剑桥无线传感器网络详解：技术挑战与应用深度

Cambridge.University.Press.

需积分: 10 139 浏览量更新于2024-07-29 收藏 2.55MB PDF 举报

"剑桥大学出版社的《无线传感器》一书深入探讨了无线传感器网络在当今信息技术领域中的前沿进展与应用。这本书的核心价值在于其对无线传感器网络这一快速发展的技术进行了全面而有组织的概述，旨在满足研究者、设计师以及工程师们对于解决该领域核心挑战的需求。无线传感器网络（Wireless Sensor Networks, WSNs）是连接虚拟世界与物理世界的关键桥梁，它们的应用范围广泛，涵盖工业过程控制、安全监控、环境感知、结构健康监测等多个重要领域。这些系统面临的三大主要挑战——能源效率、鲁棒性和自主性，是通过多层网络技术进行深入探讨的。书中详细涵盖了以下关键主题： 1. 网络部署：研究如何在特定环境中有效地部署传感器节点，确保覆盖范围和通信效果。 2. 定位技术：探讨如何通过无线信号来精确地确定传感器节点的位置，这对于许多应用如环境监测和资产追踪至关重要。 3. 时间同步：保持所有节点的时间同步对于数据一致性至关重要，尤其是在分布式系统中。 4. 无线通信特性：包括频谱利用率、抗干扰能力等无线通信技术的基础知识。 5. 介质访问控制：如何管理多个节点共享无线资源，避免冲突并提高整体网络性能。 6. 拓扑控制：通过调整网络结构来优化性能和能耗，如簇化或自组织网络架构。 7. 路由算法：设计高效的路由协议，确保数据从源到目的地的可靠传输。 8. 数据为中心的技术：强调数据处理、压缩和存储在无线传感器网络中的重要性。 9. 传输协议：选择合适的协议来支持不同场景下的数据传输，如实时性要求高的应用。《无线传感器》不仅适合研究人员寻求创新算法和协议的灵感，也为工程实践者提供了实施集成解决方案的实用指南。书中还包含丰富的习题，可供研究生学习网络理论课程时使用，有助于读者深化理解和实际操作能力的提升。作者Bhaskar Krishnamachari是一位教授，他的专业知识和经验使得本书成为无线传感器网络领域的权威参考资源。"

Networked wireless sensor devices 3

Sensors

Processor

GPS

Memory

Radio transceiver

Power source

Figure 1.2 Schematic of a basic wireless sensor network device

constraints, the embedded processors are often significantly constrained in

terms of computational power (e.g., many of the devices used currently

in research and development have only an eight-bit 16-MHz processor).

Due to the constraints of such processors, devices typically run specialized

component-based embedded operating systems, such as TinyOS. However,

it should be kept in mind that a sensor network may be heterogeneous and

include at least some nodes with significantly greater computational power.

Moreover, given Moore’s law, future WSN devices may possess extremely

powerful embedded processors. They will also incorporate advanced low-

power design techniques, such as efficient sleep modes and dynamic voltage

scaling to provide significant energy savings.

2. Memory/storage: Storage in the form of random access and read-only mem-

ory includes both program memory (from which instructions are executed

by the processor), and data memory (for storing raw and processed sensor

measurements and other local information). The quantities of memory and

storage on board a WSN device are often limited primarily by economic

considerations, and are also likely to improve over time.

3. Radio transceiver: WSN devices include a low-rate, short-range wireless

radio (10–100 kbps, <100 m). While currently quite limited in capability too,

these radios are likely to improve in sophistication over time – including

improvements in cost, spectral efficiency, tunability, and immunity to noise,

fading, and interference. Radio communication is often the most power-

intensive operation in a WSN device, and hence the radio must incorporate

energy-efficient sleep and wake-up modes.

4. Sensors: Due to bandwidth and power constraints, WSN devices primarily

support only low-data-rate sensing. Many applications call for multi-modal

sensing, so each device may have several sensors on board. The specific

4 Introduction

sensors used are highly dependent on the application; for example, they may

include temperature sensors, light sensors, humidity sensors, pressure sensors,

accelerometers, magnetometers, chemical sensors, acoustic sensors, or even

low-resolution imagers.

5. Geopositioning system: In many WSN applications, it is important for all

sensor measurements to be location stamped. The simplest way to obtain

positioning is to pre-configure sensor locations at deployment, but this may

only be feasible in limited deployments. Particularly for outdoor operations,

when the network is deployed in an ad hoc manner, such information is most

easily obtained via satellite-based GPS. However, even in such applications,

only a fraction of the nodes may be equipped with GPS capability, due to

environmental and economic constraints. In this case, other nodes must obtain

their locations indirectly through network localization algorithms.

