A Survey on Sensor Networks译文(部分)
无线传感器网络
无线传感器网络无线传感器网络（Wireless Sensor Networks, WSN）是一种由众多装备了传感器和通信设备的节点组成的、可以进行数据采集、处理和传输的网络系统。
这些节点可以相互通信，共同完成特定的监测、控制或者数据传输任务。
无线传感器网络广泛应用于环境监测、医疗健康、物联网等领域。
一、无线传感器网络的组成无线传感器网络由多个节点组成，每个节点都有独立的处理能力、通信能力和传感能力。
节点之间通过无线通信进行数据的传递和交换。
每个节点可以采集周围环境的信息，并将数据传输给其他节点，或者通过无线信号传输给数据收集中心。
在无线传感器网络中，节点可以分为三个类型：传感器节点、中心节点和路由节点。
传感器节点用于收集环境信息，如温度、湿度、光照等。
中心节点负责数据的存储和处理，是整个网络的核心。
路由节点用于传输数据，将各个传感器节点采集到的数据传输给中心节点。
二、无线传感器网络的应用无线传感器网络在各个领域都有广泛的应用。
1. 环境监测无线传感器网络可以用于环境的监测和数据的采集。
通过部署传感器节点，可以实时监测空气质量、水质状况、土壤湿度等环境因素，并将数据传输给监测站点。
这对于环境保护和资源管理非常重要。
2. 健康医疗无线传感器网络可以应用于健康监测和医疗领域。
通过佩戴传感器设备，可以实时监测人体的生理参数，如心率、血压、体温等，并将数据传输给医生或者云平台，以便于监护和诊断。
3. 物联网无线传感器网络是物联网的基础技术之一。
通过无线传感器网络，不同的物体和设备可以相互连接和通信，实现信息的交换和共享。
无线传感器网络在智能家居、智能城市等方面有着重要的应用。
三、无线传感器网络的挑战与未来发展尽管无线传感器网络在各个领域都有广泛的应用，但也面临一些挑战。
1. 能源管理由于无线传感器网络中的节点通常是由电池供电，能源管理是一个重要的问题。
如何延长节点的寿命，提高能源利用效率是当前的研究重点之一。
一种改进路由节能无线传感器网络
一种改进路由的节能无线传感器网络摘要：当前有很多路由协议就是针对延长无线传感器网络生命周期而提出的。
eaheed协议改进了heed协议，它减少了节点工作时的能量消耗。
但仍存在缺点。
因此，本文利用基站来选择簇头；同时，将网络划分为多个小矩形的思想。
经分析改进协议充分利用了网络的特点，进一步延长了网络的生命周期。
关键词：无线传感器网络；路由协议；簇头；生命周期中图分类号：tp393 文献标识码：a文章编号：1007-9599 (2013) 05-0000-02无线传感器网络（wireless sensor network）[1-3]是由大量的静止或移动的传感器组成。
无线传感器网络结合传感器技术、计算机技术和通信技术，完成网络区域内数据的收集、处理以及传输，并最终把收集的数据发送给网络所有者。
于是，无线传感器网络被应用于诸多领域。
然而，传感器节点能量有限，固而，找出一条延长传感器节点的生命周期的方法才是利用好网络的核心。
1相关问题leach[4]协议是层次路由协议中的一个典型，它在节约了节点能量，延长了网络生命周期的同时也存在一些不足之处。
pegasis[5]协议规定在传感器节点采用链式结构对数据进行传输。
与leach协议相比，它更能进一步延长网络生命周期。
eaheed[6]协议综合了leach和pegasis协议的优点，改进了heed[7]协议。
但是eaheed 协议存在簇头可能选择它所付出代价更大的簇头作为下一跳来传送数据的问题。
图1eaheed协议簇头分布设a、b、c为三个簇头节点，其位置分布如图1所示。
簇头a的权值小于簇头b的权值，簇头b的权值小于簇头c的权值。
按照eaheed协议的思想，簇头a将选择簇头c作为它传输数据的下一跳。
但如果簇头a选择簇头b作为它的下一跳，那么它所消耗的能量将更小。
因此，eaheed协议进行了改进，网络生命周期将进一步增加。
2相关改进该协议将每个回合分为三个阶段，即：簇头选择、数据传递、路由维护。
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Thesis Supervisor : Professor Wei Guoliang
October, 2016
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无线传感器网络容错目标定位算法_FTTL
a ∈ R + 次方关系，具体数值与环境相关。所以， t 时刻
位于 ( xn , yn ) 处的传感器 n 的观测值为： ，当 n = 1,..., N , t = 1,..., M ，有
在上述公式中， Vmax 和 g 是与具体的传感器设计结 构相关的参数， Vmax 表示传感器最大可测量值， 传感器的幅度增益。 rn 表示两节点间的几何距离： 表示
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A
无线传感器网络容错目标定位算法——FTTL
无线传感器网络容错目标定位算法—— FTTL
[韩丽]
摘要
文章提出了一种带有容错机制的目标定位算法，算法以传感器节点观测结果0-1 值为依据，通过一种似然估计实现定位。文章提出的算法能够获得较好的定位精 度，并在一定的节点差错概率下，保持算法性能。
关键词： 无线传感器网络 目标定位 容错算法
新 技 术
韩丽 南京邮电大学，通信与信息工程学院。
44
新 业 务
1
引言
无线传感器网络（Wireless Sensor
2 一种带有容错机制的目标定位算法—— FTTL
2.1 无线传感器网络的实验模型
对于 本文 中讨论 的用于 目标 定位的 无线 传感器网 络，我们有以下假设： （1） N 个传感器节点平均地分布在面积为
(i, j ) 进行+1或-1操作。
L(i, j ) = ∑∑ bn ,t (i, j ), for i, j = 1,..., G
一种新的无线传感器网络节点自定位技术
邮局订阅号：８２－９４６３６０元／年技术创新传感器与仪器仪表《ＰＬＣ技术应用２００例》您的论文得到两院院士关注一种新的无线传感器网络节点自定位技术ＡＮｅｗＳｅｌｆ－ｌｏｃａｌｉｚａｔｉｏｎＴｅｃｈｎｏｌｏｇｙｆｏｒＷｉｒｅｌｅｓｓＳｅｎｓｏｒＮｅｔｗｏｒｋＮｏｄｅｓ（１．清华大学；２．空军装备研究院）张正勇１梅顺良１韩岷２Ｚｈａｎｇ，ＺｈｅｎｇｙｏｎｇＭｅｉ，ＳｈｕｎｌｉａｎｇＨａｎ，Ｍｉｎ摘要：在无线传感器网络中，节点定位技术是许多应用的支撑技术，具有重要地位。
目前已经出现各种各样的定位算法，ＫＰＳ算法不需要测距，具有一定的优越性，但在某些应用中存在定位精度较低的问题。
针对这一问题，提出了一种新的改进ＫＰＳ定位精度的算法，仿真结果表明，该改进算法能够明显提高定位精度。
关键词：无线传感器网络；定位算法；锚节点中图分类号：ＴＰ１８３文献标识码：ＡＡｂｓｔｒａｃｔ：Ｉｎｗｉｒｅｌｅｓｓｓｅｎｓｏｒｎｅｔｗｏｒｋｓ，ｓｅｎｓｏｒｌｏｃａｔｉｏｎｐｌａｙｓａｃｒｉｔｉｃａｌｒｏｌｅｉｎｍａｎｙａｐｐｌｉｃａｔｉｏｎ．Ｉｎｔｈｅｐａｓｔ，ａｎｕｍｂｅｒｏｆｌｏｃａｔｉｏｎｄｉｓ－ｃｏｖｅｒｙｓｃｈｅｍｅｓｈａｖｅｂｅｅｎｐｒｏｐｏｓｅｄ．ＫＰＳｈａｓａｎａｄｖａｎｔａｇｅｂｅｃａｕｓｅｎｏｄｉｓｔａｎｃｅ／ａｎｇｌｅｍｅａｓｕｒｅｍｅｎｔａｍｏｎｇｎｏｄｅｓｉｓｉｎｖｏｌｖｅｄ，ｂｕｔｈｉｇｈｉｎａｃｃｕｒａｃｙａｓｃｏｍｐａｒｅｄｔｏｏｔｈｅｒｓ．ＴｈｉｓｐａｐｅｒｐｒｏｐｏｓｅｓａｎｅｗｌｏｃａｔｉｏｎａｌｇｏｒｉｔｈｍｔｏｉｍｐｒｏｖｅｔｈｅｉｎａｃｃｕｒａｃｙｏｆＫＰＳ．Ｔｈｉｓｐａｐｅｒｈａｓｃｏｎ－ｄｕｃｔｅｄｓｉｍｕｌａｔｉｏｎｓｔｏｅｖａｌｕａｔｅｔｈｅｓｃｈｅｍｅ．Ｋｅｙｗｏｄｓ：ｗｉｒｅｌｅｓｓｓｅｎｓｏｒｎｅｔｗｏｒｋｓ，ｌｏｃａｌｉｚａｔｉｏｎａｌｇｏｒｉｔｈｍ，ａｎｃｈｏｒｎｏｄｅ文章编号：１００８－０５７０（２００６）０８－１－０００１－０３１引言无线传感器网络（ＷｉｒｅｌｅｓｓＳｅｎｓｏｒＮｅｔｗｏｒｋ，ＷＳＮ）是将大量低成本、低功耗的微型无线传感器布置或抛撒到感兴趣的区域，传感器通过自组织快速形成的一种分布式网络，在军事和民用领域都具有广阔的应用前景。
LPR-MAC一种采用并行协商机制的低功耗多信道MAC协议
50%
active time percent
0 0.5 1 1.5 2 2.5 3 Offered Load (Mbps) 3.5 4 4.5 5
40%
30%
20%
10%
00ຫໍສະໝຸດ 0.51LPR-MAC协议 LPR-MAC协议——时间片的分配 协议——时间片的分配
1 2
休眠期
3
活跃期
4
……
休眠期
N
超帧划分为多个时间片 节点选择其中一个时间片苏醒 在其余时间片休眠 节点在初始化时确定活跃期所处时间片
LPR-MAC协议 LPR-MAC协议——节点的通信策略 协议——节点的通信策略
节点活跃期不重合 Node 1 Node 2 1 1 2 2 3 3 4 4 …… …… N N
背景——多信道 背景——多信道
Single Channel MAC
interference
Multi-Channel MAC
背景——多信道的可行性 背景——多信道的可行性
频率资源 802.15.4在2.4GHZ频段有16个可用信道 802.15.4在2.4GHZ频段有16个可用信道 硬件支持 TI公司的CC2420可以在多个信道之间方便 TI公司的CC2420可以在多个信道之间方便 切换，信道切换时间为300us. 切换，信道切换时间为300us.
