ORIGINAL ARTICLE

Design and implementation of a sensor-based wireless camerasystem for continuous monitoring in assistive environments

Nan Li • Bo Yan • Guanling Chen •

Prabhu Govindaswamy • Jie Wang

Received: 31 December 2008 / Accepted: 5 October 2009 / Published online: 26 February 2010

� Springer-Verlag London Limited 2010

Abstract Camera-based surveillance system is an

important tool for assistive environment to monitor those

who may have physical or cognitive impairment. It is,

however, expensive to deploy a wired surveillance system

and difficult to continuously monitor a moving subject in a

large facility where many cameras are deployed. In this

paper, we first evaluate the performance of streaming

camera images over wireless networks in both residential

and office environments and present the quantitative results

to show the feasibility of using wireless backbones for

camera surveillance systems. We then propose sensor-

integrated camera surveillance (SICS) to address the con-

tinuous monitoring problem. SICS uses wearable wireless

sensors to locate moving subjects and automatically selects

the camera covering the subject, allowing human operators

to focus on only one screen to monitor an individual. SICS

uses a self-organizing wireless mesh network to allow

flexible deployment at reduced cost. An on-board image-

processing algorithm is used to reduce the bandwidth

consumption. Through empirical evaluation, we found that

the automatic camera hand-off enabled by SICS was

effective for continuous camera monitoring and a sophis-

ticated wireless network management system is required to

deploy the SICS in practice.

Keywords Wireless camera networks �Continuous monitoring � Sensor-based localization

1 Introduction

Our society is facing the aging of population, where an

increasing proportion of people are over the age of 65 [1].

About 12.4% of the population are older Americans. More

than half of the older population (52%) reported having at

least one disability of some types, almost 37% of older

persons reported a severe disability, and 16% reported that

they needed some types of assistance [2]. It is thus

important to provide pervasive and intelligent technologies

to assist those with physical or cognitive impairment, to

improve their quality of life and to meet their special

personal needs [3].

Camera-based surveillance is an ideal technology that

provides most direct and effective visual information to

assist the nursing carers. The global market for video sur-

veillance system is expected to have strong growth,

reaching more than $9 billion by 2011, as security sur-

veillance and remote healthcare become more focused on

communities and households [4]. In those systems, to

continuously monitor certain individuals is one of impor-

tant features, such as to monitor the patients with Alzhei-

mer diseases or the seniors who may frequently fall to the

ground.

It is, however, particularly difficult to provide continu-

ous visual monitoring of individuals in a large facility

where hundreds of cameras are deployed. As the monitored

N. Li (&) � B. Yan � G. Chen � J. Wang

Computer Science Department, University of Massachusetts

Lowell, Lowell, MA, USA

e-mail: nli@cs.uml.edu

B. Yan

e-mail: byan@cs.uml.edu

G. Chen

e-mail: glchen@cs.uml.edu

J. Wang

e-mail: wang@cs.uml.edu

P. Govindaswamy

Qualcomm, San Diego, USA

e-mail: prabhug@qualcomm.com

123

Pers Ubiquit Comput (2010) 14:499–510

DOI 10.1007/s00779-009-0271-2

subject moves around over different camera coverage

areas, the operator has to manually figure out the current

camera screen that may cover that subject and get a visual

confirmation that the subject does show up on that camera.

This is certainly a tedious and slow task for the operator

given potentially hundreds of camera screens. When the

health carer monitors a large number of mobile users, she

could miss the best chance to help the subject. Simply

increasing the number of operators, each watching some

number of cameras, will not solve the problem since they

have to cooperate to track certain individuals.

In addition, deploying a wired camera network often

requires high installation cost due to laying out network

cables for IP cameras or coax cables for closed-circuit

television (CCTV) cameras. For example, a recent 600-

camera deployment to monitor parking lots cost more than

$8,500 per camera [5]. To deploy a wireless network

instead of wired could be one solution to reduce the

deployment cost. Existing IEEE 802.11 standards

(802.11a/g) can support 54 Mbps data transfer and the

recently ratified 802.11n can support more than 108 Mbps

bandwidth, which means that high-quality sequence images

or video transmission over wireless links is feasible today.

However, the challenge of a wireless camera network is its

relatively lower bandwidth compared to the wired back-

bone, which can be problematic if many cameras need to

share the same link. The increased network density may

also result in more channel contention, hidden terminals,

and capture effect that can significantly degrade network

performance [6].

