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A Survey of Wireless Multimedia Sensor Networks

A Survey of Wireless Multimedia Sensor Networks
A Survey of Wireless Multimedia Sensor Networks

A Survey of Wireless Multimedia Sensor Networks

Challenges and Solutions

Mariam AlNuaimi, Farag Sallabi and Khaled Shuaib

Faculty of Information Technology

United Arab Emirates University

United Arab Emirates

Mariam.alnuaimi@uaeu.ac.ae

Abstract— Wireless Multimedia Sensor Networks (WMSN) have recently gained the attention of the research community due to their wide range of applications and the advancement of CMOS cameras. In this survey paper we outline the WMSN applications, discuss their challenges and resource constraints. In addition, this paper investigates the proposed solutions by the research community to overcome challenges and constraints through architecture design and multimedia encoding paradigms. Moreover, some of the deployed examples of WMSN done by different research groups are also discussed. In addition, we provide a detail discussion of the proposed optimization solutions and outline research areas of possible improvements.

Keywords:wireless multimedia sensor networks, applications of WMSN, challenges and resource constraints in WMSNs,.

I.INTRODUCTION

Wireless Sensor Networks (WSNs) have been the focus of many researchers during the last decade due to the advances in low power and low cost hardware (i.e., micro-electro-mechanical systems (MEMS)[1]. A wireless sensor network consists of wirelessly interconnected devices that can interact with each other and with their surrounded environment by controlling and sensing physical parameters [2]. Moreover, continuously growing focus in WSNs can be endorsed to the many new applications which were deployed such as environment control [3], biomedical research [4], intelligent homes, and health applications [1, 5].

During the last few years, Wireless Multimedia Sensor Networks (WMSNs) appeared. WMSNs technology have emerged due to the production of cheap CMOS (Complementary Metal Oxide Semiconductor) cameras and microphones, which can acquire rich media content from the environment like images and videos. WMSN can be defined as networks of wirelessly interconnected sensor nodes equipped with multimedia devices, such as cameras that are capable of retrieving video and audio streams, images, and scalar sensor data [6, 31]. WMSNs are currently being used in several applications as outlined below. A.Multimedia surveillance sensor networks Multimedia surveillance applications are used to detect, recognize and track the objects in order to take appropriate actions. These applications need to continuously capture images in order to monitor certain events [7, 14]. These applications are mainly used for detecting crimes or terrorist attacks [8, 33].

B.Traffic avoidance and control systems

Traffic avoidance applications are used to monitor car traffic and provide traffic routing advice to avoid congestion. M. Jokela [9] proposed a model of three different kinds of cameras to be used in monitoring a traffic situation around a vehicle to detect problems such as a near infrared camera, a thermal imaging system for animal detection, and a regular CCTV camera for ice and snow detection.

C.Advanced health care delivery

Health and care delivery applications are used for patient monitoring and care in remote sites like monitoring patients’ facial expression, respiratory conditions or movement and forward these images to doctors in distant hospitals to make better diagnosis. In [10] a healthcare sensor periodically captures vital signs information (e.g., body temperature, Blood pressure) and sends it to the gateway. Once the information processed by the gateway, it is forwarded to doctors to help them make an initial diagnosis. After that, wireless multimedia sensor nodes used to capture and send back images or videos data to help doctors obtain more detailed information and make final diagnosis.

D.Automated parking advice

Automated parking advice applications keep track of available parking spaces and provide guidance to the drivers to allocate free parking spaces [11, 12].

E.Smart Homes

Smart homes applications used to automate the life of residences. They are usually used to adapt the house environment according to the residence preferences (e.g.,

2011 International Conference on Innovations in Information Technology

lighting or air conditioning, heating) based on detecting the presence of certain persons inside the house [13].

F.Environmental monitoring

Environmental monitoring application used for monitoring remote and unreachable areas over a long period of time. In these applications, energy-efficient operations are particularly important in order to extend monitoring over a long period of time. Most of the time cameras are combined with other types of sensors into a heterogeneous network, so that cameras are triggered only when an event is detected by other lighter sensors used in the network [27].