6. Power source: For flexible deployment the WSN device is likely to be

battery powered (e.g. using LiMH AA batteries). While some of the nodes

may be wired to a continuous power source in some applications, and energy

harvesting techniques may provide a degree of energy renewal in some cases,

the finite battery energy is likely to be the most critical resource bottleneck

in most WSN applications.

Depending on the application, WSN devices can be networked together in a

number of ways. In basic data-gathering applications, for instance, there is a node

referred to as the sink to which all data from source sensor nodes are directed.

The simplest logical topology for communication of gathered data is a single-hop

star topology, where all nodes send their data directly to the sink. In networks

with lower transmit power settings or where nodes are deployed over a large area,

a multi-hop tree structure may be used for data-gathering. In this case, some nodes

may act both as sources themselves, as well as routers for other sources.

One interesting characteristic of wireless sensor networks is that they often

allow for the possibility of intelligent in-network processing. Intermediate nodes

along the path do not act merely as packet forwarders, but may also examine and

process the content of the packets going through them. This is often done for the

purpose of data compression or for signal processing to improve the quality of

the collected information.

1.3 Applications of wireless sensor networks

The several envisioned applications of WSN are still very much under active

research and development, in both academia and industry. We describe a few

Applications of wireless sensor networks 5

applications from different domains briefly to give a sense of the wide-ranging

scope of this field:

1. Ecological habitat monitoring: Scientific studies of ecological habitats (ani-

mals, plants, micro-organisms) are traditionally conducted through hands-on

field activities by the investigators. One serious concern in these studies

is what is sometimes referred to as the “observer effect” – the very pres-

ence and potentially intrusive activities of the field investigators may affect

the behavior of the organisms in the monitored habitat and thus bias the

observed results. Unattended wireless sensor networks promise a cleaner,

remote-observer approach to habitat monitoring. Further, sensor networks,

due to their potentially large scale and high spatio-temporal density, can

provide experimental data of an unprecedented richness.

One of the earliest experimental deployments of wireless sensor networks

was for habitat monitoring, on Great Duck Island, Maine [130]. A team of

researchers from the Intel Research Lab at Berkeley, University of California

at Berkeley, and the College of the Atlantic in Bar Harbor deployed wireless

sensor nodes in and around burrows of Leach’s storm petrel, a bird which

forms a large colony on that island during the breeding season. The sensor-

network-transmitted data were made available over the web, via a base station

on the island connected to a satellite communication link.

2. Military surveillance and target tracking: As with many other information

technologies, wireless sensor networks originated primarily in military-related

research. Unattended sensor networks are envisioned as the key ingredient

in moving towards network-centric warfare systems. They can be rapidly

deployed for surveillance and used to provide battlefield intelligence regarding

the location, numbers, movement, and identity of troops and vehicles, and for

detection of chemical, biological, and nuclear weapons.

Much of the impetus for the fast-growing research and development

of wireless sensor networks has been provided though several programs

funded by the US Defense Advanced Research Projects Agency (DARPA),

most notably through a program known as Sensor Information Technology

(SensIT) [188] from 1999 to 2002. Indeed, many of the leading US researchers

and entrepreneurs in the area of wireless sensor networks today have been

and are being funded by these DARPA programs.

3. Structural and seismic monitoring: Another class of applications for sensor

networks pertains to monitoring the condition of civil structures [231]. The

structures could be buildings, bridges, and roads; even aircraft. At present the

health of such structures is monitored primarily through manual and visual

6 Introduction

inspections or occasionally through expensive and time-consuming technolo-

gies, such as X-rays and ultrasound. Unattended networked sensing techniques

can automate the process, providing rich and timely information about incip-

ient cracks or about other structural damage. Researchers envision deploying

these sensors densely on the structure – either literally embedded into the

building material such as concrete, or on the surface. Such sensor networks

have potential for monitoring the long-term wear of structures as well as

their condition after destructive events, such as earthquakes or explosions.

A particularly compelling futuristic vision for the use of sensor networks

involves the development of controllable structures, which contain actuators

that react to real-time sensor information to perform “echo-cancellation" on

seismic waves so that the structure is unaffected by any external disturbance.

4. Industrial and commercial networked sensing: In industrial manufacturing

facilities, sensors and actuators are used for process monitoring and control.

