一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.     

能量收集传感器网络中改进的能耗管理方案研究

能量收集传感器平台为网络协议的设计开辟了新的领域。为了支持网络的运行,能耗率不得高于能量收集率,否则,传感器节点会最终耗尽它们的能量。与资源处于静态的传统网络资源分配问题相比,充电速率的时间可变性带来了新的挑战。在本文中,我们首先研究了一种基于高效对偶分解和次梯度策略的算法QuickFix,计算出数据采样率和路由。然而,再次充电发生波动时的时标可能会快于传统方法的收敛时间,进而导致电池断电和溢出,这会造成采样丢失和能量收集机会丢失。为了解决这一动态问题,我们提出一种本地算法SnapIt,通过对采集率进行调节以维持电池电量在目标水平上。基于TOSSIM模拟器的性能评估表明,QuickFix和SnapIt联合起来可以跟踪网络瞬时最优效用,同时维持电池电量处于目标水平。与基于余压的IFRC相比,本文方法使总体数据速率平均提升42%,同时显著提升了网络效用。     

基于综合故障的无线传感器网络无标度容错拓扑模型研究

针对无线传感器网络实际应用中遇到的环境损毁和能量耗尽的问题,本文首先对网络综合故障进行建模,获取满足综合故障容忍能力和网络生命期双重需求的网络节点度和节点度上限值的取值规律,并结合由无标度特征导出的两者关系,从而求得最优节点度上限值,最终引入关于节点度上限值的适应度函数,提出了容忍环境损毁和能量耗尽综合故障的无标度容错拓扑演化模型.仿真实验结果表明,该模型演化生成的无标度拓扑对环境损毁和能量耗尽具有较好的容错性,并能够有效地延长网络生命期.     

一种应用于桥载设备监控网络CSMA/CA机制算法

在机场桥载设备在线监测管理网络的现场应用测试中,针对网络突发事件中高优先级节点传输延时长,节点能耗利用率低的问题,采用基于优先级调度策略思想的网络节点能量优化机制,对网络MAC层协议中的CSMA/CA算法参数做出调整,达到数据快速传输的目的,利用NS2软件进行网络仿真,结果表明该方法提高了高优先级节点的信道访问率,均衡了网络能耗并延长了网络生命周期。     

无线传感器网络中继节点布居算法的研究

本文表述的是在该应用背景下引入多约束条件, 并采用枚举法与贪婪寻优算法相结合的方法, 解决了在可以作为中继节点设置位置的预设中继节点位置集合内, 合理选择中继节点设置位置以及既存网络因添加新传感器节点所引起的中继节点追加的问题. 仿真实验表明, 本文提出的中继节点布居与追加优化算法能够保证多约束条件下网络的容错性. 同时提出的基于最小网络距离因子评价标准, 有效提高了中继节点布居算法的能效性.     

基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.     

一种基于势博弈的无线传感器网络拓扑控制算法

在实际的应用中,无线传感器网络常常由大量电池资源有限的传感器节点组成.如何降低网络功耗,最大化网络生存时间,是传感器网络拓扑控制技术的重要研究目标.随着传感节点的运行,节点的能量分布可能越来越不均衡,需要在考虑该因素的情况下,动态地调整节点的网络负载以均衡节点的能耗,达到延长网络生存时间的目的.该文引入博弈理论和势博弈的概念,综合考虑节点的剩余能量和节点发射功率等因素,设计了一种基于势博弈的拓扑控制模型,并证明了该模型纳什均衡的存在性.通过构造兼顾节点连通性和能耗均衡性的收益函数,以确保降低节点功耗的同时维持网络的连通性.通过提高邻居节点的平均剩余能量值以实现将剩余能量多的节点选择作为自身的邻居节点,提高节点能耗的均衡性.在此基础上,提出了一种分布式的能耗均衡拓扑控制算法.理论分析证明了该算法能保持网络的连通性.与现有基于博弈理论的DIA算法和MLPT算法相比,本算法形成的拓扑负载较重、剩余能量较小的瓶颈节点数量较少,节点剩余能量的方差较小,网络生存时间更长.     

