基于部分中继选择的能量收集双跳网络信息年龄优化

针对传统中继协作网络传感器节点能量受限导致信息传输延迟较高的问题,将短包通信（SPC）和能量收集技术相结合,构建了一种双跳中继无线传感器网络模型。网络中源节点和中继均需从专用能量基站收集射频信号能量以保证信息的持续传输,中继采用半双工解码转发协议将源节点发送的状态更新信息以短包形式传递到目的节点,利用中继选择策略与最大比合并技术提出了基于时效性的最早部分中继选择方案。考虑该网络信息的新鲜度,首先,分析了SPC传输性能,推导了中继和目的节点处的平均包错误率。其次,利用顺序统计量描述了源节点到中继端数据包的重传次数,基于更新过程理论推导了网络的平均信息年龄（AoI）表达式,并采用梯度下降方法优化了影响网络平均AoI的部分参数。最后,仿真分析结果表明,优化中继数量和数据包长度可以有效提高网络信息新鲜度,同时,通过对比优化部分参数得到的最优值与贪婪方法得到的最小化平均AoI理论值,可以验证部分中继协作方案在双跳状态更新网络中的可行性。      

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

Modeling and analysis of multiple access channel capacity based on hybrid energy storage and energy harvesting

Energy harvesting (EH) has been considered as a promising technology to solve the constrained energy problem in the devices of IoT with its advantages of flexible deployment and sustainable energy supply.For multiple access channel with energy harvesting,a hybrid energy storage structure model composed by super capacitor and battery was proposed for the devices of IoT.According to the peculiarities of medium access channel and energy harvesting system,an optimized energy allocation strategy with exponential-type decline (ETD) was presented,the upper and lower bounds of the average throughput were deduced,in particular,the gap of two bounds was derived to be a constant.The channel capacity was further obtained by utilizing the relationship between the average throughput and the channel capacity.In the simulations,the effect of harvested energy,storage capacity and the number of nodes on the channel capacity were analyzed respectively.Experiment results show that compared with the conventional wireless node with single battery storage,the proposed hybrid energy storage structure can improve the harvested energy value and increase the multiple access channel capacity by using adaptive modulation scheme when transmitting the signals.     

短包传输下缓冲区辅助的无线传感器状态更新系统的信息新鲜度研究

考虑到信息新鲜度对无线传感器状态更新系统设计和部署带来的影响,提出一个由两个传感器源节点和一个公共目的节点组成的状态更新系统。该系统中两个传感器源节点均配备一个单位大小的缓冲区,并根据伯努利过程在每个时隙感知对应物理过程的状态更新。随后传感器以有限块长度短包的形式将状态更新发送至目的节点。该文采用信息年龄（Age of Information,AoI）描述状态更新的新鲜度。首先,以传感器缓冲区状态组合为中心,该系统可建模为连续时间有限状态的马尔可夫过程,其中有限状态表示缓冲区的不同状态,连续时间表示与年龄相关过程的演进。因此,该系统平均AoI可采用随机混合系统（Stochastic Hybrid System,SHS）评估。最后仿真结果表明,当更新包的感知概率为0.2时,信噪比从0 dB增加至3 dB将导致平均AoI降低20.6%;当信噪比固定为4 dB时,状态更新包块长度从300 channel uses增长至400 channel uses会致使平均AoI降低8.8%。该研究可为缓冲区辅助的状态更新系统设计提供基本理论依据。     

面向物联网的深度Q网络无人机路径规划

随着无人机技术的广泛应用,基于无人机辅助数据收集的物联网架构扩展了物联网的应用范围,尤其适用于军事战场、灾害救援等极端场景。针对上述场景,该文提出一种基于深度Q网络(Deep Q-Network, DQN)框架的无人机飞行路径规划算法。该算法以无人机飞行周期内收集信息的平均信息年龄(Age of Information, AoI)为优化目标,来保证无人机收集数据的时效性。仿真结果表明,所提算法可以有效降低无人机单个飞行周期内收集数据的平均AoI。与随机算法、基于最大AoI的贪心算法、最短路径算法以及基于AoI的路径规划算法(AoI-based Trajectory Planning, ATP)相比,平均AoI分别降低了约81%, 67%, 56%和39%。该研究实现了无人机辅助物联网系统中,数据的高效、低时延采集。     

