does not need to transmit the collected data to the storage central server through the network. It is a critical technology that supports applications such as edge computing and 5G network applications, with lower network communication overhead, lower interaction delay and lower bandwidth cost. However, with the explosion of data and higher real-time requirements, the traditional Internet of Things (IoT) storage architecture cannot meet the requirements of low latency and large capacity. Non-volatile memory (NVM) presents new possibilities regarding this aspect. This paper classifies the different storage architectures based on NVM and compares the system goals, architectures, features, and limitations to explore new research opportunities. Moreover, the existing solutions to reduce the write latency and energy consumption and increase the lifetime of NVM IoT storage devices are analyzed. Furthermore, we discuss the security and privacy issues of IoT devices and compare the mainstream solutions. Finally, we present the opportunities and challenges of building IoT storage systems based on NVM.     

物联网技术研究进展

目前,物联网因其巨大的应用前景而受到各国政府、学术界和工业界的广泛重视,成为国内外信息通信领域的新研究热点;物联网产业己经成为推动世界经济增长的重要新兴产业,我国已将物联网的发展列为信息产业发展的下一个战略高点;首先总结了物联网的国内外研究进展,其次对物联网的概念、实现原理与体系结构进行了分析,归纳了物联网涉及的关键技术,然后介绍了物联网的一些典型应用,最后指出了物联网进一步研究的方向以及制约国内物联网发展的问题及解决对策。     

The yoking-proof-based authentication protocol for cloud-assisted wearable devices

Along with the development of IoT applications, wearable devices are becoming popular for monitoring user data to provide intelligent service support. The wearable devices confront severe security issues compared with traditional short-range communications. Due to the limitations of computation capabilities and communication resources, it brings more challenges to design security solutions for the resource-constrained wearable devices in IoT applications. In this work, a yoking-proof-based authentication protocol (YPAP) is proposed for cloud-assisted wearable devices. In the YPAP, a physical unclonable function and lightweight cryptographic operators are jointly applied to realize mutual authentication between a smart phone and two wearable devices, and yoking-proofs are established for the cloud server to perform simultaneous verification. Meanwhile, Rubin logic-based security formal analysis is performed to prove that the YPAP has theoretical design correctness. It indicates that the proposed YPAP is flexible for lightweight wearable devices in IoT applications.     

Characterizing application scheduling on edge,fog, and cloud computing resources

Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, edge and fog computing resources have emerged on the wide-area network as part of Internet of things (IoT) deployments. These three resource abstraction layers are complementary, and offer distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and data flow models offering a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we offer a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure and offer a taxonomy of application models, quality-of-service constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.     

A survey on data provenance in IoT

Internet of Things (IoT), as a typical representation of cyberization, enables the interconnection of physical things and the Internet, which provides intelligent and advanced services for industrial production and human lives. However, it also brings new challenges to IoT applications due to heterogeneity, complexity and dynamic nature of IoT. Especially, it is difficult to determine the sources of specified data, which is vulnerable to inserted attacks raised by different parties during data transmission and processing. In order to solve these issues, data provenance is introduced, which records data origins and the history of data generation and processing, thus possible to track the sources and reasons of any problems. Though some related researches have been proposed, the literature still lacks a comprehensive survey on data provenance in IoT. In this paper, we first propose a number of design requirements of data provenance in IoT by analyzing the features of IoT data and applications. Then, we provide a deep-insight review on existing schemes of IoT data provenance and employ the requirements to discuss their pros and cons. Finally, we summarize a number of open issues to direct future research.

     

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.      

A survey on solutions to support developers in privacy-preserving IoT development

Internet-of-Things (IoT) devices are rising in popularity and their usefulness often stems from the amount of data they collect. Data regulations such as the European General Data Protection Regulation (GDPR) require software developers to do their due diligence when it comes to privacy, as they are required to adhere to certain principles such as Privacy-by-Design (PbD). Due to the distributed and heterogeneous nature of IoT applications, privacy-preserving design is even more important in IoT environments. Studies have shown that developers are often not eager to implement privacy and generally do not see it as their duty or concern. However, developers are often left alone when it comes to engineering privacy in the realm of IoT. In this paper, we therefore survey which frameworks and tools have been developed for them, especially in the case of IoT. Our findings indicate that existing solutions are cumbersome to use, only work in certain scenarios, and are not enough to solve the privacy issues inherent IoT development. Based on our analysis, we further propose future research directions.