A Class of Cross‐Layer Optimization Design for Congestion and Energy Efficiency with Compressed Sensing in Wireless Sensing Networks

In wireless sensor networks (WSNs), the congestion problem not only causes packet loss, but also leads to an increase in delays and energy consumption. The actual performance of wireless sensor networks (WSNs) can be severely influenced by the quality of the communication channel and the bit in transmission. In this paper, the distributed protocols, which attain global optimum control for signals by the compressed sensing technique and achieve fair channel allocation by the scheduling algorithm, are proposed for WSNs. We take into account the congestion problem by robust optimization with congestion ratio for two classic aspects in energy limited WSNs: minimum transmission rate and maximum transmitted information. To achieve the goal, three protocols are developed. In the first protocol, the desired control input is designed based on the compressed sensing technique. A minimal bit of signal is provided to reduce the transmission flow for the congestion model. The second protocol is resource allocation. The resources can be allocated increasingly to the channel in order to avoid more severe congestion. This can also avoid conservative reduction of resource allocation for eliminating congestion. Channel selection abides by the fair resource allocation principle. The above protocols separately are implemented through a congestion ratio at network layer, transport layer, and MAC layer. Simulation results demonstrate that the proposed algorithm effectively relieves congestion, and achieves higher throughput and lower energy consumption.     

无线传感器网络中一种ARMA流量预测的拥塞控制算法

在无线传感器网络中,由于各节点的通信能力、计算能力、存储能力等都比较有限,使其需采取有别于传统网络的拥塞控制策略.本文针对传感网络特有的多对一、多跳通信方式经常导致网络拥塞的缺陷,提出一种基于流量预测的拥塞避免算法(SCATP).该算法通过ARMA模型分析流经各节点的当前流量,预测网络下一时刻的拥塞状况,并据此进行流量分配,从而实现拥塞控制的同时保证数据的可靠传输.仿真实验表明,SCATP算法在延迟、抖动率、吞吐量和能量有效性等方面能有效改善网络的服务质量.     

基于最优化流控模型的拥塞控制算法

在已改进的最优化流控模型和链路价格算法的基础上,提出了一种基于最优化流控模型的拥塞控制算法。NS-2模拟实验结果证明,与类似的显式精确反馈拥塞控制算法XCP相比,新算法有更好的稳定性和相同的带宽利用率。     

基于信念重用的WSNs能量高效跟踪

针对无线传感器网络(WSNs)中目标跟踪性能与传感器能量消耗难以平衡问题,提出一种信念重用的WSNs能量高效跟踪算法。使用部分可观察马尔可夫决策过程(POMDPs)对动态不确定环境下的WSNs进行建模,将跟踪性能与能量消耗平衡优化问题转化为POMDPs最优值函数求解过程;采用最大报酬值启发式查找方法获得跟踪性能的逼近最优值;采用信念重用方法避免重复获取信念,有效降低传感器通信带来的能量消耗。实验结果表明:信念重用算法能够有效优化跟踪性能与能量消耗之间的平衡,达到以较低的能量消耗获得较高跟踪性能的目的。     

Optimal replicator factor control in wireless sensor networks

For TDMA MAC protocols in wireless sensor networks (WSNs), redundancy and retransmission are two important methods to provide high end-to-end transmission reliability. Since reliable transmissions will lead to more energy consumption, there exists an intrinsic tradeoff between transmission reliability and energy efficiency. For each link, we name the number of its reserved time slots in each MAC superframe as a replicator factor. In the following paper, we propose a reliability-lifetime tradeoff framework (RLTF) for WSNs to study replicator factor control problem. First, for the redundancy TDMA MAC, we formulate replicator factor control problem as convex programming. By the gradient projection method, we develop a fully distributed algorithm to solve the convex programming. Second, for the retransmission TDMA MAC, we set the retransmission upper bound for each link according to the optimal replicator factors under the redundancy MAC and compute the total communication overhead for the retransmission MAC. Finally, we compare the communication overhead of these two MAC protocols under different channel conditions.     

一种基于TopDisc的WSNs拓扑控制算法

针对无线传感器网络（WSNs）中无线信号动态波动变化时引起网络链路不稳定和覆盖范围变小的问题,提出了一种基于TopDisc的WSNs拓扑控制算法.该算法通过引入拓扑控制参数控制网络拓扑优化以适应无线信号变化,给出了拓扑创建过程,对算法进行了仿真实验.仿真结果表明：改进的拓扑控制算法能够提升WSNs对复杂无线电环境的适应能力,提高了资源利用率和链路可靠性.     