In this paper, we first present an empirical measurement

study on the performance of using wireless networks to

support distributed camera applications in both residential

and office environment. We focus on indoor environments,

such as large households, office buildings, hospitals, and

senior care centers. Based on these quantitative results, we

propose sensor-integrated camera surveillance (SICS), a

system that implements automatic camera hand-off based

on wireless mesh network. SICS allows the human operator

to only watch a single screen to track one subject as she

moves around. The images of this screen are dynamically

changed based on which camera is covering the monitored

subject. The subject wears a small wireless sensor that

provides the location information for camera selection. The

automatic camera hand-off transforms traditional surveil-

lance model from watching a location covered by a camera

to watching a moving subject covered by many cameras. It

also is possible to integrate other indoor localization

technologies with SICS, such as tags based on RFID [7],

infrared [8], 802.11 [9], or UWB [10]. In an assistive

environment, however, a subject may already wear a sensor

that constantly monitors her vital signs [11], making SICS

appealing for this application.

To the best of our knowledge, the performance evalua-

tion is one of the first quantitative studies focusing on

wireless camera networks and SICS is the first system that

combines wireless mesh for flexible camera deployment,

on-board image analysis for reduced bandwidth consump-

tion, and sensor integration for subject tracking. All the

hardware devices used in this study, including wireless

routers, camera and wireless sensors, are off-the-shelf

devices. Our contributions include:

• the first measurement study of wireless camera net-

works deployed both in a residential and an office

environment;

• the feasibility demonstration of wireless sensor-based

localization for surveillance systems;

• quantitative performance evaluation of automatic cam-

era hand-off through an empirical implementation of

the SICS architecture;

• In the remainder of this paper, we present the related

work in Sect. 2. The system design is presented in Sect.

3. All evaluation results are described in Sect. 4. We

discuss further challenges and summarize in Sects. 5

and 6, respectively.

2 Related work

The related work can be separated into two categories. The

first category focuses on wireless measurement. Papa-

giannaki et al. [12] studied wireless networks in a home

setting, focusing on the MAC and network performance.

They assumed that the applications over the networks are

general, did not focus on surveillance related applications.

Das et al. [13] measured a 32-node outdoor mesh network,

highlighting latency and jitter problems for streaming

applications. Some projects, such as MAP [14], and Jigsaw

[15], applied distributed wireless sniffers to monitor net-

work traffic and measure the wireless network performance

by merging the distributed traces. Most of research in this

area focuses on MAC and network layer behaviors. On the

other hand, we studied the application layer quality, image

transmission performance, over wireless networks in two

typical indoor environments, to demonstrate feasibility of

SICS.

In the second category, the researchers built real wire-

less camera networks with various characteristics. Cyclops

[16] and XYZ [17] developed several prototypes with

camera modules directly mounted on wireless sensor

platforms. These low-cost camera sensor systems are ideal

for quick deployment in unmanageable spaces, such as the

battlefield for military applications and the remote areas for

habitat studies. Due to severe bandwidth constraints on the

low-power radios, however, these devices typically employ

500 Pers Ubiquit Comput (2010) 14:499–510

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lightweight on-board image processing algorithms and do

not provide continuous high-resolution images. SICS con-

nects cameras with high-speed wireless mesh network to

enable better surveillance quality for the assistive envi-

ronments, where IT infrastructures can be appropriately

managed.

Panoptes is a platform built with faster 802.11 wireless

networks with a focus on low-power consumption [18] and

Sens-Eye focuses on a tiered camera system consisting of

both sensor-based and backbone-based cameras [19].

Motorola wireless mesh networks support outdoor video

surveillance,1 where all camera videos are processed in a

central place to enable smart surveillance using video-

analysis algorithms [20]. These systems and other wireless

mesh networks for video surveillance [21], are designed to

monitor general population without explicit cooperation

from the monitored subjects. The proposed SICS system,

on the other hand, focuses on assistive environments where

subjects may have already wear sensors for location

tracking and vital signs monitoring. The integration of

sensors with camera surveillance can add more accurate

application intelligence since existing video-analysis

algorithms tend to be error prone given the potentially low-

grade camera images.

3 System design

In this section, we first describe the setup of the two

wireless camera network testbeds. These two testbeds are

used to evaluate the quality of sequential image transmis-

sion through wireless camera network in different

deployments and different indoor environments. In Sub-

sects. 3.2 and 3.3, we present the SICS system design and

localization strategy in SICS system.

