定位理论的分布式多传感器(翻译)

Sensing Human Activity:GPS Tracking感应人类活动:GPS跟踪Stefan van der Spek1,*,Jeroen van Schaick1,Peter de Bois1,2and Remco de Haan1Abstract:The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools,but also as instruments used to capture travelled routes:assensors that measure activity on a city scale or the regional scale.TU Delft developed aprocess and database architecture for collecting data on pedestrian movement in threeEuropean city centres,Norwich,Rouen and Koblenz,and in another experiment forcollecting activity data of13families in Almere(The Netherlands)for one week.Thequestion posed in this paper is:what is the value of GPS as‘sensor technology’measuringactivities of people?The conclusion is that GPS offers a widely useable instrument tocollect invaluable spatial-temporal data on different scales and in different settings addingnew layers of knowledge to urban studies,but the use of GPS-technology and deploymentof GPS-devices still offers significant challenges for future research.摘要：增强GPS技术支持使用GPS设备不仅作为导航和定位工具,但也为仪器用来捕捉旅行路线:作为传感器,测量活动在一个城市或区域范围内规模。

无线传感器节点分布式定位算法研究随着无线传感器网络技术的不断发展，无线传感器节点分布式定位算法也不断得到改进和完善。

传感器节点的位置信息对于无线传感器网络的数据采集和处理至关重要，因此分布式定位算法研究的重要性不可忽视。

一、无线传感器节点分布式定位算法的介绍无线传感器节点分布式定位算法可以实现在无需任何外部设备的情况下，对传感器节点的位置进行估计和确定。

其基本思路是将一组传感器节点彼此连接，通过对节点之间信号的传输和接收进行计算，从而得到节点的位置信息。

常见的无线传感器节点分布式定位算法有距离法、角度法和混合法等。

距离法是指通过测量节点之间信号的传输距离，推算得到节点的位置信息。

该方法简单易实现，但是受环境影响较大，且测量精度受到限制。

角度法是指通过测量节点之间信号的传输方向角度来推算节点的位置。

该方法对环境的影响较小，但是需要更多的节点参与，并且不适用于实现高精度的定位。

混合法是指通过结合距离法和角度法来实现精度更高的定位，但是实现过程较为复杂。

二、无线传感器节点分布式定位算法的应用无线传感器节点分布式定位算法在很多领域都得到广泛应用。

例如，在智能交通领域中，通过将传感器节点安装在道路上，可以实现车辆行驶的高精度定位，并且提高可视化交通管理水平。

在环境监测领域中，可以将传感器节点安装在污染源周围，实现对污染源的实时监测。

在工业自动化领域中，通过将传感器节点安装在机器设备上，可以实现设备状态的实时监测和维护。

三、无线传感器节点分布式定位算法的发展趋势随着物联网技术的不断发展，无线传感器网络的应用范围和需求也不断扩展，无线传感器节点分布式定位算法也随之发展。

未来的发展趋势主要体现在以下几个方面：1.提高定位精度：随着物联网应用场景的不断扩展，精度更高的无线传感器节点分布式定位算法将成为研究的重点。

2.实现低能耗定位：无线传感器节点的能量有限，如何通过算法实现低能耗定位，将成为研究的热点。

3.应用深度学习技术：通过应用深度学习技术，能够对数据进行更好的分析处理，提高定位精度。

无线传感器网络中的分布式目标跟踪与定位技术无线传感器网络（Wireless Sensor Networks，简称WSN）是一种由大量分布式无线传感器节点组成的网络系统。

这些节点能够感知环境中的各种物理量，并将收集到的数据通过网络传输给基站或其他节点。

WSN在农业、环境监测、智能交通等领域具有广泛的应用前景。

其中，分布式目标跟踪与定位技术是WSN中的一个重要研究方向。

目标跟踪与定位是WSN中的核心问题之一。

在许多应用场景中，需要对目标的位置进行实时监测和跟踪。

传统的目标跟踪与定位方法通常依赖于全局信息，要求节点之间进行频繁的通信，这不仅增加了能耗，还可能导致网络拥塞。

因此，研究人员提出了一系列分布式的目标跟踪与定位技术，以降低能耗并提高网络的可扩展性。

分布式目标跟踪与定位技术主要包括目标定位算法和目标跟踪算法。

目标定位算法用于确定目标的位置，而目标跟踪算法则用于跟踪目标的移动轨迹。

在WSN 中，节点通常通过测量目标到节点的距离或角度来实现目标定位。

常用的目标定位算法有多普勒测距算法、测角算法和基于信号强度的定位算法等。

这些算法可以根据不同的应用场景选择合适的方式来定位目标。

目标跟踪算法则是通过分析目标的运动特征来预测目标的下一个位置。

常见的目标跟踪算法有卡尔曼滤波算法、粒子滤波算法和扩展卡尔曼滤波算法等。

这些算法能够通过对目标的历史轨迹进行建模，从而实现对目标位置的预测和跟踪。

分布式目标跟踪与定位技术的关键问题之一是如何选择合适的节点进行目标跟踪和定位。

在WSN中，节点通常具有有限的计算和通信能力，因此需要选择一部分节点作为目标节点，负责目标跟踪和定位任务。

节点的选择可以通过节点自组织、节点自适应或节点协作等方式实现。

例如，可以通过节点之间的协作来实现目标跟踪和定位任务，即多个节点共同合作，通过相互通信和信息交换来提高目标定位和跟踪的准确性和可靠性。

此外，分布式目标跟踪与定位技术还需要考虑网络的能耗和通信开销。

无线传感器网络中的分布式定位算法研究无线传感器网络（Wireless Sensor Network, WSN）是由大量自主感知节点组成的一种网络系统，用于感知、采集和传输环境信息。