G.Telepresence systems

Tele-presence systems enable virtual visits to some locations such as museums, galleries or exhibition rooms that are monitored by a set of cameras. These applications provide the user with any current view from any viewing point, and provide him/her with the sense of being physically present at a remote location [15, 28, 29]

The rest of this paper is organized as follows. In section two we introduce challenges and resource constraints in WMSNs. In section three we present in details the effort that has been done by the research community in order to optimize and overcome challenges and resource constraints in WMSNs through architecture and multimedia coding paradigms. In section four, we introduce examples of deployed WMSNs done by different research groups. A detail discussion of the optimization solutions outlined in section three is provided in section five. Finally, we conclude the paper along with future work in section six.

II.WMSN S CHALLENGES AND RESOURCE CONSTRAINTS

In this section we discuss some of the unique requirements and challenges for WMSNs application such as high bandwidth demand, multimedia coding techniques, and application-specific QoS requirements [30].

A.High Bandwidth Demand

Multimedia content (e.g., mages and video streams), require transmission bandwidth that is higher than that supported by currently available off the shelf sensors. For example, the maximum transmission rate of state-of-the-art IEEE 802.15.4 compliant components such as Crossbow’s TelosB or MICAz [15, 45, 46] motes is 250 kbps. As a result, multimedia sensors require higher data rates than the scalar sensor, with similar power consumption.

B.Multimedia Coding Techniques

Multimedia processing and source coding has been used to handle multimedia content over wireless sensor networks and to support real time multimedia applications. These coding techniques should be designed in such a way that they meet current resource capabilities such as memory, data rate, battery, processing power and bandwidth. Thus, Multimedia coding techniques should be used to decrease the amount of multimedia content transferred over the network by extracting the useful information from the captured images and video streams while keeping the application-specific QoS requirements [17].

Recently a coding technique called distributed source coding [16] showed that a traditional balance of complex encoder and simple decoder can be reversed within the framework. The distributed source coding technique shifted the complexity to the base station or the sink, which allowed the use of simple encoders in sensor nodes. Distributed source coding will be discussed in more details in section III.

C.Application-specific QoS requirements

A WMSNs application has different requirements from the usual scalar sensor applications. In addition to data delivery required by scalar sensor networks, multimedia data include images and streaming multimedia content. Images are multimedia data obtained in a short time period. However, streaming multimedia content is generated over longer time periods and requires continuous data capturing and delivery. As a result, better hardware and coding and compression algorithms are needed in order to deliver QoS required by specific applications [6].

D.Resource Constraints

Multimedia sensors differ from the scalar sensor devices in terms of the type of data they are capturing. Video, images and audio data require more resources such as battery, memory, processing capability, and achievable data rates [2, 15, 19].

III.WMSN S O PTIMIZATION T ECHNIQUES Researchers have been working hard to overcome or minimize the effect of some of the WMSNs resource constraints and challenges mentioned in the subsections below. These efforts can be classified into two areas which are the architecture designs and optimization source coding paradigms as shown in figure 1.

Figure 1. WMSNs optimization's research areas

A. WMSNs Architectures

Different architectures were proposed to show how WMSNs can be more scalable and more efficient depending on the specific application QoS requirements and constraints [48]. Therefore, based on the designed network topology and architectures, the available resources in the network can be efficiently utilized and fairly distributed throughout the network, and the desired operations of the multimedia content can be handled. In general, network architectures for WMSNs can be divided into three different ones as outlined below [20, 34, 35, 36] composed of several components which include video and audio sensors, scalar sensors, multimedia processing hubs, storage hubs, sink, and the gateway [11].

Single-tier flat architecture

In this architecture the network consists of homogeneous sensor nodes with same capabilities and functionalities. All nodes can perform any function such as image capturing, multimedia processing and data transferring to sink over a

multi-hop path [11, 6, 20] as shown in Figure 2.