3.1 Testbeds setup

We deployed two wireless camera network testbeds, one in

a residential house and the other in our department building

at UMass Lowell. The residential home is a typical

standalone two-floor wood house with a basement, roughly

2,100 square feet in total. We call it our home testbed. In

the other testbed, each floor of our department building is

260 feet long and 85 feet wide, with hallway walls and

floors made of concrete. We call it our office testbed.

For home testbed, we deployed one wireless node in

each of the 10 rooms providing full coverage of whole

house. Each wireless node is an ASUS WL-500g Premium

wireless router, with 266 MHz CPU, 8 MB flash, 32 MB

RAM, and one Broadcom 4318 802.11b/g radio. We

replaced the ASUS operating system with OpenWrt

Kamikaze 7.09.2 The server is an IBM ThinkPad T42

laptop, which has PCMCIA 802.11a/b/g cardbus adapter

with Atheros AR5213 chipset and Madwifi 0.9.4 wireless

driver.3

Figure 1 shows the map of the deployment, with the

server marked as ‘‘S’’. Since the house is relatively small

and every node can reach the server directly, the 10 nodes

form an one-hop star network topology.

For the office testbed, we deployed 15 wireless nodes on

the second and third floor of a six-story concrete structure

building. The building is covered by the university’s

WLAN access points (APs), which use both 802.11g and

802.11a. Besides these APs, there are microwave ovens,

wireless sensors, and Bluetooth devices that may poten-

tially interfere with the wireless transmissions in our test-

bed. We expect that such a ‘‘chaotic’’ wireless environment

will be typical for a real-world deployment, given

increased popularity of home APs, wireless sensors, and

community meshes [22].

In this testbed, each wireless node is a RouterBOARD

532A device, which has MIPS 400 MHz CPU, 64 MB

RAM, and 2 GB Compact-Flash disk. Each node can have

one or two Mini PCI wireless radio cards, for which we

used Wistron Neweb CM9 with Atheros AR5213A chipset.

On all wireless nodes, we used omni-directional antennas

that have 3 and 5 dbi gains on 2.4 and 5 GHz frequencies,

respectively. Each node installed OpenWrt Kamikaze 7.09

with Linux kernel 2.6.21-5 and MadWifi 0.9.4 (r2568-

20070710 svn snapshot). The server is same as the one

used in the home testbed.

Figure 2 shows the map of the deployment in the two

floors, with the server marked as ‘‘S’’ as well. Since the

wireless link provides only about 100 feet communication

range, and the signal drops quickly as it goes through

concrete walls [23], all those wireless nodes form a multi-

hop wireless network up to three hops.

Figure 3 shows the topology of the multi-hop network.

We first measured each link’s UDP throughput with single

Fig. 1 10 nodes deployed in the home testbed (S server)

1 http://www.motorola.com/mesh/.

2 http://www.openwrt.org.3 http://www.madwifi.org.

Pers Ubiquit Comput (2010) 14:499–510 501

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radio using Iperf,4 and marked the result as link labels.

Some of nodes installed two wireless cards, which pro-

vided the possibility of multi-radio communication. By

dividing the collision domain, multi-radio mechanism

provides greater performance improvement [24, 25]. Fig-

ure 4 presents the channel assignment of office testbed

during multi-radio multi-hop measurements.

3.2 SICS system design

In general, SICS system is composed of a central server

and two types of clients, including mobile clients, and

SICS nodes. The server is a laptop with wireless adapter

which is the same as we used in both testbeds. A mobile

client is a standalone battery-powered TMote TelosB

wireless sensor, which is carried by the monitored subject.

Each SICS node contains three hardware components, a

wireless router, a wireless sensor, and a camera. Both the

sensor and camera connect to the router through USB

Fig. 2 15 nodes deployed in the

office testbed (S server). a Third

floor, b second floor

Fig. 3 Multi-hop topology with downlink/uplink throughput (Mbps)

marked on each linkFig. 4 Multi-radio topology with channel number marked on each

link

4 http://dast.nlanr.net/Projects/Iperf/.

502 Pers Ubiquit Comput (2010) 14:499–510

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interfaces. The wireless routers form the network back-

bone, which transmits not only the camera images back to

server but also camera control and sensor location mes-

sages between server and each node. These SICS routers

are strategically deployed with persistent power and typi-

cally do not move. The wireless sensors are the same as

mobile client and the cameras are Phillips QuickCam

Zoom, which can be assessed through the PWC Linux

driver. The wireless routers are the same as the router used

in the home testbed, since it provides native USB inter-

faces. The system architecture is shown in Fig. 5.

