Quality of service‐aware approaches in fog computing

In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.     

A systematic literature review of machine learning applications in IoT

The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.     

LARPA: A learning automata‐based resource provisioning approach for massively multiplayer online games in cloud environments

Nowadays, with the development of communication systems, massively multiplayer online games (MMOGs) have become very popular. In these games, the players all over the world dynamically interact with each other by sending play actions such as shootings, movements, or chatting in the form of MMOG sessions in real time through a large‐scale distributed environment. Leveraging affordable cloud computing to host such services is a widely investigated issue. It is because the arrival rate of players to the game environment has to make fluctuations, and the players expect services to be always available with an acceptable quality of service (QoS), especially in terms of the response time. Therefore, the dynamic provisioning of resources in order to deal with fluctuating demands due to variability in the arrival rate of players of the MMOG services is highly recommended. In this paper, we propose a learning‐based resource provisioning approach for MMOG services that is based on the combination of the autonomic computing paradigm and learning automata (LA). The remarkable performance of the proposed approach in terms of response time, cost, and allocated virtual machines (VMs) is assessed through simulation and comparison with the existing approaches.     

物联网中一种抗大规模天线阵列窃听者的噪声注入方案

物联网中无线传输的安全难题是制约其发展的重要瓶颈,物联网终端受限的计算能力与硬件配置以及配备大规模天线阵列的窃听者给物理层安全技术带来了新的挑战。针对该问题,该文提出一种可对抗大规模天线阵列窃听者的轻量级噪声注入策略。首先,对所提出的噪声注入策略进行介绍,并分析了该策略的安全性;然后,基于该策略得到了系统吞吐量的闭式表达式,并对时隙分配系数和功率分配系数进行优化设计。理论和仿真结果表明,通过对物联网系统参数进行设计,所提出的噪声注入策略能够实现私密信息的安全传输。

     

Data dissemination with interoperability in IoT network

Internet of Things (IoT) is connected to heterogeneous devices. Efficient adaptive scheduling with encoding and decoding of data is an unaddressed issue in IoT. This paper processes the data under three major hierarchy: namely, adaptability, scheduling of data, and network coding for that data. The reliable access to the information is ensured by a device which is a primary eminence in IoT. Device must be able to adapt itself according to the changes in the network and to maintain its reliability as well as transparency and seamless access to the resources. To enhance the performance of the data dissemination, the scheduling process is investigated using the spatial grouping in IoT devices; this is achieved by joint spatial and code domain scheduling scheme, and the novel preconfigured access scheme is coined in order to minimize the collision rate of arbitrary access; during the data dissemination, the erasure coding scheme is used for the encoding and decoding of packets which provides optimal redundancy. We carried the simulation using Contiki and it shows the proposed Polymorphic Erasure Coding with Markov decision Adaptability and Neural networks (PECMAN) improves in terms of cost, overhead, and delay when compared with Multi‐user Shared Access (EMUSA), Polynomial‐time Optimal Storage Allocation (OSA) scheme, and Event‐Aware Back pressure Scheduling Scheme (EABS).     

相似度自适应估计的物联网实体高效搜索方法

针对现有相似实体搜索方法缺乏对于观测序列长度的自适应性,且搜索过程数据存储开销过大,搜索结果准确性较低的问题,该文提出相似度自适应估计的物联网实体高效搜索方法(SAEES)。首先,设计了轻量级观测序列分段表示方法,对传感器采集的实体原始观测序列进行轻量级分段压缩表示,以降低实体观测序列的存储开销。然后,提出了观测序列相似度自适应估计方法,实现对不同观测序列长度的实体相似性的准确估计。最后,设计了高效的相似实体搜索匹配方法,依据所估计的实体相似度进行实体的准确搜索匹配。仿真结果表明,所提方法可大幅提高相似实体搜索的效率。

     

Toward intelligent resource management in dynamic Fog Computing-based Internet of Things environment with Deep Reinforcement Learning: A survey

Fog computing has already started to gain a lot of momentum in the industry for its ability to turn scattered computing resources into a large-scale, virtualized, and elastic computing environment. Resource management (RM) is one of the key challenges in fog computing which is also related to the success of fog computing. Deep learning has been applied to the fog computing field for some time, and it is widely used in large-scale network RM. Reinforcement learning (RL) is a type of machine learning algorithms, and it can be used to learn and make decisions based on reward signals that are obtained from interactions with the environment. We examine current research in this area, comparing RL and deep reinforcement learning (DRL) approaches with traditional algorithmic methods such as graph theory, heuristics, and greedy for managing resources in fog computing environments (published between 2013 and 2022) illustrating how RL and DRL algorithms can be more effective than conventional techniques. Various algorithms based on DRL has been shown to be applicable to RM problem and proved that it has a lot of potential in fog computing. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing RM. The positive impact of this work is that it can successfully provide a resource manager to efficiently schedule resources and maximize the overall performance.     

