Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the ‘full’ fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA–ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA–ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution.     

Voronoi-based localisation algorithm for mobile sensor networks

Localisation is an essential and important part in wireless sensor networks (WSNs). Many applications require location information. So far, there are less researchers studying on mobile sensor networks (MSNs) than static sensor networks (SSNs). However, MSNs are required in more and more areas such that the number of anchor nodes can be reduced and the location accuracy can be improved. In this paper, we firstly propose a range-free Voronoi-based Monte Carlo localisation algorithm (VMCL) for MSNs. We improve the localisation accuracy by making better use of the information that a sensor node gathers. Then, we propose an optimal region selection strategy of Voronoi diagram based on VMCL, called ORSS-VMCL, to increase the efficiency and accuracy for VMCL by adapting the size of Voronoi area during the filtering process. Simulation results show that the accuracy of these two algorithms, especially ORSS-VMCL, outperforms traditional MCL.     

Temporal synchronization in mobile sensor networks using image sequence analysis

This paper addresses the problem of estimating the temporal synchronization in mobile sensors’ networks, by using image sequence analysis of their corresponding scene dynamics. Unlike existing methods, which are frequently based on adaptations of techniques originally designed for wired networks with static topologies, or even based on solutions specially designed for ad hoc wireless sensor networks, but that have a high energy consumption and a low scalability regarding the number of sensors, this work proposes a novel approach that reduces the problem of synchronizing a general number $N$ of sensors to the robust estimation of a single line in ${\mathbb {R}}^{N+1}$ . This line captures all temporal relations between the sensors and can be computed without any prior knowledge of these relations. It is assumed that (1) the network’s mobile sensors cross the field of view of a stationary calibrated camera that operates with constant frame rate and (2) the sensors trajectories are estimated with a limited error at a constant sampling rate, both in the world coordinate system and in the camera’s image plane. Experimental results with real-world and synthetic scenarios demonstrate that our method can be successfully used to determine the temporal alignment in mobile sensor networks.     

Target tracking in wireless sensor networks using adaptive measurement quantization

Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth.We consider the problem of target tracking in a WSN with quantized measurements in this paper.Attention is focused on the design of measurement quantizer with adaptive thresholds.Based on the probability density function (PDF) of the signal amplitude measured at a random location and by maximizing the entropy,an adaptive design method for quantization thresholds is proposed.Due to the nonlinear measuring and quantization models,particle filtering (PF) is adopted in the fusion center (FC) to estimate the target state.Posterior Cram’er-Rao lower bounds (CRLBs) for tracking accuracy using quantized measurements are also derived.Finally,a simulation example on tracking single target with noisy circular trajectories is provided to illustrate the effectiveness of the proposed approach.     

Biologically inspired probabilistic coverage for mobile sensor networks

Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage. The problem is modeled as a multi-objective optimization that simultaneously minimizes two contradictory parameters; the total sensor moving distance and the total uncovered area. In order to resolve the aforementioned deployment problem, this study investigates the use of biologically inspired mechanisms, including evolutionary algorithms and swarm intelligence, with their state-of-the-art algorithms. Unlike most of the existing works, the coverage parameter is expressed as a probabilistic inference model due to uncertainty in sensor readings. To the best of our knowledge, probabilistic coverage of mobile sensor networks has not been addressed in the context of multi-objective bio-inspired algorithms. Performance evaluations on deployment quality and deployment cost are measured and analyzed through extensive simulations, showing the effectiveness of each algorithm under the developed objective functions. Simulations reveal that only one multi-objective evolutionary algorithm; the so-called multi-objective evolutionary algorithm with decomposition survives to effectively tackle the probabilistic coverage deployment problem. It gathers more than 78 % signals from all of the targets (and in some cases reaches 100 % certainty). On the other hand, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and non-dominated sorting particle swarm optimization show inferior performance down to 16–32 %, necessitating further modifications in their internal mechanisms.     

