A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.     

Mobile-agent-based collaborative sensor fusion

Information fusion can assist in the development of sensor network applications by merging capabilities, raw data and decisions from multiple sensors through distributed and collaborative integration algorithms. In this paper, we introduce a multi-layered, middleware-driven, multi-agent, interoperable architecture for distributed sensor networks that bridges the gap between the programmable application layer consisting of software agents and the physical layer consisting of sensor nodes. We adopt an energy-efficient, fault-tolerant approach for collaborative information processing among multiple sensor nodes using a mobile-agent-based computing model. In this model the sink/base-station deploys mobile agents that migrate from node to node following a certain itinerary, either pre-determined or determined on-the-fly, and fuse the information/data locally at each node. This way, the intelligence is distributed throughout the network edge and communication cost is reduced to make the sensor network energy-efficient. We evaluate the performance of our mobile-agent-based approach as well as that of the traditional client/server-based computing model, vis-à-vis energy consumption and execution time, through both analytical study and simulation. We draw important conclusions based on our findings. Finally, we consider a collaborative target classification application, supported by our architectural framework, to illustrate the efficacy of the mobile-agent-based computing model.     

一种目标监测的移动传感器网络覆盖分布式优化算法

针对移动传感器网络中目标监测的节点部署问题,为保证在无覆盖漏洞的同时减少覆盖冗余,以六边形棋盘结构(HTL)为网络的目标部署结构,提出一种基于群集控制的分布式部署算法.该方法只需目标的相对方向和邻居节点的相对位置、速度信息,可不依赖于通信.仿真结果表明,所提出的算法对静止和运动目标均有效,与基于虚拟力的算法相比所需信息更少,部署更均匀,对HTL的逼近效果更好,覆盖更优.     

运用异质传感器信息融合的移动机器人自定位

采用单类、单一传感器很难获得移动机器人的准确定位.为此,运用异质传感器信息融合来提高定位精度.首先,建立机器人运动方程和CCD摄像机观测模型.然后,利用扩展卡尔曼滤波器进行状态估计,选择Q,R矩阵抑制系统的模型噪声和量测噪声,并实现移动机器人的自定位.接着,建立超声波传感器的观测模型,获得机器人的自定位信息.最后,运用BP神经网络,将两种自定位信息进行融合,实现两类传感器的优缺点互补.仿真实验表明,运用异质传感器信息融合能明显地提高移动机器人的自定位精度.     

<Emphasis Type="Italic">Smart CAPs for Smart Its</Emphasis> – Context Detection for Mobile Users

Context detection for mobile users plays a major role for enabling novel, human-centric interfaces. For this, we introduce a context detection scheme applicable in a self-organized sensor network, which is formed of disseminated, computer empowered sensors, referred to as Smart-Its [1]. Context-detection takes place without requiring any central point of control, and supports push as well as pull modes. Our solution is based on an in-network composition approach relying on so-called smart Context-Aware Packets (sCAPs). These packets act as a uniform interchange format, and allow single sensors to share sensed data and to cooperate to build up a meaningful context model from manifold inputs. sCAPs travel through the sensor network governed by an enclosed retrieving plan, specifying which sensors to visit in order to gain a specific piece of context information. For enhanced flexibility, the retrieving plan itself may be dynamically altered in accordance with past sensor readings.     

基于无线传感器网络的海洋环境监测系统研究

针对海洋环境理需求,提出并开发了基于智能无线传感器网络的监测系统,旨在完成海洋生态环境监测,数据实时处理、各类海洋气象与灾害的数值预报预测,各类海水指标检测控制.该系统综合运用无线传感网络技术,嵌入式计算技术、现代网络技术、无线通信技术和分布式智能信息处理技术,将功能相同或不同的传感器构成智能化传感器网络,大大提高了传感器的监测能力.模拟实验结果表明了该系统的有效性及良好的稳定性.     

基于静态和移动传感器的WSN的k-覆盖研究

针对无线传感器网络的k-覆盖问题进行了研究。首先定义一个表征网络覆盖效率的过度提供因子,并在此基础上对静态传感器网络和全移动传感器网络的k-覆盖问题进行分析,得到这两种情形下的过度提供因子以及全移动传感器网络中移动传感器的最大移动距离;进而提出一种由静态传感器和少量移动传感器构成的混合网络结构,并得到了这种网络结构下不依赖于网络大小的k-覆盖以及调度移动传感器移动的分布式移动调度算法,从而实现有效覆盖。仿真结果表明,提出的混合网络结构不仅能够实现精确的k-覆盖,而且相比于其他k-覆盖算法,有更高的覆盖率。     

