具有能量收集管理的无线传感器节点设计

传感器网络作为物联网的底层核心和感知前端,在实际应用中要求能够长时间连续工作,但是基于电池的有限能源供给严重限制了其灵活设计和长期部署,成为许多领域中影响其应用的重大挑战.能量收集技术使得节点可以从环境获取能量给自身充电,为延长网络寿命提供了一个有效的技术手段.本文设计了一种基于CC2650的无线传感器网络节点,结合基于LTC3331的多源环境能量收集技术,形成了一套完整的带能量补充的微功耗传感器节点.实验通过太阳能板作为能量收集前端进行验证,结果表明其可以实现环境能量的收集和累积存储并驱动节点运行.该节点体积小,自治工作时间长,可以广泛用于环境和结构监测等领域.     

基于射频能量收集的无线中继网络资源分配

针对现有射频能量收集网络资源分配研究局限于单个数据源场景,无法适配于多数据源网络的问题,提出了一种适用于多数据源场景的射频能量收集中继网络传输协议框架,在该框架内节点可作为源节点或中继节点传输自身数据或转发数据,并在其他节点的数据传输过程中完成射频能量收集。以协议框架为基础,分别以系统吞吐量及用户公平性为优化目标设计两种资源分配方案。仿真表明,两种方案可有效改善网络吞吐量及资源分配公平性。     

基于压缩感知的WSNs长生命周期数据收集方法

该文针对基于事件驱动的无线传感器网络(WSNs)数据收集查询的长期应用需求,基于压缩感知理论将混合压缩感知的数据收集技术与数据收集树的构建过程相结合,设计出一种长生命周期数据收集方法。该方法在数据收集查询到达时,构造一棵数据收集树。建树过程中,利用混合压缩感知思想,在分析转发节点和融合节点能耗的基础上,以收集查询后节点最小剩余能量最大化为目标,构造最大数据收集树集合。仿真实验表明,该方法能够充分利用节点能量资源,显著提高网络能量效率,达到延长网络生命周期的目标。     

无线供能反向散射通信中能耗最小化资源分配方法

针对无线供能反向散射通信网络,提出了一种满足传感设备通信需求及能量因果的专用能量站能耗最小化资源分配方法。在考虑非线性能量收集模型及不完美串行干扰消除基础上,通过联合优化专用能量站发射功率、传感设备反向散射通信时间、反向散射系数及其能量收集时间,构建了一个专用能量站能耗最小化的非凸多维资源分配问题。首先,构建辅助变量对反向散射系数与时间进行解耦,再利用目标函数是关于专用能量站发射功率的单调递减函数这一特性来设计一种基于二分法的迭代算法来获取原问题的最优解。仿真验证了所提算法的快速收敛性,同时,与同类算法相比,所提方法可为专用能量站节约更多的能量。     

具有能量收集功能的无线中继网络资源分配

针对具有能量收集功能的无线中继网络,通过分析有限能量存储约束和能量消耗的因果约束对资源分配的影响,提出了基于马尔科夫决策的功率分配算法以最大化中继网络的吞吐量。所提算法根据无线信道状态信息和电池容量信息对功率分配进行动态调整,合理利用收集到的绿色能源,实现网络吞吐量的极大提升。对具有不同能量收集效率的场景进行了仿真,验证了所提算法的优越性。     

一种新的遥感图像混合像元分解方法

提出一种新的遥感图像混合像元分解方法,通过最小化一种带约束条件的能量函数,可实现多通道遥感图像中混合像元更精确的分解.同时针对所提议的算法进行了模拟和实际数据的实验验证,并将结果与BP神经网络的分解结果进行比较,结果表明,本文所提出的带约束条件的能量函数最小化方法在分解准确性和抗噪声能力方面,明显优于基于BP神经网络的分解方法.     

无线携能保密网络下的中继协作波束形成算法

本文研究了无线携能通信网络中的多窃听者场景下,基于无线能量收集放大转发中继的协作机密信息安全传输问题.提出了两种低复杂度的零空间波束形成方案.分别利用接收者或窃听者方位先验信息,将待优化的目标矢量投射到接收者或窃听者信道的零空间.与原协作波束形成方法相比,由于降低了待优化目标矢量的空间自由度,本文方法运算复杂度较低.特别的,由于第二种方法目标函数中将窃听者的交互信息迫零,将原始拟凸优化问题转化为凸问题求解,进一步降低了运算量.实验研究验证了本文方法的合理性.     

