水声传感网中的高效调制解调器的设计

为适应水声传感网络节点密集、规模大、工作时间长的特点,提出了一种具有小体积、低成本、低功耗等特点的水声调制解调器。采用数字幅度调制2ASK调制解调方式,硬件使用低功耗设计方案,电池供电,使用市场上量产的收发一体式小型水声换能器,使功耗和成本得到大幅度降低。经实际测试,整机最大功耗为90 mW,数据传输速率为2kbps,具备代替市场上昂贵水声调制解调器的潜力,在水声传感网技术中有良好的应用前景。     

一种基于缓存交互的命名数据网络拥塞控制算法

研究了命名数据网络(NDN)的拥塞控制。为了解决突发流量问题和提高吞吐量及网络资源利用率,考虑了路由器缓冲区大小与拥塞控制机制的相互影响以及NDN内部署缓存这一重要特性,提出了一种基于缓存交互的NDN拥塞控制算法。该算法通过利用NDN中的路由器缓存,在逻辑上动态扩充缓冲区大小并控制Data包的发送速率,同时与现有的NDN拥塞控制算法相结合,动态调整Interest包发送速率阈值,以平滑突发流量,缓解网络拥塞。基于ndn SIM的仿真实验结果表明,该算法能有效提高NDN的传输效率、吞吐量和网络资源利用率。     

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.     

Voronoi-based relay placement scheme for wireless sensor networks

Energy consumption is a crucially important issue in battery-driven wireless sensor networks (WSNs). In most sensor networks, the sensors near the data collector (i.e. the sink) become drained more quickly than those elsewhere in the network since they are required to relay all of the data collected in the network to the sink. Therefore more balanced data paths to the sink should be established in order to extend the lifetime of the sensor network. Accordingly, a novel relay deployment scheme for WSNs based on the Voronoi diagram is proposed. The proposed scheme is applicable to both two-dimensional and three-dimensional network topologies and establishes effective routing paths that balance the traffic load within the sensor network and alleviate the burden on the sensors around the sink. Simulation results indicate that the number of relays deployed in the proposed scheme is similar to that deployed in the predetermined location scheme and is significantly less than that deployed in the minimum set cover scheme. Furthermore, the lifetime of the sensor network containing relay nodes deployed using the current scheme is longer than that achieved using either the predetermined location scheme or the minimum set cover scheme.     

EAMTR: energy aware multi-tree routing for wireless sensor networks

IEEE 802.15.4 is the prevailing standard for low-rate wireless personal area networks. It specifies the physical layer and medium access control sub-layer. Some emerging standards such as ZigBee define the network layer on top of these lower levels to support routing and multi-hop communication. Tree routing is a favourable basis for ZigBee routing because of its simplicity and limited use of resources. However, in data collection systems that are based on spanning trees rooted at a sink node, non-optimal route selection, congestion and uneven distribution of traffic in tree routing can adversely contribute to network performance and lifetime. The imbalance in workload can result in hotspot problems and early energy depletion of specific nodes that are normally the crucial routers of the network. The authors propose a novel light-weight routing protocol, energy aware multi-tree routing (EAMTR) protocol, to balance the workload of data gathering and alleviate the hotspot and single points of failure problems for high-density sink-type networks. In this scheme, multiple trees are formed in the initialisation phase and according to network traffic, each node selects the least congested route to the root node. The results of simulation and performance evaluation of EAMTR show significant improvement in network lifetime and traffic distribution.     

Network lifetime-aware data collection in Underwater Sensor Networks for delay-tolerant applications

Development of energy-efficient data collection and routing schemes for Underwater Wireless Sensor Networks (UWSNs) is a challenging issue due to the peculiarities of the underlying physical layer technology. Since the recharging or replacement of sensor nodes is almost impossible after deployment, the critical issue of network lifetime maximization must be considered right from the beginning of designing the routing schemes. We propose a mobile sink (MS)-based data collection scheme that can extend network lifetime, taking into account power-constrained sensor nodes, partitioned networks with geographically distant data collection points and periodic monitoring applications with delay-tolerance. Lifetime extension is achieved by mitigating the ‘sink neighbourhood problem’ and by deferring the data transmissions until the MS is at the most favourable location for data transfer. Unlike the models available for terrestrial WSNs, we consider non-zero travel time of the MS between data collection points, thus making our model more realistic for UWSNs, both connected and partitioned. The performance of the proposed mobility-assisted data collection scheme is thoroughly investigated using both analytical and simulation models. The analytical results are compared to those of existing models to assess their effectiveness and to investigate the trade-offs. Results show that, with a network size of 60 nodes, the network lifetime achieved by the proposed model is 188% higher than that of static sink model and 91% higher than that of mobile sink model (MSM). The proposed maximum lifetime routing scheme is implemented in the network simulation platform OMNET++, for validating the analytical results as well as for evaluating other performance metrics that are not tractable via analytical methods. Both analytical and simulation results demonstrate the superiority of the proposed model in capturing realistic network conditions and providing useful performance indicators prior to network deployment.     