基于虚拟力的混合感知网节点部署

感知网一般是由静态的或移动的节点组成,为保证感知网的感知功能,节点应该有自部署和自修复能力.然而全部由移动传感器组成的感知网的成本太高,为保证感知网的覆盖功能和低成本,提出了一种在静态传感器节点中加入移动传感器节点的混合感知网形式.为了更好地部署这些节点,最大化覆盖待感知区域,提出了一种基于节点间虚拟力的移动节点部署方法,利用静态节点和移动节点以及移动节点之间的虚拟人工势场产生的作用力来控制移动节点的运动,使移动节点能够在较短的时间内,以较少的能量消耗到达自己合适的位置.在理论上分析了算法的可行性,用仿真实验验证了此算法的有效性,并和其他3种类似算法进行了性能比较.     

能耗均衡的移动传感器节点派遣算法

在混合无线传感器网络中,移动传感器节点最耗能的操作是移动,如何减少移动传感器节点的移动距离同时能让其完成任务是一个富有挑战性的研究课题。本文提出了一个移动传感器节点的派遣算法,旨在均衡各个移动传感器节点的移动负载,并且能按优先级响应事件地点,适用于任意数量的移动传感器节点和事件地点的情况。当移动传感器节点数量大于事件地点数量时,将其转化为一个带权完全二分图上的最大匹配问题。当事件地点数量大于移动传感器节点的数量时,本文提出的算法先将事件地点聚类分簇,然后派遣移动传感器节点到各个簇中分别完成访问任务。为了减少传感器节点之间的消息传输量,本文在集中式算法的基础上又提出了一个分布式算法。仿真实验结果表明本文提出的分布式算法能有效降低传感器节点之间的消息传输量,算法能够使得整个混合无线传感器网络的生存寿命延长20%左右。     

Decomposition algorithms for maximizing the lifetime of wireless sensor networks with mobile sinks

We address the problem of maximizing the lifetime of a wireless sensor network with energy-constrained sensors and a mobile sink. The sink travels among discrete locations to gather information from all the sensors. Data can be relayed among sensors and then to the sink location, as long as the sensors and the sink are within a certain threshold distance of each other. However, sending information along a data link consumes energy at both the sender and the receiver nodes. A vital problem that arises is to prescribe sink stop durations and data flow patterns that maximally prolong the life of the network, defined as the amount of time until any node exhausts its energy. We describe linear programming and column generation approaches for this problem, and also for a version in which data can be delayed in its transmission to the sink. Our column generation approach exploits special structures of the linear programming formulations so that all subproblems are shortest path problems with non-negative costs. Computational results demonstrate the efficiency of the proposed algorithms.     

物联网环境下多智能体决策信息支持技术

随着物联网技术的不断发展,传感器网络得到了广泛的应用并成为信息技术领域重要的基础设施。尤其是传感网络提供的实时感知信息,为许多智能应用提供了充分的信息支持和必要的决策依据。然而,由于智能应用的实时感知信息需求通常无法转化为简单的查询请求与传感器底层查询接口准确匹配,因此,基于物联网的智能决策常常无法准确获取到决策相关的实时信息。针对此问题,提出一个基于语义覆盖网的物联网信息资源描述、推理和应用模型,并以多智能体系统决策支持为应用基础,研究了新型物联网环境下的多智能体决策信息支持技术。该技术以基于多智能体系统的团队导向规划的任务分解方法为核心,将复杂任务分解为若干简单子任务,并基于本体推理方法把子任务执行时需要的决策信息转化为精确、完备的传感器信息查询,从而实现从物联网中准确定位具体的传感器并获取相应感知信息的实时决策信息支持机制。     

强度调制型光纤网络温度传感器多路检测技术

本文结合时分复用形式的网络用频率域的方法处理 ,设计了一个具有两个强度调制型传感器的系统 ,并对实际光纤网络温度传感器系统响应进行了测量和数据处理 ,并又分析和计算出强度受调制的两路光的通光率 ,从而完成了对温度的测量     

Environmental map building for a mobile robot using infrared range-finder sensors

This paper presents a methodology for building a high-accuracy environmental map using a mobile robot. The design approach uses low-cost infrared range-finder sensors incorporating with neural networks. To enhance the map quality, the errors occurring from the sensors are corrected. The non-linearity error of the sensors is compensated using a backpropagation neural network and the random error of readings including the uncertainty of the environment is taken into a sensor model as a probabilistic approach. The map is represented by an occupancy grid framework and updated by the Bayesian estimation mechanism. The effectiveness of the proposed method is verified through a series of experiments.     

