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### 无线传感器网络中多媒体流传输的关键技术与挑战 #### 引言随着嵌入式系统及无线通信技术的进步，无线传感器网络（Wireless Sensor Networks, WSNs）已成为研究热点之一。WSNs由低成本、低功耗的多功能传感器节点（Sensor Nodes, SNs）组成，这些微型传感器具备感知、数据处理和无线通信的能力。它们被广泛部署在监测区域，支持如健康监控、军事战场监视等多种应用场景。近年来，实时多媒体应用的需求逐渐增长，这些应用对于端到端延迟和丢包率有着严格的要求。因此，在无线传感器网络中实现高效可靠的多媒体流传输成为了一个重要的研究方向。 #### 多媒体流传输需求分析多媒体流传输的应用对网络性能提出了较高的要求。主要表现在以下几个方面： 1. **端到端延迟**：多媒体应用通常要求低延迟，尤其是实时视频和音频流传输。 2. **丢包率**：高质量的多媒体流传输需要尽可能减少数据包丢失。 3. **带宽效率**：无线传感器网络资源有限，需要高效的编码和压缩技术来提高带宽利用率。 4. **能量消耗**：由于传感器节点通常由电池供电，能量管理是另一个关键问题。 #### 网络分层机制为了满足上述要求，研究人员针对不同网络层提出了一系列解决方案。 - **应用层**：该层主要关注多媒体数据的格式化、编码和压缩。例如，使用H.264或MPEG-4等标准进行视频编码，以及使用AAC或MP3等格式进行音频压缩。 - **网络层**：这一层着重于路由协议的设计，旨在减少延迟和丢包率。典型的路由协议包括Ad hoc On-demand Distance Vector (AODV) 和 Dynamic Source Routing (DSR) 等。 - **MAC层**：介质访问控制（Medium Access Control, MAC）层负责协调多个节点对共享无线信道的访问。常见的MAC协议有IEEE 802.11（Wi-Fi）、Zigbee等。 #### 层间协作策略除了单一层面上的技术改进外，跨层设计也是优化多媒体流传输性能的重要手段。跨层方法通过在不同的网络层之间共享信息并进行协调，从而提高整个系统的性能。例如： - **应用层与MAC层之间的协作**：应用层可以提供关于多媒体流特性（如数据速率和丢包容忍度）的信息给MAC层，以便后者能够更好地调整其参数以适应应用需求。 - **网络层与MAC层之间的交互**：网络层可以向MAC层发送有关网络拥塞状况的信息，MAC层则可以根据这些信息动态调整信道接入策略。 - **多层协同优化**：综合考虑多个层面上的性能指标，采用联合优化的方法来提高多媒体流传输的整体质量。 #### 错误控制与恢复在无线环境中，信号干扰和噪声可能导致数据包丢失。为了解决这一问题，可以采取以下措施： - **前向纠错（Forward Error Correction, FEC）**：在数据传输之前添加冗余信息，接收端可以利用这些冗余信息纠正传输过程中的错误。 - **自动重传请求（Automatic Repeat reQuest, ARQ）**：当检测到数据包丢失时，接收端会向发送端请求重新发送丢失的数据包。 - **混合ARQ（Hybrid ARQ, HARQ）**：结合了FEC和ARQ的优点，既减少了重传次数，又提高了传输效率。 #### 结论无线传感器网络中多媒体流传输的研究涵盖了多个层面的技术挑战和解决方案。通过在各个网络层上的技术创新以及跨层优化策略的应用，可以显著提升多媒体流传输的质量。未来的研究方向可能包括更加智能的路由算法、自适应的编码与压缩技术以及更高效的能量管理方案等。随着技术的进步，我们可以期待无线传感器网络在多媒体应用领域发挥更大的作用。

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A survey of multimedia streaming in Wireless

Sensor Networks

Satyajayant Misra

†

, Martin Reisslein

‡

, Guoliang Xue

†

Abstract

A wireless sensor network is comprised of small, low powered self-organizing sensor nodes, densely

deployed in the area to be monitored. These networks now support a wide range of applications, such as

health monitoring and surveillance of a military battleﬁeld. Some of these applications may be augmented

by the use of real-time multimedia. Real-time multimedia applications have stringent requirements for

end-to-end delay and loss during network transport. These requirements can be categorized on the basis

of the layer of the network stack at which they arise. In this survey, we classify the mechanisms that

have been proposed for multimedia streaming in wireless sensor networks at each layer of the stack.

Speciﬁcally, we consider the mechanisms operating at the application, network, and MAC layers. We also

review existing cross-layer approaches and propose a few possible cross-layer solutions that combine the

best approaches at each of the layers, to optimize the performance of a given wireless sensor network

for multimedia streaming applications.