随着无线通信技术的发展和传感器节点的不断减小与廉价化，WSN已经广泛应用于环境监测、智能交通、农业等领域。

其中，分布式定位算法对于WSN的定位精度和能耗具有重要影响，一直是学术研究的热点之一。

一、无线传感器网络中的位置定位问题在无线传感器网络中，节点的位置信息对于各类应用具有重要意义。

如智能交通系统中，精确的车辆定位能够帮助减少交通事故的发生；在环境监测中，节点的位置信息能够帮助准确地监测到污染源的位置。

然而，WSN的节点数量众多、部署范围广泛、环境复杂多变，使得分布式定位问题变得非常具有挑战性。

二、基于距离测量的分布式定位算法基于距离测量的分布式定位算法是一类常用的分布式定位算法。

该算法通过测量节点之间的相对距离来实现节点的位置信息的估计。

常见的距离测量方法包括收发信号强度(RSSI)、时间差(TDOA)和时间差方向(TDOA)等。

1. RSSI定位算法RSSI定位算法是利用接收信号强度指示(RSSI)来估计节点之间的距离。

该方法简单易用，但受到多径效应、信号衰减等干扰因素的影响较大，导致定位精度较低。

2. TDOA定位算法TDOA定位算法则是通过测量节点收到信号的时间差来求解节点的位置。

该算法在理论上具备较高的定位精度，但需要节点之间的时钟同步，且对于移动传感器节点的定位较为困难。

三、基于角度测量的分布式定位算法除了使用距离测量外，基于角度测量的分布式定位算法也是一种常见的实现方式。

基于角度的分布式定位算法通过测量节点之间的相对角度来推测节点的位置。

1. CDI定位算法CDI定位算法是基于角度测量的一种分布式定位算法。

该算法通过测量节点之间的相对方向来计算节点的位置。

CDI算法具有较好的定位精度，且不需要时钟同步。

分布式传感器网络的研究与应用近年来，分布式传感器网络技术在智能化物联网领域得到了广泛的应用。

由于其具有自组织、自适应、自修复、可靠性高等优点，使得它们可以用于环境监测、智能交通、农业自动化、智能家居等领域。

在本文中，我将围绕着分布式传感器网络的研究与应用，从以下几个方面进行探讨。

一、分布式传感器网络的基本概念分布式传感器网络是由无线传感器节点组成的一种分布式控制系统，它通过感知环境并将信息传输到其他节点，以实现数据的采集、传输和处理。

它的节点通常由多个嵌入式处理器、传感器、存储器、通讯模块等组成，具有能耗低、开销小、可扩展等特点。

二、分布式传感器网络的工作原理在分布式传感器网络中，节点通常是通过无线通信互联的。

当一个节点检测到一个事件时，它会将事件信息发送到它的邻居节点，并由邻居节点将信息传递到目标节点。

一些节点可能会被选为汇聚点进行协调，以便将传感器收集到的数据发送到云服务器进行存储和处理。

由于节点能耗是分布式传感器网络设计的重点，所以许多研究都集中在如何优化能耗，如调整节点的传输功率、感知参数或任务调度等。

三、分布式传感器网络的技术难点在分布式传感器网络的研究与应用中，主要存在以下几个技术难点：1. 能量受限问题：由于分布式传感器网络节点有限的电量，所以节点的能耗是其设计的关键问题。

