移动物联网非完整约束中继的协同任务规划

为实现突发事件的应急决策和态势评估,满足对重点区域的地面全景或空间立体信息可靠节能地远距离传输要求,本文将无人机（Unmanned Aerial Vehicle,UAV）作为空基感测平台,研究应急移动物联网应用中无人机编队能量受限条件下的远距离通信问题,提出面向非完整约束多地面移动中继的协同任务规划方法.首先,对该类物联网进行系统建模;其次,根据所建模型中无人机空基平台立体地分布于重点空域的特点,研究了地面移动中继区域分配算法、位置更新算法.在提供可靠节能通信的同时,确定其覆盖范围和最优布署方案.再次,针对地面移动中继非完整约束导致的对UAV空基平台分簇覆盖的拓展性不足,研究了多个地面移动中继的联合运动策略,以应对部分地面移动中继失效或因降低移动物联网的成本而出现地面移动中继数量减少的状况.最后,通过实验验证了本文所提方法的有效性.     

物联网数据收集中基于负载均衡的无人机-车联合轨迹规划

为了提升大规模物联网数据收集的效率,提出了一种基于负载均衡区域划分的多无人机-车联合轨迹规划算法,其中,无人机作为空中基站收集物联网设备的数据,地面无人车作为移动电池更换站以弥补无人机能量的不足。为了缩短整体任务完成时间,优化目标为最小化所有无人机-车中最长的任务完成时间,将该问题建模为多站点车辆路由问题的一个变种,并从负载均衡的角度对其进行求解。具体来说,首先通过负载均衡区域划分算法将物联网设备分配到无人机-车的服务区,在此基础上,多站点无人机-车的轨迹规划问题退化为多个独立的单站点单组无人机-车的轨迹规划问题,进而设计联合轨迹规划策略优化各个服务区中的路径。数值结果验证了所提算法在任务完成时间和负载均衡度方面优于对比算法。     

基于5G的无人机智能组网的应急通信技术开发及应用

在成立国家应急管理部后,国家越来越重视灾害事故后应急通信保障能力。为了使灾后地面通信设备被损害,从而无法对正常通信进行保证的问题得到解决,根据无人机机载基站快速创建天地一体化网络应急通信方案也备受重视。以此,本文就将5G技术作为基础,开发无人机智能组网的应急通信方案。     

基于全球星实现无人机远程通信

根据新型无人机对通信带宽和通信距离的特殊要求,通过对全球星通信性能的分析,提出基于全球星实现无人机远程通信的解决方案,并且针对一些关键技术给出实现方法.系统由机裁通信设备、地面通信设备、地面监控设备、全球星卫星、网关等组成,可实现无人机远程通信.该解决方案可以在无人机通信设备的研制中使用,对提高其远程通信能力有一定的参考价值.     

加权能耗最小化的无人机辅助移动边缘计算资源分配策略

针对无人机(UAV)辅助的移动边缘计算(MEC)系统,考虑到无人机能耗与地面设备能耗不在一个数量级,该文提出通过给地面设备能耗增加一个权重因子以平衡无人机能耗与地面设备能耗。同时在满足地面设备的任务需求下,通过联合优化无人机轨迹、系统资源分配以最小化无人机和地面设备的加权能耗。该问题高度非凸,为此提出一个基于交替优化算法的两阶段资源分配策略解决该非凸问题。第1阶段在给定地面设备的卸载功率下,利用连续凸逼近(SCA)方法求解无人机轨迹规划、CPU频率资源分配及卸载时间分配;第2阶段求解地面设备的卸载功率分配。通过两阶段的交替和迭代优化找到原问题的次优解。仿真结果验证了所提算法在降低系统能耗方面的有效性。     

基于移动捎带的5G广域物联网信息传输方法研究

针对偏远地区低速率、时延非敏感业务的入网问题,提出了一种基于移动捎带的广域物联网信息传输方法。利用移动载体为物联网设备提供无感接入支持,扩展了网络覆盖范围。在无基站信号覆盖区域,移动载体与传感器建立连接,获取传感器数据并存储,具备5G接入条件时,将感知信息卸载实现信息入网。提出了一种基于代价函数的数据缓存策略,提高了信息捎带效率,分析了移动捎带方案的覆盖性能,并设计了一种基于远距离无线电（long range radio,LoRa）技术的硬件实现方案。仿真结果表明,基于移动捎带的广域物联网信息传输方法能够提高物联网信息捎带效率,并将5G网络的物联网覆盖范围扩大4.98倍,实地测试验证了所提方法的有效性,移动捎带节点可以实现半径4 km的通信覆盖。     

本期导读

<正>随着通信技术的发展以及市场需求的多样化,移动蜂窝网络除主要满足人与人之间的通信需求外,也越来越多地开始承载物联网应用。然而,基于移劝蜂窝通信技术的物联网设备存在功耗大、成本高等劣势,移动网络的承载能力很难满足物与物的连接需求,在这种情况下,若要满足日益增长的远距离物联网设备连接需求,必须引入一些新的技术来增强移动网     