无线传感网络中拥塞控制与路由的跨层设计:分布式牛顿法

无线传感网络应用广泛, 其性能与路由选择和拥塞控制密切相关. 致力于拥塞控制与多径路由的跨层优化, 以实现在链路容量受限和节点能量受限情况下的无线传感网络效用最大化. 针对对偶次梯度算法具有收敛速度慢与信息交互量大等缺陷, 设计了具有二阶收敛性能的分布式牛顿算法来实现网络效用最大化. 通过矩阵分裂技术, 实现了只需单跳信息交互的牛顿对偶方向的分布式求解方法. 仿真结果表明, 分布式牛顿算法的收敛性能显著优于对偶次梯度算法.     

基于量子行为粒子群优化算法的定位技术研究

针对无线传感器网络(WSNs)节点定位问题,阐述了WSNs的分布迭代式定位方法研究。这种方法将每次迭代后定位的节点作为其余未知节点的参考节点.同时将基于测距定位问题看成一个多维优化问题,并提出利用具有快速收敛能力的量子行为粒子群优化(QPSO)算法进行求解。最后将仿真实验结果与粒子群优化(PSO)算法进行比较,表明QPSO算法在优化性能上优于PSO算法,有效提高了节点定位精度,证明该方法的有效性。     

无线传感器网络动态节点选择优化策略

无线传感器网络的能耗和有效覆盖率是衡量其性能的两个重要指标.无线传感器网络动态节点选择优化策略通过合理配置各无线传感器节点状态,平衡网络能耗和有效覆盖率,提高网络能效性,延长网络寿命.提出一种结合了Hopfield网络与遗传算法的动态节点选择优化策略,简称为HN-GA.该策略通过遗传算法实现全局搜索,采用Hopfield网络缩小遗传算法的搜索范围,保证遗传算法中每个基因对应待选解的有效性,并针对动态节点选择优化提出一种基于无线传感器网络能耗、寿命和有效覆盖率的综合指标.仿真实验表明,HN-GA算法能有效完成无线传感器网络动态节点选择优化,并在确保网络有效覆盖率的前提下,通过动态配置各无线传感器节点状态,降低网络能耗,延长网络寿命.与遗传算法和Hopfield网络相比,HN-GA算法不仅全局搜索能力强,且收敛速度快、耗时少.     

移动Sink的传感器网络路径优化策略

在无线传感器网络（WSNs）中引入移动 Sink 可以避免网络拥塞和能量空洞并降低网络能耗,但由于移动速度的限制导致时延较大。针对这一问题,提出了时延约束下的移动 Sink 路径优化策略,根据时延和网络能耗之间的关系设计了可调节的节点权重,通过模拟退火遗传算法得到最优节点权重,并依据此权重通过迭代得到汇聚节点和最佳移动路径。仿真结果表明：该策略能保证在满足时延约束的前提下降低网络能耗,且收敛速度快。     

传感器网络感知数据自适应去噪方法

在传感器网络研究领域中,去除感知数据含有的噪声是个重要的研究课题。现存的去噪算法没有考虑节点密度不均匀及信息拥塞的情况,从而过多地消耗了能量。考虑这两个因素,使用时间维加权的方法,提出了一个基于节点密度的网内自适应去噪算法－DHA（density-based hybrid approach）。DHA能够根据节点密度来进行算法决策,并且在时间维进行加权,能够对数据变化作出快速反应并且提高数据精度。实验结果表明,DHA方法能够在保证良好的去噪效果、快速响应时间的前提下,比目前最好的去噪算法WMA（weighted moving average-based）更节省能量。     

无线传感网络移动节点位置并行微粒群优化策略

网络节点位置优化是无线传感网络研究的核心问题之一.无线传感网络通常由固定节点和少量移动节点构成,传统的虚拟力导向算法无法解决固定节点对移动节点优化的约束.该文针对这一问题,提出了基于并行微粒群算法的优化策略.微粒群算法具有适于解决连续空间多维函数优化问题、能快速收敛至全局最优解的特点.并行框架提高了算法的运行效率,降低了算法的运算复杂度,使算法能够满足无线传感网络的需求.通过并行微粒群算法搜索不同状态下无线传感节点的最优位置,使无线传感网络能够利用移动节点实现网络结构的动态重组,最大化网络覆盖范围,提高网络测量可靠性.实验证明,并行微粒群优化策略能快速有效地实现无线传感网络移动节点位置优化.     

基于加强学习与联想记忆粒子群优化算法的节点定位

提出了加强学习与联想记忆的粒子群优化算法,并将该算法应用到无线传感器网络的节点定位中。在RSSI模型测距产生的不同误差情况下,分别比较极大似然估计法和加强学习与联想记忆的粒子群优化算法产生的定位误差、定位方差,证明了加强学习与联想记忆的粒子群优化算法是一种收敛快、精度高、稳定性好的优化算法,适合应用在无线传感器网络节点定位中。     

Jointly optimized congestion control,forwarding strategy,and link scheduling in a named-data multihop wireless network

As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-dataMWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.     

