一种新型的基于数据平面共享环的IP over ASTN机制

提出了一种重叠模型下新型的基于数据平面共享环的IP over ASTN机制,其优点是利用IP网络中路由器自身具备的能力和在ASTN边缘节点引入基于以太网的二层交换能力,以较低的环路带宽代价实现各IP网间可达性信息传达,同时避免了对路由器的基本运行机制的较复杂修改。分析了相应的组网形式、边缘节点结构和网络运行机制,并对其中具有关键意义的带宽限制参数提出两种参数确定算法。     

事件驱动型传感器网络能量有效数据融合算法

从降低网络能耗和平衡网络负载的角度,提出了网络的一种能量有效的数据融合算法EFDAA,可应用于节点数量及覆盖度均较大的事件驱动型无线传感器网络.该算法采用正六边形网格划分方法,基于全网能量消耗模型计算所需的融合节点数,解决由于无规则选取融合节点数量而造成的网络能耗增加问题,并且能够优化融合节点的分布;为平衡网格内节点负载,以节点剩余能量、邻节点度和移动性作为选取融合节点的权重因子,基于距离信息自适应调整网格内节点间的单跳通信级别.仿真实验结果表明,融合节点数量的优选,降低了网络总的能量消耗;相比较于HEED算法,EFDAA有效延长了网络生命期.     

基于遗传算法的传感器网络拓扑控制研究

无线传感器网络的首要设计目标是延长网络生命期,网络的拓扑控制是实现这一目标的支撑 基础。针对传统拓扑控制方案所获拓扑的连通冗余度高或结构健壮性低等弊端,将问题转化 为多判据最小生成树模型,提出了一种基于遗传算法的拓扑控制方案。仿真实验结果 表明,该方案可获得具有网络整体功耗低、结构健壮性高和节点间通信干扰小等特点的拓扑 结构,因而能够有效地延长传感器网络生命期。     

探究非均匀分布下无线传感器网络节点调度机制

传统的无线网络传感器网络节点过于依赖精确的位置信息,在没有具体的位置信息时其正常运行就会受到很大的限制。针对这一问题,结合节点部署的理论分析,现提出无线传感器网络节点调度机制,该机制是基于非均匀分布情况下的,利用节点与邻居节点距离信息,判别节点覆盖冗余。本文就简要介绍该机制的设计方案,并通过仿真实验进行验证。     

基于WDM宽带无源光网络中IP业务的接入

为了进一步满足ATMPON增加带宽的需求 ,同时考虑到现行WDM器件性能的限制 ,提出了一种结合WDM与ATMPON技术的新型光纤接入网络结构。通过对IPOA的简化 ,给出了IP数据包在PON中传送的具体优化实施方案     

支持动态带宽约束的Ad Hoc网络QoS路由协议

针对已有的Ad Hoc网络QoS路由协议不能有效支持具有动态资源需求的特性业务的问题,提出了一种路径上传输带宽可动态调整的带宽约束QoS路由协议.路由建立后,若路径上的需求带宽增加,该协议将启动升级进程,各带宽瓶颈节点根据该协议的动态前向算法求解释放时隙集合,通过释放这部分当前处于传输状态的时隙增加路径上的预留带宽:而...     

带有带宽感知的自适应转发节点集合建立机制

研究了多点中继(MPR)集合建立机制对移动自组织网络(MANET)性能的影响,提出了一种基于可用带宽感知的集合建立方法,其特点是节点在MPR集合计算过程中自适应地为两跳邻居选择可用带宽较大的中继节点,降低节点拥塞程度,而算法的复杂度与原有算法的复杂度相同.仿真结果表明,虽然MPR集合平均元素数量和广播数据包数量略有增加,但是通过文中提出的转发节点集合建立方法,网络中的分组投递率以及端到端延迟性能得到了明显改善,此外该机制还能够根据网络当前状态实时调整,更加适应状态时变的网络特性.     

传感器网络时钟同步中基于多父节点的卡尔曼滤波算法

指出了节点每接收一个来自父节点的同步包等效于获得一个全局参考时钟的观测值,提出了使用矢量卡尔曼滤波器对来自多个父节点的观测值进行融合的算法.在多跳的无线传感器网络中,缓解了由于同步误差的逐层传递使全局时钟同步精度发生的恶化,避免网络边缘的节点产生较大的时钟误差,从而放宽了由误差传递导致的对网络规模的限制.硬件实现与数值仿真均表明相比现有协议,该算法能大幅度减小全局时钟误差,且随着节点跳数的增加而越发显著,有效地减轻了同步误差的传递问题.     