一些研究表明，在传输中，节点的能量消耗大约在数据处理消耗和通讯中占60% ~ 80%。

2. 数据质量问题：由于传感器网络中存在许多噪声和干扰，因此在数据采集和传输方面需要考虑这些噪声和干扰的影响，从而提高数据的质量和准确性。

3. 网络拓扑优化问题：节点之间的通信和数据传输的效率取决于网络的拓扑结构。

因此，如何在不损失网络扩展能力的情况下，优化网络拓扑结构是设计分布式传感器网络时需要考虑的问题。

四、分布式传感器网络的应用范围分布式传感器网络的应用领域非常广泛，包括环境监测、智能交通、农业自动化、智能家居等。

在环境监测方面，它可以用于水质监测、大气污染监测、土壤湿度监测、地震监测等。

基于多数据融合传感器的分布式温度控制系统摘要：在过去的几十年，温度控制系统已经被广泛的应用。

对于温度控制提出了一种基于多传感器数据融合和CAN总线控制的一般结构。

一种新方法是基于多传感器数据融合估计算法参数分布式温控系统。

该系统的重要特点是其共性，其适用于很多具体领域的大型的温度控制。

实验结果表明该系统具有较高的准确性、可靠性，良好的实时性和广泛的应用前景。

关键词：分布式控制系统；CAN总线控制；智能CAN节点；多数据融合传感器。

1介绍分布式温度控制系统已经被广泛的应用在我们日常生活和生产,包括智能建筑、温室、恒温车间、大中型粮仓、仓库等。

这种控制保证环境温度能被保持在两个预先设定的温度间。

在传统的温度测量系统中，我们用一个基于温度传感器的单片机系统建立一个RS-485局域网控制器网络。

借助网络,我们能实行集中监控和控制.然而,当监测区域分布更广泛和传输距离更远,RS-485总线控制系统的劣势更加突出。

在这种情况下，传输和响应速度变得更低，抗干扰能力更差。

因此，我们应当寻找新的通信的方法来解决用RS-485总线控制系统而产生的问题。

在所有的通讯方式中，适用于工业控制系统的总线控制技术，我们可以突破传统点对点通信方式的限制、建立一个真正的分布式控制与集中管理系统，CAN总线控制比RS-485总线控制系统更有优势。