data channel
Control channel
McMAC协议 McMAC协议
不依赖控制信道，避免了控制信道的瓶颈问题 避免多个节点在控制信道上竞争 避免控制信道受到干扰，影响整个网络
无线传感器网络实验报告
无线传感器网络实验报告无线传感器网络实验报告引言：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，并将所感知到的信息通过无线通信传输给基站或其他节点。
WSN广泛应用于农业、环境监测、智能交通等领域。
本实验旨在通过搭建一个简单的无线传感器网络系统，了解其工作原理和性能特点。
一、实验背景无线传感器网络是现代信息技术的重要组成部分，其应用领域广泛且前景十分广阔。
通过实验，我们可以深入了解WSN的工作原理和应用场景，为今后的研究和开发提供基础。
二、实验目的1. 掌握无线传感器网络的基本概念和原理；2. 理解无线传感器网络的组网方式和通信协议；3. 了解无线传感器网络的性能特点和应用领域。
三、实验设备1. 无线传感器节点：本实验使用了10个无线传感器节点，每个节点都具备感知和通信功能；2. 基站：作为无线传感器网络的中心节点，负责接收并处理来自传感器节点的数据；3. 电脑：用于控制和监控整个无线传感器网络系统。
四、实验步骤1. 搭建无线传感器网络：将10个传感器节点分别放置在不同的位置，并保证它们之间的通信范围有重叠部分；2. 配置传感器节点参数：通过电脑连接到基站，对每个传感器节点进行参数配置，包括通信频率、传输功率等；3. 数据采集与传输：传感器节点开始感知环境中的物理量，并将采集到的数据通过无线通信传输给基站；4. 数据处理与展示：基站接收到传感器节点的数据后，进行数据处理和分析，并将结果展示在电脑上。
五、实验结果与分析通过实验，我们成功搭建了一个简单的无线传感器网络系统，并进行了数据采集和传输。
我们发现，传感器节点能够准确地感知环境中的物理量，并将数据可靠地传输给基站。
基站对接收到的数据进行了处理和分析，展示了环境中物理量的变化趋势。
六、实验总结通过本次实验，我们深入了解了无线传感器网络的工作原理和性能特点。
传感器相关英语文献
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.。
A Survey on Wireless Body Area Networks
A survey on wireless body area networksBenoıˆt Latre ´•Bart Braem •Ingrid Moerman •Chris Blondia •Piet DemeesterPublished online:11November 2010ÓSpringer Science+Business Media,LLC 2010Abstract The increasing use of wireless networks and the constant miniaturization of electrical devices has empow-ered the development of Wireless Body Area Networks (WBANs).In these networks various sensors are attached on clothing or on the body or even implanted under the skin.The wireless nature of the network and the wide variety of sen-sors offer numerous new,practical and innovative applica-tions to improve health care and the Quality of Life.The sensors of a WBAN measure for example the heartbeat,the body temperature or record a prolonged ing a WBAN,the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital.This paper offers a survey of the concept of Wireless Body Area Networks.First,we focus on some applications with special interest in patient monitoring.Then the communi-cation in a WBAN and its positioning between the different technologies is discussed.An overview of the current research on the physical layer,existing MAC and network protocols is given.Further,cross layer and quality of service is discussed.As WBANs are placed on the human body and often transport private data,security is also considered.An overview of current and past projects is given.Finally,the open research issues and challenges are pointed out.Keywords Wireless body area networks ÁRouting ÁMAC1IntroductionThe aging population in many developed countries and the rising costs of health care have triggered the introduction of novel technology-driven enhancements to current health care practices.For example,recent advances in electron-ics have enabled the development of small and intelligent (bio-)medical sensors which can be worn on or implanted in the human body.These sensors need to send their data to an external medical server where it can be analyzed and ing a wired connection for this purpose turns out to be too cumbersome and involves a high cost for deployment and maintenance.However,the use of a wireless interface enables an easier application and is more cost efficient [1].The patient experiences a greater physical mobility and is no longer compelled to stay in a hospital.This process can be considered as the next step in enhancing the personal health care and in coping with the costs of the health care system.Where eHealth is defined as the health care practice sup-ported by electronic processes and communication,the health care is now going a step further by becoming mobile.This is referred to as mHealth [2].In order to fully exploit the benefits of wireless technologies in telemedicine and mHealth,a new type of wireless network emerges:a wire-less on-body network or a Wireless Body Area Network (WBAN).This term was first coined by Van Dam et al.[3]and received the interest of several researchers [4–8].A Wireless Body Area Network consists of small,intelligent devices attached on or implanted in the body which are capable of establishing a wireless communica-tion link.These devices provide continuous health moni-toring and real-time feedback to the user or medical personnel.Furthermore,the measurements can be recorded over a longer period of time,improving the quality of the measured data [9].tre´(&)ÁI.Moerman ÁP.Demeester Department of Information Technology,Ghent University/IBBT,Gaston Crommenlaan 8,Box 201,9050Gent,Belgium e-mail:tre@intec.ugent.beB.Braem ÁC.BlondiaDepartment of Mathematics and Computer Science,University of Antwerp/IBBT,Middelheimlaan 1,2020Antwerp,Belgium e-mail:bart.braem@ua.ac.beWireless Netw (2011)17:1–18DOI 10.1007/s11276-010-0252-4Generally speaking,two types of devices can be dis-tinguished:sensors and actuators.The sensors are used to measure certain parameters of the human body,either externally or internally.Examples include measuring the heartbeat,body temperature or recording a prolonged electrocardiogram(ECG).The actuators(or actors)on the other hand take some specific actions according to the data they receive from the sensors or through interaction with the user,e.g.,an actuator equipped with a built-in reservoir and pump administers the correct dose of insulin to give to diabetics based on the glucose level measurements.Inter-action with the user or other persons is usually handled by a personal device,e.g.a PDA or a smart phone which acts as a sink for data of the wireless devices.In order to realize communication between these devi-ces,techniques from Wireless Sensor Networks(WSNs) and ad hoc networks could be used.However,because of the typical properties of a WBAN,current protocols designed for these networks are not always well suited to support a WBAN.The following illustrates the differences between a Wireless Sensor Network and a Wireless Body Area Network:•The devices used have limited energy resources avail-able as they have a very small form factor(often less than1cm3[10]).Furthermore,for most devices it is impossible to recharge or change the batteries althougha long lifetime of the device is wanted(up to severalyears or even decades for implanted devices).Hence, the energy resources and consequently the computa-tional power and available memory of such devices will be limited;•All devices are equally important and devices are only added when they are needed for an application(i.e.no redundant devices are available);•An extremely low transmit power per node is needed to minimize interference and to cope with health concerns[11];•The propagation of the waves takes place in or on a (very)lossy medium,the human body.As a result,the waves are attenuated considerably before they reach the receiver;•The devices are located on the human body that can be in motion.WBANs should therefore be robust against frequent changes in the network topology;•The data mostly consists of medical information.Hence,high reliability and low delay is required;•Stringent security mechanisms are required in order to ensure the strictly private and confidential character of the medical data;•Andfinally the devices are often very heteroge-neous,may have very different demands or may requiredifferent resources of the network in terms of data rates, power consumption and reliability.When referring to a WBAN where each node comprises a biosensor or a medical device with sensing unit,some researchers use the name Body Area Sensor Network (BASN)or in short Body Sensor Network(BSN)instead of WBAN[12].These networks are very similar to each other and share the same challenges and properties.In the following,we will use the term WBAN which is also the one used by the IEEE[13].In this article we present a survey of the state of the art in Wireless Body Area Networks.Our aim is to provide a better understanding of the current research issues in this emergingfield.The remainder of this paper is organized as follows.First,the patient monitoring application is dis-cussed in Sect.2.Next,the characteristics of the commu-nication and the positioning of WBANs amongst other wireless technologies is discussed in Sect.4.Section5 gives an overview of the properties of the physical layer and the issues of communicating near or in the body. Existing protocols for the MAC-layer and network layer are discussed in Sects.6and7,respectively.Section8 deals with cross-layer protocols available for WBANs.The Quality of Service(QoS)and possible security mechanisms are treated in Sects.9and10.An overview of existing projects is given in Sect.11.Finally,the open research issues are discussed in Sects.12and13concludes the paper.2Patient monitoringThe main cause of death in the world is CardioVascular Disease(CVD),representing30%of all global deaths. According to the World Health Organization,worldwide about17.5million people die of heart attacks or strokes each year;in2015,almost20million people will die from CVD.These deaths can often be prevented with proper health care[14].Worldwide,more than246million people suffer from diabetes,a number that is expected to rise to 380million