基于WDM0宽带无源光网络中IP业务的接入

为了进一步满足ATM PON增加带宽的需求,同时考虑到现行WDM器件性能的限制,提出了一种结合WDM与ATM PON技术的新型光纤接入网络结构。通过对IPOA的简化,给出了IP数据包在PON中传送的具体优化实施方案。     

数据网格资源选择优化模型

为了满足网格数据服务的可靠性和传输时间约束,同时兼顾网络负载及节点资源的有效利用,提出了一种网格数据资源选择优化模型,该模型以网络负载和资源代价为优化目标函数,以节点的传输速度、可靠性、传输距离、网络状态、客户端带宽和容忍度阈值为输入,进而决策出参与服务的最优节点集合,同时模型中设定了权重因子来均衡网络负载与资源代价....     

Matheuristic approaches for Q-coverage problem versions in wireless sensor networks

This article deals with sensor coverage scheduling in wireless sensor networks subject to Q-coverage constraints. The main concern is to maximize the network lifetime, while ensuring that each target is covered by a given number of sensors. Three different variations of this problem are considered. Column generation based exact approaches are developed for those problems where the auxiliary problem is solved by a two-level approach comprising a genetic algorithm and an integer linear programming formulation. The genetic algorithm takes advantage of the auxiliary problem structure and appears to be very efficient at providing the master problem with attractive columns. The auxiliary problem integer linear programming (ILP) formulation is then mostly used for proving the optimality status of the current master problem solution. The proposed approaches are shown to be significantly faster than column generation approaches relying only on the auxiliary problem ILP formulation.     

Controlled deployments for wireless sensor networks

In wireless sensor networks (WSNs), the operation of sensor nodes has to rely on a limited supply of energy (such as batteries). To support long lifetime operation of WSNs, an energy-efficient way of sensor deployment and operation of the WSNs is necessary. A new controlled layer deployment (CLD) protocol to guarantee coverage and energy efficiency for a sensor network is proposed. CLD outperforms previous similar protocols in that it can achieve the same performances and guarantee full area coverage and connection using a smaller number of sensors. It can also ameliorate the 'cascading problem' that reduces the whole network lifetime. Finally, analysis and simulation results show that CLD can use fewer sensor nodes for coverage and also increases the lifetime of the sensor network when compared with the probing environment and adapting sleeping (PEAS) protocol.     

Architectural design of a sensory node controller for optimized energy utilization in sensor networks

The increasing complexity of manufacturing machines and the continued demand for high productivity have led to growing applications of sensor networks to enable more reliable, timely, and comprehensive information gathering from the machines being monitored. An effective and efficient utilization of sensor networks requires new sensor designs that enable adaptive event-driven information gathering based on the condition of the machines, as well as a coordinated information distribution adjusted to the available communication bandwidth of the network. This paper investigates several fundamental aspects regarding the architectural design of a sensory node controller (SNOC). The SNOC is the key element in a large-scale sensor network that coordinates the operation of individual sensors and the communication among various sensing clusters to realize distributed intelligent sensing. A parametric SNOC design that dynamically adjusts the power supply and the data-acquisition procedure to reduce the overall energy consumption of the sensor network is presented. Considerations on both the hardware and software aspects of the design to achieve energy efficiency are described, and analytical formulations are derived. Simulation results for a sensor network consisting of 40 SNOCs, each coordinating eight physical sensors, have shown that the design is able to reduce the energy consumption by about 43%, as compared to traditional techniques. A prototype SNOC was designed and implemented, based on the platform of a commercially available microcontroller, and experimentally tested for its ability to dynamically adjust the power consumption. The study has provided a concrete input to the design optimization and experimental realization of an SNOC-based sensor network for machine-system monitoring.     

Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method.     

Impact Location and Quantification on a Composite Panel using Neural Networks and a Genetic Algorithm

Abstract: The problem of impact detection in composite panels using artificial neural networks is addressed in this paper. The data were taken from an experiment in which time dependent strain data were recorded on a network of surface-mounted piezoceramic sensors when the plate was impacted. Neural networks were trained to locate and quantify the impact event when presented with features extracted from the measured data. An important problem for detection systems like this is that of optimal sensor placement; this is solved here by means of a Genetic Algorithm. The study shows that a relatively small number of sensors can be used to detect reliably impacts on a composite plate.     

Optimum Sensor Placement for Impact Location Using Trilateration

A key problem associated with structural health monitoring (SHM) is the placement of sensors upon a structure to detect the existence, location, and the extent of any damage. Because input data coming from the sensors are groups of measurements, it is arguable that the most widely used approach to SHM nowadays is to consider it as a statistical pattern recognition problem. Artificial neural networks have made a great impact on pattern recognition practice. A problem associated with this monitoring strategy is to find a good compromise between the quality of information achieved by the sensor network, increasing with the sensor density, and the need to keep the minimum weight and instrumentation cost. Thus, the number of sensors must be kept under control, and a search of the optimal location of such sensors needs to be performed. All these aspects have been taken into account in the present work, dealing with the problem of optimum sensor placement for impact location on a multilayered composite structure. Multilayered composite structures may suffer particularly relevant trauma when subject to low‐velocity impacts, as they may produce non‐visible or barely visible damage on the structure surface, while remarkable subsurface delaminations may be present. Such hidden damage, when remaining undetected, may grow to catastrophic failure. To overcome this issue, a neural network approach has been used here to predict the impact locations on a composite panel from time‐dependent data recorded on a set of surface‐mounted piezoelectric sensors during an experimental impact test. A genetic algorithm has been used to find the optimal sensor layout that minimised the error in predicting the impact location. A new approach, based on trilateration, is discussed and compared with the traditional one and is shown to provide the same degree of accuracy at reduced computational cost.     

