Intelligent UAV Based Energy Supply for 6G Wireless Powered IoT Networks

In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.

OFDM Based Bidirectional Multi-Relay SWIPT Strategy for 6G IoT Networks

6G IoT networks aim for providing significantly higher data rates and extremely lower latency.However,due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices(IoDs)deployed,6G IoT networks face two critical challenges,i.e.,energy limitation and severe signal attenuation.Simultaneous wireless information and power transfer(SWIPT)and cooperative relaying provide effective ways to address these two challenges.In this paper,we investigate the energy self-sustainability(ESS)of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks.In the proposed strategy,the transmission process is equally divided into two phases.Specifically,in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy,respectively.In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy.We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation.Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WSNs may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized decision.On the other side the proliferation of these devices will soon cause radio spectrum shortage.So,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these networks.CR can sense the unutilized spectrum of licensed users and then use these empty bands when required.In order to keep the IoT nodes functional all time,continuous energy is required.For this reason the energy harvested techniques are preferred in IoT networks.Mainly it is preferred to harvest Radio Frequency(RF)energy in the network.In this paper a region based multi-channel architecture is proposed.In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region.The Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy.To harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel.From the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel.We also show that channel load can be highly reduced and channel utilization is proved to be more proficient.Thus,this proves the proposed architecture cost-effective and energy-efficient.

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network.Therefore,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone domains.In ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes.This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination.The proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources.Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application throughput.The end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

Enabling IoT Network Slicing with Network Function Virtualization

Numerous Internet of Things (IoT) devices are being connected to the net-works to offer services. To cope with a large diversity and number of IoT ser-vices, operators must meet those needs with a more flexible and efficient net-work architecture. Network slicing in 5G promises a feasible solution for this issue with network virtualization and programmability enabled by NFV (Net-work Functions Virtualization). In this research, we use virtualized IoT plat-forms as the Virtual Network Functions (VNFs) and customize network slices enabled by NFV with different QoS to support various kinds of IoT services for their best performance. We construct three different slicing systems including: 1) a single slice system, 2) a multiple customized slices system and 3) a single but scalable network slice system to support IoT services. Our objective is to compare and evaluate these three systems in terms of their throughput, aver-age response time and CPU utilization in order to identify the best system de-sign. Validated with our experiments, the performance of the multiple slicing system is better than those of the single slice systems whether it is equipped with scalability or not.

Ultra-low-power backscatter-based software-defined radio for intelligent and simplified IoT network

The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things(IoT)networks that provide ultra-low-power communication for numerous miniaturized devices.Although the research community has paid great attention to wireless protocol designs for these networks,researchers are handicapped by the lack of an energy-efficient software-defined radio(SDR)platform for fast implementation and experimental evaluation.Current SDRs perform well in battery-equipped systems,but fail to support miniaturized IoT devices with stringent hardware and power constraints.This paper takes the first step toward designing an ultra-low-power SDR that satisfies the ultra-low-power or even battery-free requirements of intelligent and simplified IoT networks.To achieve this goal,the core technique is the effective integration ofµW-level backscatter in our SDR to sidestep power-hungry active radio frequency chains.We carefully develop a novel circuit design for efficient energy harvesting and power control,and devise a competent solution for eliminating the harmonic and mirror frequencies caused by backscatter hardware.We evaluate the proposed SDR using different modulation schemes,and it achieves a high data rate of 100 kb/s with power consumption less than 200µW in the active mode and as low as 10µW in the sleep mode.We also conduct a case study of railway inspection using our platform,achieving 1 kb/s battery-free data delivery to the monitoring unmanned aerial vehicle at a distance of 50 m in a real-world environment,and provide two case studies on smart factories and logistic distribution to explore the application of our platform.

Task-Specific Feature Selection and Detection Algorithms for IoT-Based Networks

As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of applying Machine Learning-based cybersecurity in IoT devices is feature selection as most IoT devices are resource-constrained. This paper studies two feature selection algorithms: Information Gain and PSO-based, to select a minimum number of attack features, and Decision Tree and SVM are utilized for performance comparison. The consistent use of the same metrics in feature selection and detection algorithms substantially enhances the classification accuracy compared to the non-consistent use in feature selection by Information Gain (entropy) and Tree detection algorithm by classification. Furthermore, the Tree with consistent feature selection is comparable to the ensemble that provides excellent performance at the cost of computation complexity.