MAC Protocols for Energy Harvesting Wireless Sensor Networks: Survey

Energy harvesting (EH) technology in the field of wireless sensor networks (WSNs) is gaining increasing popularity through removing the burden of having to replace/recharge depleted energy sources by energy harvester devices. EH provides an alternative source of energy from the surrounding environment; therefore, by exploiting the EH process, WSNs can achieve a perpetual lifetime. In view of this, emphasis is being placed on the design of new medium access control (MAC) protocols that aim to maximize the lifetime of WSNs by using the maximum possible amount of harvested energy instead of saving any residual energy, given that the rate of energy harvested is greater than that which is consumed. Various MAC protocols with the objective of exploiting ambient energy have been proposed for energy‐harvesting WSNs (EH‐WSNs). In this paper, first, the fundamental properties of EH‐WSN architecture are outlined. Then, several MAC protocols proposed for EH‐WSNs are presented, describing their operating principles and underlying features. To give an insight into future research directions, open research issues (key ideas) with respect to design trade‐offs are discussed at the end of this paper.     

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.     

Transient content caching and updating with modified harmony search for Internet of Things

Internet of Things (IoT) has emerged as one of the new use cases in the 5th Generation wireless networks. However, the transient nature of the data generated in IoT networks brings great challenges for content caching. In this paper, we study a joint content caching and updating strategy in IoT networks, taking both the energy consumption of the sensors and the freshness loss of the contents into account. In particular, we decide whether or not to cache the transient data and, if so, how often the servers should update their contents. We formulate this content caching and updating problem as a mixed 0–1 integer non-convex optimization programming, and devise a Harmony Search based content Caching and Updating (HSCU) algorithm, which is self-learning and derivative-free and hence stipulates no requirement on the relationship between the objective and variables. Finally, extensive simulation results verify the effectiveness of our proposed algorithm in terms of the achieved satisfaction ratio for content delivery, normalized energy consumption, and overall network utility, by comparing it with some baseline algorithms.     

基于信息年龄的工业无线传感器网络混合数据调度方法

在工业无线传感器网络(IWSN)中，实时交付工业现场的周期性控制/传感数据流与非周期性事件数据流，是保障生产安全高效运行的关键。信息年龄(AoI)作为一种新兴的数据新鲜度衡量指标，能够从目标节点角度全面地度量网络数据交付的实时性。针对周期性和非周期性数据混合的工业无线传感器网络，该文在引入网络数据整体新鲜度指标的同时，考虑到周期性数据新鲜度在超过阈值后可能会对工业生产造成负面影响，建立了最小化系统平均AoI和周期性数据AoI逾期概率的联合优化模型，并将优化问题表述为马尔可夫决策过程(MDP)进行求解。由于传统基于相对值迭代的最优求解方法在大规模网络中因为维度灾难难以实施，因此采用深度强化学习(DRL)降低优化问题的状态空间维度，并改进决策探索机制以加快学习速度，提出了基于优化决策探索的深度强化学习(DRL-ODE)调度方法。仿真结果表明，所提方法能够提高网络数据交付的实时性，并有效减少周期性数据的AoI逾期概率。     

SWIPT in multi-hop amplify-and-forward wireless sensor networks

ABSTRACT

Energy harvesting (EH) is an eminent solution to perpetuate the lifetime of energy-constrained relay nodes in wireless sensor networks (WSNs). This paper considers a multi-hop amplify-and-forward (AF) network in which relay nodes with EH capability harvest energy inherent in the source transmitted radio frequency (RF) signal and use the harvested energy for signal transmission. Based on the time switching and power splitting EH receiver designs, we have examined the performance of, (i) time switching based relaying (TSR) and (ii) power splitting based relaying (PSR) protocols in multi-hop AF network, with throughput as the figure of merit. The numerical analysis reveals that, PSR outperforms TSR at high signal-to-noise ratio (SNR) whereas TSR outperforms PSR at low SNR, in multi-hop AF-WSNs with energy harvesting.     