Optimal design of sensor networks for vehicle detection, classification, and monitoring

The tracking and identification of vehicles for the purpose of surveillance is a widespread application. Observations from a network of sensors can be used to make decisions regarding the identity of vehicles, as well as their trajectories. Generally, the information provided by a sensor network is limited, so vehicles may be misclassified, go undetected, and/or their trajectories may not be determined uniquely. Often, assumptions are made regarding, for example, traffic composition and possible vehicle trajectories. Because the performance of a sensor network can be sensitive to these assumptions, the conclusions made by the network about the identity and trajectory of vehicles can be highly inaccurate. In this paper, these assumptions are treated as possible models of reality that are subsequently evaluated in a decision framework. Mathematical models for vehicle movement and sensor behavior are developed. Candidate designs for the sensor network are considered, where each design is defined by the number, location, and range of the sensors. Methods from decision theory are used to determine the optimal design for the sensor network.     

Multi-sensor task allocation framework for supply networks security using task administration protocols

This research proposes a multi-sensor task allocation framework for security of supply networks aimed to maximise the number of correctly detected and reported security events (defined as tasks). The framework includes a double layer system consisting of a process layer and a monitoring layer. The process layer allocates sensors to tasks using an ant colony algorithm. The monitoring layer applies four task administration protocols (TAPs) specially developed and implemented to deal with high time-consuming tasks, conflicts in task priorities and sensor failure, defined in this research as overloading, deception and tampering of sensors, respectively. A system objective function for sensor to task allocation was developed to allow computation of the expected value of system performance given the sensor and the task parameters. Sensory limitations evaluated including reliability, distance coverage and the limited number of sensors are addressed in the decision-making process. The framework enables detection of tasks as soon as they occur in every location along the supply network, based on the sensor network distribution. The dual layer system analyses reveal that TAPs increase the systems performance in the scenarios of deception, tampering and overloading by more than 64% with respect to the number of unallocated tasks in comparison to a single layer system. Overall availability was analysed using Monte Carlo simulation and the fault tolerant system yielded significantly increased number of treated tasks (by 11%, p = 0.02).     

Robust Deployment of Dynamic Sensor Networks for Cooperative Track Detection

The problem of cooperative track detection by a dynamic sensor network arises in many applications, including security and surveillance, and tracking of endangered species. Several authors have recently shown that the quality-of-service of these networks can be statically optimized by placing the sensors in the region of interest (ROI) via mathematical programming. However, if the sensors are subject to external forcing, such as winds or currents, they may be rapidly displaced, and their quality-of-service may be significantly deteriorated over time. The novel approach presented in this paper consists of placing the sensors in the ROI based on their future displacement, which can be estimated from environmental forecasts and sensor dynamic models. The sensor network deployment is viewed as a new problem in dynamic computational geometry, in which the initial positions of a family of circles with time-varying radii and positions are to be optimized subject to sets of algebraic and differential equations. When these equations are nonlinear and time-varying, the optimization problem does not have an exact solution, or global optimum, but can be approximated as a finite-dimensional nonlinear program by discretizing the quality-of-service and the dynamic models with respect to time. Then, a near-optimal solution for the initial sensor positions is sought by means of sequential quadratic programming. The numerical results show that this approach can improve quality-of-service by up to a factor of five compared to existing techniques, and its performance is robust to propagated modeling and deployment errors.      

Research on Sensor Network Coverage Enhancement Based on Non-Cooperative Games

Coverage is an important issue for resources rational allocation, cognitive tasks completion in sensor networks. The mobility, communicability and learning ability of smart sensors have received much attention in the past decade. Based on the deep study of game theory, a mobile sensor non-cooperative game model is established for the sensor network deployment and a local information-based topology control (LITC) algorithm for coverage enhancement is proposed. We both consider revenue of the monitoring events and neighboring sensors to avoid nodes aggregation when formulating the utility function. We then prove that the non-cooperative game is an exact potential game in which Nash Equilibrium exists. The proposed algorithm focuses on the local information of the neighboring sensors and decides sensors’ next action based on the actions of the other sensors, which maximizes its own utility function. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate that the proposed algorithm can enlarge the coverage of the entire monitoring area while achieving effective coverage of the events.