比如更好的纠错能力、改善实时的能力，低成本等。

目前，它正被广泛的应用于实现分布式测量和范围控制。

随着传感器技术的发展，越来越多的系统开始采用多传感器数据融合技术来提高他们的实现效果。

多传感器数据融合是一种范式对多种来源整合数据,以综合成新的信息，比其他部分的总和更加强大。

无论在当代和未来，系统的低成本，节省资源都是传感器中的一项重要指标。

2分布式架构的温度控制系统分布式架构温度控制系统如图中所示的图1。

可以看出，这系统由两个模块——两个智能CAN节点和一个主要的控制器组成。

每个模块部分执行进入分布式架构。

DiMo:Distributed Node Monitoring in WirelessSensor NetworksAndreas Meier†,Mehul Motani∗,Hu Siquan∗,and Simon Künzli‡†Computer Engineering and Networks Lab,ETH Zurich,Switzerland∗Electrical&Computer Engineering,National University of Singapore,Singapore‡Siemens Building T echnologies,Zug,SwitzerlandABSTRACTSafety-critical wireless sensor networks,such as a distributed fire-or burglar-alarm system,require that all sensor nodes are up and functional.If an event is triggered on a node, this information must be forwarded immediately to the sink, without setting up a route on demand or having tofind an alternate route in case of a node or link failure.Therefore, failures of nodes must be known at all times and in case of a detected failure,an immediate notification must be sent to the network operator.There is usually a bounded time limit,e.g.,five minutes,for the system to report network or node failure.This paper presents DiMo,a distributed and scalable solution for monitoring the nodes and the topology, along with a redundant topology for increased robustness. Compared to existing solutions,which traditionally assume a continuous data-flow from all nodes in the network,DiMo observes the nodes and the topology locally.DiMo only reports to the sink if a node is potentially failed,which greatly reduces the message overhead and energy consump-tion.DiMo timely reports failed nodes and minimizes the false-positive rate and energy consumption compared with other prominent solutions for node monitoring.Categories and Subject DescriptorsC.2.2[Network Protocols]:Wireless Sensor NetworkGeneral TermsAlgorithms,Design,Reliability,PerformanceKeywordsLow power,Node monitoring,Topology monitoring,WSN 1.INTRODUCTIONDriven by recent advances in low power platforms and protocols,wireless sensor networks are being deployed to-day to monitor the environment from wildlife habitats[1] Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on thefirst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.MSWiM’08,October27–31,2008,Vancouver,BC,Canada.Copyright2008ACM978-1-60558-235-1/08/10...$5.00.to mission-criticalfire-alarm systems[5].There are,how-ever,still some obstacles in the way for mass application of wireless sensor networks.One of the key challenges is the management of the wireless sensor network itself.With-out a practical management system,WSN maintenance will be very difficult for network administrators.Furthermore, without a solid management plan,WSNs are not likely to be accepted by industrial users.One of the key points in the management of a WSN is the health status monitoring of the network itself.Node failures should be captured by the system and reported to adminis-trators within a given delay constraint.Due to the resource constraints of WSN nodes,traditional network management protocols such as SNMP adopted by TCP/IP networks are not suitable for sensor networks.In this paper,we con-sider a light-weight network management approach tailored specifically for WSNs and their unique constraints. Currently,WSN deployments can be categorized by their application scenario:data-gathering applications and event-detection applications.For data-gathering systems,health status monitoring is quite straight forward.Monitoring in-formation can be forwarded to the sink by specific health status packets or embedded in the regular data packets.Ad-ministrators can usually diagnose the network with a helper program.NUCLEUS[6]is one of the network management systems for data-gathering application of WSN.Since event-detection deployments do not have regular traffic to send to the sink,the solutions for data-gathering deployments are not suitable.In this case,health status monitoring can be quite challenging and has not been discussed explicitly in the literature.In an event-detection WSN,there is no periodic data trans-fer,i.e.,nodes maintain radio silence until there is an event to report.While this is energy efficient,it does mean that there is no possibility for the sink to decide whether the net-work is still up and running(and waiting for an event to be detected)or if some nodes in the network have failed and are therefore silent.Furthermore,for certain military ap-plications or safety-critical systems,the specifications may include a hard time constraint for accomplishing the node health status monitoring task.In an event-detection WSN,the system maintains a net-work topology that allows for forwarding of data to a sink in the case of an event.Even though there is no regular data transfer in the network,the network should always be ready to forward a message to the sink immediately when-ever necessary.It is this urgency of data forwarding that makes it undesirable to set up a routing table and neighborlist after the event has been detected.The lack of regular data transfer in the network also leads to difficulty in de-tecting bad quality links,making it challenging to establish and maintain a stable robust network topology.While we have mentioned event-detection WSNs in gen-eral,we accentuate that the distributed node monitoring problem we are considering is inspired by a real-world ap-plication:a distributed indoor wireless alarm system which includes a sensor for detection of a specific alarm such as fire(as studied in[5]).To illustrate the reporting require-ments of such a system,we point out that regulatory speci-fications require afire to be reported to the control station within10seconds and a node failure to be reported within 5minutes[9].This highlights the importance of the node-monitoring problem.In this paper,we present a solution for distributed node monitoring called DiMo,which consists of two functions: (i)Network topology maintenance,introduced in Section2, and(ii)Node health status monitoring,introduced in Sec-tion3.We compare DiMo to existing state-of-the-art node monitoring solutions and evaluate DiMo via simulations in Section4.1.1Design GoalsDiMo is developed based on the following design goals:•In safety critical event monitoring systems,the statusof the nodes needs to be monitored continuously,allow-ing the detection and reporting of a failed node withina certain failure detection time T D,e.g.,T D=5min.•If a node is reported failed,a costly on-site inspectionis required.This makes it of paramount interest todecrease the false-positive rate,i.e.,wrongly assuminga node to have failed.•In the case of an event,the latency in forwarding theinformation to the sink is crucial,leaving no time toset up a route on demand.We require the system tomaintain a topology at all times.In order to be robustagainst possible link failures,the topology needs toprovide redundancy.•To increase efficiency and minimize energy consump-tion,the two tasks of topology maintenance(in par-ticular monitoring of the links)and node monitoringshould be combined.•Maximizing lifetime of the network does not necessar-ily translate to minimizing the average energy con-sumption in the network,but rather minimizing theenergy consumption of the node with the maximal loadin the network.In particular,the monitoring shouldnot significantly increase the load towards the sink.•We assume that the event detection WSN has no reg-ular data traffic,with possibly no messages for days,weeks or even months.Hence we do not attempt to op-timize routing or load balancing for regular data.Wealso note that approaches like estimating links’perfor-mance based on the ongoing dataflow are not possibleand do not take them into account.•Wireless communications in sensor networks(especially indoor deployments)is known for its erratic behav-ior[2,8],likely due to multi-path fading.We assumesuch an environment with unreliable and unpredictablecommunication links,and argue that message lossesmust be taken into account.1.2Related WorkNithya et al.discuss Sympathy in[3],a tool for detect-ing and debugging failures in pre-and post-deployment sen-sor networks,especially designed for data gathering appli-cations.The nodes send periodic heartbeats to the sink that combines this information with passively gathered data to detect failures.For the failure detection,the sink re-quires receiving at least one heartbeat from the node every so called sweep interval,i.e.,its lacking indicates a node fail-ure.Direct-Heartbeat performs poorly in practice without adaptation to wireless packet losses.To meet a desired false positive rate,the rate of heartbeats has to be increased also increasing the communication cost.NUCLEUS[6]follows a very similar approach to Sympathy,providing a manage-ment system to monitor the heath status of data-gathering applications.Rost et al.propose with Memento a failure detection sys-tem that also requires nodes to periodically send heartbeats to the so called observer node.Those heartbeats are not directly forwarded to the sink node,but are aggregated in form of a bitmask(i.e.,bitwise OR operation).The ob-server node is sweeping its bitmask every sweep interval and will forward the bitmask with the node missing during the next sweep interval if the node fails sending a heartbeat in between.Hence the information of the missing node is disseminated every sweep interval by one hop,eventually arriving at the sink.Memento is not making use of ac-knowledgements and proactively sends multiple heartbeats every sweep