移动性无线传感器网络吞吐量跨层优化

采用跨层优化方法,分析了移动性无线传感器网络点对点链路的吞吐量优化问题。首先推导出吞吐量的理论表达式;以最大化吞吐量为目标,采用一种基于分层优化的数学框架,分别得到物理层最优符号速率、最优调制星座体积和MAC层最优数据包长;在此基础上,通过联合优化物理层参数和MAC层参数实现了链路吞吐量的最大化;最后提出了一种基于节点间通信距离的自适应跨层优化策略。仿真结果表明,与传统的分层优化相比,跨层优化后链路的吞吐量性能得到了明显提高。     

A farmland multimedia data collection method using mobile sink for wireless sensor networks

Wireless sensor networks are applied to collect the information about farmland required to achieve unmanned agriculture. The major purpose of the deployed sensors is to collect data. However, if the data collected by the sensors are too large, such as an image, sensors quickly become unavailable. In this paper, a novel method for collecting image data from deployed sensors by using a mobile sink is proposed. Unlike the existing data gathering methods, in which sensors deliver data to a sink via long distance transmission, in the proposed method the mobile sink walks within the region of interest (ROI) to harvest the data. A virtual-grid method is proposed to determine the visiting locations of the mobile sink. An algorithm to eliminate redundant locations that uses set-conjunction operations is also proposed for reducing the number of unnecessary visiting locations. In addition, a cross-edge adjusting trajectory scheduling (CATS) algorithm is proposed to reduce the moving distance of the mobile sink. Simulation results show that the proposed virtual-grid method can effectively reduce the number of visiting locations by about 15–20 % as compared to the cluster-centroid method. The CATS algorithm can also shorten the moving distance of the mobile sink by about 25 % as compared to that of a heuristic minimum spanning tree method.     

一种自适应无线传感器网络路由协议

无线传感器网络应用场合拓扑结构复杂,针对特定路径数据双向传输的需求,提出了一种加权的自适应无线传感器网络路由协议,并针对协议建立了相应的仿真模型.仿真结果表明根据网络规划确认的权值所建立的路由,由权值决定下一跳,达到了适应不同的拓扑结构的目的.同时加权结点位置变化时,无需向全网广播数据,只要修改局部路由,降低了因结点移动而再路由所产生的通信量.     

无线传感器网络中移动传感器的再定位技术

回顾了在传感器网络中引入移动传感器的过程。介绍了移动传感器再定位技术可解决传感器网络中的事件深入感知、传感器失效和非精确投放等问题。详细阐述了现有的移动传感器移动至感兴趣区域深入感知、扩大网络覆盖面积和修补网络覆盖洞三类技术。剖析了现有技术中存在的问题。总结分析了主要解决方法和模型,并对未来研究方向进行了展望。     

A secure routing scheme for mobile wireless sensor networks

Frequency-hopping (FH) is a well-known spread-spectrum method of transmitting radio signals by hopping frequency channels along a predefined hopping sequence known to both transmitter and receiver. Although FH is resistant to jamming by external malicious nodes which have no knowledge of the sequence, it is of no effect against attacks by internal compromised nodes which know the sequence. In this article, we propose a secure scheme for creating the hopping sequence for mobile wireless sensor networks. The proposed scheme is based on the idea of a statistical en-route filtering (SEF). SEF exploits collective decision making by multiple detecting nodes in the dense deployment of large sensor networks. We demonstrate the effectiveness of our scheme thorough simulations.     

Distributed network control for mobile multi-modal wireless sensor networks

A sensor network operates on an infrastructure of sensing, computation, and communication, through which it perceives the evolution of events it observes. We propose a fusion-driven distributed dynamic network controller, called MDSTC, for a multi-modal sensor network that incorporates distributed computation for in-situ assessment, prognosis, and optimal reorganization of constrained resources to achieve high quality multi-modal data fusion. For arbitrarily deployed sensors, a certain level of data quality cannot be guaranteed in sparse regions. MDSTC reallocates resources to sparse regions; reallocation of network resources in this manner is motivated by the fact that an increased density of sensor nodes in a region of interest leads to better quality data and enriches the network resilience. Simulation results in NS-2 show the effectiveness of the proposed MDSTC. ¹     

能量自适应的无线传感器网络分簇路由协议

为有效解决无线传感器网络中的"热区"问题,提出了一种能量自适应的非均匀分簇路由协议.协议采取非均匀的分簇结构,使靠近基站的簇范围减小到合理的范围,即在靠近基站的区域内,每个簇由较少的无线传感器节点构成,这些节点的主要工作是负责转发其它簇的信息,从而有效减少其能量消耗速率.同时,协议在通信路由的过程中还综合考虑了簇头与中转节点的距离以及中转节点的能量消耗率.实验结果表明,该协议有效解决了"热区"问题,并且延长了无线传感器网络的生命周期.     