基于NB-IoT技术的环境监测系统优化设计

为进一步提升工业生产过程的安全系数,提出一种基于窄带物联网NB-IoT的环境监测系统。其中,以NB-IoT技术作为系统的主要通信,以传感器为主要的环境数据采集工具,以改进的BP神经网络作为预测方法进行环境风险预测。实验结果表明,与传统的BP神经网络相比,经过粒子群算法PSO优化的BP神经网络具有更高的预测精度,且稳定性较好,将其应用于环境风险的预测时误差始终保持在1%的误差范围内。设计的基于NB-IoT的环境监测系统能够进行准确的数据采集和风险预测,能够进一步保障生产安全,可行性较高。

Approximate Data Aggregation in Sensor Equipped IoT Networks

As Internet-of-Things(IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.

基于IPv6-IOT技术的地震专网无线传输组网研究

文章以山东地震行业无线传输网建设为目标,基于山东地震行业专网的现状,利用IPv6-IOT技术规划和建设山东地震行业无线传输网。研究成果不仅可以为山东地震行业实现下一代IPv6组网的平滑演进和升级奠定坚实基础,还可以为基于IPv6-IOT无线传输的组网提供模型依据和技术支撑。

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

基于自适应控制算法的NB-IoT网络性能优化研究

随着物联网技术的快速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)技术因其低功耗、广覆盖、大容量等特性,成为物联网的重要连接方式。针对NB-IoT网络性能优化问题,提出基于自适应控制算法的优化方案,提高其可靠性、容量及能效。通过仿真实验,验证该方案的有效性和性能优势。此外,基于该算法,采用终端感知、网络通信、数据处理以及应用表现4层系统设计架构,设计基于自适应控制算法的NB-IoT物联网系统,满足不断增长的物联网应用需求。

Tackling IoT Scalability with 5G NFV-Enabled Network Slicing

With emerging large volume and diverse heterogeneity of Internet of Things (IoT) applications, the one-size-fits-all design of the current 4G networks is no longer adequate to serve various types of IoT applications. Consequently, the concepts of network slicing enabled by Network Function Virtualization (NFV) have been proposed in the upcoming 5G networks. 5G network slicing allows IoT applications of different QoS requirements to be served by different virtual networks. Moreover, these network slices are equipped with scalability that allows them to grow or shrink their instances of Virtual Network Functions (VNFs) when needed. However, all current research only focuses on scalability on a single network slice, which is the scalability at the VNF level only. Such a design will eventually reach the capacity limit of a single slice under stressful incoming traffic, and cause the breakdown of an IoT system. Therefore, we propose a new IoT scalability architecture in this research to provide scalability at the NS level and design a testbed to implement the proposed architecture in order to verify its effectiveness. For evaluation, three systems are compared for their throughput, response time, and CPU utilization under three different types of IoT traffic, including the single slice scaling system, the multiple slices scaling system and the hybrid scaling system where both single slicing and multiple slicing can be simultaneously applied. Due to the balanced tradeoff between slice scalability and resource availability, the hybrid scaling system turns out to perform the best in terms of throughput and response time with medium CPU utilization.

ZTE and Innofidei Achieve Industry's First Field IOT on Multiple TD-LTE USB Dongles in a Mobile Network Cell

ZTE Corporation, a leading global provider of telecommunications equipment and networking solutions, announced on May 11,2010 that ZTE Corporation and Innofidei have jointly delivered a significant breakthrough for the Time Division Long Term Evolution （TD-LTE） industry with the industry＇s first successful Inter-Operability Test（IOT） of multiple TD-LTE USB dongles in a single mobile network cell. The successful test was first performed in Hong Kong,