一种优化无线传感器网络生命周期的容错拓扑研究

<正>由于无线传感器网络的节点能量受限,优化网络生命周期成为设计网络拓扑时首要考虑的问题.通过分析节点的剩余能量和负载量对节点生命周期的影响,提出了一种可延长无线传感器网络生命期的容错拓扑演化模型,并得出了在节点满足网络生存时间的条件下负载调节系数的取值范围.仿真实验结果表明,基于无标度网络的演化拓扑结构具有较好的容错性,并能够均衡网络节点能耗和延长网络生命周期.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

Vibro-acoustic control with a distributed sensor network

The purpose of this work is to demonstrate the ability of a distributed control system, based on a smart sensor network, to reduce acoustic radiation from a vibrating structure. The platform from which control is effected consists of a network of smart sensors, each referred to as a node. Each node possesses its own computational capability, sensor, actuator and the ability to communicate with other nodes via a wired or wireless network. The primary focus of this work is to employ existing group management middleware concepts to enable vibro-acoustic control with such a distributed network. Group management middleware is distributed software that provides for the establishment and maintenance of groups of distributed nodes and that provides for the network communication among such groups. The control objective is met by designing distributed feedback compensators that take advantage of node groups in order to effect their control. The node groups are formed based on physical proximity. The global control objective is to minimize the radiated sound power from a rectangular plate. Results of this investigation demonstrate that such a distributed control system can achieve attenuations comparable to those achieved by a centralized controller.     

Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.     

基于质心理念的无线传感网络路由协议的研究与改进*

在无线传感器网络中存在诸如节点能量损耗以及数据传输时所存在的时延等问题,因此在深入研究PEGASIS以及相关改进算法的基础上,并结合力学中质心的概念而提出一种基于质心理念的链式算法CMC-PEGASIS(Center of mass concept-PEGASIS)。该算法将整个传感器区域分成等宽的五个子区域,通过计算每个子区域内节点至基站的距离并按照距离与基站的远近来成链。其次引入质心的概念找到每个区域节点的能量中心,同时结合节点自身的能耗以及每个节点与基站的距离关系,得到每个子区域中的最佳的簇头节点,最后每个区域的簇头节点直接与基站完成信息的传输。经过理论分析和仿真结果得知：CMC-PEGASIS算法降低了全局能耗、减少了信息延迟率同时延长了网络的生命周期。     

Design of artificial genetic regulatory networks with multiple delayed adaptive responses*

Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.     

一种针对无线传感器网络能量洞的节点分布策略

在无线传感器网络中,sink节点周围出现的能量空洞问题直接影响着网络的生存寿命。从延长网络生命周期的角度,对网络部署时距离和节点密度等因素进行了研究,设计了一种节点初始能量不同的能量均衡机制,通过合理地部署节点数量和相对位置,使得整个末端网络总能耗尽可能少;对于多跳过程中的单个节点,提出了一种基于剩余能量与距离的比重的方法,选择适当的转发节点。通过推导仿真,这种节点分布策略能够有效提高末端网络总体效能,对物联网末端网络不间断工作具有良好效果。     

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network(WSN), the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption(PCCAGM). This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption(PCCAA) is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.     

多尺度传感网络失效节点检测系统设计

对多尺度传感网络中的失效节点进行准确检测与定位,实现故障节点的高效检测,保障传感网络的可靠运行。提出一种基于多传感器量化融合跟踪滤波检测的失效节点检测算法,并进行检测系统优化设计。构建多尺度传感网络的节点分布实体对象模型,进行失效节点检测系统总体设计和技术指标分析。设计基于多传感器量化融合跟踪滤波检测的失效节点检测算法。进行系统的硬件设计,包括A/D模块设计、时钟电路设计、程序加载电路设计、传感器通信模块设计和系统电源模块设计。在ARM Cortex?-M0平台上进行检测系统软件开发。系统仿真结果表明,该系统进行多尺度传感网络失效节点检测的准确度较高,提高了传感器网络的寿命周期。     

能量异构的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均和如何高效获得节点和边的负载问题,提出一种局域范围内能量异构的加权无标度拓扑演化模型.通过对节点能量与负载、能耗的关系建模,建立节点能量与点权和边权的联系,进而结合点权和加权模型给出网络的演化方式,推出点权、度和边权的幂率分布规律,最终根据网络获得的点权和边权来分析负载和能耗.仿真结果表明,提出的模型不仅能够准确计算点边的负载,而且缓解了无标度网络的节点能耗不均衡问题.     

Age-Aware Utility Maximization in Relay-Assisted Wireless Powered Communication Networks

This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.     

用于水声传感器网络自组织的询问式泛洪广播算法

水声传感器网络(Underwater acoustic sensor networks,UASN)通常由随机散布的传感器节点组成。需要通过自组织算法将这些节点组成具有一定功能的网络。目前,已有较多成熟的用于陆地无线传感器网络(Wireless sensor networks,WSN)的自组织算法。但水声通信中存在的严重的传播损失、较高的背景噪声、有限的通信带宽、较长的传播时延、复杂的多途信道等,使得大多数适用于WSN的自组织算法难以适用于UASN。本文提出了一种改进的自组织算法,在简单泛洪广播算法中附加一段询问过程。通过OPNET仿真证明了在相同的条件下,相比于简单泛洪与概率泛洪广播算法,本算法可以在较短的时间内建立起有效路由,降低了水声网络在自组织阶段的能量消耗。