基于移动自组网络的无人机通信设计

为了解决由于自然灾害所造成的通信故障问题,文章建立一种应急通信系统,对于受灾地区的救援工作可以起到重要作用。本文以移动自组网络为主,建立一种无人机通信系统,利用无人机监测特殊地区的真实情况,完成视频的传输功能。利用无人机在空间、地理位置上的优势,感应并监测特殊地区的损坏数据,利用层次化的自组网,实现信息的回传、共享和下载等功能。结果表明,该系统可以实现无人机之间传输数据的低时延功能,很好地满足了应急通信的应用场景。     

泊松点距离约束下无人机辅助地面应急网络覆盖性能研究

在用户数量激增的应急通信场景下,为保证地面用户的通信质量,提出了基于距离约束的用户自适应接入方案。首先采用泊松点距离约束策略(Poisson Point under Distance Constraint, PPDC)对无人机(Unmanned Aerial Vehicle, UAV)的位置进行建模,避免无人机区域重叠带来的干扰问题。其次,引入基站负载传输协议(Base Station Load Transfer Protocol, BSLTP),当接入基站的用户数量超过给定阈值时,超载用户由无人机提供服务。此外,分别分析了地面基站和无人机的覆盖性能,得到了系统整体覆盖概率,并研究了无人机高度、覆盖半径、激增用户密度对网络覆盖性能的影响。最后,通过仿真验证了理论结果的正确性,且所提部署方案能够有效提升网络覆盖性能。     

IoT网络中基于多天线技术的无人机通信传输性能优化

为了提高无人机通信传输性能，研究了在物联网中结合多天线技术对无人机通信的上下行链路功率控制。采用可配置波束宽度和功率的阵列天线，对无人机通信的上行链路和下行链路的传输性能进行分析和优化。对于下行链路，提出一种基于地面传感器设备位置信息的自适应功率分配算法，通过研究地面传感器设备接收性能与无人机发射功率和相对位置之间的关系，分析无人机采用固定功率发射时地面传感器设备获得通信速率与其位置的关系，从而最大化下行链路的传输容量。同时针对实际工程需求，考虑覆盖范围内所有用户的服务质量，提出一种基于地面传感器设备位置的功率预均衡算法，从而获取良好的QoS。对于上行链路，考虑在频谱共享环境下的通信机制，提出一种基于地面传感器设备位置的非合作博弈功率控制方法，根据相对位置和最小通信速率需求设计效用函数，证明纳什均衡的存在性和唯一性。最后，针对提出的上下行链路功率控制优化算法，通过仿真结果验证了其正确性，有效保证了系统的传输性能。     

CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.     

UAV-assisted multi-hop D2D relaying for communication in large disaster area

An unmanned aerial vehicle (UAV)-assisted communication is a promising technique for enabling ground users in a non-functional area (NFA) or disaster area to communicate. All the base stations in the disaster area may be partially or fully damaged due to the natural calamity. Device-to-device (D2D) communication can be a viable solution for immediate connection between users in an NFA. We propose a UAV-assisted multi-hop D2D communication following a hybrid power-time switching (PTS) in this paper. Moreover, a D2D user of a cluster can communicate with another D2D user in a different cluster through UAVs. However, D2D users can harvest energy from their respective ad hoc energy stations and forward the information to the nearby D2D user following a hybrid PTS-based strategy. We propose a time frame for the same and show a node-based energy harvesting strategy. The expressions of outage probability, throughput, end-to-end energy consumption, and energy efficiency are developed for the Rician and Nakagami-m faded channel. The impact of several network parameters such as energy harvesting factor, energy harvesting efficiency, and fading parameter on the network performance is also indicated.     

A survey on communication protocols and performance evaluations for Internet of Things

The Internet of Things (IoT) is a large-scale network of devices capable of sensing, data processing, and communicating with each other through different communication protocols. In today's technology ecosystem, IoT interacts with many application areas such as smart city, smart building, security, traffic, remote monitoring, health, energy, disaster, agriculture, industry. The IoT network in these scenarios comprises tiny devices, gateways, and cloud platforms. An IoT network is able to keep these fundamental components in transmission under many conditions with lightweight communication protocols taking into account the limited hardware features (memory, processor, energy, etc.) of tiny devices. These lightweight communication protocols affect the network traffic, reliability, bandwidth, and energy consumption of the IoT application. Therefore, determining the most proper communication protocol for application developers emerges as an important engineering problem. This paper presents a straightforward overview of the lightweight communication protocols, technological advancements in application layer for the IoT ecosystem. The survey then analyzes various recent lightweight communication protocols and reviews their strengths and limitations. In addition, the paper explains the experimental comparison of Constrained Applications Protocol (CoAP), Message Queuing Telemetry (MQTT), and WebSocket protocols, more convenient for tiny IoT devices. Finally, we discuss future research directions of communication protocols for IoT.     