interval,whereas this number is estimated based on the link’s estimated worst-case performance and the tar-geted false positive rate.Hence Memento and Sympathy do both send several messages every sweep interval,most of them being redundant.In[5],Strasser et al.propose a ring based(hop count)gos-siping scheme that provides a latency bound for detecting failed nodes.The approach is based on a bitmask aggre-gation,beingfilled ring by ring based on a tight schedule requiring a global clock.Due to the tight schedule,retrans-missions are limited and contention/collisions likely,increas-ing the number of false positives.The approach is similar to Memento[4],i.e.,it does not scale,but provides latency bounds and uses the benefits of acknowledgements on the link layer.2.TOPOLOGY MAINTENANCEForwarding a detected event without any delay requires maintaining a redundant topology that is robust against link failures.The characteristics of such a redundant topology are discussed subsequently.The topology is based on so called relay nodes,a neighbor that can provide one or more routes towards the sink with a smaller cost metric than the node itself has.Loops are inherently ruled out if packets are always forwarded to relay nodes.For instance,in a simple tree topology,the parent is the relay node and the cost metric is the hop count.In order to provide redundancy,every node is connected with at least two relay nodes,and is called redundantly con-nected.Two neighboring nodes can be redundantly con-nected by being each others relay,although having the same cost metric,only if they are both connected to the sink. This exception allows the nodes neighboring the sink to be redundantly connected and avoids having a link to the sinkas a single point of failure.In a(redundantly)connected network,all deployed nodes are(redundantly)connected.A node’s level L represents the minimal hop count to the sink according to the level of its relay nodes;i.e.,the relay with the least hop count plus one.The level is infinity if the node is not connected.The maximal hop count H to the sink represents the longest path to the sink,i.e.,if at every hop the relay node with the highest maximal hop count is chosen.If the node is redundantly connected,the node’s H is the maximum hop count in the set of its relays plus one, if not,the maximal hop count is infinity.If and only if all nodes in the network have afinite maximal hop count,the network is redundantly connected.The topology management function aims to maintain a redundantly connected network whenever possible.This might not be possible for sparsely connected networks,where some nodes might only have one neighbor and therefore can-not be redundantly connected by definition.Sometimes it would be possible tofind alternative paths with a higher cost metric,which in turn would largely increase the overhead for topology maintenance(e.g.,for avoiding loops).For the cost metric,the tuple(L,H)is used.A node A has the smaller cost metric than node B ifL A<L B∨(L A=L B∧H A<H B).(1) During the operation of the network,DiMo continuously monitors the links(as described in Section3),which allows the detection of degrading links and allows triggering topol-ogy adaptation.Due to DiMo’s redundant structure,the node is still connected to the network,during this neighbor search,and hence in the case of an event,can forward the message without delay.3.MONITORING ALGORITHMThis section describes the main contribution of this paper, a distributed algorithm for topology,link and node monitor-ing.From the underlying MAC protocol,it is required that an acknowledged message transfer is supported.3.1AlgorithmA monitoring algorithm is required to detect failed nodes within a given failure detection time T D(e.g.,T D=5min).A node failure can occur for example due to hardware fail-ures,software errors or because a node runs out of energy. Furthermore,an operational node that gets disconnected from the network is also considered as failed.The monitoring is done by so called observer nodes that monitor whether the target node has checked in by sending a heartbeat within a certain monitoring time.If not,the ob-server sends a node missing message to the sink.The target node is monitored by one observer at any time.If there are multiple observer nodes available,they alternate amongst themselves.For instance,if there are three observers,each one observes the target node every third monitoring time. The observer node should not only check for the liveliness of the nodes,but also for the links that are being used for sending data packets to the sink in case of a detected event. These two tasks are combined by selecting the relay nodes as observers,greatly reducing the network load and maximiz-ing the network lifetime.In order to ensure that all nodes are up and running,every node is observed at all times. The specified failure detection time T D is an upper bound for the monitoring interval T M,i.e.,the interval within which the node has to send a heartbeat.Since failure detec-tion time is measured at the sink,the detection of a missing node at the relay needs to be forwarded,resulting in an ad-ditional maximal delay T L.Furthermore,the heartbeat can be delayed as well,either by message collisions or link fail-ures.Hence the node should send the heartbeat before the relay’s monitoring timer expires and leave room for retries and clock drift within the time window T R.So the monitor-ing interval has to be set toT M≤T D−T L−T R(2) and the node has to ensure that it is being monitored every T M by one of its observers.The schedule of reporting to an observer is only defined for the next monitoring time for each observer.Whenever the node checks in,the next monitoring time is announced with the same message.So for every heartbeat sent,the old monitoring timer at the observer can be cancelled and a new timer can be set according the new time.Whenever,a node is newly observed or not being observed by a particular observer,this is indicated to the sink.Hence the sink is always aware of which nodes are being observed in the network,and therefore always knows which nodes are up and running.This registration scheme at the sink is an optional feature of DiMo and depends on the user’s requirements.3.2Packet LossWireless communication always has to account for possi-ble message losses.Sudden changes in the link quality are always possible and even total link failures in the order of a few seconds are not uncommon[2].So the time T R for send-ing retries should be sufficiently long to cover such blanks. Though unlikely,it is possible that even after a duration of T R,the heartbeat could not have been successfully for-warded to the observer and thus was not acknowledged,in spite of multiple retries.The node has to assume that it will be reported miss-ing at the sink,despite the fact it is still up and running. Should the node be redundantly connected,a recovery mes-sage is sent to the sink via another relay announcing be-ing still alive.The sink receiving a recovery message and a node-missing message concerning the same node can neglect these messages as they cancel each other out.This recov-ery scheme is optional,but minimizes the false positives by orders of magnitudes as shown in Section4.3.3Topology ChangesIn the case of a new relay being announced from the topol-ogy management,a heartbeat is sent to the new relay,mark-ing it as an observer node.On the other hand,if a depre-cated relay is announced,this relay might still be acting as an observer,and the node has to check in as scheduled.How-ever,no new monitor time is announced with the heartbeat, which will release the deprecated relay of being an observer.3.4Queuing PolicyA monitoring buffer exclusively used for monitoring mes-sages is introduced,having the messages queued according to a priority level,in particular node-missing messagesfirst. Since the MAC protocol and routing engine usually have a queuing buffer also,it must be ensured that only one single monitoring message is being handled by the lower layers atthe time.Only if an ACK is received,the monitoring mes-sage can be removed from the queue(if a NACK is received, the message remains).DiMo only prioritizes between the different types of monitoring messages and does not require prioritized access to data traffic.4.EV ALUATIONIn literature,there are very few existing solutions for mon-itoring the health of the wireless sensor network deployment itself.DiMo is thefirst sensor network monitoring solution specifically designed for event detection applications.How-ever,the two prominent solutions of Sympathy[3]and Me-mento[4]for monitoring general WSNs can also be tailored for event gathering applications.We compare the three ap-proaches by looking at the rate at which they generate false positives,i.e.,wrongly inferring that a live node has failed. False positives tell us something about the monitoring pro-tocol since they normally result from packet losses during monitoring.It is crucial to prevent false positives since for every node that is reported missing,a costly on-site inspec-tion is required.DiMo uses the relay nodes for observation.Hence a pos-sible event message and the regular heartbeats both use the same path,except that the latter is a one hop message only. The false positive probability thus determines the reliability of forwarding an event.We point out that there are other performance metrics which might be of interest for evaluation.In addition to false positives,we have looked at latency,message overhead, and energy consumption.We