基于介电弹性体的风力发电机理及性能分析

设计并研究分析了一种基于介电弹性体发电机的新型风力发电装置，它可以从风能收集装置叶片中嵌入的介电弹性体膜中获取能量。介绍了该系统的能量采集过程，从理论上建立了风致旋转下该系统的电力学模型，然后推导了该系统在旋转运动下的输出电压和电能增量。通过测量所提出系统的输出电压和电能增量，实验验证了理论分析的可操作性。在此基础上，通过Design Expert中的响应面法分析了该系统在模拟环境下的电能增量，研究了激励参数、外内半径比、输入电压参数对能量采集的影响。结果表明，在较大的激励参数和输入电压下，系统表现出较好的能量收集性能，同时增大外内半径比也可提高系统的能量收集性能。在给定激励参数60 mm、输入电压1 000 V和外内半径比为2时，实验结果表明，单个周期内的发电量可达26.61 mJ。研究结果可为介电弹性体的能量收集和发电机设计提供参考。     

无线传感器网络能量收集技术分析

对无线传感器网络节点的能量供应及其管理技术的现状进行了讨论.分析研究了无线传感器网络节点的能量收集原理、技术与方法,认为无线传感器网络节点能量管理应从节能与供能两方面去解决.对环境中存在的各种能源的收集原理与方法进行了分析,这些能源包括太阳能、风能、声能、振动、热电以及电磁场能等.最后提出了传感器节点应该采用尽可能多的方法从环境中吸取能量,以确保传感器节点能够长期、稳定、可靠地工作.     

能量采集的前景及最新动向

正能量收集:物联网领域的新兴技术"能量收集技术是指把大自然中的一些能源收集起来转化成电能给系统供电,这些能量主要包括动能、光能、电磁能、热能的收集。"Spansion公司微控制器与模拟业务部门市场部营销总监(上海)王钰说。微型能量收集技术主要应用在物联网的无线传感器网络、结构健康监测、植入式医疗监测、有源射频传感器及识别等方面。这些市场目前在中国正在迅速发     

Reliability modeling and planning of energy harvesting based on uncertainty theory

Energy harvesting network is a new form of computer networks.It can convert the environmental energy into usable electric energy,and supply the electrical energy as a primary or secondary power source to the electronic device for network communication.However,the energy harvesting process has great volatility and uncertainty,the traditional analytical method based on probability distribution function to describe the energy collection process can not accurately simulate the actual situation,resulting in higher depletion probability of nodes,then the reliability cannot be guaranteed as a result.For this,the energy harvesting reliability of energy harvesting nodes was defined,represented with the degree of normal operation,respectively set up the node reliability models with no battery and infinite battery.As an example for maximum node achievable rate,the uncertain multilevel programming model based on node reliability was put forward,then the network efficiency was improved under the premise of ensuring node reliability.An energy average allocation (EAA) algorithm was proposed and the upper bound of competitive ratio of the algorithm was proved theoretically.Finally,the actual wind power data was taken as an example to verify the feasibility and effectiveness of the proposed model and method.     

Energy harvesting,cooperative spectrum sensing,and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

In this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.     

无线EH认知物联网网络资源分配研究

由于5G技术的快速发展,基于能量采集的认知物联网(Internet of things,IOT)具有很大的应用前景。针对无线物联网网络中存在的"doubly-near-far"问题,考虑在认知物联网网络中将能量采集与信息接收站分开管理,提出了一个基于能量采集的异构认知物联网网络模型,实现用户能效的最大化。此外,该模型不仅考虑能量采集,还考虑了频谱利用率低、频谱资源稀缺的问题,针对该网络优化问题,提出了一个混合整数非线性规划问题,通过信道分配、功率控制以及时间分配的联合优化的方法,考虑用户数据速率、干扰约束的同时,最大化能效。仿真实验结果证明了所提出模型以及算法的有效性。     

基于能量采集异构蜂窝网络的功率分配算法研究

针对能量采集异构蜂窝网络,由于能量到达和信道状态的随机性导致离线功率分配算法只能取得理论最优,本文提出了一种在线功率分配算法.算法在每个时隙开始时,基站控制器通过能量判别选出满足开启条件的小蜂窝基站,然后采用基于拉格朗日乘子的两层迭代算法对所选择的小蜂窝基站分配发射功率,能够实际最大化系统在每个时隙的能效.仿真表明在满足基站开启条件的情况下,所提算法可以为密集异构网络提供更高的能量效率.该算法适用于信道状态和能量状态不可预测的网络.     