Figure 5 shows the SICS node architecture. When SICS

node starts up, the wireless sensor periodically receive the

localization messages from mobile client and passes them

to wireless router by USB interface. The SerialForwarder is

a process for transmitting the messages to server via

wireless mesh network. The MoteTrack process, running

on server, calculates the mobile client’s location based on

the pre-stored knowledge of network deployment and

decides which camera is covering the subject. Once the

decision is made, the control message is sent back to

camera control process in the selected wireless router, and

trigger the motion detection process5 that retrieves the

images from web camera via USB and transmits the images

back to server for display. By applying on-board motion

detection, the router does not transmit images unless there

is target movement detected to save the wireless band-

width. All wireless backbone installed OLSRD6 for auto-

matic mesh routing.

We deployed eight SICS nodes in third floor of our

department building to demonstrate the feasibility of SICS.

All the nodes were deployed in the hallway, and the camera

coverage overlapped.

3.3 Sensor-based localization

To select an appropriate camera that covers a moving

subject, we need to determine the current location of the

subject with reasonable accuracy and relatively short delay.

If the localization algorithm lacks accuracy, we may end up

selecting the wrong camera. If it takes a long time to obtain

the localization result, the subject may have already moved

to another place before we switch the camera.

It is a challenging task to use radio signals for indoor

localization because of the irregular RF propagation caused

by plenty of absorbing, scattering, and multi-path effects. It

is thus difficult to derive a simple correlation function

between the distance and the radio signal strength.

Many existing solutions require a manual process to

build a RF map to achieve meter-level localization accu-

racy [26, 27]. Namely, it is necessary to measure the RF

signatures that are signal strength samples from strategi-

cally deployed stationary beacons. While this approach

could be labor-intensive for a large facility, the RF data-

base is only needed to be built once and the room-level

localization accuracy is suitable for the purpose of SICS

applications.

We use MoteTrack [28] to build the RF signature data-

base. All sensors attached with SICS node broadcast beacon

messages and mobile client receives beacons. Different than

the original MoteTrack, which requires the mobile sensor

be attached to a laptop that stores RF database, we want the

subject to carry only a sensor, rather than a heavyweight

laptop, for the targeted assistive environment applications.

We modified the MoteTrack so the mobile client sends its

RF signature to a nearby SICS node with the strongest

signal strength. That node will then forward the RF signa-

ture to the central server for location determination.

MoteTrack increases the localization accuracy by

broadcasting the beacon messages on a set of frequency

channels C with a set of transmission power levels P. The

beacon is transmitted on all (ci, pj) combinations, for every

ci [ C and pj [ P. The beacon messages contain the

identifier, the frequency channel ci, and the power level pj.

The rational behind using more frequencies and power

levels is to increase ‘‘uniqueness’’ of the RF signatures,

since RF signals tend to have different propagation char-

acteristics on different frequency channels and have dif-

ferent propagation distances with different transmission

power levels.

Although increasing the number of transmission fre-

quency and power levels can improve the localization

Fig. 5 SICS node architecture

5 http://motion.sourceforge.net.6 http://www.olsr.org.

Pers Ubiquit Comput (2010) 14:499–510 503

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accuracy, mobile client needs to wait enough time to

receive all beacon messages at all power levels P and then

iterate through all frequency channels C. Thus it may take a

while for the mobile sensor to compute a RF signature

before it sends it to the server for location determination. If

this delay is too long, the calculated location may be

irrelevant since the subject may have moved, leading to

incorrect camera selection. We evaluate the trade-off

between the size of C, P and the localization accuracy in

Sect. 4.2.

4 Evaluation

4.1 Transmission quality

In this subsection, we describe the measurement results of

image transmission over wireless camera networks. Since

we are interested in the quality of image transmission over

wireless networks, we measured the Peak Signal-to-Noise

Ratio (PSNR) in both home and office testbeds and delays

in the office testbed.

