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.

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.

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.

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.

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。

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。

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。

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.

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的智慧巷道水位监测系统研究

研究提出一个基于NB-IoT技术的物联网巷道水位监测系统架构及模型设计,尝试将传感网技术应用于解决巷道水灾的监测问题上。系统利用树莓派作为传感节点中各监测节点的控制核心,收集、过滤巷道周边传感器获取的信息,并通过传感网传递信息给后台服务器,记录所有水位信息与处理结果。系统包含一个能够识别出水位信息的传感网络模型,通过架设于巷道中的传感器网络去感知水位情况后,通过NB-IoT网络传递信息,记录异常信息与处理结果,如果出现信息异常,传感节点可自动报警。

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NB-IoT技术的智能农业环境监测系统设计

在农业生产管理中,利用农业物联网可以高效地获取农作物的生长环境信息,但是农田中终端节点之间分布距离较远,同时受限于接入容量,设备功耗等,从而给农作物环境监测带来一定的困扰。针对智慧农业存在的问题,利用NB-IoT技术组建无线传感网络对农作物生长环境进行实时采集和监测,同时研究基于多传感器数据融合方法,利用自适应加权平均融合以及神经网络方法对各区域内的传感器数据进行融合,从而得到农作物环境状况的综合判断。该方法可以针对不同农作物及其生存环境需求,有效提升农作物生存状态预测精度,本方法相比传统方法预测误差降低45%以上,验证本方法的有效性和优越性。

轨道区段占用检测系统的NB-IoT网络通信节点设计

针对轨道区段占用检测系统的网络通信需求,研发一种高效可靠的基于阿里云平台的窄带物联网(Narrow Band Internet of Things,NB-IoT)网络通信节点。利用光纤光栅传感器、超声波探头等设备采集轨道区段的列车占用信息和钢轨裂缝探伤检测信息,通过基于移远BC260Y模组所设计的NB-IoT无线通信节点将数据上传到阿里云平台进行数据记录。通过上位机和App程序,用户可以实时查看轨道区段的占用检测信息和钢轨的损伤情况。所设计的NB-IoT网络通信节点主要包含4个方面,分别为NB-IoT模块电路、串口通信模块电路、USIM接口模块电路和电源模块电路。不同于传统的“两跳”方案,所设计的NB-IoT网络通信节点无需中间网关设备,具备广泛的覆盖范围、低终端功耗以及高成本效益等特点,可有效提升高铁轨道区段占用检测的网络通信效率和可靠性。

基于ZigBee和NB-IoT融合网络的实验室智能监控系统实现及应用

针对传统实验室监控系统设备配置繁琐、用户无法及时获悉示警信息等问题,该文设计了一种实验室智能监控系统。系统构建了以ZigBee和NB-IoT融合网络为核心的传感层、网络层、应用层3层网络拓扑,集成信息采集、数据处理、无线通信、终端执行单元等模块。经试验,该系统实现了对实验室环境信息数据的实时监测、采集传输、波形显示、可靠存储、分析示警及实验室设备的智能调控,达到了预期效果。借助该系统,用户在移动端即可实时远程监控实验室的环境状况,及时获得异常示警并做出反馈,为有效规避实验室突发事故提供极大便利。

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IOT的生猪智能养殖集成系统设计

随着生猪饲喂技术从机械化向自动化、智能化方向的发展,国内劳动密集型生猪养殖方法逐渐被智能养殖设备替代。我国在生猪智能化饲喂设备方面起步较晚,而国外的智能饲喂设备价格昂贵且不符合我国养猪业“小聚集”的现状。基于此,本文研究设计了一套基于IOT的生猪智能养殖集成系统。该系统主要包括电子识别与精准饲喂系统和猪舍环境监控系统。精准饲喂设备可以根据RFID射频识别出猪的身份ID号实现个体差异化的精准投料,同时为进食猪只提供全封闭的采食环境;猪舍环境监控系统能够实时监控猪舍环境变化,调控猪只生活环境。试验表明,该系统能够实现生猪的精准饲喂和猪舍环境的有效改善,满足群养生猪个体精准饲喂的设计要求。

基于边云协同的消防设施智慧感知系统

火灾发生时,消火栓欠压、消防水箱无水等消防设施故障会致使火灾进一步扩大,造成不必要的生命财产损失。针对该问题,本系统选用低功耗蓝牙技术构建无线Mesh网络的组网方案感知消防设施状态参数,数据通过NB-IoT上传至云平台。用户可以通过电脑端或移动端实现对消防设施的远程监测,当消防设施状态异常时,用户端自动推送通知,从而实现第一时间检修。通过在边缘计算主机上部署火焰识别算法模型处理视频监控数据,当识别到火情时通过云平台及时推送给用户,实现火情的联动报警和快速响应。经测试,所设计的系统使用便捷、运行稳定、具有较高的应用价值。