考虑多节点拥堵的城市道路网级联失效仿真

为量化城市路网在多个节点突发拥堵时出现的级联失效现象,解决网络重要节点拥堵而引起的连锁反应问题,本文构建了双层网络配流的网级联失效模型,采用计算路网拥堵度完成该情景下的级联失效仿真研究的方法,对级联失效现象进行分析。首先采用原始法构建路网的几何拓扑图,依次从拓扑图中删除拥堵节点,然后利用连通度、最大连通子图相对大小与圈数率3个鲁棒性评价指标衡量路网通行效率,计算出通行效率不低于10%条件下的最大节点失效比例,再运用双层网络配流模型测算级联失效时的路网拥堵度,最后通过Transcad 4.5和Matlab 2012a完成对包含54个节点、92条路段的成都市某地中等规模路网的仿真实验。实验结果表明：路网在最不利条件下的最大节点失效比例为19.3%;相比于单个拥堵节点,2个以上节点同时拥堵更易造成路网出现级联失效现象;当拥堵节点数目超过7个后,拥堵度逐渐趋于稳定并使路网到达其鲁棒性的极限。该研究旨在为多节点失效条件下的路网管控措施提供仿真技术参考。     

A Mobile Host Approach for Wireless Powering and Interrogation of Structural Health Monitoring Sensor Networks

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can provide the data collection necessary for rapid structural assessment after an event such as a natural disaster puts the reliability of civil infrastructure in question. Technical challenges affecting deployment of such a network include ensuring power is maintained at the sensor nodes, reducing installation and maintenance costs, and automating the collection and analysis of data provided by a wireless sensor network. In this work, a new "mobile host" WSN paradigm is presented. This architecture utilizes nodes that are deployed without resident power. The associated sensors operate on a mechanical memory principle. A mobile host, such as a robot or unmanned aerial vehicle, is used on an as-needed basis to charge the node by wireless power delivery and subsequently retrieve the data by wireless interrogation. The mobile host may be guided in turn to any deployed node that requires interrogation. The contribution of this work is the first field demonstration of a mobile host wireless sensor network. The sensor node, referred to as THINNER, capable of collecting data wirelessly in the absence of electrical power was developed. A peak displacement sensor capable of interfacing with the THINNER sensor node was also designed and tested. A wireless energy delivery package capable of being carried by an airborne mobile host was developed. Finally, the system engineering required to implement the overall sensor network was carried out. The field demonstration took place on an out-of-service, full-scale bridge near Truth-or-Consequences, NM.     

An Adjust Duty Cycle Method for Optimized Congestion Avoidance and Reducing Delay for WSNs

With the expansion of the application range and network scale of wireless sensor networks in recent years, WSNs often generate data surges and delay queues during the transmission process, causing network paralysis, even resulting in local or global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order to improve the resource utilization rate of network nodes, we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy. Using this strategy to ensure that the network lifetime remains the same, can minimize system delay and maximize energy efficiency. Firstly, the problems of the existing RED algorithm are analyzed. We introduce the improved SIG-RED algorithm into the ADCOC mechanism. As the data traffic changes, the RED protocol cannot automatically adjust the duty cycle. A scheduler is added to the buffer area manager, referring to a weighted index of network congestion, which can quickly determine the status of network congestion. The value of the weighting coefficient W is adjusted by the Bayesian method. The scheduler preferably transmits severely urgent data, alleviating the memory load. Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically. By simulating the algorithm, it shows that it has faster convergence speed and smaller queue jitter. Finally, we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams. Thus, the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.     