能量采集技术在主动窃听系统中的应用研究

结合传统物理层安全中的能量采集技术,考虑了基于能量采集技术的可疑通信网络下的主动窃听场景,该场景中无源主动窃听者将从可疑发送者采集到的能量用于合法干扰与窃听。接着分析推导了这一场景下的窃听不中断概率的半闭合表达式,并仿真验证了各因素对窃听性能的影响情况。结果表明,能量采集时间、采集效率与可疑接收者的发射功率都是影响窃听者性能的主要因素,且相互之间都存在正比关系,但窃听性能与发送者采集效率无关。     

能量收集双向中继网络的中继选择和功率分配

结合能量收集技术,研究了放大转发双向中继网络的系统性能。基于双向中继系统中的两个端到端信噪比平衡准则,推导出了单中继选择情况下信源最优的功率分配方案和中继最优的能量收集比例。仿真结果证明所提方法能够实现最好的系统性能。通过比较发现,能量收集双向中继网络比传统双向中继网络能够实现更高的传输速率。     

Achieving energy‐neutral data transmission by adjusting transmission power for energy‐harvesting wireless sensor networks

Recently, benefiting from rapid development of energy harvesting technologies, the research trend of wireless sensor networks has shifted from the battery‐powered network to the one that can harvest energy from ambient environments. In such networks, a proper use of harvested energy poses plenty of challenges caused by numerous influence factors and complex application environments. Although numerous works have been based on the energy status of sensor nodes, no work refers to the issue of minimizing the overall data transmission cost by adjusting transmission power of nodes in energy‐harvesting wireless sensor networks. In this paper, we consider the optimization problem of deriving the energy‐neutral minimum cost paths between the source nodes and the sink node. By introducing the concept of energy‐neutral operation, we first propose a polynomial‐time optimal algorithm for finding the optimal path from a single source to the sink by adjusting the transmission powers. Based on the work earlier, another polynomial‐time algorithm is further proposed for finding the approximated optimal paths from multiple sources to the sink node. Also, we analyze the network capacity and present a near‐optimal algorithm based on the Ford–Fulkerson algorithm for approaching the maximum flow in the given network. We have validated our algorithms by various numerical results in terms of path capacity, least energy of nodes, energy ratio, and path cost. Simulation results show that the proposed algorithms achieve significant performance enhancements over existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

Performance of cognitive relay network with energy harvesting relay under imperfect CSI

This paper evaluates the performance of an underlay cognitive relay network under imperfect channel state information (CSI) where a secondary user (SU) transmits using a secondary relay (SR) based on decode and forward scheme. The outage probability (OP) of SU is investigated in a scenario where the decode and forward relay harvests energy from radio frequency signal of SU. The relay uses a fraction of time for harvesting in time switching–based relaying (TSR) while a fraction of received power is used for harvesting in power splitting–based relaying (PSR) scheme. The SU and relay control their transmit power using a scaling factor, based on CSI of the interfering links (ie, links from SU transmitter and SR to the primary user [PU] receiver) to protect the quality of service of PU. The available CSI at the SU and SR are imperfect due to practical limitation. Analytical expressions of the OP are derived for TSR‐ and PSR‐based schemes. The impact of harvesting time, power splitting ratio, imperfect CSI, PU outage constraint and interference threshold on the OP of the SU network, and average transmit power of SR is indicated. Further, the impact of multiple SRs is also shown.     

能量收集双向中继网络的高能效联合中继选择和功率分配算法

为了实现双向中继系统在满足传输速率要求时的最小功率消耗,基于功率分割中继协议,在完美和非完美的信道估计两种不同的情况下,提出了能量收集双向中继网络的高能效联合中继选择和功率分配算法,得到了两个信源的最优功率分配和中继节点最优的能量收集比例.仿真结果表明,信道估计误差会增加系统的功率消耗;与传统双向中继比较发现,能量收集双向中继能够实现更少的系统功率消耗.     

Energy Harvesting in Cognitive Networks with Cooperative Beamforming: Power Allocation and Stability Analysis

Energy harvesting (EH) is a promising technology to improve both energy efficiency and spectral efficiency in cognitive radio (CR) networks. However, due to the randomness of the harvested energy and the interference constraint at the primary users (PUs), the limited transmission power of secondary users (SUs) may reduce the service rate of SUs. To solve this problem, this paper investigates a cooperative transmission method where a zero-forcing beamforming method is used in the EH based secondary network. Considering the transmission power constraint and energy causality, we derive the closed-form solution of the optimal transmission power for the secondary source and relays, which achieves the maximal stable throughput of the secondary network. Numerical results show the impact of different system parameters to the maximal stable throughput. In addition, compared with the traditional decode-and-forward (DF) scheme, the cooperative beamforming method achieves higher stable throughput under an high quality source-to-relay channel.