A low power high speed MTJ based non-volatile SRAM cell for energy harvesting based IoT applications

Powering billions of devices is one of the most challenging barrier in achieving the future vision of IoT. Most of the sensor nodes for IoT based systems depend on battery as their power source and therefore fail to meet the design goals of lifetime power supply, cost, reliable sensing and transmission. Energy harvesting has the potential to supplant batteries and thus prevents frequent battery replacement. However, energy autonomous systems suffer from sudden power variations due to change in external natural sources and results in loss of data. The memory system is a main component which can improve or decrease performance dramatically. The latest versions of many computing system use chip multiprocessor (CMP) with on-chip cache memory organized as array of SRAM cell. In this paper, we outline the challenges involved with the efficient power supply causing power outage in energy autonomous/self-powered systems. Also, various techniques both at circuit level and system level are discussed which ensures reliable operation of IoT device during power failure. We review the emerging non-volatile memories and explore the possibility of integrating STT-MTJ as prospective candidate for low power solution to energy harvesting based IoT applications. An ultra-low power hybrid NV-SRAM cell is designed by integrating MTJ in the conventional 6T SRAM cell. The proposed LP8T2MTJ NV-SRAM cell is then analyzed using multiple key performance parameters including read/write energies, backup/restore energies, access times and noise margins. The proposed LP8T2MTJ cell is compared to conventional 6T SRAM counterpart indicating similar read and write performance. Also, comparison with the existing MTJ based NV-SRAM cells show 51–78% reduction in backup energy and 42–70% reduction in restore energy.     

定量和定性相结合的物联网漏洞分类方法研究

为解决物联网漏洞数量规模巨大、分类方法欠缺问题,针对已有漏洞分类方法应用于物联网漏洞存在覆盖不完全、交叉重叠现象严重的现状,提出从物联网设备、同源跨平台漏洞以及漏洞的影响效果和漏洞利用复杂度3个维度对物联网漏洞进行科学分类的方法——VCECI。首先研究传统漏洞分类方法的特点和物联网产品研发固有特点,分析物联网漏洞分类不完善的原因。其次,对VCECI方法定量和定性相结合的分类过程进行深入论述。最后,结合实验分析该方法的应用效果。实验结果表明,VCECI方法对物联网漏洞具有较好的标识和去重能力,能够有效表示物联网漏洞的异构多样性特点。     

物联网核心技术与应用场景

首先对物联网的概念、组成部分以及服务体系进行简要介绍,使读者首先对物联网在整体上有较为清晰的认识,其次,依托对传感器无线射频识别技术（RFID）两种主要数据采集手段的坚实论述,融合ZigBee、WIA-PA两种常用标准,着重阐述物联网的核心-传感器网络,为读者详细论述了传感器网络中的数据采集和处理以及无线通信这两个方面。最后,展望物联网的发展前景,剖析过程中的机遇与挑战。     

Task offloading in an optimized power-performance manner

The cloud computing systems, such as the Internet of Things (IoT), are usually introduced with a three-layer architecture (IoT-Fog-Cloud) for the task offloading that is a solution to compensate for resource constraints in these systems. Offloading at the right location is the most significant challenge in this field. It is more appropriate to offload tasks to fog than to cloud based on power and performance metrics, but its resources are more limited than the resources of the cloud. This paper tries to optimize these factors in the fog by specifying the number of usable servers in the fog. For this purpose, we model a fog computing system using the queueing theory. Furthermore, binary search and reinforcement learning algorithms are proposed to determine the minimum number of servers with the lowest power consumption. We evaluate the cost of the fog in different scenarios. By solving the model, we find that the proposed dispatching policy is very flexible and outperformed the known policies by up to 31% and in no case is it worse than either of them, and the overall offloading cost increases when fog rejects tasks with a high probability. Our offloading method is more effective than running all fog servers simultaneously, based on simulation results. It is evident from the similarities between the simulation results and those derived from the analytical method that the model and results are valid.     

Edge computing technologies for Internet of Things: a primer

With the rapid development of mobile internet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and non-adaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. In particular, the standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. From the viewpoint of radio access network, the differences between mobile edge computing and fog computing are highlighted, and the characteristics of fog computing-based radio access network are discussed. Finally, open issues and future research directions are identified as well.     