基于NB-IoT的水电站大规模洞室群体智能照明研究

本研究以大型水电站的地下洞室群体照明为对象,针对其在特殊运行环境中的管理需求,研发了一套综合性照明管理系统。该系统充分考虑了洞室群在电力传输、交通、应急逃生、通风换气及生产生活用水等方面关键功能,同时应对由于隧洞布局的复杂性带来的挑战。采用分层分区的控制策略,并整合智能预测与强化学习技术,优化照明布局并实现亮度的动态调节。通过多平台的互联互通以及云平台的数据管控,系统不仅确保了洞室内部工作的有效进行,还注重资源的合理配置和能源的节约,有效地解决了大规模洞室群照明系统在管理控制和能源消耗方面的问题。该系统的实施,为水电站洞室群体管理的高效智能化提供了新的视角,对提高电站的稳定性和安全性有着重要的贡献。

A Review of Artificial Intelligence Applications in Contemporary Computer Network Technologies

Rapid advancement in science and technology has seen computer network technology being upgraded constantly, and computer technology, in particular, has been applied more and more extensively, which has brought convenience to people’s lives. The number of people using the internet around the globe has also increased significantly, exerting a profound influence on artificial intelligence. Further, the constant upgrading and development of artificial intelligence has led to the continuous innovation and improvement of computer technology. Countries around the world have also registered an increase in investment, paying more attention to artificial intelligence. Through an analysis of the current development situation and the existing applications of artificial intelligence, this paper explicates the role of artificial intelligence in the face of the unceasing expansion of computer network technology.

IOT环境下网络实验课程群及实验综合平台建设

物联网(IOT)是下一代信息技术的核心,互联网是物联网的基础,在这种新形式下,承担网络知识模块的计算机网络课程群成为了高校信息类专业中最重要的专业课程群之一,但传统的实验教学模式与体系已经无法适应物联网环境下网络课程群建设的需要。本文通过对计算机网络课程群实验教学内容和特点的分析,针对目前课程群实验教学中存在的不足,根据"创新优先,能力主导,课程协同,项目驱动"的实验课程群教学模式,研究并提出一种分层次的网络实验课程群体系结构,构建了一套新的符合人才培养需求的综合实验平台,可以为师生提供一个高水平和高效率的教学科研平台。

引入区块链的SDN-IoT网络安全:架构、方案与挑战

基于软件定义网络架构的物联网(SDN-IoT)顺应了物联网从软件上实现网络功能的发展趋势,但随着网络应用服务需求的类型与数量日益剧增,物联网易受攻击设备数量不断增加,软件定义网络自身特性带来更多更新的安全威胁.具有去中心化、交易安全性等特性的区块链技术为保证SDN-IoT网络的安全性提供新的思路与策略.首先从物联网、软件定义网络以及两者结合的3个方面来解析SDN-IoT面临的安全风险与网络攻击,讨论引入区块链后所能解决的问题;对现有基于区块链的SDN-IoT安全架构进行梳理,并从部署区块链的网络层面进行分类.此外,分别针对数据安全、通信安全、软件定义网络控制安全、节点安全和特有攻击,对现有解决方案及最新研究进展进行全面介绍.最后结合现有的解决思路,从技术融合、区块链自身安全、架构完善等3方面,分析基于区块链的SDN-IoT网络安全的研究方向与挑战.

面向智慧城市的NB-IoT网络规划设计虚拟仿真实验教学系统建设

该文以典型的智慧城市应用为场景,将最新的5G通信技术引入“移动通信”课程实践教学,建设了窄带物联网规划设计虚拟仿真实验教学系统。该实验教学系统可从城市和社区两个方面开展NB-IoT组网规划设计,能够使学生掌握最新移动通信系统的原理、技术与应用,提高学生解决复杂工程问题和考虑非技术因素能力,建立工程系统观和工程伦理观。

基于ZigBee Mesh网络和NB-IoT城市地下空间传输系统的研究

为解决城市地下空间无网络信号及洪涝期间信息传输不稳定的问题,提出一种基于ZigBee自适应、高冗余Mesh网络和NB-IoT的无线传输方法。通过新型物联网技术比较,以ARM为系统监测平台,ZigBee为终端节点,采用Mesh无中心同频自组网络,NB-IoT为中继协调节点将数据传输至监控中心,组建城市地下空间信息传输系统,实现实时监测与高效传输。经应用与分析,该传输系统在地下空间无网络信号或洪涝期间信息传输不稳定的情况下,数据平均到报率达到99.50%以上,为城市洪涝监测预报预警和应急抢险提供快速及时的信息支持。