车载自组织网络信息流量特性分析

为了揭示车载自组织网络动态拓扑特性,预测车载自组织网络行为和缓解网络拥堵,本文从微观角度分析了信息流量分布的一般特性,研究了在不同交通需求下车载自组织网络信息流量分布特性及信息流量与节点度值之间的关系.首先提出度、度分布指数与信息流量的关系,用复杂网络理论以静态、动态两种方式建立无容量限制的无标度网络模型,采用数值模拟实验和仿真实验方法讨论了参数变化时车载自组织网络信息流量变化规律;其次,借助非线性动力学理论研究了考虑容量限制的信息流量分布特性.研究结果表明,信息流量分布指数是无标度网络的通用特征参数,度值大的节点对网络影响较大,传播信息更快,信息流量与度值之间满足幂律分布规律;当信息流量需求增大时,Hub节点可能负担过重,许多信息流量可能会选择其他节点避免与Hub节点连通,这时较小度值的节点承担这部分信息流量.     

Q-Learning Based Routing Protocol for Congestion Avoidance

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state, Q-value, and reward function to set the desired path toward the destination. A new reward function that consists of a buffer occupancy, link reliability and hop count is considered. Moreover, look ahead algorithm is employed to update the Q-value with values within two hops simultaneously. This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account, accordingly. Finally, the simulation results presented approximately 20% higher packet delivery ratio and 15% shorter end-to-end delay, compared to those with the existing scheme by avoiding congestion adaptively.     

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System (ITS) is essential for effective identification of vulnerable units in the transport network and its stable operation. Also, it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things (IoT). Previous research on vulnerability has no congestion effect on the peak time of urban road network. The cascading failure of links or nodes is presented by IoT monitoring system, which can collect data from a wireless sensor network in the transport environment. The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure (V2I) channels to simulate key segments and their failure probability. Finally, the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors. The two indices are standardized by calculating the relative change rate, and the comprehensive index of the consequence after road network unit is in a failure state. Therefore, by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state, the comprehensive vulnerability of road network can be evaluated by a risk calculation formula. In short, the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.     

基于粒子群算法的水声通信网络能量优化方法

水声通信网络节点功耗是影响网络节点寿命的重要因素之一。针对水声通信网络的能量优化问题,基于网络节点发射功率与传输距离的非线性关系,结合改进的粒子群算法建立了一种能量优化方法。该方法改进了网络模型,在网络运行中根据存活节点数量和节点剩余能量的变化情况,自适应动态优化每个节点的信息传输路径。仿真结果表明,所提优化方法能有效降低网络节点总功耗,延缓首个节点的死亡,减缓网络中节点的死亡速率,也即减缓了网络有效覆盖面积随着网络运行而减小的速率。     

基于WOSS的NS-Miracle水声信道模拟方法扩展

将世界海洋仿真系统（World Ocean Simulation System,WOSS）与声射线模型Bellhop结合并引入海洋噪声经验公式对NS-Miracle仿真系统的水声传输信道模拟方法进行扩展。为了验证扩展后的仿真系统,实验采用了基于水声信道特征经验模型、Bellhop射线模型及WOSS结合Bellhop射线模型的3种仿真方法对5个节点集中式拓扑结构的水下传感器网络进行了计算机仿真。结果表明,在相同的网络环境中,利用Bellhop信道仿真模型的两组实验的平均吞吐量、平均延时非常接近,而利用经验信道模型的第一组仿真得到的平均吞吐量和延时与后两组数据的差别稍大,三者的平均误包率非常接近。同时实验仿真了3个不同海域相同结构水下传感器网络的运行情况,分析引入WOSS后对仿真结果的影响。结果显示：在不同海域引入WOSS的系统仿真结果比未引入WOSS的系统仿真结果更具有实时动态性,扩展后的NS2/NS-Miracle仿真系统能够更准确地模拟实际水声网络的相关特性。另外,实验还利用扩展后的系统对ALOHA-CS、CSMA/CA和DACAP三种MAC协议进行了仿真和分析,结果显示,CSMA/CA协议相较于其他两种协议更适合集中式结构的水下传感器网络。     

Spatial econometrics models for congestion prediction with in-vehicle route guidance

The congestion dependence relationship among links using microsimulation is explored, based on data from a real road network. The work is motivated by recent innovations to improve the reliability of Dynamic Route Guidance (DRG) systems. The reliability of DRG systems can be significantly enhanced by adding a function to predict the congestion in the road network. The application of spatial econometrics modelling to congestion prediction is also explored, by using historical traffic message channel (TMC) data stored in the vehicle navigation unit. The nature of TMC data is in the form of a time series of geo-referenced congestion warning messages, which is generally collected from various traffic sources. The prediction of future congestion could be based on the previous year of TMC data. Synthetic TMC data generated by microscopic traffic simulation for the network of Coventry are used in this study. The feasibility of using spatial econometrics modelling techniques to predict congestion is explored. The results are presented at the end.     