面向6G低轨卫星物联网的能效优先的多波束鲁棒预编码设计

如今,物联网已经应用在经济社会的各个领域,但是由于空间、环境等限制,地面物联网在一些应用场景中表现出了服务能力严重不足的问题。针对这个问题,第六代移动通信技术（6th generation mobile networks, 6G）,提出将卫星通信与地面通信融合,从而实现全球无缝覆盖。对于卫星通信,卫星通常由太阳能供电,导致能量有限,因此想要实现大规模设备高质量的通信,卫星的能量效率设计非常必要。本文为6G低轨（low earth orbit, LEO）卫星物联网（Internet of Things, IoT）设计了一个能量有效的非正交多址接入（non-orthogonal multiple access, NOMA）框架,以支持广域分布设备的大规模机器通信（massive machine-type communications, mMTC）。考虑到LEO卫星的能量有限性和信道状态信息（channel state information, CSI）不准确,本文建立了一个在功率和信干噪比（signal-to-interference-plus-noise ratio, SINR）约束下最大化能效的优化问题。通过将分数形式问题转换为等效的减法形式优化问题,提出了一种鲁棒的联合波束成形和功率分配算法,以在存在信道不确定性的情况下最大化能量效率。理论分析和仿真结果均验证了所提算法的有效性和鲁棒性。      

UAV-IoT collaboration: Energy and time-saving task scheduling scheme

UAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV-IoT collaborative system has become a current hot research topic. However, other concerns require in-depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision-making, energy harvesting, and so forth. Here, we suggest a task-scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA-WS in terms of energy use (67%) and delays (36%).     

UAV-based LoRaWAN flying gateway for the internet of flying things

The Internet of Things (IoT) is one of the paradigms related to the evolution of telecommunication networks which is contributing to the evolution of numerous use cases, such as smart city and smart agriculture. However, the current communication infrastructure and wireless communication technologies are not always able to guarantee a proper service for these IoT scenarios. Smart solutions are needed to overcome current terrestrial network limitations offering a cost-effective way to extend the current terrestrial network coverage. For example, temporary extensions “on-request” of the terrestrial infrastructure may be a viable solution to allow collecting data generated by nodes outside the current network coverage. Flying objects can help achieve this goal. Various studies supported the use of unmanned aerial vehicles (UAVs) as intermediate nodes between IoT devices and the network. However, such solutions have not been exhaustively tested yet in real-case scenarios. This paper proposes an efficient solution to collect data from multiple IoT sensors in rural and remote areas based on UAVs. It describes the implementation of the proposed UAV-based Long RangeWide Area Network (LoRaWAN) flying gateway able to collect data directly from LoRaWAN sensors during its flight, keep them stored in an onboard memory, and forward them at the end of its flying path to a platform where the authorized users can access them. A prototype of the gateway has been developed to assess the proposed solution through both indoor and outdoor tests aiming to test its feasibility both in terms of communication performance and UAV-required hardware resources.     

Efficient Deployment of Multiple UAVs for IoT Communication in Dynamic Environment

The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.     

NOMA物联网下多UAV辅助MEC系统的资源分配

为解决偏远地区或突发灾害等场景中的物联网(Internet of Things, IoT)设备的任务计算问题,构建了一个非正交多址接入(Non-orthogonal Multiple Access, NOMA)-IoT(NOMA-IoT)下多无人机(Unmanned Aerial Vehicle, UAV)辅助的NOMA多址边缘计算(Multiple Access Edge Computing, MEC)系统。该系统中设备的计算能耗、卸载能耗和MEC服务器计算能耗直接受同信道干扰、计算资源和发射功率的影响,可通过联合优化卸载策略、计算资源和发射功率最小化系统加权总能耗。根据优化问题的非凸性和复杂性,提出了一种有效的迭代算法解决：首先,对固定卸载策略,计算资源和发射功率分配问题可通过连续凸逼近转化为可解的凸问题;其次,对固定计算资源和发射功率,利用联盟形成博弈解决卸载策略问题,以最小化IoT设备之间的同信道干扰。仿真结果表明,较OMA接入方式,NOMA接入方式减少本地计算能耗、卸载能耗及计算能耗约20%;较无卸载策略方法,包含卸载策略方法在减少系统加权总能耗方面效果较为明显。     

An Efficient FIR Filter Based on Hardware Sharing Architecture Using CSD Coefficient Grouping for Wireless Application

Current research emphasis on IoT (Internet-of-Things) to enable physical objects to connect, co-ordinate among themselves in a ubiquitous manner. Specifically, for this type of applications, the quality of wireless communication is indeed considered vital to enhance the reliability of battery operated IoT devices. Moreover, these devices operate on unlicensed bands creating heavy congestion on spectrum. Interestingly operating these devices on Cognitive Radio band avoids the spectrum shortage problem by exploiting the dynamic spectrum access capabilities. Cognitive relaying for IoT applications ensures reliable communication through interference mitigation among co-existing IoT devices. In this paper, we therefore propose spectrally efficient two-path decode and amplify relaying technique in three time slots through network-coded co-operation of Primary user and Secondary User. To address the interference issues, optimum power allocation is formulated as a convex optimization problem solved through a modeling system named CVX. Thereby we achieve co-operative diversity without the need for additional antennas for IoT networks. Performance evaluation compares the Bit Error Rate for different relaying schemes with and without optimum power allocation. Our simulation results ensure that the probability of error is minimized and rate is maximized by our proposed algorithm.