by2025[15].Frequent monitoring enables proper dosing and reduces the risk of fainting and in later life blindness,loss of circulation and other complications [15].These two examples already illustrate the need for continuous monitoring and the usefulness of WBANs. Numerous other examples of diseases would benefit from continuous or prolonged monitoring,such as hypertension, asthma,Alzheimer’s disease,Parkinson’s disease,renal failure,post-operative monitoring,stress-monitoring,pre-vention of sudden infant death syndrome,etc[9,16,17]. These applications can be considered as an indicator for thesize of the market for WBANs.The number of people suffering from diabetics or CVD and the percentage of people in the population age60years and older will grow in the future.Even without any further increase in world population by2025this would mean a very large number of potential customers.WBAN technology could provide the connectivity to support the elderly in managing their daily life and medical conditions[18].A WBAN allows continuous monitoring of the physiological parameters. Whether the patient is in the hospital,at home or on the move,the patient will no longer need to stay in bed,but will be able to move around freely.Furthermore,the data obtained during a large time interval in the patient’s natural environment offers a clearer view to the doctors than data obtained during short stays at the hospital[9].An example of a medical WBAN used for patient moni-toring is shown in Fig.1.Several sensors are placed in clothes,directly on the body or under the skin of a person and measure the temperature,blood pressure,heart rate,ECG, EEG,respiration rate,SpO2-levels,etc.Next to sensing devices,the patient has actuators which act as drug delivery systems.The medicine can be delivered on predetermined moments,triggered by an external source(i.e.a doctor who analyzes the data)or immediately when a sensor notices a problem.One example is the monitoring of the glucose level in the blood of diabetics.If the sensor monitors a sudden drop of glucose,a signal can be sent to the actuator in order to start the injection of insulin.Consequently,the patient will experience fewer nuisances from his disease.Another example of an actuator is a spinal cord stimulator implanted in the body for long-term pain relief[19].A WBAN can also be used to offer assistance to the disabled.For example,a paraplegic can be equipped with sensors determining the position of the legs or with sensors attached to the nerves[20].In addition,actuators posi-tioned on the legs can stimulate the muscles.Interaction between the data from the sensors and the actuators makes it possible to restore the ability to move.Another example is aid for the visually impaired.An artificial retina,con-sisting of a matrix of micro sensors,can be implanted into the eye beneath the surface of the retina.The artificial retina translates the electrical impulses into neurological signals.The input can be obtained locally from light sen-sitive sensors or by an external camera mounted on a pair of glasses[21].Another area of application can be found in the domain of public safety where the WBAN can be used byfire-fighters,policemen or in a military environment[22].The WBAN monitors for example the level of toxics in the air and warns thefirefighters or soldiers if a life threatening level is detected.The introduction of a WBAN further enables to tune more effectively the training schedules of professional athletes.Next to purely medical applications,a WBAN can include appliances such as an MP3-player,head-mounted (computer)displays,a microphone,a camera,advanced human-computer interfaces such as a neural interface,etc [20].As such,the WBAN can also be used for gaming purposes and in virtual reality.This small overview already shows the myriad of pos-sibilities where WBANs are useful.The main characteristic of all these applications is that WBANs improve the user’s Quality of Life.3Taxonomy and requirementsThe applications described in the previous section indicate that a WBAN consists of several heterogeneous devices.In this section an overview of the different types of devices used in a WBAN will be given.Further the requirements and challenges are discussed.These include the wide var-iability of data rates,the restricted energy consumption,the need for QoS and reliability,ease-of-use by medical pro-fessionals and security and privacy issues.3.1Types of devices(Wireless)sensor node:A device that responds to and gathers data on physical stimuli,processes the data if necessary and reports this information wirelessly.It consists of several components:sensor hardware,a power unit,a processor,memory and a transmitter or transceiver[23].(Wireless)actuator node:A device that acts according to data received from the sensors or throughinteractionwith the user.The components of an actuator are similar to the sensor’s:actuator hardware(e.g.hardware for medicine administration,including a reservoir to hold the medicine),a power unit,a processor,memory and a receiver or transceiver.(Wireless)personal device(PD):A device that gathers all the information acquired by the sensors and actuators and informs the user(i.e.the patient,a nurse,a GP,etc.) via an external gateway,an actuator or a display/LEDS on the device.The components are a power unit,a (large)processor,memory and a transceiver.This device is also called a Body Control Unit(BCU)[4],body-gateway or a sink.In some implementations,a Personal Digital Assistant(PDA)or smart phone is used.Many different types of sensors and actuators are used in a WBAN.The main use of all these devices is to be found in the area of health applications.In the following,the termnodes refers to both the sensor as actuator nodes.The number of nodes in a WBAN is limited by nature of the network.It is expected that the number of nodes will be in the range of20–50[6,24].3.2Data ratesDue to the strong heterogeneity of the applications,data rates will vary strongly,ranging from simple data at a few kbit/s to video streams of several Mbit/s.Data can also be sent in bursts,which means that it is sent at higher rate during the bursts.The data rates for the different applications are given in in Table1and are calculated by means of the sampling rate,the range and the desired accuracy of the measure-ments[25,26].Overall,it can be seen that the application data rates are not high.However,if one has a WBAN with several of these devices(i.e.a dozen motion sensors,ECG, EMG,glucose monitoring,etc.)the aggregated data rate easily reaches a few Mbps,which is a higher than the raw bit rate of most existing low power radios.The reliability of the data transmission is provided in terms of the necessary bit error rate(BER)which is used as a measure for the number of lost packets.For a medical device,the reliability depends on the data rate.Low data rate devices can cope with a high BER(e.g.10-4),while devices with a higher data rate require a lower BER(e.g. 10-10).The required BER is also dependent on the criti-calness of the data.3.3EnergyEnergy consumption can be divided into three domains: sensing,(wireless)communication and data processing [23].The wireless communication is likely to be the most power consuming.The power available in the nodes is often restricted.The size of the battery used to store the needed energy is in most cases the largest contributor to the sensor device in terms of both dimensions and weight. Batteries are,as a consequence,kept small and energy consumption of the devices needs to be reduced.In some applications,a WBAN’s sensor/actuator node should operate while supporting a battery life time of months or even years without intervention.For example,a pacemaker or a glucose monitor would require a lifetime lasting more than5years.Especially for implanted devices,the lifetime is crucial.The need for replacement or recharging induces a cost and convenience penalty which is undesirable not only for implanted devices,but also for larger ones.The lifetime of a node for a given battery capacity can be enhanced by scavenging energy during the operation of the system.If the scavenged energy is larger than the average consumed energy,such systems could run eternally.How-ever,energy scavenging will only deliver small amounts of energy[5,28].A combination of lower energy consumption and energy scavenging is the optimal solution for achieving autonomous Wireless Body Area Networks.For a WBAN, energy scavenging from on-body sources such as body heat and body vibration seems very well suited.In the former,a thermo-electric generator(TEG)is used to transform the temperature difference between the environment and the human body into electrical energy[27].The latter uses for example the human gait as energy source[29].During communication the devices produce heat which is absorbed by the surrounding tissue and increases the temperature of the body.In order to limit this temperature rise and in addition to save the battery resources,the energy consumption should be restricted to a minimum. The amount of power absorbed by the tissue