Index Terms

Wireless sensor networks, video, audio, streaming, multimedia, aggregation, layering, medium access

control, error control, encoding, compression, cross-layer.

† Satyajayant Misra and Guoliang Xue are with the Department of Computer Science and Engineering, Arizona State

University, Tempe, AZ 85287-8809. Email: {satyajayant, xue}@asu.edu. This research was supported in part by ARO grant

W911NF-04-1-0385 and NSF grants CCF-0431167 and ANI-0312635. The information reported here does not reﬂect the position

or the policy of the federal government.

‡ Martin Reisslein is with the Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287-8809. Email:

reisslein@asu.edu.

I. INTRODUCTION

Recent advances in embedded systems and wireless communications have led to the creation of wireless

sensor networks (WSNs), consisting of low-cost, low-power, multi-functional sensor nodes (SNs), that are

small in size and communicate over short distances [6]. These tiny sensors have sensing, data processing

and communication components and are able to communicate wirelessly over multiple hops with the help

of their neighboring SNs. They can be used for continuous sensing, event detection, location sensing, and

local control of actuators. These nodes run algorithms to self-organize into a network and communicate

among themselves and the BS(s). A WSN is composed of a large number of SNs, deployed densely and

most times randomly in the area being monitored. In general, the SNs in a WSN sense data and convey

them to one or more high power nodes called the sink or the base station (BS) which do most of the

complex processing. The sink (or BS) might be the ﬁnal destination of the data or might act as a hub

from where the data is sent to users over the wired network.

Improved versions of the SNs have the ability to perform local processing computations, sending only

the relevant part of the sensed data. This is an important improvement over their predecessors as it has

been shown that the SNs typically spend most of their energy in transmitting data to the sink (or neighbor

node in case of multi-hop scenario). Thus, in-house processing often results in a reduction in the overall

energy consumption.

Data sent at a periodic rate from a source across the network to a destination is commonly referred to

as a data stream [30]. Data streaming is an inherent feature of a WSNs because most sensors send data

across the network at a periodic rate towards the BSs. Sensors continue sending data so long as they

detect the entity they are observing or there is a requirement for data upstream. With the wireless video

sensor nodes coming of age, a new albeit nascent ﬁeld of research has sprung up, namely exploration

of the possibility of use of video/acoustic sensors to set up WSNs, to monitor and convey data in the

form of only video, only audio, or both video and audio streams. Video and audio streams are often

referred to as real-time data streams. These streams are similar to the data stream with two important

differences, they are larger in size and require various guarantees for bandwidth, delay and jitter from

the network. Streaming of real-time data in the WSNs expands the realm of usability of the currently

available SNs. Real-time data in the form of sound and/or video, along with the other sensed information

that the WSNs already provide, have the ability to augment the ﬁeld of remote surveillance, structural

monitoring, and a host of other WSN applications.

The ability of WSNs to provide support for video/audio streaming is restricted due to the hardware,

bandwidth, and power limitations of the SNs that make-up the network. Hardware is limited because

the currently available cameras for WSNs cannot be made small in size and image obtained from

them are of low quality having limited use. Even with good image quality, multimedia communication

inherently requires high bandwidth. A WSN is labored to provide enough end-to-end bandwidth for such

communications. In addition, SNs are power-starved as they run on small batteries that have limited

power. In such a scenario, the goals of being able to send multimedia data streams over the network

and maintain/extend the lifetime of the sensors become two diametrically opposite requirements. This

implies that there is a need to deﬁne the requirements of a real-time multimedia communication in a

WSN keeping the abilities of the network in mind. The requirements for multimedia streaming need to

be tuned to the properties of the WSN to ensure that the nodes in the network survive longer and provide

high quality sensed data.

A few applications, such as [13] and [49], have been proposed for multimedia streaming in WSNs.

These applications address some of the issues that need attention for supporting multimedia streaming

in a WSN environment. In most of these applications video/audio sensors are used in conjunction with

other sensors, such as motion, acoustic, heat, and light sensors. These other sensors do the initial sensing

and identify and locate the target. Once the target is located, the video/audio sensors can be used to send

periodic high quality data updates of the target. This mechanism helps in conserving the energy of the

SNs while also improving network lifetime.

The motivation for this survey is to provide an insight into the basic requirements for real-time

multimedia streaming in WSNs and how they have been satisﬁed. We also highlight the issues that

we believe need to be resolved. We approach the issues on a per-layer basis, associating each issue with

the layer of the network stack at which it arises. We classify the solutions proposed at each layer based

on the techniques used, giving their salient features and their merits and demerits. In addition, we also

indicate open issues at each layer.