Although PSNR is neither the only nor the best metric

for image (or video) applications, it does give us an

objective measurement to compare the application perfor-

mance of different network configurations. It is defined via

the mean squared error (MSE) for two m 9 n monochrome

images I and K, which could be the original image and

received image in our experiment:

MSE ¼ 1

mn

Xm�1

i¼0

Xn�1

j¼0

jjIði; jÞ � Kði; jÞjj2 ð1Þ

The PSNR is defined as:

PSNR ¼ 10� log10

MAX2I

MSE

� �¼ 20� log10

MAXIffiffiffiffiffiffiffiffiffiffiMSEp� �

ð2Þ

Here, MAXI is the maximum possible pixel value of the

image. Typical values for the PSNR in lossy image

compression are between 30 and 50 dB, where higher

value represents better quality [29]. In our calculations, we

assigned PSNR to be 100 dB for two identical images

(where MSE is 0.).

From Eq. 1, we need to compare the received image

with the original image to calculate the PSNR, which

means that it is not appropriate to transmit the real-time

images directly captured from external cameras while

measuring the transmission quality. We used a webcam to

take snapshots (one per second) for over 100 s in our

laboratory. Each image’s resolution is 640 9 480 pixels,

and the size in JPEG format is about 42 K bytes. These

images were saved both on the server and the wireless

nodes. During the experiments, every node sequentially

read these images, segmented them by a given packet size

limit (experiment parameter) and sent them according to a

given speed (such as one image per second) through UDP.

On the server side, the receiver reassembled the image

files, recorded packet loss, and calculated PSNR by com-

paring the received images with the stored images.

We chose to use UDP for image transport, since TCP is

known to have bad performance over wireless links, par-

ticularly over multi-hop wireless networks [13]. Because

UDP does not guarantee reliable delivery, the packet loss

would result in lower PSNR.

4.1.1 Home testbed

In this experiment, we activated all 10 nodes to continu-

ously transmit one image per second simultaneously in

802.11g mode. The total workload (42 KB 9 8 bits/

B 9 10nodes = 3.28 Mbps) is much smaller than the wire-

less link capacity (54 Mbps for 802.11g).

As mentioned above, each images size is about 42 KB,

and the wireless packet size is up to 1,500 B. Thus each

image would be segmented into multiple packets to trans-

mit. We varied the UDP packet length as 450, 950, and

1,450 bytes to quantify the impact of packet length over

network and application-level performance.

Figure 6a shows the average and standard deviation of

the packet length impact on image PSNR. The results show

that the image quality had improved with larger packet

length for those nodes with lower throughput, such as

nodes H2, H3, H4, H6, H7, H8, and H10 (we use Hi for

labeled nodes in the home testbed). The packet length does

not impact the relatively better transmission quality nodes,

such as nodes H1, H5, and H9.

We chose the long packet length, 1,450 bytes, to com-

pare the image quality with and without the (Request to

Send/Clear to Send) RTS/CTS mechanism. 802.11 MAC

uses RTS/CTS to reduce packet collisions caused by the

hidden terminal problem [30]. Figure 6b shows the average

and standard deviation of images PSNR with and without

the RTS/CTS mechanism. The results show that RTS

mechanism improves the PSNR more than 50% in most

nodes. Every node achieves at least 95 dB PSNR, which

means that the image transmission quality is nearly perfect.

4.1.2 Office testbed

In the office testbed, we first study the one hop nodes

transmission performance to provide a comparable result

with the home testbed. The one hop nodes (see Fig. 3),

including O3, O6, O7, O8, O9, and O11 (we use Oi for

labeled nodes in the office testbed), form a star wireless

network. We started the six nodes to transmit two images

504 Pers Ubiquit Comput (2010) 14:499–510

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per second in order to make the overall network workload

close to the home testbed experiment.

Figure 7 shows the PSNR results in different packet

lengths and with/without RTS/CTS. Comparing with

Fig. 6, all nodes achieved good and similar performance,

except for O3. The RTS/CTS mechanism even degraded

the performance for O3. We noted that the wireless link

between O3 and the server was relatively bad, and turning

on RTS/CTS added additional traffic overhead that led to

reduced quality. From Fig. 3, the throughput from O3 to the

server is only 1/4 of the best uplink throughput between O8

and the server.

From these comparison results, we can conclude that the

choice of packet length and the benefits of RTS/CTS

depend on the different wireless environment, and we need

automatic tools to determine the packet length and RTS/

CTS mechanism by measuring and monitoring link quality.