基于可信计算构筑物联网安全边界

近年来，中国物联网政策支持力度不断加大，技术创新成果接连涌现，各领域应用持续深化，产业规模保持快速增长。本论文以物联网接入边界为切入点，探讨了物联网安全接入问题的解决方案。设计了基于可信计算3.0的物联网可信网关，以及安全管理中心，构建了可信的物联网安全边界接入系统。为各种异构物联网终端设备提供了安全屏障，隔绝了针对于物联网设备的网络安全威胁。     

Towards asynchronous federated learning for heterogeneous edge-powered internet of things

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-time data and deploying machine learning models. Nonetheless, an individual IoT device may not have adequate computing resources to train and deploy an entire learning model. At the same time, transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and privacy. Federated learning, a distributed machine learning framework, is a promising solution to train machine learning models with resource-limited devices and edge servers. Yet, the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication connections. In this paper, we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication network. Particularly, we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks effectively. The proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication condition. Extensive experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed (i.i.d.) and non-i.i.d. data distribution.     

认知物联网短包通信中双向中继系统的信息年龄分析

未来认知物联网(IoT)将存在大量用于监控的时间敏感类短包信息。针对认知物联网短包通信场景,该文分析了认知次用户对在双向中继短包通信系统中的信息新鲜度。该文采用信息年龄(AoI)作为衡量信息新鲜度的性能指标。根据短包通信理论,该文推导出系统的误包率和平均峰值AoI(PAoI)表达式,并得出了系统在高信噪比情况下的表达式。在此基础上,考虑认知物联网短包通信中频谱检测性能的非理想,采用交替迭代优化算法对感知包长和传输包长进行联合优化以最小化PAoI加权和。仿真结果验证了理论分析的正确性。该研究发现,对于双向中继系统次用户对的PAoI加权和,感知包长和传输包长存在折中关系,该文所采用的交替迭代优化算法能够有效提升系统PAoI性能。     

Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.     

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

Energy harvesting (EH) has been considered as one of the promising technologies to power Internet of Things (IoT) devices in self‐powered IoT networks. By adopting a typical harvest‐then‐transmit mode, IoT devices with the EH technology first harvest energy by using wireless power transfer (WPT) and then carry out wireless information transmission (WIT), which leads to the coordination between WPT and WIT. In this paper, we consider optimizing energy consumption of periodical data collection in a self‐powered IoT network with non‐orthogonal multiple access (NOMA). Particularly, we take into account time allocation for the WPT and WIT stages, node deployment, and constraints for data transmission. Moreover, to thoroughly explore the impact of different multiple access methods, we theoretically analyse and compare the performance achieved by employing NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) in the considered IoT network. To validate the performance of the proposed method, we conduct extensive simulations and show that the NOMA outperforms the FDMA and TDMA in terms of energy consumption and transmission power.     

Design of a wideband Y-shaped antenna for the application in IoT and 5G communication

A small wideband Y-shaped antenna is presented in this paper. A monopole of Y-shaped with two rectangular-shape frequency shifting strip is used to produce a compact dimension of 10 mm × 12 mm on a 1-mm-thick FR4 substrate. The antenna has an impedance bandwidth (measured below ?10 dB from 39.57 to 44.63 GHz), a gain more than 4.9 dB, radiation efficiency of 81%, and voltage standing wave ratio (VSWR) < 2, within the bandwidth of interest, making it a viable option for 5G applications. The use of a (01.7850 × 2.6775 × 00.02) mm³ metallic strip located above the feed line is also shown to efficiently increase the antenna bandwidth to values greater than 5 GHz without affecting the other antenna parameters. Additionally, the measured results in comparison with the simulated results reveal negligible changes, confirming that the proposed antenna is also suitable for the applications of 5G with Internet of Things.     

物联网应用层关键技术研究

物联网网络架构可划分为3层,作为物联网架构两端的感知层和应用层是物联网的显著特征和核心所在,感知层强调利用感知技术与智能装置对物理世界进行感知识别,应用层则侧重于对感知层采集数据的计算、处理和知识挖掘,从而达到对物理世界实时控制、精确管理和科学决策的目的,而目前从学术研究到应用领域都呈现出"重物联轻应用"的现象。本文从应用层角度分析物联网的需求特征,对应用层的关键技术和目前存在的突出问题进行了研究,主要包括云计算技术、软件和算法、标识与解析技术、信息和隐私安全技术,同时还对应用层的标准体系进行了初步研究。