Structural environmental informatization using sensor network with heterogeneous sensors

In the present paper, a configuration of a sensor network system for monitoring the usage patterns of a house is proposed. The sensor network system has many active type radio-frequency identifications (RFIDs) as a network node. The RFID has advantages of low cost and low power consumption. Also the RFID can connect various sensors for measuring environment information, since having I/O port as external interface. Environment information is stored on a database through the RFID’s wireless radio communication function. The sensor types in this system are variety from slow changing information like temperature and humidity data to event notification like a human detection data. The paper will be described a acquisition procedure for various characteristic sensor data on the same sensor network. Also, the installation locations of the sensors are critical for highly accurate detection of the environmental conditions. Finally, based on the sensor data, capability of analysis of the usage pattern for a house will be discussed.     

Efficient Data Reporting in Intelligent Transportation Systems

Recently, intelligent transportation systems (ITSs) have emerged. These systems can improve traditional transportation systems and provide traffic information to travelers. In the area of transportation, wireless sensor networks (WSNs) can replace the existing wired sensors and expensive traffic monitoring systems to mitigate the time and costs of installing such systems. However, accurate and on-time traffic information delivery is a major challenge, considering the energy constraints of sensor nodes. In this paper, we propose a two-tier architecture that includes a network of mobile objects (vehicles) in the upper layer and a hierarchical WSN in the bottom layer. Using this approach, a portion of loads on the low-power static sensor nodes can be transferred to mobile objects, such as powerful mobile devices. Moreover, to provide accurate and timely traffic information, a QoS-aware link cost function has been proposed and used for data transmission between the static sensor nodes. In addition, due to the mobility of the objects and the probability of losing packets in the mobile object tier, a reliable data forwarding mechanism has been proposed for this tier. In this mechanism, data packets are forwarded to the neighbors, which enhance the probability of the packets’ being received. The performance evaluation results indicate the effectiveness of the proposed architecture and data reporting mechanism for use in ITS applications.     

基于局部信息的移动感知网覆盖方法*

移动感知网是一个由许多带有传感器的自主移动机器人组成的分布式传感器网络。为了更好地部署这些移动机器人节点,形成最大化覆盖感知区域,提出了一种基于机器人局部信息的分布式感知网覆盖方法。每个节点利用与邻居节点之间的虚拟人工势场产生的虚拟作用力来控制移动节点的运动和节点间的避碰,使移动节点能够在允许的时间内,以较少的能量消耗移动到各自理想的位置。采用李亚普诺夫函数进行了感知网节点势场梯度的理论分析,用计算机仿真实验验证了该方法的有效性,并与模拟退火算法进行了性能比较。     

Scenes: Abstracting interaction in immersive sensor networks

Pervasive computing deployments are increasingly using sensor networks to build instrumented environments that provide local data to immersed mobile applications. These applications demand opportunistic and unpredictable interactions with local devices. While this direct communication has the potential to reduce both overhead and latency, it deviates significantly from existing uses of sensor networks that funnel information to a static central collection point. This pervasive computing driven perspective demands new communication abstractions that enable the required direct communication among mobile applications and sensors. This paper presents the scene abstraction, which allows immersed applications to create dynamic distributed data structures over the immersive sensor network. A scene is created based on application requirements, properties of the underlying network, and properties of the physical environment. This paper details our work on defining scenes, providing an abstract model, an implementation, and an evaluation.     

Adaptive behavior navigation of a mobile robot

Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities     

基于物联网的智能家居远程控制系统

该文结合了物联网和智能家居的相关技术,将传感器、控制器等设备通过无线技术连接和采集数据信息的处理,实现了使用传感器采集室内温度,光线等数据,在嵌入式终端中进行信息的读取和处理,做出相应的操作。整个过程涉及到基于ZigBee的无线传感网络和GPRS手机通信模块的应用,实现了对家居环境中的光线、温度、烟雾等因素的实时监测,并通过GPRS通信模块和管理系统作出相应的控制,以提升家居环境的安全性、舒适性、便利性。     

Ultrasonic sensor data integration and its application to environment perception

To move in an unknown or uncertain environment, a mobile robot must collect information from various sensors and use it to construct a representation of the external world. Ultrasonic sensors can provide range data for this purpose in a simple and cost-effective way. However, most ultrasonic sensors are not sufficient for environment recognition because of their large beam opening angles. In this article the beam-opening-angle problem is solved by fusing data from multiple ultrasonic sensors. We propose two methods for sensor data fusion. One uses an artificial neural network (ANN), and the other is based on a mathematical model. Simulations and experiments show that the mathematical model is more accurate when there is no noise in the sensor readings, but the ANN method is better when the sensors are subject to much noise. To extract line segments from the ultrasonic image, we develop a line extractor that is more efficient than traditional line fitting methods in this application. Experimental results show that this method is effective for environment perception in a robotic system. © 1996 John Wiley & Sons, Inc.