无线多媒体传感器网络技术综述

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"这篇文档是一份关于无线多媒体传感器网络（Wireless Multimedia Sensor Networks，WMSNs）的调查报告，由Ian F. Akyildiz、Tommaso Melodia和Kaushik R. Chowdhury等人撰写，来自美国佐治亚理工学院的宽带和无线网络实验室。文章对WMSNs领域的最新进展、算法、协议和硬件进行了全面概述，并讨论了未来的研究挑战。" 在当前的科技环境中，由于低功耗CMOS摄像头和麦克风等低成本硬件的普及，无线多媒体传感器网络得到了快速发展。这些网络由能够无处不在地捕获环境中的多媒体内容（如视频流、音频流、静态图像和标量传感器数据）的无线设备组成。本文的主要内容包括以下几个方面： 1. **算法与协议的状态**: 文章回顾了用于WMSNs的传输、处理和管理多媒体数据的算法与通信协议。这涵盖了数据采集、编码、压缩、路由、多播和能量效率等方面的技术。 2. **硬件发展**: 报告详细列出了现有的商业硬件以及研究原型，分析了它们的特点和局限性。硬件部分可能涉及传感器节点设计、能源管理系统、无线通信模块和多媒体处理单元等。 3. **网络架构探讨**: 探讨了各种WMSNs的架构设计，如星型、网状、树形等，分析了它们的优缺点。这些架构对系统性能、扩展性和可靠性有显著影响。 4. **开放研究问题**: 文章深入讨论了WMSNs领域目前面临的挑战，如带宽限制、能源效率、延迟敏感性、安全性和隐私保护等，这些是推动该领域进一步发展的关键问题。 5. **解决方案与未来方向**: 作者列举了一些已有的解决方案，并对未来的研究方向给出了展望，可能涉及到新型的传感器技术、自适应的多媒体编码策略、网络协同机制以及优化的能效管理方法。通过这篇综述，读者可以了解到WMSNs的最新发展动态，为相关领域的研究者和工程师提供了深入理解和探索这一技术的基础。同时，它也为未来的研究工作指明了可能的路径和关注点。

application needs, which may result in energy sav-

ings since by storing it locally, the multimedia

content does not need to be wirelessly relayed to

remote locations. The wireless gateway is also

connected to a central sink, which implements the

software front-end for netw ork querying and

tasking. The second cloud represents a single-tiered

clustered architecture of heterogeneous sensors

(only one cluster is depicted). Video, audio, and

scalar sensors relay data to a central clusterhead,

which is also in charge of performing intensive mul-

timedia processing on the data (processing hub).

The clusterhead relays the gathered content to the

wireless gateway and to the storage hub. The last

cloud on the right represents a multi-tiered network,

with heterogeneous sensors. Each tier is in charge

of a subset of the functionalities. Resource-con-

strained, low-power scalar sensors are in charge

of performing simpler tasks, such as detecting

scalar physical measurements, while resource-rich,

high-power devices are responsible for more com-

plex tasks. Data processing and storage can be

performed in a distributed fashion at each diﬀerent

tier.

3.2. Single -tier vs. multi-tier sensor deployment

One possible approach for designing a multime-

dia sensor application is to deploy homogeneous

sensors and program each sensor to perform all pos-

sible application tasks. Such an approach yields a

ﬂat, single-tier network of homogeneous sensor

nodes. An alternative, multi-tier approach is to use

heterogeneous elem ents [69]. In this approach,

resource-constrained, low-power elem ents are in

charge of performing simpler tasks, such as detect-

ing scalar physical measurements, while resource-

rich, high-power devices take on more complex

tasks. For instance, a survei llance application can

rely on low-ﬁdelity cameras or scalar acoustic sen-

sors to perform motion or intrusion detection, while

high-ﬁdelity cameras can be woken up on-demand

for object recognition and tracking. In [68],a

multi-tier architecture is advocated for video sensor

networks for surveillance applications. The architec -

ture is based on multiple tiers of cameras with diﬀer-

ent functionalities, with the lower tier constituted of

low-resolution imaging sensors, and the higher tier

composed of high-end pan-tilt-zoom cameras. It is

argued, and shown by means of experiments, that

such an architecture oﬀers considerable advantages

with respect to a single-tier architecture in terms

of scalability, lower cost, better coverage, higher

functionality, and better reliability.

3.3. Coverage

In traditional WSNs, sensor nodes collect infor-

mation from the environment within a pre-deﬁned

sensing range, i.e., a roughly circular area deﬁned

by the type of sensor being used.

Multimedia sensors generally have larger sensing

radii and are also sensitive to the direction of data

acquisition. In particular, cameras can capture

images of objects or parts of regions that are not

necessarily close to the camera itself. However, the

image can obviously be captured only when there

is an unobstructed line-of-sight between the event

and the sensor. Furthermore, each multimedia

sensor/camera perceives the environment or the

observed object from a diﬀerent and unique view-

point, given the diﬀerent orientations and positions

of the cameras relative to the observed event or

region. In [118], a preliminary investigation of the

coverage problem for video sensor networks is con-

ducted. The concept of sensing range is replaced

with the camera’s ﬁeld of view , i.e., the maximum

volume visible from the camera. It is also shown

how an algorithm designed for traditional sensor

networks does not perform well with video sensors

in terms of coverage preservation of the monitored

area.