传感器网络多路径传输效用-寿命折中优化研究

网络效用和网络寿命是无线传感器网络速率控制研究中两个极为重要而又互相冲突的设计目标.为兼顾网络性能需对二者进行折衷处理.通过引入折衷因子,建立网络效用和寿命的组合优化模型,利用拉格朗日对偶分解方法对优化问题进行求解,设计分布式的最优速率控制算法.仿真结果表明,通过调节折衷因子,可实现网络寿命和效用的均衡,并验证了提出算法的收敛性能及全局最优性.     

一种基于GM(1,1)的分簇WSNs拥塞检测与控制方案

针对分簇结构WSNs下中继簇首流量负担过重易引发局部拥塞的问题,提出了一种基于簇首数据吞吐量短期预测的拥塞检测与控制算法CMETR.该算法通过建立GM(1,1)灰色模型分析流经各簇首的当前流量,预测簇首未来的拥塞程度,并以调整簇内节点数据采集频率的方式减小簇首的数据传输压力,从而达到控制网络拥塞的目的.仿真结果表明:该算法有较好的预测精度,对即将到来的网络拥塞能够进行提前处置,且在网络繁忙时能够缓解链路压力,相对CODA算法有更好的稳定性和能耗特性.     

New coordination strategy for on-line hierarchical optimizing control of large scale industrial processes

A new coordination strategy for hierarchical optimizing control is presented, Unlike the price coordination method this approach is not a primal and dual method and its convergence behaviour does not depend on the feature of the saddle point of the problem lagrangian. Compared with the price coordination method, this approach has two advantages. Firstly, its applicability conditions are easier to satisfy and secondly, its convergence behaviour is more desirable in terms of convergence rate and insensitivity of the hessian structure of the problem. A variable augmentation technique is employed to increase the flexibility of the iterative gain selection and to improve further the convergence behaviour of the method. Optimality and convergence analysis are provided for each different version of the algorithm presented. A comparative study between different versions of the algorithm presented and a single iterative integrated system optimization and parameter estimation (ISOPE) method with global feedback is also provided using computer simulation.     

Primal and dual-based algorithms for sensing range adjustment in WSNs

Coverage and tracking of multiple targets, are viewed as important challenges in WSNs, mainly aimed for future ubiquitous and pervasive applications. Target coverage in WSNs with large numbers of sensor nodes and targets, and with a predefined placement of sensors, may be conducted through adjusting the sensing range and considering the energy consumption related to this operation. In this paper, we encounter the problem of multiple target coverage in WSNs by determining the sensing range of each sensor node to maximize the total utility of the network. We solve this Network Utility Maximization (NUM) problem via two approaches, primal and dual decompositions, which result in iterative distributed price-based algorithms. Convergence of sensing ranges to optimal values is proved by means of stability analysis and simulation experiments. Simulation results show convergence to optimal values in few iterations, with near optimal values for the total objective function and energy consumption of nodes. These results show scalability of our algorithm, in terms of the number of iterations needed for convergence when compared with the other two methods. Furthermore, the distributed algorithm based on dual decomposition is used to cover efficiently moving targets in consecutive time intervals.     

基于跨层设计的认知无线Mesh网络拥塞反馈研究

在认知无线Mesh网络中,由于信道状态变化导致的链路负载差异,网络拥塞成为影响认知无线Mesh网络系统性能的重要因素。针对这一问题,提出了基于最大最小公平策略的拥塞反馈算法。该算法通过综合分析基于随机搜索-遗传算法的多速率编解码调制、多重数据流的信道分配机制,以及优化的路由选择三种机制的网络资源分配约束条件,来构建跨层模型,计算网络拥塞。同时,通过拥塞值反馈,实现对物理层、链路层和网络层的联合跨层优化,最大程度避免网络拥塞。仿真结果表明,该算法在网络发生拥塞时收敛更快,能够有效避免拥塞,均衡负载,并能提升网络吞吐量。     

基于压缩感知的无线传感器网络数据传输跨层优化算法

针对无线传感器网络中数据传输问题,提出一种基于压缩感知的数据传输跨层优化算法.首先,为了剔除原始数据时间、空间冗余性,构造一种时空动态感知矩阵,降低采样频率的同时使得传感器采集的数据包含全部有用的信息.其次,以最小传输数据量为目标,以链路容量、功率、路由选择为约束条件,建立跨层的优化模型,通过求解优化模型,得到最优的功率控制、链路容量和路由选择策略.仿真结果表明,所提算法能够降低数据传输量,克服传统数据传输算法中由于数据处理不均衡导致的网络拥塞问题.