is expressed Table1Examples of medical WBAN applications[21,25,26,27] Application Data rate Bandwidth(Hz)Accuracy(bits) ECG(12leads)288kbps100–100012ECG(6leads)71kbps100–50012EMG320kbps0–10,00016EEG(12leads)43.2kbps0–15012 Blood saturation16bps0–18 Glucose monitoring1600bps0–5016 Temperature120bps0–18 Motion sensor35kbps0–50012 Cochlear implant100kbps––Artificial retina50-700kbps––Audio1Mbps––Voice50-100kbps––by the specific absorption rate(SAR).Since the device may be in close proximity to,or inside,a human body,the localized SAR could be quite large.The localized SAR into the body must be minimized and needs to comply with international and local SAR regulations.The regulation for transmitting near the human body is similar to the one for mobile phones,with strict transmit power requirements [11,30].3.4Quality of service and reliabilityProper QoS handling is an important part in the framework of risk management of medical applications.A crucial issue is the reliability of the transmission in order to guarantee that the monitored data is received correctly by the health care professionals.The reliability can be con-sidered either end-to-end or on a per link base.Examples of reliability include the guaranteed delivery of data(i.e. packet delivery ratio),in-order-delivery,…Moreover, messages should be delivered in reasonable time.The reliability of the network directly affects the quality of patient monitoring and in a worst case scenario it can be fatal when a life threatening event has gone undetected [31].3.5UsabilityIn most cases,a WBAN will be set up in a hospital by medical staff,not by ICT-engineers.Consequently,the network should be capable of configuring and maintaining itself automatically,i.e.self-organization an self-mainte-nance should be supported.Whenever a node is put on the body and turned on,it should be able to join the network and set up routes without any external intervention.The self-organizing aspect also includes the problem of addressing the nodes.An address can be configured at manufacturing time(e.g.the MAC-address)or at setup time by the network itself.Further,the network should be quickly reconfigurable,for adding new services.When a route fails,a back up path should be set up.The devices may be scattered over and in the whole body.The exact location of a device will depend on the application,e.g.a heart sensor obviously must be placed in the neighborhood of the heart,a temperature sensor can be placed almost anywhere.Researchers seem to disagree on the ideal body location for some sensor nodes,i.e.motion sensors,as the interpretation of the measured data is not always the same[32].The network should not be regarded as a static one.The body may be in motion(e.g.walking, running,twisting,etc.)which induces channel fading and shadowing effects.The nodes should have a small form factor consistent with wearable and implanted applications.This will make WBANs invisible and unobtrusive.3.6Security and privacyThe communication of health related information between sensors in a WBAN and over the Internet to servers is strictly private and confidential[33]and should be encrypted to protect the patient’s privacy.The medical staff collecting the data needs to be confident that the data is not tampered with and indeed originates from that patient.Further,it can not be expected that an average person or the medical staff is capable of setting up and managing authentication and authorization processes. Moreover the network should be accessible when the user is not capable of giving the password(e.g.to guarantee accessibility by paramedics in trauma situations).Security and privacy protection mechanisms use a significant part of the available energy and should therefor be energy efficient and lightweight.4Positioning WBANsThe development and research in the domain of WBANs is only at an early stage.As a consequence,the terminology is not always clearly defined.In literature,protocols devel-oped for WBANs can span from communication between the sensors on the body to communication from a body node to a data center connected to the Internet.In order to have clear understanding,we propose the following defi-nitions:intra-body communication and extra-body com-munication.An example is shown on Fig.2.The former controls the information handling on the body between the sensors or actuators and the personal device[34–37],the Fig.2Example of intra-body and extra-body communication in a WBANlatter ensures communication between the personal device and an external network[32,38–40].Doing so,the medical data from the patient at home can be consulted by a phy-sician or stored in a medical database.This segmentation is similar to the one defined in[40]where a multi-tiered telemedicine system is presented.Tier1encompasses the intra-body communication,tier2the extra-body commu-nication between the personal device and the Internet and tier3represents the extra-body communication from the Internet to the medical server.The combination of intra-body and extra-body communication can be seen as an enabler for ubiquitous health care service provisioning.An example can be found in[41]where Utility Grid Com-puting is combined with a WBAN.Doing so,the data extracted from the WBAN is sent to the grid that provides access to appropriate computational services with highbandwidth and to a large collection of distributed time-varying resources.To date,development has been mainly focused on building the system architecture and service platform for extra-body communication.Much of these implementa-tions focus on the repackaging of traditional sensors(e.g. ECG,heart rate)with existing wireless devices.They consider a very limited WBAN consisting of only a few sensors that are directly and wirelessly connected to a personal device.Further they use transceivers with a large form factor and large antennas that are not adapted for use on a body.In Fig.3,a WBAN is compared with other types of wireless networks,such as Wireless Personal(WPAN), Wireless Local(WLAN),Wireless Metropolitan(WMAN) and Wide Area Networks(WAN)[42].A WBAN is operated close to the human body and its communication range will be restricted to a few meters,with typical values around1–2m.While a WBAN is devoted to intercon-nection of one person’s wearable devices,a WPAN is a network in the environment around the person.The com-munication range can reach up to10m for high data rate applications and up to several dozens of meters for low data rate applications.A WLAN has a typical communi-cation range up to hundreds of meters.Each type of net-work has its typical enabling technology,defined by the IEEE.A WPAN uses IEEE802.15.1(Bluetooth)or IEEE 802.15.4(ZigBee),a WLAN uses IEEE802.11(WiFi)and a WMAN IEEE802.16(WiMax).The communication in a WAN can be established via satellite links.In several papers,Wireless Body Area Networks are considered as a special type of a Wireless Sensor Network or a Wireless Sensor and Actuator Network(WSAN)with its own requirements1.However,traditional sensor networks do not tackle the specific challenges associated with human body monitoring.The human body consists of a complicated internal environment that responds to and interacts with its external surroundings,but is in a way separate and self-contained.The human body environment not only has a smaller scale,but also requires a different type and fre-quency of monitoring,with different challenges than those faced by WSNs.The monitoring of medical data results in an increased demand for reliability.The ease of use of sensors placed on the body leads to a small form factor that includes the battery and antenna part,resulting in a higher need for energy efficiency.Sensor nodes can move with regard to each other,for example a sensor node placed on the wrist moves in relation to a sensor node attached to the hip.This requires mobility support.In brief,although challenges faced by WBANs are in many ways similar to WSNs,there are intrinsic differences between the two,requiring special attention.An overview of some of these differences is given in Table2.A schematic overview of the challenges in a WBAN and a comparison with WSNs and WLANs is given in Fig.4.5Physical layerThe characteristics of the physical layer are different for a WBAN compared to a regular sensor network or an ad-hoc network due to the proximity of the human body.Tests with TelosB motes(using the CC2420transceiver)showed lack of communications between nodes located on the chest and nodes located on the back of the patient[46]. This was accentuated when the transmit power was set to a minimum for energy savings reasons.Similar conclusions where drawn with a CC2420transceiver in[47]:when a person was sitting on a sofa,no communication was pos-sible between the chest and the ankle.Better results were obtained when the antenna was placed1cm abovethe Fig.3Positioning of a Wireless Body Area Network in the realm of wireless networks1In the following,we will not make a distinction between a WSAN and a WSN although they have significant differences[43].body.As the devices get