4. Multimedia sensor hardware

In this section, we review and classify existing

imaging, multimedia, and processing wireless

devices that will ﬁnd application in next generation

wireless multimedia sensor networks. In particular,

we discuss existing hardware, with a particular

emphasis on video capturing devices, review existing

implementations of multimedia sensor networks,

and discuss current possibilities for energy harvest-

ing for multimedia sensor devices.

4.1. Enabli ng hardware platforms

High-end pan-tilt-zoom cameras and high resolu-

tion digital cameras are widely available on the mar-

ket. However, while such sophisticated devices can

ﬁnd application as high-quality tiers of multimedia

sensor networks, we concentrate on low-cost, low-

energy consumption imaging and processing devices

that will be densely deployed and provide detailed

I.F. Akyildiz et al. / Computer Networks 51 (2007) 921–960 927

visual information from multiple disparate view-

points, help overcoming occlusion eﬀects, and thus

enable enhanced interaction with the environment.

4.1.1. Low-resolution imaging motes

The recent availability of CMOS imaging sensors

[61] that capture and process an optical image within

a single integrated chip, thus eliminating the need for

many separate chips required by the traditional

charged-coupled device (CCD ) technology, has

enabled the massive deployment of low-cost visual

sensors. CMOS image sensors are already in many

industrial and consumer sectors, such as cell phones,

personal digital assistants (PDAs), consumer and

industrial digital cameras. CMOS image quality is

now matching CCD quality in the low- and mid-

range, while CCD is still the technology of choice

for high-end image sensors. The CMOS technology

allows integrating a lens, an image sensor and image

processing algorithms, including image stabilization

and image compression, on the same chip. With

respect to CCD, cameras are smaller, lighter, and

consume less power. Hence, they constitute a suit-

able technology to realize imaging sensors to be

interfaced with wireless motes.

However, existing CMOS imagers are still

designed to be interfaced with computationally rich

host devices, such as cell phones or PDAs. For this

reason, the objective of the Cyclops module [103] is

to ﬁll the gap between CMOS cameras and compu-

tationally constrained devices. Cyclops is an elec-

tronic interface between a CMOS camera module

and a wireless mote such as MICA2 or MICAz,

and contains programmable logic and memory for

high-speed data communication. Cyclops consists

of an imager (CMOS Agilent ADCM-1700 CIF

camera), an 8-bit ATMEL ATmeg a128L microcon-

troller (MCU), a complex programmable logic

device (CPLD), an external SRAM and an external

Flash. The MCU controls the imager, conﬁgures its

parameters, and performs local processing on the

image to produce an inference. Since image capture

requires faster data transfer and address generation

than the 4 MHz MCU used, a CPLD is used to pro-

vide access to the high-speed clock. Cyclops ﬁrm-

ware is written in the nesC language [48], based on

the TinyOS libraries. The module is connected to

a host mote to which it provides a high level inter-

face that hides the complexity of the imaging device

to the host mote. Moreover, it can perform simple

inference on the image data and present it to the

host.

Researchers at Carnegie Mellon University are

developing the CMUcam 3, which is an embedded

camera endowed with a CIF Resolution (352 · 288)

RGB color sensor that can load images into memory

at 26 frames per second. CMUcam 3 has software

JPEG compression and has a basic image manipula-

tion library, and can be interface with an 802.15.4

compliant TelosB mote [6].

In [41], the design of an integrated mote for wire-

less image sensor networks is described. The design

is driven by the need to endow motes with adequate

processing power and memory size for image sens-

ing applications. It is argued that 32-bit processors

are better suited for image processing than their 8-

bit counterpart, which is used in most existing

motes. It is shown that the time needed to perform

operations such as 2-D convolution on an 8-bit pro-

cessor such as the ATMEL ATmega128 clocked at

4 MHz is 16 times higher than with a 32-bit

ARM7 device clocked at 48 MHz, while the power

consumption of the 32-bit processor is only six times

higher. Hence, an 8-bit processor turns out to be

slower and more energy-consuming. Based on these

premises, a new image mote is developed based on

an ARM7 32-bit CPU clocked at 48 MHz, with

external FRAM or Flash memory, 802.15.4 compli-

ant Chipcon CC2420 radio, that is interfaced with

mid-resolution ADCM-1670 CIF CMOS sensors

and low-resolution 30 · 30 pixel optical sensors.

The same conclusion is drawn in [81], where the

energy consumption of the 8-bit Atmel AVR pro-

cessor clocked at 8 MHz is compared to that of

the PXA255 32-bit Intel processor, embedded on a

Stargate platform [10] and clocked at 400 MHz.

Three representative algorithms are selected as

benchmarks, i.e., the cyclic redundancy check, a

ﬁnite impulse response ﬁlter, and a fast Fourier

transform. Surprisingly, it is shown that even for

such relatively simple algorithms the energy con-

sumption of an 8-bit processor is between one and

two orders of magnitude higher.

4.1.2. Medium-resolution imaging motes based on the

Stargate platform

Intel has developed several prototypes that con-

stitute important building platform for WMSN

applications. The Stargate board [10] is a high-per-

formance processing platform designed for sensor,

signal processing, control, robotics, and sensor net-

work applications. It is designed by Intel and pro-

duced by Crossbow. Stargate is based on Intel’s

PXA-255 XScale 400 MHz RISC processor, which

928 I.F. Akyildiz et al. / Computer Networks 51 (2007) 921–960

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