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.     

Topology control algorithm for underwater wireless sensor networks using GPS-free mobile sensor nodes

Underwater wireless sensor networks (UWSNs) have been developed for underwater applications, such as resource exploration, pollution monitoring, and tactical surveillance. The topology control techniques of UWSNs and terrestrial wireless sensor networks are significantly different because of the particularity of underwater environments and acoustic communication, such as mobility pattern, propagation delay and energy consumption. Due to multifarious factors of underwater environments, node mobility becomes a non-negligible issue. However, GPS may not be feasible because of the limitations of satellite coverage or obstructions in adverse underwater environments. In particular, anchored sensor nodes towed by wires are prone to offset around their static positions, causing each node to move within a spherical crown surface (spherical crown mobility pattern). Nevertheless, most previous studies have not focused on this specific mobility pattern. In the current paper, a mobility model for UWSNs nodes is constructed, and three representative topology control objectives are attained. A distributed radius determination algorithm is designed for the mobility-based topology control problem. Results of theoretic analysis prove that the proposed algorithm is convergent, and it has preferable approximate ratios and polynomial complexity. Performance of the algorithm is analyzed through simulation experiments, which indicate a well-constructed topology. Every objective can still be upgraded without the dynamic location information of mobile nodes.     

Coverage boundary of unknown environment using mobile sensor networks

In this article, we consider the problem of optimal coverage of unknown environmental boundary using sensor networks. Since the boundary is unknown to all sensors, it is necessary for the sensors to find it first. We give a new distributed estimate policy by tracking a virtual agent using the sensor networks. Then we consider the problem of optimal coverage of the estimate boundary instead of the actual one. Moreover, an algorithm is given to deploy the sensors to the optimal configuration corresponding to the coverage problem considered in this article.     

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.     

Distributed patching for mobile wireless sensor networks

Mobile wireless sensor networks (MWSNs), the continuous movement of sensor nodes, may cause complete disconnection of the network or at best a part of it. The design of such networks should guarantee that all sensor nodes at all times have a path to the sink node(s). Prolonging the lifetimes of the MWSNs is a crucial design issue but should not be at the expense of other essential functions such as connectivity. In this paper, we propose an energy efficient distributed framework for connectivity maintenance of MWSNs. In the proposed framework, sensor nodes of the MWSNs schedule and control their radio frequency (RF) modules based on dynamic coordinated reconstruction mechanism, some senor nodes may lose their connectivity with the network due to mobility, depletion of energy and/or incurred faults. Two protocols are developed to patch up the disconnection of the MWSNs. The first protocol reestablishes the network connection consuming far less energy than state-of-the-art alternatives. However, it is capable of reestablishing the connection when no more than 20% of the MWSN backbone nodes lose connection concurrently. The second protocol extends this limit to operate when up to 35% of the MWSN backbone nodes lose connection simultaneously.     

传感器网络中一种实时的自适应时隙调度算法

无线传感器网络节点数目众多,MAC协议为节点分配工作时隙面临能量利用不高、节点延时较长等方面的难题。目前基于时隙调度的MAC协议一般采用等长的时隙大小,不能适应数据流量变化大的网络且忽略与网络层的融合,没有利用路由层信息来减低时隙分配算法性能代价。提出一种基于路由转发树的时隙调度算法（ATSA）,网络采用簇结构,在簇内构造一棵路由转发树,根据路由转发树形成的路径信息对节点实时获取节点每轮需要发送的数据量大小,根据节点的数据量大小来分配节点每轮需要的时隙,然后由簇头据此动态地为成员节点分配时隙,降低时隙划分的能量和时间代价,减少空闲侦听时间,避免串音。仿真表明,该算法有效地提高了网络能量利用效率,延长了网络生存周期,降低数据包的延时。     

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.     

Distributed estimation for adaptive sensor selection in wireless sensor networks

Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.     

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.     

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.     

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。     

延迟容忍移动无线传感器网络路由分析

延迟容忍移动无线传感器网络DTMSN（Delay Tolerant Mobile Sensor Networks）用于广泛数据收集,传统传感器网络的数据收集方法在DTMSN中并不适用。为此研究了DTMSN的特性,分析了目前常用的几种DTMSN路由算法的特点,并通过详细的仿真实验给出了这几种算法的性能指标,如数据的平均传输成功率,传输能耗、传输延迟及网络寿命。     

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.     

基于熵权系数法的无线传感器网络自适应QoS路由算法

针对无线传感器网络中不同业务对服务质量（QoS）指标的不同要求,以及QoS指标在网络运行过程中实时变化的特点,提出一种基于熵权系数法的自适应QoS路由（EAQR）算法。算法将路由建立过程抽象成多指标加权评分的问题,选取节点负载、平均能量势、通信时延作为QoS评价指标,采用熵权系数法自适应地确定指标的权重,选择最优节点转发数据。仿真实验显示,与有序分配路由(SAR)、能量感知QoS路由(EQR)算法相比,EAQR算法可以有效降低网络平均端到端延迟,减少丢包率,延长网络寿命。     

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.     

Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

多帧融合自适应核回归图像去噪

图像去噪是数字图像处理的重要内容,常用的传统方法包括空域中值滤波和维纳滤波,近年来基于小波变换、核回归等的去噪方法备受关注,基于单帧处理的实验发现核回归方法有更好的去噪效果。在理论上将核回归方法推广到多帧情况,并进行了对比实验,结果表明多帧处理能够进一步改进去噪效果。     

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。