能量收集认知无线电网络下的多接入用户信息年龄研究

针对信道资源有限的多接入信道无线传感器网络场景,实时信息的传送需要考虑信道环境和信息新鲜度问题。该文基于认知无线电物联网（Cognitive Radio-Internet of Things, CR-IoT）系统,构建了一个具有频谱访问权限的主用户（Primary User, PU）和两个可共享PU频谱次用户（Secondary User, SU）的网络模型。在考虑PU工作状态和SU数据队列稳定的条件下,提出了一个以最小化节点平均AoI为目标的优化问题。其次使用两种策略进行优化,包括概率随机接入策略（Probabilistic Random Access Policy, PRA）,该策略下两个SU节点根据相应的概率分布做出独立的传输决策;以及基于李雅普诺夫优化框架优化时隙内调度决策的漂移加罚策略（Drift Plus Penalty Policy, DPP）。仿真结果可知,DPP策略下得到的平均AoI的值要明显低于PRA策略,表明使用DPP策略对平均AoI的优化更加显著,可以有效提升数据包的时效性和新鲜度。      

基于信息年龄优化的多信道无线网络调度方法

信息年龄(AoI)是近年来针对时间敏感类应用所提出的一种描述数据交付及时性的度量标准,它是从网络中目标节点的角度出发,衡量其最新收到的数据包的新鲜程度.在信道资源受限的多信道无线网络场景中,基于信息年龄的链路调度问题需要考虑信道资源和链路冲突的约束.针对上述问题,该文提出一种逐时隙调度链路进行数据传输的调度方法,以最小...     

Alternate energy harvesting and information relaying in cooperative AF networks

This paper studies an energy harvesting (EH) based cooperative relaying system, where two half-duplex relays operate with EH and alternately amplify and forward source data to the destination. When one relay joins in the cooperative data transmission, the other relay will harvest wireless energy by overhearing the transmissions from both the source and the transmitting relay. Both the time-switching and power-splitting architectures are considered for the EH and data reception at relays. Since the EH can be implicitly performed by each relay through listening the ongoing transmissions, more energy can be harvested for the cooperative data transmission. The outage probability and throughput of the proposed scheme are derived. Simulation results are provided to verify the correctness of our theoretical analysis and show that our scheme can significantly outperform the single-relay system in terms of throughput.     

基于无人机辅助的认知无线电网络信息年龄研究

考虑到信息时效性和数据新鲜度对各类实时状态更新系统带来的挑战,提出了一种无人机（Unmanned Aerial Vehicle, UAV）中继辅助的认知无线电网络（Cognitive Radio Network, CRN）信息年龄（Age of Information, AoI）最小化方案。为了降低CRN中次用户接收机处的AoI,在不影响主用户正常通信的前提下,利用UAV作为移动中继来辅助次用户发射机将数据包传输到对应的接收机。通过联合优化UAV的飞行轨迹和资源分配来确保信息新鲜度。由于联合设计问题是非凸的,故采用连续凸逼近的高效迭代算法得出问题的最优解。仿真结果表明,所提方案可以有效提高信息新鲜度,并获得峰值AoI的最小值。同时,数据包的大小和数量与AoI呈线性关系,对UAV的飞行轨迹也有影响。      

Downlink energy efficiency modeling and optimization with backhaul awareness and interference price in ultra cellular HetNet