For example, in a multi-stage chemical processing plant there may be sensors

placed at different points in the process in order to monitor the temperature,

chemical concentration, pressure, etc. The information from such real-time

monitoring may be used to vary process controls, such as adjusting the amount

of a particular ingredient or changing the heat settings. The key advantage

of creating wireless networks of sensors in these environments is that they

can significantly improve both the cost and the flexibility associated with

installing, maintaining, and upgrading wired systems [131]. As an indication

of the commercial promise of wireless embedded networks, it should be noted

that there are already several companies developing and marketing these

products, and there is a clear ongoing drive to develop related technology

standards, such as the IEEE 802.15.4 standard [94], and collaborative industry

efforts such as the Zigbee Alliance [244].

1.4 Key design challenges

Wireless sensor networks are interesting from an engineering perspective,

because they present a number of serious challenges that cannot be adequately

addressed by existing technologies:

1. Extended lifetime: As mentioned above, WSN nodes will generally be

severely energy constrained due to the limitations of batteries. A typical alka-

line battery, for example, provides about 50 watt-hours of energy; this may

translate to less than a month of continuous operation for each node in full

active mode. Given the expense and potential infeasibility of monitoring and

Key design challenges 7

replacing batteries for a large network, much longer lifetimes are desired.

In practice, it will be necessary in many applications to provide guarantees

that a network of unattended wireless sensors can remain operational without

any replacements for several years. Hardware improvements in battery design

and energy harvesting techniques will offer only partial solutions. This is the

reason that most protocol designs in wireless sensor networks are designed

explicitly with energy efficiency as the primary goal. Naturally, this goal

must be balanced against a number of other concerns.

2. Responsiveness: A simple solution to extending network lifetime is to operate

the nodes in a duty-cycled manner with periodic switching between sleep and

wake-up modes. While synchronization of such sleep schedules is challenging

in itself, a larger concern is that arbitrarily long sleep periods can reduce

the responsiveness and effectiveness of the sensors. In applications where

it is critical that certain events in the environment be detected and reported

rapidly, the latency induced by sleep schedules must be kept within strict

bounds, even in the presence of network congestion.

3. Robustness: The vision of wireless sensor networks is to provide large-

scale, yet fine-grained coverage. This motivates the use of large numbers of

inexpensive devices. However, inexpensive devices can often be unreliable

and prone to failures. Rates of device failure will also be high whenever

the sensor devices are deployed in harsh or hostile environments. Protocol

designs must therefore have built-in mechanisms to provide robustness. It is

important to ensure that the global performance of the system is not sensitive

to individual device failures. Further, it is often desirable that the performance

of the system degrade as gracefully as possible with respect to component

failures.

4. Synergy: Moore’s law-type advances in technology have ensured that device

capabilities in terms of processing power, memory, storage, radio transceiver

performance, and even accuracy of sensing improve rapidly (given a fixed

cost). However, if economic considerations dictate that the cost per node

be reduced drastically from hundreds of dollars to less than a few cents, it

is possible that the capabilities of individual nodes will remain constrained

to some extent. The challenge is therefore to design synergistic protocols,

which ensure that the system as a whole is more capable than the sum of

the capabilities of its individual components. The protocols must provide

an efficient collaborative use of storage, computation, and communication

resources.

5. Scalability: For many envisioned applications, the combination of fine-

granularity sensing and large coverage area implies that wireless sensor

剩余214页未读，继续阅读

s466501480

粉丝: 1
资源: 2

剑桥无线传感器网络详解：技术挑战与应用深度

邦纳 Wireless Sensors.pdf

Event-Driven Analog-to-Digital Converter for Ultra Low Power Wearable Wireless Biomedical Sensors

ESP8266-Wireless-Sensors:ESP8266 模块用作微型传感器，定期记录数据。 （温度、湿度、气压等）

Power Aware Wireless Sensors-开源

Battery-free RFID-enabled Wireless Sensors

A Novel Complex-Coefficient In-Band Interference Suppression Algorithm for Cognitive Ultra-Wide Band Wireless Sensors Networks

The effects of wireless communication failures on group behavior of mobile sensors

Charging utility maximization in wireless rechargeable sensor networks by charging multiple sensors simultaneously

Symmetric Game for Distributed estimation in Energy Harvesting Wireless Sensor Net-works with Selfish Sensors

Experimental Results with two Wireless Power Transfer Systems.PDF

最新资源

ESP8266-Wireless-Sensors:ESP8266 模块用作微型传感器，定期记录数据。（温度、湿度、气压等）