present the evaluation of false positives below.4.1Analysis of False PositivesIn the following analysis,we assume r heartbeats in one sweep for Memento,whereas DiMo and Sympathy allow sending up to r−1retransmissions in the case of unac-knowledged messages.To compare the performance of the false positive rate,we assume the same sweep interval for three protocols which means that Memento’s and Sympa-thy’s sweep interval is equal to DiMo’s monitoring interval. In the analysis we assume all three protocols having the same packet-loss probability p l for each hop.For Sympathy,a false positive for a node occurs when the heartbeat from the node does not arrive at the sink in a sweep interval,assuming r−1retries on every hop.So a node will generate false positive with a possibility(1−(1−p r l)d)n,where d is the hop count to the sink and n the numbers of heartbeats per sweep.In Memento,the bitmask representing all nodes assumes them failed by default after the bitmap is reset at the beginning of each sweep interval. If a node doesn’t report to its parent successfully,i.e.,if all the r heartbeats are lost in a sweep interval,a false positive will occur with a probability of p l r.In DiMo the node is reported missing if it fails to check in at the observer having a probability of p l r.In this case,a recovery message is triggered.Consider the case that the recovery message is not kept in the monitoring queue like the node-missing messages, but dropped after r attempts,the false positive rate results in p l r(1−(1−p l r)d).Table1illustrates the false positive rates for the three protocols ranging the packet reception rate(PRR)between 80%and95%.For this example the observed node is in afive-hop distance(d=5)from the sink and a commonPRR80%85%90%95% Sympathy(n=1) 3.93e-2 1.68e-2 4.99e-3 6.25e-4 Sympathy(n=2) 1.55e-3 2.81e-4 2.50e-5 3.91e-7 Memento8.00e-3 3.38e-3 1.00e-3 1.25e-4 DiMo 3.15e-4 5.66e-5 4.99e-67.81e-8Table1:False positive rates for a node with hop count5and3transmissions under different packet success rates.number of r=3attempts for forwarding a message is as-sumed.Sympathy clearly suffers from a high packet loss, but its performance can be increased greatly sending two heartbeats every sweep interval(n=2).This however dou-bles the message load in the network,which is especially substantial as the messages are not aggregated,resulting in a largely increased load and energy consumption for nodes next to the paring DiMo with Memento,we ob-serve the paramount impact of the redundant relay on the false positive rate.DiMo offers a mechanism here that is not supported in Sympathy or Memento as it allows sending up to r−1retries for the observer and redundant relay.Due to this redundancy,the message can also be forwarded in the case of a total blackout of one link,a feature both Memento and Sympathy are lacking.4.2SimulationFor evaluation purposes we have implemented DiMo in Castalia1.3,a state of the art WSN simulator based on the OMNet++platform.Castalia allows evaluating DiMo with a realistic wireless channel(based on the empiricalfindings of Zuniga et al.[8])and radio model but also captures effects like the nodes’clock drift.Packet collisions are calculated based on the signal to interference ratio(SIR)and the radio model features transition times between the radio’s states (e.g.,sending after a carrier sense will be delayed).Speck-MAC[7],a packet based version of B-MAC,with acknowl-edgements and a low-power listening interval of100ms is used on the link layer.The characteristics of the Chipcon CC2420are used to model the radio.The simulations are performed for a network containing80 nodes,arranged in a grid with a small Gaussian distributed displacement,representing an event detection system where nodes are usually not randomly deployed but rather evenly spread over the observed area.500different topologies were analyzed.The topology management results in a redun-dantly connected network with up to5levels L and a max-imum hop count H of6to8.A false positive is triggered if the node fails to check in, which is primarily due to packet errors and losses on the wireless channel.In order to understand false positives,we set the available link’s packet reception rate(PRR)to0.8, allowing us to see the effects of the retransmission scheme. Furthermore,thisfixed PRR also allows a comparison with the results of the previous section’s analysis and is shown in Figure1(a).The plot shows on the one hand side the monitoring based on a tree structure that is comparable to the performance of Memento,i.e.,without DiMo’s possibil-ity of sending a recovery message using an alternate relay. On the other hand side,the plot shows the false positive rate of DiMo.The plot clearly shows the advantage of DiMo’s redundancy,yet allowing sending twice as many heartbeats than the tree approach.This might not seem necessarily fair atfirst;however,in a real deployment it is always possible(a)Varying number of retries;PRR =0.8.(b)Varying link quality.Figure 1:False positives:DiMo achieves the targeted false positive rate of 1e-7,also representing the reliability for successfully forwarding an event.that a link fails completely,allowing DiMo to still forward the heartbeat.The simulation and the analysis show a slight offset in the performance,which is explained by a simulation artifact of the SpeckMAC implementation that occurs when the receiver’s wake-up time coincides with the start time of a packet.This rare case allows receiving not only one but two packets out of the stream,which artificially increases the link quality by about three percent.The nodes are observed every T M =4min,resulting in being monitored 1.3e5times a year.A false positive rate of 1e-6would result in having a particular node being wrongly reported failed every 7.7years.Therefore,for a 77-node net-work,a false positive rate of 1e-7would result in one false alarm a year,being the targeted false-positive threshold for the monitoring system.DiMo achieves this rate by setting the numbers of retries for both the heartbeat and the recov-ery message to four.Hence the guard time T R for sending the retries need to be set sufficiently long to accommodate up to ten messages and back-offtimes.The impact of the link quality on DiMo’s performance is shown in Figure 1(b).The tree topology shows a similar performance than DiMo,if the same number of messages is sent.However,it does not show the benefit in the case of a sudden link failure,allowing DiMo to recover immedi-ately.Additionally,the surprising fact that false positives are not going to zero for perfect link quality is explained by collisions.This is also the reason why DiMo’s curve for two retries flattens for higher link qualities.Hence,leaving room for retries is as important as choosing good quality links.5.CONCLUSIONIn this paper,we presented DiMo,a distributed algorithm for node and topology monitoring,especially designed for use with event-triggered wireless sensor networks.As a de-tailed comparative study with two other well-known moni-toring algorithm shows,DiMo is the only one to reach the design target of having a maximum error reporting delay of 5minutes while keeping the false positive rate and the energy consumption competitive.The proposed algorithm can easily be implemented and also be enhanced with a topology management mechanism to provide a robust mechanism for WSNs.This enables its use in the area of safety-critical wireless sensor networks.AcknowledgmentThe work presented in this paper was supported by CTI grant number 8222.1and the National Competence Center in Research on Mobile Information and Communication Sys-tems (NCCR-MICS),a center supported by the Swiss Na-tional Science Foundation under grant number 5005-67322.This work was also supported in part by phase II of the Embedded and Hybrid System program (EHS-II)funded by the Agency for Science,Technology and Research (A*STAR)under grant 052-118-0054(NUS WBS:R-263-000-376-305).The authors thank Matthias Woehrle for revising a draft version of this paper.6.REFERENCES[1] A.Mainwaring et al.Wireless sensor networks for habitatmonitoring.In 1st ACM Int’l Workshop on Wireless Sensor Networks and Application (WSNA 2002),2002.[2] A.Meier,T.Rein,et al.Coping with unreliable channels:Efficient link estimation for low-power wireless sensor networks.In Proc.5th Int’l worked Sensing Systems (INSS 2008),2008.[3]N.Ramanathan,K.Chang,et al.Sympathy for the sensornetwork debugger.In Proc.3rd ACM Conf.Embedded Networked Sensor Systems (SenSys 2005),2005.[4]S.Rost and H.Balakrishnan.Memento:A health monitoringsystem for wireless sensor networks.In Proc.3rd IEEE Communications Society Conf.Sensor,Mesh and Ad Hoc Communications and Networks (IEEE SECON 2006),2006.[5]M.Strasser,A.Meier,et al.Dwarf:Delay-aware robustforwarding for energy-constrained wireless sensor networks.In Proceedings of the 3rd IEEE Int’l Conference onDistributed Computing in Sensor Systems (DCOSS 2007),2007.[6]G.Tolle and D.Culler.Design of an application-cooperativemanagement system for wireless sensor networks.In Proc.2nd European Workshop on Sensor Networks (EWSN 2005),2005.[7]K.-J.Wong et al.Speckmac:low-power decentralised MACprotocols for low data rate transmissions in specknets.In Proc.2nd Int’l workshop on Multi-hop ad hoc networks:from theory to reality (REALMAN ’06),2006.[8]M.Zuniga and B.Krishnamachari.Analyzing thetransitional region in low power wireless links.In IEEE SECON 2004,2004.[9]Fire detection and fire alarm systems –Part 25:Componentsusing radio links.European Norm (EN)54-25:2008-06,2008.。

