State-centric programming for sensor-actuator network systems

Distributed embedded systems such as wireless sensor and actuator networks require new programming models and software tools to support the rapid design and prototyping of sensing and control applications. Unlike centralized platforms and Web-based distributed systems, these distributed sensor-actuator network (DSAN) systems are characterized by a massive number of potentially failing nodes, limited energy and bandwidth resources, and the need to rapidly respond to sensor input. We describe a state-centric, agent-based design methodology to mediate between a system developer's mental model of physical phenomena and the distributed execution of DSAN applications. Building on the ideas of data-centric networking, sensor databases, and proximity-based group formation, we introduce the notion of collaboration groups, which abstracts common patterns in application-specific communication and resource allocation. Using a distributed tracking application with sensor networks, we'll demonstrate how state-centric programming can raise the abstraction level for application developers.     

An Automatic Fusion Mechanism for Variable-Length List Skeletons in SkeTo

Skeletal parallel programming is a promising approach to easy parallel programming in which users can build parallel programs simply by combining parts of a given set of ready-made parallel computation patterns called skeletons. There is a trade-off for this easiness in the form of an efficiency problem caused by the compositional style of the programming. One solution to this problem is fusion transformation that optimizes naively composed skeleton programs by eliminating redundant intermediate data structures. Several parallel skeleton libraries have automatic fusion mechanisms. However, there have been no automatic fusion mechanisms proposed for variable-length list (VLL) skeletons, even though such skeletons are useful for practical problems. The main difficulty is that previous fusion mechanisms are not applicable to VLL skeletons, and so the fusion cannot be completed. In this paper, we propose a novel fusion mechanism for VLL skeletons that can achieve both an easy programming interface and complete fusion. The proposed mechanism has been implemented in our skeleton library, SkeTo, by using the expression templates technique, experimental results have shown that it is very effective.     

Research on adaptive target tracking in vehicle sensor networks

For resource-constrained vehicle sensor networks (VSNs), consisting of vehicle nodes and monitor nodes, the crucial task is to track the movement of vehicles in the monitor area. In this paper, we study the problem of how to achieve the precise vehicle tracking with minimized energy consumption. We first demonstrate that the precise vehicle tracking cannot be realized when only one type sensor is equipped on monitor node. Then we employ Gaussian mixture model (GMM) to describe the distribution of the different sensory data collected by various sensors, while the mean-shift algorithm is adopted to predict the location. To obtain better performance, we further use maximum likelihood estimation to improve the monitor precision. Finally, we propose an adaptive vehicle tracking algorithm (AVT), which uses sector awakened area and sigmoid function to reduce the number of participated monitor nodes for the sake of reducing the energy. Extensive experiments are carried out to evaluate AVT with several performance criteria. Our experiment results show that the proposed AVT algorithm can effectively track mobile vehicle and perform high efficiency in conserving energy.     

Energy-balanced multiple-sensor collaborative scheduling for maneuvering target tracking in wireless sensor networks

An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimat...     

WSN中面向非均匀形状事件的动态检测方案

高效且准确地事件检测与跟踪是无线传感器网络研究的热点问题之一,现有的事件检测方案无法实现对形状不定的区域事件的检测与跟踪,为了解决这一问题,提出了一种改进的动态事件检测和跟踪方案.首先,该方案利用事件质心确定事件的具体位置,然后,考虑了节点的读数强度及该读数与事件质心的距离,提出节点动量概念以引导事件融合和分割检测.仿真实验结果表明,该方案可在各传感器结点中分布式运行,延迟低,能效高,在各种条件下均有很高的准确度.不论事件大小和事件数量如何,它始终可以确定正确的事件数量,给出正确的事件形状.     

Curve-skeleton extraction using iterative least squares optimization

A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object's geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies, 2) extracting curve skeletons through the thinning algorithm, and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the pre-computational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other well-known methods. Experimental results show advantages when using our method over other techniques.     

Cooperative distributed target tracking algorithm in mobile wireless sensor networks

The paper proposes a cooperative distributed target tracking algorithm in mobile wireless sensor networks.There are two main components in the algorithm:distributed sensor-target assignment and sensor motion control.In the key idea of the sensor-target assignment,sensors are considered as autonomous agents and the defined objective function of each sensor concentrates on two fundamental factors:the tracking accuracy and the tracking cost.Compared with the centralized algorithm and the noncooperative distrib...     

Interacting multiple model filter-based distributed target tracking algorithm in underwater wireless sensor networks

This paper deals with the problem of accurately tracking a single target, which has various trajectories, moving through the environment of underwater wireless sensor networks (UWSNs). This paper addresses the issues of estimating the states of the target, improving energy efficiency by using a distributed architecture. Each underwater wireless sensor node composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides algorithms increasing the energy efficiency of each sensor node by using the proposed Wake-Up/Sleep (WUS) scheme. An interacting multiple model (IMM) filter is applied to the proposed distributed architecture in order to cope with a target maneuver. Simulation results illustrate the performance of the proposed tracking filter according to the various target maneuver patterns.     

ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.     

Collaborative signal processing for target tracking in distributed wireless sensor networks

Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time.     