The rest of this survey is structured as follows: In Section II we describe the characteristics of the

WSN and the SNs that make up the network. In Section III, we present the quality of service (QoS)

requirements for multimedia streaming applications in WSNs. Section IV highlights the requirements

of a multimedia streaming application (MSA) from the application layer perspective, we classify the

approaches proposed and analyze them. In Section V, we highlight the requirements of an MSA from the

network layer perspective and survey the QoS solutions at the network layer. In Section VI, we present

the requirements of an MSA from the MAC layer and we review the schemes. Section VII discusses

cross-layer optimization approaches to solve the issues for supporting MSAs in a WSN. We also propose

a few solutions based on the cross-layer optimization approach. In Section VIII, we conclude our survey

and outline possible directions of research.

II. CHARACTERISTICS OF WIRELESS SENSOR NETWORKS

Even though the WSNs are generally considered to belong to the family of wireless mobile ad hoc

networks they have many characteristics that differentiate them from mobile ad hoc wireless networks.

WSNs are more resource constrained, deployed in a much larger scale, and are data centric. In general,

they consist of low power, multi-functional SNs (e.g., TelosB motes [36]), powered by small irreplaceable

batteries. These SNs are densely deployed and are capable of sensing and conveying data towards the

BS(s). The BS (sink) in a WSN is assumed to have power and memory that are orders of magnitude

more than that of the SNs.

The wireless medium in use is characterized by high path loss, channel fading, interference, noise

disturbances, and bit error rate (BER), these result in the wireless channel having much lesser capacity

than wired channels [24]. The throughput of a wireless channel ﬂuctuates signiﬁcantly owing to its time-

varying characteristics, making long-term bandwidth predictions difﬁcult. In addition, the SNs may be

mobile to various extents. For instance, in some WSNs the sink or the clusterheads may be mobile [51].

Consequently, the topology in a WSN is highly dynamic, resulting in frequent changes to the routes.

The small transmission power of the SNs necessitates multi-hop communication to reach the BS. In fact

the path loss and channel fading characteristic of the wireless medium make multi-hop communication

economical [6]. However, multi-hop communication tends to generate more interference, delay, and both

higher packet loss and error during transmission. Interference and high packet loss rate on the path affect

the bandwidth and delay values of the route.

WSNs have a data centric nature, the data delivery model in use deﬁnes the energy requirements to a

great extent. The data delivery models in WSNs could be categorized as continuous, event driven, query

driven, or hybrid [44]. In the continuous model each SN transmits data periodically. In the event and query

driven model the data transmission is triggered by an event or a query respectively from the BS. The

hybrid model is a combination of both continuous and event or query driven model. The MSAs conform

to the hybrid data model, deﬁned by a combination of continuous and event driven models. In addition,

as a result of the high deployment density, a stimulus is sensed by more than one SN. This results in

highly correlated data containing both spatial and temporal redundancy. Given the high correlation in

data, aggregation is a desired property. Each intermediate SN on the path from a source SN to the BS(s)

can aggregate the data from its neighbors and send it towards the BS(s). The protocols and aggregation

algorithms running on the SNs in the network are required to be simple, while using less memory and

computation power, to be able to execute in the low capacity processing unit of the SNs.

Each WSN uses mechanisms for power control and topology control to ensure longevity of the network.

Power control [25] helps to prolong the lifetime of the SNs by allowing the nodes to transmit at lower

power levels when possible. Topology control [7], [33], prolongs the lifetime of the network by letting a

subset of the nodes to sleep, while others are active. Topology control also reduces interference as lesser

number of nodes are active in any given neighborhood.

Given the outlined characteristics, the important considerations for an application in a WSN become

different from those of the same application in a mobile ad hoc network. Any application for WSNs has

to consider the above characteristics and suitably tune its objectives to provide the end users with the

expected service satisfaction.

III. QUALITY OF SERVICE REQUIREMENTS FOR STREAMING APPLICATIONS

Every application has certain service requirements from the network, these requirements are deﬁned

as its expected Quality of Service (QoS) [32]. The real-time MSAs also have a set of QoS requirements,

primarily aimed at sustaining transmission of high quality data at a high bit-rate. They require strict

end-to-end delay, bandwidth, and jitter guarantees. To ensure this, the requirements of the real-time

applications need to be mapped accurately to related variables in each layer of the stack. We believe

that the system and the protocols need to be aware of this mapping, commonly referred to as cross-layer

mapping, to attempt to optimize system performance.

Real-time streaming trafﬁc intrinsically has multiple priorities. For instance, in a video stream some

packets contain the intra-frames (I), these have the highest priority, because the application has lower

tolerance to delay of the I frames, while the predictor frames (P ) or the bi-directional (B) frames

have a lesser priority, as the application can recover from some delay in these cases. In addition, encoded

multimedia data being generally larger in size than other sensed data, requires more memory for buffering,

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