smaller and more ubiquitous,a direct connection to the personal device will no longer be possible and more complex network topologies will be needed.In this section,we will discuss the characteristics of the propagation of radio waves in a WBAN and other types of communication.5.1RF communicationSeveral researchers have been investigating the path loss along and inside the human body either using narrowband radio signals or Ultra Wide Band(UWB).All of them came to the conclusion that the radio signals experience great losses.Generally in wireless networks,it is known that the transmitted power drops off with d g where d represents the distance between the sender and the receiver and g the coefficient of the path loss(aka propagation coefficient)[48].In free space,g has a value of2.Other kinds of losses include fading of signals due to multi-path propagation.The propagation can be classified according to where it takes place:inside the body or along the body.5.1.1In the bodyThe propagation of electromagnetic(EM)waves in the human body has been investigated in[49,50].The body acts as a communication channel where losses are mainly due to absorption of power in the tissue,which is dissipated as heat.As the tissue is lossy and mostly consists of water, the EM-waves are attenuated considerably before they reach the receiver.In order to determine the amount of power lost due to heat dissipation,a standard measure of how much power is absorbed in tissue is used:the specific absorption rate(SAR).It is concluded that the path loss is very high and that,compared to the free space propaga-tion,an additional30–35dB at small distances is noticed.A simplified temperature increase prediction scheme based on SAR is presented in[50].It is argued that considering energy consumption is not enough and that the tissue is sensitive to temperature increase.The influence of a patient’s body shape and position on the radiation pattern from an implanted radio transmitter has been studied in [51].It is concluded that the difference between bodyTable2Schematic overview of differences between Wireless Sensor Networks and Wireless Body Area Networks,based on[45] Challenges Wireless sensor network Wireless body area networkScale Monitored environment(m/km)Human body(cm/m)Node number Many redundant nodes for wide area coverage Fewer,limited in spaceResult accuracy Through node redundancy Through node accuracy and robustnessNode tasks Node performs a dedicated task Node performs multiple tasksNode size Small is preferred,but not important Small is essentialNetwork topology Very likely to befixed or static More variable due to body movementData rates Most often homogeneous Most often heterogeneousNode replacement Performed easily,nodes even disposable Replacement of implanted nodes difficultNode lifetime Several years/months Several years/months,smaller battery capacityPower supply Accessible and likely to be replaced moreeasily and frequentlyInaccessible and difficult to replaced in an implantable setting Power demand Likely to be large,energy supply easier Likely to be lower,energy supply more difficultEnergy scavenging source Most likely solar and wind power Most likely motion(vibration)and thermal(body heat) Biocompatibility Not a consideration in most applications A must for implants and some external sensorsSecurity level Lower Higher,to protect patient informationImpact of data loss Likely to be compensated by redundant nodes More significant,may require additional measures to ensure QoSand real-time data deliveryWireless technology Bluetooth,ZigBee,GPRS,WLAN,…Low power technologyrequired。
无线传感器网络中基于簇的混合式路由协议
2．相关进展
下面将简要介绍几种无线传感器网络中具有代表性的路由协议！
2.1 LEACH ( Low-Energy Adaptive Clustering Hierarchy)
LEACH 是第一个在无线传感器网络中提出的层次式路由协议。其后的很多层次式路由 协议都是在它基础上提出来的。因此这里我们着重介绍 LEACH 协议[1]。 它是一种基于分簇的路由协议， 由于网络中节点轮流作簇头因此均衡了网络中能量的消 耗。LEACH 的实现是以轮(round)为单位进行的，每一轮又分为簇建立阶段和数据传输阶段 （平衡阶段） 。并且，平衡阶段的持续时间要远远大于成簇所耗的时间。 在簇建立阶段， 每个节点都检查它自己在过去的 1/p-1 (其中 p 为簇头数目与网络节点总 数目的比例)轮是否成为过节点。如果没有，节点将产生一个介于 0 到 1 之间的随机数，如 果该随机数小于一个预定义的门限值 T(n)，那么该节点在本轮中将成为簇头，T(n)的定义如 下：
1
摘要：提出一种以数据为中心的路由协议算法 THRL，在 OMNeT++上进行仿真验证，并将其与 传统路由协议进行比较。 通过比较得出， 在相同条件下， THRL 比 LEACH 和 Directed Diffusion 在能耗方面都有较大的改善。 关键词：无线传感器网络; 混合式路由协议；OMNeT++仿真平台；分簇；门限 中图分类号：TN915.03 文献标识码 ：A
4.1
-3-
type = temperature instance = >100℉ location = [125,200] timestamp = 01:06:10 由于此前 Sink 掌握全网信息，因此，这一消息可以单播出去，而非像 Directed Diffusion 中那样每次查询都要广播。目的区域的簇头在接收到 interest-DATA 消息后，将本簇中产生 的数据融合，并发送给 Sink 。这样，由于查询信息的发送频度与用户需求有关，可以大大 减少网络中不必要数据的传送。减少了能量的消耗。 同时，为了保证网络在监测区域有突发性事件发生时，能够及时的将这一信息反馈给远 端的用户，我们在每一个节点的传感模块增加了两个门限值：硬门限（HT ）和软门限（ST ） [4]。如果监测对象感知值变化大于硬门限或者其相对变化值大于软门限，节点就会被触发。 开始向上一级发送数据，直至 Sink ，这样又保证了数据的及时性。另外，由于簇头处拥有 到达 Sink 的一条梯度最大的路径，因此避免了新一轮的广播，有助于节能。
MIMO系统各态历经信道容量的分析与仿真
收稿日期：2006-03-06；修订日期：2006-04-29作者简介：贺中堂（1971-），男，江苏丰县人，讲师，博士研究生，主要研究方向：通信信号处理、空时信号处理； 张力军（1942-），男，浙江文章编号：1001-9081（2006）08-1799-03MIMO 系统各态历经信道容量的分析与仿真贺中堂1，张力军1，陈自力2（1.南京邮电大学通信与信息工程学院，江苏南京210003；2.徐州空军学院电工电子教研室，江苏徐州221000）(hezta@)摘 要：以信息论的观点为基础，在假设信道状态信息仅收端已知的情况下，采用等功率发射方案，研究了瑞利衰落信道下MIMO（多输入多输出）系统各态历经信道容量，推导了三种特殊MIMO 信道的各态历经信道容量表达式，以及在小信噪比下等收发天线MIMO 系统的容量近似公式，并通过仿真进行了验证，仿真结果表明该近似公式比较精确。
关键词：多输入多输出；各态历经信道容量；Wishart 分布函数；Gamma 函数中图分类号：TP393.03 文献标识码：AAnalysis and simulations for ergodic capacity channel of MIMO systemsHE Zhong-tang 1,ZHANG Li-jun 1,CHEN Zi-ii 2（1.School of Communication and Information Engineering,Nanjing Uniuersity of Posts and Telecommunications,Nanjing Jiangsu 210003,China ；2.Electrical and Electronic Teaching and Researching Offices,Xuzhou Airforce College,Xuzhou Jiangsu 221000,China ）Abstract:Under the assumption that the channei state information was avaiiabie oniy at the receiver,the capacity of MIMO systems in Rayieigh fading channei was investigated on the basis of information theory.Three speciai expressions for the MIMO capacity over ergodic fiat fading channei were derived.An asymptotic formuia for MIMO systems with eguai number of transmit and receive antennas in smaii SNR was aiso given.Simuiation resuits show that this approximation is reiativeiy accurate.Key words:MIMO;ergodic channei capacity;Wishart distribution function;Gamma function0 引言采用MIMO （多输入多输出）的无线通信系统可以带来比SISO （单发单收）系统更多的优点，比如增加通信系统容量、提高频谱利用效率，下一代（4G ）无线移动通信已考虑在移动台和基站设置多个收发天线。
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英文翻译——采用红外传感器网络对移动目标的计数英文翻译
Moving Object Counting with an InfraredSensor NetworkBy Chi-Keung KiABSTRACTWireless Sensor Network (WSN) has become a hot research topic recently.Great benefit can be gained through the deployment of the WSN over a wide range of applications,covering the domains of commercial, military as well as residential. In this project, we design a counting system which tracks people who pass through a detecting zone as well as the corresponding moving directions.Such a system can be deployed in traffic control, resource management, and human flow control. Our design is based on our self-made cost-effective Infrared Sensing Module board which co-operates with a WSN.The design of our system includes Infrared Sensing Module design, sensor clustering, node communication, system architecture and deployment.We conduct a series of experiments to evaluate the system performance which demonstrates the efficiency of our Moving Object Counting system. KEYWORDS Infrared radiation;Wireless Sensor Node1 Wireless Sensor Network1.1 Introduction to InfraredInfrared radiation is a part of the electromagnetic radiation with a wavelength lying between visible light and radio waves.Infrared have be widely used nowadays including data communications,night vision,object tracking and so on.People commonly use infrared in data communication,since it is easily generated and only suffers little from electromagnetic interference.Take the TV remote control as an example,which can be found in everyone's home.The infrared remote control systems use infrared light-emitting diodes (LEDs) to send out an IR (infrared) signal when the button is pushed.A different pattern of pulses indicates the corresponding button being pushed. To allow the control of multiple appliances such as a TV,VCR,and cable box,without interference,systems generally have a preamble and an address to synchronize the receiver and identify the source and location of the infrared signal.To encode the data, systems generally vary thewidth of the pulses (pulse-width modulation) or the width of the spaces between the pulses (pulse space modulation).Another popular system,bi-phase encoding,uses signal transitions to convey information.Each pulse is actually a burst of IR at the carrier frequency. A 'high' means a burst of IR energy at the carrier frequency and a 'low' represents an absence of IR energy.There is no encoding standard. However, while a great many home entertainment devices use their own proprietary encoding schemes, some quasi-standards do exist. These include RC-5, RC-6, and REC-80.In addition,many manufacturers,such as NEC,have also established their own standards.1.2 Wireless sensor networkWireless sensor network (WSN) is a wireless network which consists of a vast number of autonomous sensor nodes using