多传感器融合定位技术原理英文回答：Multi-sensor fusion positioning technology is a technology that uses multiple sensors to obtain theposition information of an object. It is widely used in many fields, such as navigation, robotics, and autonomous driving.The principle of multi-sensor fusion positioning technology is to use the complementary advantages of different sensors to improve the positioning accuracy and reliability. For example, GPS can provide accurate absolute positioning, but it is easily affected by environmental factors such as buildings and trees. Inertial navigation system (INS) can provide continuous positioning, but itwill drift over time. By fusing GPS and INS, the advantages of both sensors can be utilized to achieve high-precision and reliable positioning.Multi-sensor fusion positioning technology can be divided into two categories: centralized fusion and decentralized fusion. Centralized fusion is to collect the data from all sensors and process them in a central processor. Decentralized fusion is to process the data from each sensor separately and then fuse the results. Centralized fusion has higher accuracy but higher computational cost, while decentralized fusion has lower accuracy but lower computational cost.The key technologies of multi-sensor fusion positioning technology include sensor data preprocessing, sensor calibration, sensor data fusion, and positioning algorithm. Sensor data preprocessing is to remove noise and outliers from the sensor data. Sensor calibration is to correct the errors of the sensors. Sensor data fusion is to fuse the data from different sensors to obtain more accurate and reliable information. Positioning algorithm is to calculate the position of the object based on the fused data.中文回答：多传感器融合定位技术是一种利用多个传感器获取物体位置信息的技术，广泛应用于导航、机器人、自动驾驶等领域。