基于线性回归的无线传感器网络分布式数据采集优化策略

事件监测是无线传感器网络中最重要的应用之一,部署在监测区域内的传感器节点通过对感知数据信息的采集、处理和传输等基本操作完成具体的监测任务,在各种操作中,节点之间的数据传输是最消耗能量的.为了减少节点之间的通信数据量,达到降低网络能耗和延长网络生命周期的目的,该文提出了一种能量高效的基于线性回归的无线传感器网络分布式数据采集优化策略,通过应用线性回归分析方法构建感知数据模型,保持感知数据的特征,使节点仅传输回归模型的参数信息,代替传输实际监测的感知数据信息.仿真实验结果表明,文中提出的数据采集优化策略能通过较小的通信量有效地实现事件监测区域感知数据的预测和估计,降低网络的总能量消耗,延长网络的生命周期.     

无线传感器网络主动分布式节点定位算法

在研究现有无线传感器定位算法基础上提出了主动分布式传感器节点定位策略(ADLA),该算法借鉴集中式凸规划算法的思路,提出了一种方法简单的分布式位置估计方法.该方法计算简单,内存需求少且通信开销低.经过试验证明,该算法的定位精度在锚节点连接度为18个的情况下,可以达到0.6倍节点通信距离的精度,完全可以满足无线传感器网络一般的定位需求.     

无线传感网中混合业务下的触发式流控制算法

无线传感器网络中,节点所具有的能量和通信能力等都十分有限,如何设计有效的协议及算法,利用有限的资源高效地完成诸多任务,成为无线传感器网络设计所面临的一大挑战.考虑接收容量模型,研究了无线传感器网络在节点接收容量和能量联合受限情况下,面向混合业务时的效用公平流控制问题,并针对传统对偶分解算法存在着收敛速度慢、步长不易调节、通信负荷大等缺陷,进一步提出了基于事件触发的分布式求解算法.理论分析与仿真验证均表明:使用事件触发算法时,传感节点的平均广播周期比使用对偶分解算法时大很多,大幅度降低了无线传感器网络节点间的通信量,减少了网络的通信开销.仿真结果显示:与对偶分解算法相比,分布式事件触发算法具有收敛速度快、对网络规模扩展的适应性强等优势;与传统的速率公平流控制机制相比,所提的效用公平流控制模型能够更加适应弹性与非弹性业务共存的网络场景.     

分布式粒子滤波算法在目标跟踪中的应用

针对无线传感器网络中目标跟踪的精度与网络能耗这一对矛盾,提出了一种改进的分布式粒子滤波算法。通过调整滤波器的似然分布保持粒子的多样性,同时将无线传感器网络中的跟踪机制进行改进,采用根据跟踪精度自适应调整动态簇内工作的传感器节点的数目。仿真结果表明:提出的改进算法在提高跟踪性能的同时减少了能量损耗,延长了网络的使用寿命。     

Connectivity and coverage maintenance in wireless sensor networks

One of the main design challenges for wireless sensor networks (WSNs) is to obtain long system lifetime without sacrificing system original performance such as communication connectivity and sensing coverage. A large number of sensor nodes are deployed in redundant fashion in dense sensor networks, which lead to higher energy consumption. We propose a distributed framework for energy efficient connectivity and coverage maintenance in WSNs. In our framework, each sensor makes self-scheduling to separately control the states of RF and sensing unit based on dynamic coordinated reconstruction mechanism. A novel energy-balanced distributed connected dominating set algorithm is presented to make connectivity maintenance; and also a distributed node sensing scheduling is brought forward to maintain the network coverage according to the surveillance requirements. We implemented our framework by C++ programming, and the simulation results show that our framework outperforms several related work by considerably improving the energy performance of sensor networks to effectively extend network lifetime.     

INS/vSLAM system using distributed particle filter

In implementing an INS/SLAM integrated navigation system based on the vision sensor, a suboptimal nonlinear filter is used to figure out the nonlinear characteristics in measurement and noise model. When a conventional centralized filter is used, however, the entire state vectors need to be reconfigured in every necessary cycle as the number of feature points changes, which is hard to isolate potential faults. Furthermore, any change in the number of feature points and a subsequent increase in the dimension of state variables may result in an exponential growth in computation quantities. In order to address these issues, this paper presents a distributed particle filter approach for implementing a vision sensor based INS/SLAM system. The proposed system has several local filters which are subject to change flexibly by the number of feature points, and separates state vectors into sub-states for vehicle dynamics and feature points so that minimum state vectors can be estimated in the master filter. Simulation results show that the distributed particle filter performs competitively as with the centralized particle filter and is capable of improving computation quantities.     

A comparative analysis of video codecs for multihop wireless video sensor networks

Wireless video sensor networks (WVSNs) have drawn significant attention in recent years due to the advent of low-cost miniaturized cameras, which makes it feasible to realize large-scale WVSNs for a variety of applications including security surveillance, environmental tracking, and health monitoring. However, the conventional video coding paradigms are not suitable for WVSNs due to resource constraints such as limited computation power, battery energy, and network bandwidth. In this paper, we evaluated and analyzed the performance of video codecs based on emerging video coding paradigms such as distributed video coding and distributed compressive video sensing for multihop WVSNs. The main objective of this work was to provide an insight into the computational (encoding/decoding) complexity, energy consumption, node and network lifetime, processing and memory requirements, and the quality of reconstruction of these video codecs. Based on the findings, this paper also provides some guidelines for the selection of appropriate video codecs for a given WVSN application.     

On computing mobile agent routes for data fusion in distributed sensor networks

The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.