We also evaluated the performance of the multi-hop

network in the office testbed where the topology is descri-

bed in Subsect. 3.1. We activated all nodes to transmit one

image per second with packet length 1450 bytes and RTS/

CTS disabled. We found that some of nodes, which were

more than one hops to the server, achieved relatively bad

performance. Since the RouterBOARD 532A provides

multiple Mini-PCI interfaces, it is feasible to study the

multi-radio performance in wireless mesh network. We

installed up to two wireless radio cards in wireless nodes,

and configured the transmission channel as shown in Fig. 4

where the channel number is marked as label.

Figure 8 shows the multi-hop wireless network mea-

surement results. For each node, the left bars are single

radio image transmission PSNR and the right bars are

multi-radio result. The first six nodes on x axis are the

direct neighbors of the server, the next 6 nodes are the two-

hop nodes, and the last three nodes are three-hop away

from the server (see Fig. 3). All one-hop nodes’ perfor-

mance was much better than others, and some of the two-

hop nodes, such as O16 and O17, had better performance

than three-hop nodes. All three-hop nodes had unaccept-

able performance with a single radio. The performance

unfairness existed even as the channel bandwidth is much

larger than the total workload generated, which is due to

self-interference in a dense network as all radios transmit

over the same frequency at the same time [31]. Adding

additional radios and assign them with different channels

can separate collision domains and improve the wireless

mesh network performance [32]. By comparison of the two

bars for each node, we observed significant PSNR

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 Ave

rage

and

Std

_Dev

iatio

n of

PSN

R (

dB)

Index of Node

Std_Deviation 450 Bytes Average 450 Bytes

Std_Deviation 950 Bytes Average 950 Bytes

Std_Deviation 1450 Bytes Average 1450 Bytes

(a)

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10

Ave

rage

and

Std

-Dev

of

PSN

R (

dB)

Index of Node

Std-Dev without RTS/CTSAverage without RTS/CTS

Std-Dev with RTS/CTSAverage with RTS/CTS

(b)

Fig. 6 Home testbed image transmission quality. a Packet length

impact, b RTS/CTS impact

0

20

40

60

80

100

120

140

160

180

3 6 7 8 9 11 Ave

rage

and

Std

_Dev

iatio

n of

PSN

R (

dB)

Index of Node

Std_Deviation 450 UDP length Average 450 UDP length

Std_Deviation 950 UDP length Average 950 UDP length

Std_Deviation 1450 UDP length Average 1450 UDP length

(a)

0

20

40

60

80

100

120

140

3 6 7 8 9 11

Ave

rage

and

Std

-Dev

of

PSN

R (

dB)

Index of Node

Std-Dev without RTS/CTSAverage without RTS/CTS

Std-Dev with RTS/CTSAverage with RTS/CTS

(b)

Fig. 7 Office testbed one hop nodes image transmission quality. aPacket length impact, b RTS/CTS impact

Pers Ubiquit Comput (2010) 14:499–510 505

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improvement (about 50%) by using multiple radios, except

for O9, O13, and O17. Because the node O9 has good

wireless link to the server, it achieved good PSNR either

with single radio or multiple radios. We also measured the

network throughput of node O13 and O17, and found that

the improvements of throughput were 6 times and about

35%, respectively by using multiple radios. Since the

relationship between PSNR and throughput is not linear,

one fragment loss could cause the serious image distortion,

thus the throughput improvement does not guarantee pro-

portional PSRN improvement. All wireless nodes, except

O13, O17, and O18, achieved more than 40 dB average

PSNR with multi-radio configuration, which lead to

acceptable image quality [29].

To study the PSNR distribution, we also calculated the

cumulative distribution function (CDF) of image trans-

mission PSNR. Figure 9 shows two typical results from

two-hop node O2 and three-hop node O19, respectively. All

other nodes’ results are similar except that the proportion

of 100 dB PSNR depends on node transmission quality. We

noticed that most of broken images’ PSNR fall in the range

of 9–15 dB. Images with this quality are not useful for a

surveillance system. Because of the compressive JPEG

coding, small packet loss can cause large image PSNR

dropping. This motives a possible improvement technique.

Once the transmitting node finds that too many packets are

lost in the same image, it should stop sending additional

packets belongs to this image since the image is unable to

recover with reasonable quality. This mechanism could

improve the system performance by avoiding unnecessary

traffic.

Besides the image quality, the other important metric is

transmission delay in surveillance system. In wireless

networks, especially those using multi-hop topologies,

there are several reasons causing increased packet trans-

mission delay, such as MAC-layer back-off, frame

retransmission, and node congestion. We deployed three

wireless network sniffers near the nodes, O9, O10, and O5,

respectively. All those sniffers can log every packet sent

and received by wireless nodes with time-stamp marked.