sensors to monitor physical or environmental conditions, such as temperature,acoustics,vibration,pressure,motion or pollutants,at different locations.Each node in a sensor network is typically equipped with a wireless communications device,a small microcontroller, one or more sensors,and an energy source, usually a battery.The size of a single sensor node can be as large as a shoebox and can be as small as the size of a grain of dust,depending on different applications.The cost of sensor nodes is similarly variable,ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity requirement of the individual sensor nodes.The size and cost are constrained by sensor nodes,therefore,have result in corresponding limitations on available inputs such as energy,memory, computational speed and bandwidth.The development of wireless sensor networks (WSN) was originally motivated by military applications such as battlefield surveillance.Due to the advancement in micro-electronic mechanical system technology (MEMS),embedded microprocessors,and wireless networking,the WSN can be benefited in many civilian application areas,including habitat monitoring,healthcare applications,and home automation.1.3 Types of Wireless Sensor NetworksWireless sensor network nodes are typically less complex than general-purpose operating systems both because of the special requirements of sensor network applicationsand the resource constraints in sensor network hardware platforms.The operating system does not need to include support for user interfaces. Furthermore,the resource constraints in terms of memory and memory mapping hardware support make mechanisms such as virtual memory either unnecessary or impossible to implement.Tiny OS is possibly the first operating system specifically designed for wireless sensor networks.Unlike most other operating systems,Tiny OS is based on an event-driven programming model instead of multithreading.Tiny OS programs are composed into event handlers and tasks with run to completion-semantics.When an external event occurs,such as an incoming data packet or a sensor reading,TinyOS calls the appropriate event handler to handle the event.The TinyOS and programs are both written in a special programming language called NesC which is an extension to the C programming language.NesC is designed to detect race conditions between tasks and event handlers. There are also operating systems that allow programming in C. Examples of such operating systems include Contiki ,and MANTIS. Contiki is designed to support loading modules over the network and run-time loading of standard ELF files.The Contiki kernel is event-driven,like TinyOS, but the system supports multithreading on a per-application basis. Unlike the event-driven Contiki kernel,the MANTIS kernel is based on preemptive multithreading.With preemptive multithreading, applications do not need to explicitly yield the microprocessor to other processes.1.4 Introduction to Wireless Sensor NodeA sensor node, also known as a mote, is a node in a wireless sensor network that is capable of performing processing, gathering sensory information and communicating with other connected nodes in the network.Sensor node should be in small size,consuming extremely low energy,autonomous and operating unattended,and adaptive to the environment.As wireless sensor nodes are micro-electronic sensor device, they can only be equipped with a limited power source.The main components of a sensor node include sensors,microcontroller,transceiver,and power source.Sensors are hardware devices that can produce measurable response to a change in a physical condition such as light density and sound density.The continuous analog signal collected by the sensors is digitized by Analog-to-Digital converter.The digitized signal is then passed to controllers for furtherprocessing.Most of the theoretical work on WSNs considers Passive and Omni directional sensors.Passive and Omni directional sensors sense the data without actually manipulating the enviro nment with active probing,while no notion of “direction”is involved in these monly people deploy sensor for detecting heat (e.g. thermal sensor), light (e.g. infrared sensor), ultra sound (e.g. ultrasonic sensor), or electromagnetism (e.g. magnetic sensor).In practice,a sensor node can equip with more than one sensor. Micro-controller performs tasks,processes data and controls the operations of other components in the sensor node.The sensor node is responsible for the signal processing upon the detection of the physical events as needed or on demand.It handles the interruption from the transceiver.In addition, it deals with the internal behavior, such as application-specific computation.The function of both transmitter and receiver are combined into a single device known as transceivers that are used in sensor nodes.Transceivers allow a sensor node to exchange information between the neighboring sensors and the sink node (a central receiver).The operational states of a transceiver are Transmit,Receive,Idle and Sleep. Power is stored either in the batteries or the capacitors.Batteries are the main source of power supplying for the sensor nodes.Two types of batteries used are chargeable and non-rechargeable. They are also classified according to electrochemical material used for electrode such as Nickel-cadmium,Nickel-zinc,Nickel metal hydride,and Lithium-Ion. Current sensors are developed which are able to renew their energy from solar to vibration energy.Two major power saving policies used are Dynamic Power Management and Dynamic V oltage Scaling. DPM takes care of shutting down parts of sensor node which are not currently used or active.DVS scheme varies the power levels depending on the non-deterministic workload. By varying the voltage along with the frequency, it is possible to obtain quadratic reduction in power consumption.1.5 ChallengesThe major challenges in the design and implementation of the wireless sensor network are mainly the energy limitation, hardware limitation and the area of coverage.Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSN nodes.WSNnodes are meant to be deployed in large numbers in various environments, including remote and hostile regions,with ad-hoc communications as key.For this reason, algorithms and protocols need to be lifetime maximization,robustness and fault tolerance and self-configuration.The challenge in hardware is to produce low cost and tiny sensor nodes. With respect to these objectives,current sensor nodes usually have limited computational capability and memory space. Consequently,the application software and algorithms in WSN should be well-optimized and condensed.In order to maximize the coverage area with a high stability and robustness of each signal node, multi-hop communication with low power consumption is preferred.Furthermore,to deal with the large network size, the designed protocol for a large scale WSN must be distributed.1.6 Research IssuesResearchers are interested in various areas of wireless sensor network, which include the design, implementation and operation.These include hardware,software and middle-ware,which means primitives between the software and the hardware.As the WSNs are generally deployed in the resources-constrained environments with battery operated nodes,the researchers are mainly focus on the issues of energy optimization, coverage areas improvement,errors reduction,sensor network application,data security,sensor node mobility, and data packet routing algorithm among the sensors.In literature, a large group of researchers devoted a great amount of effort in the WSN.They focused in various areas, including physical property,sensor training,security through intelligent node cooperation, medium access,sensor coverage with random and deterministic placement, object locating and tracking, sensor location determination,addressing,energy efficient broadcasting and active scheduling,energy conserved routing,connectivity,data dissemination and gathering,sensor centric quality of routing, topology control and maintenance, etc.REFERENCE[1] G . 5 . Cheung , J . Y . M , Azzi , 0 . Intelligenc in building : the prtential advanced modelling Loveday . D . L . Virk . Automation in Construction . 1997:447-461.[2] Kirill Yelizarov v . home security System . Microchip Technology InC .1998:44-48.[3] B.D.Moore. Tradeoffs in selecting IC temperature sensors. Microprocessors and Microsystems, 1999, 23:181-184.[4] AT89C51 DATA SHEEP Philips Semiconductors 1999:55-58.[5]Yang. Y., Yi. J., Woo, Y.Y., and Kim. B·Optimum design for linearity and efficiency of microwave Doherty amplifier using a new load matching technique, Microw. J., 2001, 44:20–36.采用红外传感器网络对移动目标的计数作者Chi-Keung Ki摘要近来，无线传感器网络(WSN)已经成为一个热点的研究方向。
传感器英语
当这些设备部署在一个广泛的地理区域时，它们可以收集关于环 境的信息，并通过形成一个分布式通信网络，既无线传感器网络 （WSM）有效地协作处理这些信息，如图1.1所示。
A WSN is a special case of an ad-hoc wireless network, and assumes a multi-hop communication framework with no common infrastructure, where the sensors spontaneously cooperate to deliver information by forwarding packets from a source to a destination.