Figure 2. Single tier flat architecture

Single-tier clustered architecture

Single-tier clustered architecture consists of heterogeneous sensors such as camera, audio and scalar sensors grouped together to form a cluster. All heterogeneous sensors belonging to the same cluster send their sensed data to the cluster head which has more resources and can perform complex data processing. The cluster head is connected either directly or indirectly to the sink or the gateway through multi-hop path as shown in Figure 3[11, 6, 20].

Multi-tier architecture

In this architecture, the first tier consists of scalar sensors that perform simple tasks, like measuring scalar data from surrounding environment (e.g., light, temperature..etc), the second tier consists of camera sensors that perform more complex tasks such as image capturing or object recognition, and at the third tier consists of more powerful and high

resolution video camera sensors that are capable of performing more complex tasks, like video streaming or object tracking [11, 20, 41]. Each tier has a central hub for data processing and communicating with the upper tier. The third tier is connected with the sink or the gateway through a multi-hub

path [47, 42, 43] as shown in Figure 4.

Figure 3. Single-tier clustered architecture

B. Coding paradigms:

Multimedia applications require more resources (e.g., high processing capabilities, extensive encoding and decoding high bandwidth…etc) than data sensing applications in wireless sensor networks. The goal of researchers in the area of WMSNs coding is to find a coding paradigm that has low complexity, produces a low output bandwidth, tolerates loss, and consumes as little power as possible. There are two different types of coding paradigms discussed here as outlined below.

Individual source coding

Individual source coding is the paradigm that is used in multimedia coding where each node codes its information independently of other nodes [19]. Individual source coding is simple and does not require any kind of interaction between the nodes. However, when there is a high concentration of multimedia sensors on a specific event, individual source coding results in large redundancies. This is because all sensors attempt to transmit similar data at the best possible quality to the sink or the base station, resulting in a large number of copies of the same data. These copies might cause major congestion and energy exhaustion in the network. Thus, individual source coding is still an open research area for improvement [18, 6].

Distributed source coding.

Distributed source coding refers to the compression of

multiple correlated sensor outputs from sensors and the joint decoding at a central decoder at the base station or the sink node [6, 18]. Distributed source coding reverse the traditional one-to-many video coding paradigm used in most video encoders/decoders, such as MPEGx and H.26x into many-to-one paradigm. In the one-to-many paradigm, the encoders have complex encoding while the decoders are simpler. However, distributed source coding uses a many to- one coding paradigm, and exchanges the complex encoder for a complex decoder. Therefore, the encoders at the video sensor nodes can be designed to be simple requiring less resource, while the sink node or base station has the more complex

decoder [20, 24, 32].

Figure 4. Multi-Tier architecture

IV. E XAMPLE OF CURRENT DEPLOYED WMSN

A. IresNet-Intel Research Pittsburgh

IresNet is a platform for a two-tiered heterogeneous WMSN developed by Intel research group in Pittsburgh. Video sensors and scalar sensors are spread throughout the environment and collect potentially useful data. IrisNet allows users to perform Internet-like queries to video and scalar sensors. It reduces the bandwidth consumed: instead of transferring the raw data across the network, IresNet sends only a potentially small amount of processed data through the use of distributed filtering. Sensor data are represented in the Extensible Markup Language (XML). The user views the sensor network as a single unit that can be queried through a high-level language, simple query statements or more complex forms involving arithmetic and database operators [12].

B. Sens-Eye University of Massachusetts

Sense-Eye is a three-tier network of heterogeneous WMSN for surveillance applications developed by the University of Massachusetts. The lowest tier consists of MICA2 Motes and Scalar sensors, e.g. vibration sensors. The second tier is made up of motes equipped with low camera sensors. The third tier consists of Stargate nodes equipped with [25]. The Sense-Eye surveillance application consists of three tasks object detection, object recognition, object tracking

V.