As Fig. 3 shows, the three nodes form a three-hop wireless

path. By merging all traces collected from different snif-

fers, we can calculate each packet transmission delay as it

passes through multiple nodes.

We calculated the average end-to-end packet delay,

which is the difference between the time when packet

departs from the sender and the time when the packet

arrives to the server. We also calculated the image delay,

which is the difference between the time when the first

packet of an image departs and the time when the last

packet of that image arrives at the server, because one

image could be segmented into multiple packets. The

results are shown in Table 1.

We found that the multi-hop nodes suffered more that 10

times longer packet delay than the one-hop node both in

single radio and multiple radios configurations. The reason

is that the packet from a multi-hop node, needs more times

to compete for wireless media. For example, the packets

sent from O5 need to contend for wireless channel access

three times, each time it may need to back off or retransmit.

The multi-radio mechanism did help all nodes to reduce the

packet delay because of the reduced interference.

Figure 10 shows the CDF of image transmission delay

in single radio and multiple radios configurations. We

found that the CDF in multiple radios are steeper than

single radio, which means that the multiple radio mecha-

nism helps the image transmission more smoothly. The

packets sent from three hops nodes in single radio network

suffered two obviously different delay ranges, about 40%

images delay 0.2–0.9 s and another 40% images delay 5–

20 s. By checking the image transmission traces, we also

found the long image delay usually happened continuously.

This burst delay could seriously degrade the video sur-

veillance system’s performance. For example, this delay

could cause monitoring operator to miss the important

events. By using multiple radios, the delay distributions are

much evener than the single radio network, all delays

collapse into a smaller range.

4.2 Localization quality

We selected the distance errors in meters as a metric to

evaluate the quality of localization, which is the difference

between the actual location and the estimated location. For

each measurement, we placed the mobile client at multiple

different positions and let the system estimate the client’s

positions. The average estimate errors are considered as the

measurement results.

We first calculated the distance errors against a varying

number of transmission power levels. We also made sure

that the mobile sensor could hear from at least 6 beacon

0

20

40

60

80

100

120

140

160

3 6 7 8 9 11 2 10 13 15 16 17 5 18 19Ave

rage

and

Std

_Dev

iatio

n of

PSN

R (

dB)

Index of Node

Std_Deviation Single RadioAverage Single Radio

Std_Deviation Multiple RadiosAverage Multiple Radios

Fig. 8 Office Testbed multi-hop transmission quality

506 Pers Ubiquit Comput (2010) 14:499–510

123

sensors, as suggested by Mote-Track authors [29]. Here we

fixed the frequency channel to be 1 and varied the number

of power levels from 1 to 5.

Figure 11 shows that increasing the number of trans-

mission power levels decreases the distance errors of

localization. It is also clear that using two transmission

power levels reduces error in distance by 14% approxi-

mately, by comparing with using only a single transmission

power level. Using additional power levels, however, we

can only further reduce the error distance by 2–5%. Con-

sidering that using more power levels causes longer cal-

culation time, we chose two transmission power levels for

the SICS system, which gives us a room-level localization

accuracy and relatively shorter delay.

Figure 12 shows the distance errors against the number

of transmission frequencies by fixing transmission power

levels at two. From these results, we found that using 3 or 4

frequency channels could give us distance errors of less

than 4 m, which is comparable to a single camera’s typical

coverage range and thus is sufficient for automatic camera

selection. Reducing the number of frequency channels used

by the sensors will also limit the potential interference to

other channels, such as those used by SICS 802.11 mesh

network, since both 802.15.4 and 802.11 work in 2.4 GHz.

Having found the desired number of transmission power

levels and frequencies, we further evaluated the impact of

number of beacons being heard by the mobile client at any

point to the localization errors. It is important to note that

the beacons should be well spread in the space. Doing so

helps us to get varying signal strengths from beacons.

Hence, we used standalone wireless sensor powered by

batteries at the ends of a hallway in the office testbed

(Fig. 13).

Figure 13 shows that increasing number of beacons does

not improve the localization significantly. We believe that

a typical indoor camera network deployment, with one

beacon per camera, should be sufficient for sensor

localization.