无线传感器网络
WIRELESS SENSOR NETWORKS
vocabulary
sensor
[ˈsensə(r)]
deploy
[dɪˈplɔɪ]
collaborate
[kəˈlæ bəreɪt]
illustrate
[ˈɪləstreɪt]
status
[ˈsteɪtəs]
无线传感网络
无线传感网络综述张梓轩Survey on Wireless Sensor NetworkZhang ZixuanAbstract Wireless sensor network has a wide application future. Both academia and industries are very interested in it. The paper describes the basic concept and characteristics for sensor network. Then the network protocol architecture is introduced. Detailed progress in data link layer protocol, network routing protocol is also introduced. Besides, this paper also gives a brief review of recent progress in wireless sensor network applications.摘要无线传感网络具有广阔的应用前景，因此受到学术界和工业界的广泛重视。
介绍了无线传感器网络的基本概念以及无线传感网的主要特点。
总结了网络协议体系结构框架，重点介绍了数据链路层MAC协议和网络层路由协议。
本文还对目前无线传感网的一些应用做了简要的介绍。
关键词无线传感网特点体系结构数据链路协议网络协议应用1简介无线传感网络（WSN）是一个涉及传感器技术、嵌入式计算技术、现代网络及无线通信技术、分布式信息处理等学科的前沿热点研究领域，目的是实时地监测、感知和采集节点部署区域中观察者感兴趣的各种信息（如温度、湿度、噪音等），并将这些信息通过自组织无线通信网络[1]发送出去。
一个典型的无线传感网络的系统架构包括分布式传感器节点群、接收发送器汇聚节图1 无线网络的系统架构无线传感网络有十分广阔的应用前景，在军事国防、环境监测、危险区域远程控制等许多重要领域都有潜在的使用价值。
传感器技术外文文献及中文翻译
传感器技术外文文献及中文翻译引言传感器是现代检测技术的重要组成部分，它能将物理量、化学量等非电信号转换为电信号，从而实现检测和控制。
传感器广泛应用于工业、医疗、军事等领域中，如温度、湿度、气压、光强度等参数检测。
随着科技的发展，传感器不断新型化、微型化和智能化，已经涵盖了人体所有的感官，开启了大规模的物联网与智能化时代。
本文将介绍几篇与传感器技术相关的外文文献，并对其中较为重要的内容进行中文翻译。
外文文献1标题“Flexible Sensors for Wearable Health: Why Materials Matter”作者Sarah O’Brien, Michal P. Mielczarek, and Fergal J. O’Brien文献概述本文主要介绍了柔性传感器在可穿戴健康监测中的应用，以及传感材料的选择对柔性传感器性能的影响。
文章先介绍了柔性传感器的基本工作原理和常见的柔性传感材料，然后重点探讨了传感材料对柔性传感器灵敏度、稳定性、响应速度等性能的影响。
最后，文章提出未来柔性传感器材料需满足的性能要求，并对可能的研究方向和应用进行了展望。
翻译摘要柔性传感器是可穿戴健康监测中重要的成分，通过将身体状态转化为电信号进行检测。
选择合适的传感材料对柔性传感器产品的成本、性能及标准化有着面向未来的影响。
本文对柔性材料的常见种类 (如: 聚合物、金属、碳复合材料等) 进行了介绍，并重点探讨了传感材料选择的影响因素，如对柔性传感器的灵敏度、特异性和响应时间等。
此外，文章还探讨了柔性传感器的性能要求和建议未来的技术方向。
外文文献2标题“Smart sensing system for precision agriculture”作者Olivier Strauss, Lucas van der Meer, and Benoit Figliuzzi文献概述本文主要介绍智能传感系统在精准农业中的应用。
一种新的无线传感器混合网络系统设计及验证
一种新的无线传感器混合网络系统设计及验证陈三风;陈全义;胡涛【摘要】为了验证基于弹簧粒子模型及其衍生定位算法的性能,本文设计了一种新的无线传感器混合网络系统,并进行了相关的实验验证.本文的混合网络是基于CC2431传感器节点和智能手机高级节点组成的混合网络,其中CC2431用于数据采集与节点定位等,智能手机iPhone网络用于传达任务、获取数据以及数据显示.实验研究结果表明,本混合网络系统是稳定可靠的,且系统的测距和定位算法的精度较高,网络的路由算法稳定可靠,可满足一般机器人导航等无线传感器网络应用场景.【期刊名称】《深圳信息职业技术学院学报》【年(卷),期】2018(016)002【总页数】6页(P30-35)【关键词】无线传感器网络;弹簧粒子模型定位算法;CC2431;网络节点;混合网络【作者】陈三风;陈全义;胡涛【作者单位】深圳信息职业技术学院信息技术研究所,广东深圳 518172;深圳市可视媒体处理与传输重点实验室,广东深圳 518172;深圳信息职业技术学院信息技术研究所,广东深圳 518172;深圳市可视媒体处理与传输重点实验室,广东深圳518172;深圳信息职业技术学院信息技术研究所,广东深圳 518172;深圳市可视媒体处理与传输重点实验室,广东深圳 518172【正文语种】中文【中图分类】TP212.9;TN929.5前言目前，大部分无线传感器网络定位算法[1-3]的计算复杂度、通信复杂度和时间复杂度会随着网络规模的扩大而大大增加, 这会导致大规模网络系统瘫痪。
大规模无线传感器网络对网络节点的计算复杂度要求非常高，有关学者提出了基于质量-弹簧模型的定位算法[4]，其要求网络中每个节点都具有测距能力，网络中没有锚节点，比较灵活，但是其计算复杂度比较高、收敛速度较慢、定位精度也不高。
对此，有关研究者提出一种基于弹簧粒子网络模型的定位算法[5-6] (localization algorithm based on a spring particle model，LASPM)。
压缩感知在无线传感网络的应用综述
压缩感知在无线传感网络的应用综述包明杰;张浩然;王妃【摘要】随着信息技术的发展,近些年压缩感知技术格外引人瞩目,在图像视频编码、雷达及微波辐射成像、气象卫星、图像加密、物联网等领域展现出强大的功能与发展前景.首先介绍了压缩感知在无线传感网络领域的发展及研究现状,然后从压缩感知仿真实验和实例、压缩感知的测量方案、压缩感知的解压缩方案、压缩感知在无线传感网络的具体应用四个方面阐明了压缩感知在无线传感网络领域的优势,最后对压缩感知的前景进行了展望.【期刊名称】《微型机与应用》【年(卷),期】2016(035)014【总页数】3页(P16-18)【关键词】压缩感知;无线传感网络;数据压缩【作者】包明杰;张浩然;王妃【作者单位】浙江师范大学数理与信息工程学院,浙江金华321000;浙江师范大学数理与信息工程学院,浙江金华321000;浙江师范大学数理与信息工程学院,浙江金华321000【正文语种】中文【中图分类】TP3;TP212近年来，无线传感器网络(Wireless Sensor Networks, WSNs)得到很大的关注，它使得人们与这个世界进行远程交互的能力得到提升[1-3]。
但是该项技术在实现方面遇到一些问题。
增加的节点数量使得通信路线变得异常复杂，甚至导致无法正常工作；传感器节点的单价并没有降到一个可以接受的范围之内；电池的续航时间不够长(在较理想的情况下正常运行也只能工作数月)。
不过随着越来越多的无线传感网络产品上市，这些问题正在逐步解决。
人们对数据需求的剧增，使信息技术面临着巨大考验。
模拟化的现实世界和数字化的信号处理工具，导致信号的采集必须从获取模拟信号入手，然后再进行数字化处理。
但信号的数字化会使得数据量变得十分巨大，若不对其进行有效的压缩就难以得到实际应用。
在传统采样过程中,采样频率要求不得低于信号最高频率的2倍[4]。
数字图像和视频需求的增加，使得数据采集量剧增，存储和传输的代价变得十分高昂。
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A Survey on Sensor Networks传感器网络调查摘要在这篇文章中主要介绍传感器网络的当前发展水平并根据相关协议栈部分讨论解决方案。
本文还指出了开放性的研究问题和这一领域的发展。
介绍最近无线通信的发展使低成本，低功耗，多功能传感器，节点体积小的短距离通信得到发展。
这些微型传感器节点利用传感器网络的理念，由传感、数据处理和交流组件组成。
传感器网络相对于传统的传感器来说是一个重大的改进。
一个传感器网络由大量的传感器节点组成。
传感器节点的位置不是设计好的或预定的。
允许随机排列。
另一方面,这也意味着传感器网络协议和算法必须具备自组织功能。
传感器的另一个独特之处是网络传感器的节点之间互相合作，配备机载传感器节点处理器。
他们只使用本地处理进行简单的计算和部分数据处理，而不发送负责融合的原始数据节点。
以上描述的这些特性能够确保传感器网络的应用范围足够宽。
一些应用的区域包括卫生、军事、家庭。