D ISCUSSION

In this section, we will discuss the performance of optimization solutions which were outlined in section III. A. Architecture design

The objective of the architecture design in WMSNs is to design an architecture so that the available resources in the network can be efficiently utilized and fairly distributed throughout the network, to be scalable enough to handle the growing size in the network and to extend the energy life time of the nodes in the network [50].

In a single tear flat architecture, a set of homogeneous sensors are deployed and each sensor is programmed to perform all possible application tasks from image capturing through multimedia processing to data relaying toward the sink in multi-hop basis. Therefore, the energy of sensors will be quickly depleted preventing these sensors from achieving their objectives. Furthermore, the single tier flat architecture is not scalable enough to handle the complex and dynamic range of applications offered over WMSNs.

In a single tier clustered architecture, the cluster head performs all intensive multimedia processing on the data gathered by all sensors in the same cluster. Therefore, the processing and storage capability is determined by cluster head resources. Moreover, the energy of the cluster head will be depleted quickly [37].

In a multitier architecture approach, different tasks are distributed throughout the network. The resource-constrained, low-power elements are in charge of performing simpler tasks, while resource rich high-power sensors perform complex tasks. Most of the time, the rich high power sensors in the upper tier are triggered only when an event is detected by the low-power sensors to save their energy and reduce the amount of data sent. Moreover, Data processing and storage can be performed in a distributed fashion at each different tier. It was shown in [27] through experiments, that a multitier architecture has significant advantages over the other single-tier architectures in terms of scalability, lower cost, lower consumed energy, better coverage, higher functionality, and better reliability[47]. Table 1, represents a summary comparison of the three types of the architectures in terms of kind of sensors, processing types and storage types [41, 42].

T ABLE 1.C OMPARING DIFFERENT TYPES OF ARCHITECTURES

Single tier flat architecture Single tier clustered

architecture

Multitier

architecture

Sensors Homogenous

sensors Heterogeneous

sensors

Heterogeneous

sensors

Processing Distributed

processing Centralized

processing

Distributed

processing

Storage Centralized

storage Centralized storage Distributed

storage

B.Coding paradigms

The objective of coding paradigms is to find coding techniques that has low complexity coding, requires less processing, produces a low output bandwidth and consumes as little power as possible [40, 44]. Table 2, represents a comparison between individual source coding and distributed source coding techniques. Distributed source coding techniques require less encoder resources which in case of WMSNs is the sensor. Moreover, if more than one multimedia sensors were monitoring the same scene, they communicate between each other to send only one copy of the sensed data. As a result, the amount of data being sent in the distributed source coding is reduced and the network will not be congested [49]. However, in individual source coding each node codes its information independently without communicating with other nodes. As a result, if they were monitoring the same scene a lot of redundant data will be transmitted through the network causing it to be congested.

T ABLE 2.C OMPARISON OF ENCODING TECHNIQUES

Individual source coding Distributed source coding

The stream of data encoded by one source (sensor) and decoded by many destination. The stream of data encoded by many sensors and decoded by the rich-resources device(base station)

The encoders at the video sensor nodes are complex and requiring a lot of resources. The encoders at the video sensor nodes are simple requiring fewer resources.

The decoder is simple and requiring less resources The decoder at the base station is complex and requiring more resources

No communication exist

between sensors

There is communication between sensors Example MPEGx and H.26x Example Slepian-Wolf, Wyner-Ziv

C.Physical layer protocol standards for WMSN

Physical layer technologies can be classified based on the modulation scheme and bandwidth consideration [38] as three groups: Narrow band, Spread spectrum, Ultra-Wide band (UWB) technologies). Also they can be classified based on standard protocols into IEEE 802.15.4 ZigBee, IEEE 802.15.1 Bluetooth, IEEE 802.11 WiFi and 802.15.3a UWB. Table 3 summarizes the specifications of the different physical Layer Standards. ZigBee [39] is the most popular standard radio protocol used in wireless sensor networks because of its low-cost and low-power characteristics. However, the ZigBee standard is not suitable for high data rate applications such as multimedia streaming over WMSN and for WMSNs application-specific QoS. Therefore, researcher like Akyildiz, [6], believes that UWB should be used in WMSNS as standard protocol for physical layer. UWB has low power consumption, high data-rate of short range wireless communication.