A power scheme of a wireless sensor network node for debris flow monitoring with move-triggered wake-up

In countries threatened by debris flow disasters, using wireless sensor networks (WSN) for debris flow monitoring and warning has become an important research topic. To detect the initial movement and subsequent moving status of monitored debris or rocks, a two-phase power scheme for a self-developed wireless inertial sensor has been proposed, implemented, and analyzed in this study. During the first phase, the power of the sensor node is disconnected to ensure that the sensor remains stationary. In the second phase, a hybrid scheme with periodic and move-triggered wake-up is actuated if the sensor is moved by debris or hit by rocks. A simple move detection algorithm is used to determine whether the node should enter sleep mode. The power models of the components of the proposed sensor node have also been discussed. Based on those models, the power consumption of the proposed power scheme has been determined and verified according to measurements. Analysis of the proposed hybrid power scheme can be easily generalized for WSN applications with sensor-triggered schemes. The relationship between the moving rate of monitored objects and the wake-up period in the moving detection algorithm was found to be nearly inversely proportional. The results can be employed to estimate and design sensor node power schemes for other similar applications, such as value asset monitoring and tracking.     

Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks

This paper presents an intelligent, dynamic power conservation scheme for sensor networks in which the sensor network operation is adaptive to both changes in the objects under measurement and the network itself. The conservation scheme switches sensor nodes between a sleep and an active mode in a manner such that the nodes can maximize the time they spend in a power-efficient sleep state, which corresponds to a nonmeasuring and/or nontransmitting mode, while not missing important events. A switching decision is made based on changes (or their absence) in the signals sensed from the environment by an intelligent agent that has been trained to determine whether or not a special event has occurred. This intelligent agent is based on a novel neural network topology that allows for a significant reduction in the resource consumption required for its training and operation without compromising its change detection performance. The scheme was implemented to control a sensor network built from a number of Telos rev. B motes currently available on the market. A few new utilities including an original neural network-based intelligent agent, a “visualizer,” a communication manager, and a scheduler have been designed, implemented, and tested. Power consumption measurements taken in a laboratory environment confirm that use of the designed system results in a significant extension of sensor network lifetime (versus “always on” systems) from a few days to a few years.      

一种改进的唤醒信号能量检测算法及其应用研究

为了减少水下无线传感器网络的功耗,在notch滤波器算法原理基础上,结合帕斯瓦尔定理和子带能量目标检测算法分析环境噪声,得到了检测门限随环境噪声变化的唤醒信号检测算法,进行了理论分析和仿真。在微功耗处理器上实现了该算法并讨论了其在水下无线传感器网络的应用。试验证明该算法可以灵活调度网络中的各个节点状态,降低了传感器网络的功耗。     

A scheduling scheme for wireless sensor networks based on social insect colonies

Sensor networks employ a large amount of wireless sensor nodes to provide sensing power with high redundancy. Such redundancy makes sensor networks robust under changing environments. However, without proper scheduling, the surplus sensing power will cost tremendous energy consumption to the wireless sensor nodes. A scheduling scheme based on social insect colonies is proposed here. The proposed scheme is a kind of adaptive 'periodic on-off' scheduling scheme that uses only local information for making scheduling decisions. The scheme is evaluated in terms of averaged detection delay, target 3-coverage hit-rate and energy consumption per successful target detection. Simulation results show that, when comparing with other generic scheduling schemes, the proposed scheme can reduce energy consumption from a minimum of 7.49% to a maximum of 90.81% and improve the target hit-rate from a minimum of 15.7% to a maximum of 58.9%. Optimisation of the network lifetime and other performances is possible by adjusting some parameters.     

水声通信系统中分布式空时分组码的研究

协作传输可以获得无线信道内在的空间分集,有效改善通信系统的性能,增加通信系统的覆盖范围。对水声通信系统中两中继节点协作的译码转发方案进行了研究,提出了基于译码转发协议(DF)的分布式空时分组扩频编码(DSTBSC)方案,该方案可以克服多径衰落对分布式空时分组码(DSTBC)信号正交性的影响。给出了DSTBSC方案的系统模型,着重分析了在采用两中继译码转发时,中继节点的误码对水声通信系统性能的影响。在接收端不考虑信号冲突的情况进行仿真,结果表明,与直接传输方案相比,采用两中继节点、译码转发的DSTBSC方案,在两中继节点处均无误码和一个中继有误码时在相同的比特误码率(BER)条件下均可获得约4dB的分集增益,增加了传输距离。