gsn的名词解释GSN（Global Sensor Networks，全球传感器网络）是一种由分布式传感器节点组成的网络系统。

传感器节点可以接收和传输环境信息，通过数据采集和处理来实现对环境的监测和控制。

GSN的目标是监测和解决全球性问题，例如气候变化、环境污染和自然灾害等。

通过在多个地点部署分布式传感器节点，GSN可以实时收集大量的环境数据。

这些数据可以用于科学研究、政策制定和应急响应等领域。

GSN的核心组成部分是传感器节点。

传感器节点通常包括传感器、数据处理单元和通信模块。

传感器用于感知环境信息，例如温度、湿度和空气质量等。

数据处理单元负责处理和分析传感器收集到的数据。

通信模块则用于与其他传感器节点进行数据交换和网络通信。

GSN的优势之一是其分布式的特性。

由于传感器节点可以在广泛的地理区域内部署，GSN可以提供广泛的覆盖范围和高分辨率的环境数据。

这使得GSN成为研究人员、政府和企业等利益相关者的重要工具。

GSN不仅可以监测环境，还可以用于其他应用领域。

例如，GSN可以用于智能交通系统，在道路上部署传感器节点，实时监测交通流量和道路状况，从而优化交通管理和减少交通拥堵。

此外，GSN还可以应用于农业领域。

通过在农田内部署传感器节点，农民可以实时监测土壤湿度、温度和养分等参数，从而优化灌溉和施肥的管理，提高农作物的产量和质量。

然而，GSN也面临一些挑战。

首先是传感器节点的能源供应问题。

传感器节点通常是由电池供电，但电池寿命有限，长期的数据收集可能导致电池耗尽。

因此，如何进行有效的能量管理，延长节点的使用寿命成为一个关键问题。

其次，GSN面临的另一个挑战是数据处理和存储。

由于传感器节点收集到的数据量很大，如何高效地处理数据、存储和传输数据成为一个挑战。

同时，数据的可靠性和安全性也需要得到保障。

为了解决这些问题，研究人员们正在开展一系列的研究和开发工作。

例如，他们正在探索新型的能源供应和管理技术，如利用太阳能和振动能等。

Sensor technologyA sensor is a device which produces a signal in response to its detecting or measuring a property ,such as position , force , torque ，pressure , temperature ，humidity , speed ，acceleration ，or vibration 。

Traditionally ，sensors （such as actuators and switches )have been used to set limits on the performance of machines .Common examples are (a）stops on machine tools to restrict work table movements ,(b) pressure and temperature gages with automatics shut-off features ，and （c）governors on engines to prevent excessive speed of operation . Sensor technology has become an important aspect of manufacturing processes and systems 。

It is essential for proper data acquisition and for the monitoring ，communication ，and computer control of machines and systems 。

Because they convert one quantity to another , sensors often are referred to as transducers .Analog sensors produce a signal , such as voltage ,which is proportional to the measured quantity .Digital sensors have numeric or digital outputs that can be transferred to computers directly 。