无线传感器网络中一种竞争窗口自适应MAC协议

媒体访问控制(MAC)协议是保证无线传感器网络(WSNs)高效通信的关键网络协议之一。MAC层协议设计的是否合理将严重影响网络的性能。介绍了现有MAC协议分类和主要MAC协议。分析了WSNs中典型的S-MAC协议,针对S-MAC协议在载波侦听时采用固定竞争窗口的弊端,提出了一种新型的能够根据流量变化对竞争窗口进行动态调整的新的MAC协议ASMAC,利用NS2对ASMAC进行了仿真,证明了新的ASMAC不仅能够显著地提高吞吐量,降低时延,还能有效提高能量效率,在提高网络性能的同时达到节能的目的。     

无线传感器网络自适应性拓扑控制的研究

自适应拓扑控制方法用到多跳两层无线传感器网络(WSNs),在每个簇中用两类传感器,有效且低开销的传感器节点N感知环境现象信息,并传输它们的信息到汇聚节点S,所有Ss协同工作去除随机信息并传输数据到基站BS。因为覆盖范围依赖于它的汇聚节点的工作情况,而汇聚节点的能耗在网络的生命期中是关键性因素。这个方法主要是从节点路由能量匹配角度出发,设计可控制数据流路由路径,用于尽可能有效地保持网络能量,并不是仅仅考虑路径的最优选择,而是考虑能效的最优方式选择路由,从而增加整个网络的生命期。     

水下移动无线传感器网络研究综述

水下移动传感器网络是水下机器人与水下传感器网络的结合,解决了传统水下监控网络存在的无法自动实现节点投放与回收,容易存在监测盲区以及不能动态组网等问题,按照自下而上的体系结构,对水下移动传感器网络的研究内容及发展方向进行了归纳阐述,包括水下节点设计、节点互联和动态组网,即点、线、面的层次结构;着重分析了节点移动特性对水下传感器网络的影响及相应的解决方法.虽然水下移动传感器网络的研究面临很多挑战,但它必将具有广阔的应用前景.     

Learning bounds for kernel regression using effective data dimensionality

Kernel methods can embed finite-dimensional data into infinite-dimensional feature spaces. In spite of the large underlying feature dimensionality, kernel methods can achieve good generalization ability. This observation is often wrongly interpreted, and it has been used to argue that kernel learning can magically avoid the curse-of-dimensionality phenomenon encountered in statistical estimation problems. This letter shows that although using kernel representation, one can embed data into an infinite-dimensional feature space; the effective dimensionality of this embedding, which determines the learning complexity of the underlying kernel machine, is usually small. In particular, we introduce an algebraic definition of a scale-sensitive effective dimension associated with a kernel representation. Based on this quantity, we derive upper bounds on the generalization performance of some kernel regression methods. Moreover, we show that the resulting convergent rates are optimal under various circumstances.     

Multivariate context collection in mobile sensor networks

We focus on the treatment of quality-stamped contextual information in mobile sensor networks. Sensing nodes capture and forward context for consumption by mobile context aware applications. Due to the dynamic network topology the quality indicators seen by consumers vary over time. Context quality is a decreasing function of time and context can be consumed with a certain delay from its capturing time. We propose the sequential assessment of the network-circulated context information according to the Generalized Secretary Problem, a known paradigm in the Optimal Stopping Theory. The consumer node delays the processing (consumption) of incoming context until better quality is attained. We extend this basic model to include the cardinality of contextual components (i.e., different types of measurements coming from, possibly, different sources). Hence, the consumer node is interested not only in the higher possible quality of context but also in the widest possible range of context parameters (context “quantity”). We compare our findings to simple consumption strategies and pinpoint the advantages of the proposed model.     

Epidemic-based reliable and adaptive multicast for mobile ad hoc networks

An emerging approach to distributed systems exploits the self-organization, autonomy and robustness of biological epidemics. In this article, we propose a novel bio-inspired protocol: EraMobile (Epidemic-based Reliable and Adaptive Multicast for Mobile ad hoc networks). We also present extensive performance analysis results for it. EraMobile supports group applications that require high reliability. The protocol aims to deliver multicast data reliably with minimal network overhead, even under adverse network conditions. With an epidemic-based multicast method, it copes with dynamic and unpredictable topology changes due to mobility. Our epidemic mechanism does not require maintaining any tree- or mesh-like structure for multicasting. It requires neither a global nor a partial view of the network, nor does it require information about neighboring nodes and group members. In addition, it substantially lowers overhead by eliminating redundant data transmissions. Another distinguishing feature is its ability to adapt to varying node densities. This lets it deliver data reliably in both sparse networks (where network connectivity is prone to interruptions) and dense networks (where congestion is likely). We describe the working principles of the protocol and study its performance through comparative and extensive simulations in the ns-2 network simulator.