II.C ONCLUSIONS AND F UTURE W ORK

In this paper, we introduced WMSNs technologies and their different applications and discussed major challenges and resources constraints pertained to these WMSNs. We surveyed and classified optimization techniques that have been investigated by researchers to overcome certain challenges. In addition, we discussed these optimization solutions in details showing their suitability for WMSNs. We also surveyed the existing off-the-shelf examples of WMSNs deployments. Our future work will focus on experimental deployment of WMSNs for particular applications. In addition to that, performance analysis and enhancement of existing technologies will be studied and proposed.

T ABLE 3.S PECIFICATIONS OF THE P HYSICAL L AYER S TANDARDS IN WMSN S

Zigbee Bluetooth WLAN UWB Data Rate 250 Kbps 1 Mbps

3 Mbps

54 Mbps 250 Mbps Output power 1-2 mW 1-100 mW 40-200 mW 1 mW Range 10-100 m 1-100 m 20-100 m < 10 m Frequency 2.4 GHz

915 MHz

868 MHz

2.4 GHz 2.4 GHz

3.1 GHz

10.6 GHz

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multimedia sensor networks. In Springer J. Supercomput.(JoS).

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无线信道相对于有线信道通信质量差很多。有限信道典型的信噪比约为46dB,(信号电平比噪声电平高4万倍)。无限信道信噪比波动通常不超过2dB,同时有多重因素会导致信号衰落(骤然降低)。引起衰落的因素有环境有关。 2.1无线信道的传播机制 无线信道基本传播机制如下: ①直射:即无线信号在自由空间中的传播; ②反射:当电磁波遇到比波长大得多的物体时,发生反射,反射一般在地球表面,建筑物、墙壁表面发生; ③绕射:当接收机和发射机之间的无线路径被尖锐的物体边缘阻挡时发生绕射; ④散射:当无线路径中存在小于波长的物体并且单位体积内这种障碍物体的数量较多的时候发生散射。散射发生在粗糙表面、小物体或其它不规则物体上,一般树叶、灯柱等会引起散射。 2.2无线信道的指标 (1)传播损耗:包括以下三类。 ①路径损耗:电波弥散特性造成,反映在公里量级空间距离内,接收信号电平的衰减(也称为大尺度衰落); ②阴影衰落:即慢衰落,是接收信号的场强在长时间内的缓慢变化,一般由于电波在传播路径上遇到由于障碍物的电磁场阴影区所引起的; ③多径衰落:即快衰落,是接收信号场强在整个波长内迅速的随机变化,一般主要由于多径效应引起的。 (2)传播时延:包括传播时延的平均值、传播时延的最大值和传播时延的统计特性等; (3)时延扩展:信号通过不同的路径沿不同的方向到达接收端会引起时延扩展,时延扩展是对信道色散效应的描述; (4)多普勒扩展:是一种由于多普勒频移现象引起的衰落过程的频率扩散,又称时间选择性衰落,是对信道时变效应的描述; (5)干扰:包括干扰的性质以及干扰的强度。 2.3无线信道模型 无线信道模型一般可分为室内传播模型和室外传播模型,后者又可以分为宏蜂窝模型和微蜂窝模型。 (1)室内传播模型:室内传播模型的主要特点是覆盖范围小、环境变动较大、不受气候影响,但受建筑材料影响大。典型模型包括:对数距离路径损耗模型、Ericsson多重断点模型等; (2)室外宏蜂窝模型:当基站天线架设较高、覆盖范围较大时所使用的一类模型。实际使用中一般是几种宏蜂窝模型结合使用来完成网络规划; (3)室外微蜂窝模型:当基站天线的架设高度在3~6m时,多使用室外微蜂窝模型;其描述的损耗可分为视距损耗与非视距损耗。

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合肥学院第六届电子设计大赛

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