The cellular heterogeneous network(HetNet) with ultra dense small cells is called ultra cellular HetNet.The energy efficiency for this network is very important for future green wireless communications.The data rates and power consumptions for three parts(i.e.,macro cells,small cells,and mixed backhaul links) in ultra cellular HetNet are jointly formulated to model downlink energy efficiency considering the active base stations(BSs) and inactive BSs.Then,in order to decrease the downlink co-channel interference,the interference price functions are also jointly set up for the three parts in ultra cellular HetNet.Next,energy efficiency optimization iterative algorithm scheme using the fractional programming and Lagrangian multiplier with constraints for density of ultra dense small cells and fraction of mixed backhaul links is presented with interference pricing.The convergence and computation complexity are also proved in this scheme.The numerical simulations finally demonstrate convergence behavior of the proposed algorithm.By comparison,some conclusion can be drawn.Maximizing energy efficiency of system is lower as the density of small cell is high.The effect on maximizing energy efficiency with interference price outperforms that without interference price.And the energy efficiency increases as the fraction of mixed backhaul links is higher because of more power consumption in the microwave backhaul links.     

Outage Performance Analysis of Energy Harvesting Wireless Sensor Networks for NOMA Transmissions

Mobile Networks and Applications - In this paper, we investigate radio frequency (RF) energy harvesting (EH) in wireless sensor networks (WSNs) using non-orthogonal multiple access (NOMA) uplink...     

雾无线接入网中基于内容流行度和信息新鲜度的缓存更新策略

将边缘缓存技术引入雾无线接入网,可以有效减少内容传输的冗余。然而,现有缓存策略很少考虑已缓存内容的动态特性。该文提出一种基于内容流行度和信息新鲜度的缓存更新算法,该算法充分考虑用户的移动性以及内容流行度的时空动态性,并引入信息年龄(AoI)实现内容的动态更新。首先,所提出算法根据用户的历史位置信息,使用双向长短期记忆网络(Bi-LSTM)预测下一时间段用户位置;其次,根据预测得到的用户位置,结合用户的偏好模型得到各位置区的内容流行度,进而在雾接入点进行内容缓存。然后,针对已缓存内容的信息年龄要求,结合内容流行度分布,通过动态设置缓存更新窗口以实现高时效、低时延的内容缓存。仿真结果表明,所提算法可以有效地提高内容缓存命中率,在保障信息的时效性的同时最大限度地减小缓存内容的平均服务时延。     

应用于能量收集的DC-DC电路研究综述

随着物联网技术(IoT)的发展,越来越多的IoT传感器被应用于环境检测、物流管理、生产制造和医疗保健等领域。IoT传感器的使用年限取决于内部电池的使用时间,而内部电池是不可拆卸的,因此一旦电池损坏或者电量耗尽,则需要更换新IoT传感器,这大大提高了成本。能量收集技术的发展为解决该难题提供了新方向,该技术将环境中的微弱能量收集起来,通过相关技术转换成电能,为IoT传感器供电,摆脱了其对电池的依赖。文章综述了能量收集系统的关键技术,比较了不同电路结构的优缺点,并且总结了最大功率追踪技术(MPPT),表明MPPT技术从一维MPPT技术发展到三维MPPT技术,现在已有更多的辅助技术来提高MPPT电路的跟踪精度。为了实现更广泛的应用,多输入多输出的转换器已然成为研究重点,文章综述了多能量源输入多输出电压的转换器结构,静态功耗和动态范围等性能指标成为关注的重点。     

基于新型电源供电的无线传感网络节点设计

基于新型集成式压电能量收集模块LTC3588-1,结合压电自发电单元特性,进行了面向环境监测的无线传感网路节点的设计.实验结果表明,与改进后的电荷捕获电路相比,系统具有能量收集效率高、传输距离较远等优点,有效地解决了无线传感网络节点能源供电的问题,达到了节能环保的目的.