在军事方面，传感器网络的快速部署、自组织和容错特性能够对军事指挥、控制、通信、计算机、情报、监视、侦察和目标系统提供巨大的帮助。
在卫生方面，也可以放置传感器节点监控病人并帮助残疾人。
还有其他一些商业应用包括库存管理、产品质量监控和灾区监控。
SENSOR NETWORKS COMMUNICATION ARCHITECTURE传感器网络通信体系结构传感器节点通常是分散在一个传感器如图1所示。
这些分散的传感器节点有收集数据的能力并且能把数据送回汇点。
数据路由通过多次反射回到汇点如图1所示。
汇点可能与任务管理器通过互联网或卫星节点进行通信。
传感器网络的设计如图1会受到许多因素的影响，包括容错,可扩展性、生产成本、操作环境、传感器网络拓扑结构、硬件限制,传播媒体和功耗。
DESIGN FACTORS设计因素本文谈到许多学者处理设计因素的方法。
然而,这些研究没有一个能够全面的整合传感器网络和节点设计的所有因素。
这些因素非常重要因为他们是设计一个协议或传感器网络算法的指南。
此外，这些影响因素可以用来比较不同的方案。
容错——一些传感器节点由于缺乏能源,物理伤害或环境干扰可能会导致其失效或成块。
传感器节点的失效不能影响传感器网络的总体任务。
这就是一个可靠性或者说是容错问题。
容错是能维持传感器网络的功能而不带有任何由于传感器节点失效而产生的中断[1,2]。
可靠性Rk（t）或一个传感器节点的容错建模[2]通过泊松分布来捕获无错的可能性时间间隔(0,t):R(t)= e -λk t(1)其中λk是传感器节点的失败率，k 和t是时间。
可伸缩性——在所研究的现象中，传感器节点的数量可能达到数百或数千。
根据应用程序也可能会达到一个数百万的极端值。
新的方案必须能够与这个节点数量相匹配。
他们还必须利用高密度传感器网络。
密度的范围可以从几个传感器节点到几百个传感器节点,直径小于10米。
密度μ可以根据[3]μ(R)=(N⋅πR2)/A计算，(2)其中N是分散的传感器节点的数量在A中的范围,R是无线电传输的范围。
基本上,μ(R)给出了传输半径内节点的数量。
生产成本——因为传感器网络由大量节点组成，所以单个节点的成本是衡量网络总成本的重要依据。
如果使用网络的成本比使用传统传感器更贵，那么传感器网络久不是最优的。
因此,每个传感器节点的成本必须足够低。
最先进的技术允许蓝牙无线电系统的价格少于10美元[4]。
一个传感器的成本节点应该远低于1美元才能保证传感器网络被选用。
蓝牙无线电是一种已知的低成本设备，但它却比一个传感器节点的目标价格贵10倍。
硬件的限制——传感器节点由四个基本组件如图2:传感单元、处理单元、收发器单元和动力装置组成。
此外他们还可能与这些部分有关比如定位系统，发电机和移动装置。
传感单元通常由两部分组成：传感器和模拟数字转换器(adc)。
这些传感器根据观察到的现象产生的模拟信号通过ADC转换为数字信号，然后送入处理单元。
处理单元通常与一个小的存储单元有关，控制过程使传感器节点与其他节点合作完成分配的传感任务。
一个收发器单元把它的节点连到网络。
对于一个传感器来说，节点最重要的组成部分是动力装置。
功率单元是能量清除单元就像太阳能电池。
除此之外还有一些其他有关的子单元。
大多数传感器网络路由技术和遥感任务需要高精度的位置信息。
因此，一个传感器节点拥有定位系统是很常见的。
当需要执行分配的任务时，传动装置有时可能需要移动传感器节点。
所有这些单元可能需要适应火柴盒大小的模块[5]。
有的甚至可能会小于一立方厘米[6]，使得光能够保持在空气中悬浮。
除了大小之外，还有其他一些对于传感器节点的严格约束。
这些节点必须[7]能耗极低，能够在体积密度高操作，产量成本低，对环境具有适应性。
传感器网络拓扑——成百上千的节点配置在传感器上。
他们被配置在十分之一英尺之内的范围中[5]。
节点密度可能高达20节点/ m3[8]。
配置大量的节点需要小心的处理拓扑结构维护。
我们需要检查的问题包括拓扑维护和三个阶段的变化:•预部署和部署阶段:传感器节点可以放置成一团或者在传感器域一个接一个的放置。
他们可以放置成从一架飞机下降，部署交给一个炮弹、火箭或导弹，并放置一个接一个的人类或机器人。
•部署后阶段:部署后，拓扑结构变化是由传感器节点位置的变化[5],可达性(由于干扰,噪声、运动障碍等),可用能源，故障和任务的细节产生的。
•重新部署附加节点的阶段:节点发生故障或者任务产生动态变化时，额外的传感器节点可以随时被重新放置。
环境——传感器节点部署的非常密集，有点节点之间距离非常近，有的直接部署在观察对象中。
因此，他们通常远程应用在地理无人值守区域。
他们可能会被应用于大型机械的内部，海洋的底部，生物或化学污染的区域，敌后战线，家里或大型建筑中。
传播媒体——在一个多次反射的传感器网络中，通信节点通过无线媒体连接在一起。
这些链接可以由无线电、红外线或光学媒体形成。
这些网络要确保能全球操作,选择的传播媒介必须在全球范围内可用。
目前许多传感器节点的硬件都基于射频电路设计。
μAMPS无线传感器节点使用蓝牙技术[8]2.4 GHz收发器提供了一个集成的频率合成器。
低功耗传感器装置[9]以一个916 MHz的单通道射频收发器的形式被使用。
无线集成网络传感器(WINS)架构[6]也使用无线电线路来通信。
另一个传感器网络节点之间可用的通信方式是红外线。
红外通信是免授权的并且对电子设备会产生强劲的干扰。
基于红外线的收发器更便宜并且更容易构建。
另一个令人关注的发展是智能微尘[7],它具有自主感知功能，在计算和通信系统中利用光学介质传播。
红外线和光线都对发送方和接收方之间的视线有要求。
功耗——无线传感器节点作为微电子设备只能配备一个有限的电源(< 0.5Ah,1.2 V)。
在某些应用场合中，电力资源鲜有补充的机会。
因此,传感器节点的寿命严重依赖于电池的寿命。
在一个特定的多次反射传感器网络中,每个节点扮演着发起数据和数据路由的双重角色。
几个节点的故障会造成重大的拓扑变化并且可能需要数据包的传送路径切换和网络重组。
因此，电源保护和电源管理也很重要。
正是因为这些原因，研究人员目前专注于设计传感器网络的节能协议和算法。
在传感器领域节点的主要任务是探测事件，迅速执行本地数据处理，然后传输数据。
电力消耗因此可以划分为三大类:传感、通信和数据处理。
PROTOCOL STACK协议栈使用的协议栈汇点和传感器节点图1所示。
这个协议栈结合能源和路由，集成数据和网络协议，无线通信电源高效率的通过介质,促进传感器节点分工合作。
协议栈包括物理层，数据链路层，网络层，传输层，应用层，电源管理平面，移动性管理平面和任务管理平面。
物理层地址需要简单但强大的调制、传输和接收技术。
因为环境嘈杂并且传感器节点可以移动，媒体访问控制(MAC)协议必须具有能量感知功能，能够减少与相邻广播的冲突。
网络层负责路由传输层提供的数据。
在传感器网络应用有需要的时候，传输层可以帮助它维护数据流。
根据传感任务，在应用层会构建和使用不同类型的应用软件。
此外，能源，流动性和任务管理平面监控能源，传送等任务分布在传感器节点之间。
这些平面帮助传感器节点调整传感任务并降低总功耗。
电源管理平面用来控制一个传感器节点如何使用它的能源。
例如，传感器节点在收到一个不是来自本身的消息时可能会关掉它的接收端。
这是为了避免消息的重复。
同样的，当传感器节点的功率很低,传感器节点向周围的其他节点广播自己的低功率，并且不能参与路由消息。
剩下的能源可以用来传感。
移动管理平面用来检测和寄存传感器节点的移动状态，所以返回用户的路线始终被保持着，而且传感器节点可以与周围的传感器节点保持联系。
通过了解周围传感器节点的状态，它可以使自己的能源和任务量保持平衡。
任务管理平面在一个特定的区域平衡和安排传感任务。
并不是所有的传感器节点必须同一时间在该地区执行传感任务。
所以，一些传感器节点执行任务比其他的节点多取决于他们的功率级。
这些管理平面是有用的，所以传感器节点必须能在高效的能源下协同工作，在移动传感器网络路由数据，以及在传感器节点之间共享资源。
THE PHYSICAL LAYER物理层物理层负责选频、载波频率生成、信号检测、调制和数据加密。
到目前为止，915 MHz的工业、科学、和医学(ISM)频带已经广泛的应用于传感器网络。
频率生成和信号检测与底层硬件和收发器的设计有关，超出了本文讨论的范围。
在接下来的讨论中，我们专注于信号的传播效应，功率效率和传感器网络的调制方案。
众所周知，根据能源的需求和安装的复杂性，长距离的无线通信费用很昂贵。
在设计传感器网络的物理层时，能量最小化的重要性体现在传播和衰落效应上。
在一般情况下，最小输出功率发送信号所需的距离d与dn成正比，2 n< =n< 4。
指数n接近四，低洼的天线和近地信道[6]，是典型的传感器网络通信。
这可以归因于局部信号取消了地面的反射线。
传输的测量值[10]表明在天线高度低距离短的时候功率开始以更高的指数级衰减。
试图解决这些问题，最重要的是设计者能意识到内在的差异并能充分利用。
例如，如果节点密度是足够高，在传感器网络中多次反射的传播可以有效地克服跟踪和路径损耗的影响。
同样的，尽管传输损耗和通道容量会限制数据的可靠性，这一现象仍可以被用于空间频率复用。
研究人员目前正在追求物理层的节能方案。
尽管其中的一些话题在学术报告中被讨论到，它仍然是无线传感器网络中一个很大的未开拓领域。
下面是现有的一些想法的讨论。
选择一个好的调制方案是传感器网络可靠通信的关键。
通过对二进制和m进制调制方案的比较发现[8]，m进制的方案通过发送多个比特位/符号可以减少传输持续时间,但会导致电路复杂并增加无线电功率消耗。
这些权衡参数在[8]中得到阐述并得出在功率控制条件下二进制调制方案更节能。