4.3 Overall quality of SICS

Due to the hardware and camera driver’s restrictions, we

set each SICS node to transmit five images per second and

each image’s resolution to be 160 9 128. We chose packet

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10 100

CD

FPSNR (dB)

single radio multiple radios

(a)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10 100

CD

F

PSNR (dB)

single radio multiple radios

(b)

Fig. 9 Cumulative distribution

function (CDF) of two typical

nodes’ image PSNR. a Node 2,

bNode 19

Table 1 Average transmission delay (s)

Hop Packet delay (s) Image delay (s)

Single radio Multiple radios Single radio Multiple radios

1 0.0034 0.0018 0.0382 0.0286

2 0.0396 0.1567 0.9100 0.4794

3 3.4272 0.5081 6.7805 3.0943

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.001 0.01 0.1 1 10 100

CD

F

Image delay (s)

1 hop 2 hops 3 hops

(a)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.001 0.01 0.1 1 10 100

CD

F

Image delay (s)

1 hop 2 hops 3 hops

(b)

Fig. 10 Cumulative

distribution function (CDF) of

image transmission delay. aSingle radio delay, b Multi-

radio delay

Pers Ubiquit Comput (2010) 14:499–510 507

123

length to be 1,450 bytes with RTS/CTS disabled. For

sensor localization, we chose two transmission power

levels and four frequency channels.

The localization accuracy is around 3 m, which is suf-

ficient for the camera decision. It takes almost one to cal-

culate and deliver a single location report; i.e. every second

we have a location update from the MoteTrack. Although

we do not have the original image to calculate the PSNR,

we observed that the images’ quality is acceptable.7

Figure 14 show three sample images with different packet

loss degrees. Most of received images achieved same

quality as Fig. 14a and very few images as b. Almost no

images were as bad as Fig. 14c. Even there were some

screens pause on server side during hand off because of

transmission or calculation delay, the cameras successfully

captured the subject, which was moving with regular

walking speed, and was shown on server screen in time.

5 Discussions

Even though we have shown that the wireless network can

provide reasonable image transmission quality in typical

indoor environments for SICS system and the TMote Te-

losB sensor-based localization strategy is also feasible to

implement a continuous monitoring for assistive environ-

ments, there are still some questions to be answered.

In practice, the wireless mesh network usually uses

license-free frequency band, which means that they may

suffer significant interference from nearby devices. Other

potential hardware and software faults include buggy

firmware, routing and MAC layer protocol misbehaviors,

and malicious attacks could degrade or even break the

networks. Comparing with the more reliable wired net-

work, wireless networks need more sophisticate networks

monitoring and management tools.

Recent work on mesh management includes DAMON

[33], where mesh routers periodically send their state to a

central server for analysis. Mesh-Mon provides a novel

monitoring solution even if the routing protocol completely

fails by leveraging mobile users to ferry management

packets between disconnected network partitions [34]. Qiu

et al. [35] propose a simulation-based diagnosis engine that

can automatically troubleshoot common mesh problems. In

SICS system, we expect an integrated network manage-

ment solution, which could provide cross-layer manage-

ment. For example, when a SICS node detects that a broken

wireless link from the link layer, the network management

system not only performs the link recover process, but also

caches the images into local storage media, such as flash

drive connected through USB interface. Once the link is

recovered, the SICS node can transmit the saved images to

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Loca

lizat

ion

Err

or (

met

er)

Number of Transmission Frequency

Std. DevMean

Fig. 12 Loc. error vs. trans frequencies

0

0.5

1

1.5

2

2.5

3

3.5

4

0 1 2 3 4 5 6

Loca

lizat

ion

Err

or (

met

er)

Number of Transmission Beacon

Std. DevMean

Fig. 13 Loc. error vs. trans beacons

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Loca

lizat

ion

Err

or (

met

er)

Number of Transmission Power

Std. DevMean

Fig. 11 Loc. error vs. trans power levels

7 A video demo can be found from http://www.cs.uml.edu/

glchen/sics/.

508 Pers Ubiquit Comput (2010) 14:499–510

123

server to compensate the loss due to link failure. We plan

to investigate this research direction as future work.

The all wireless design of SICS systems also introduce

security issues. For example, the camera images may be

easily intercepted and the wireless links can be easily

disrupted [14]. Both security protocols and wireless intru-

sion detection systems must be used to address these issues.

Privacy is another challenge in SICS-like systems where

users location, activity, and medical information are

tracked and recorded. While we envision the patients may

be willing to sacrifice some privacy for better healthcare in

assisted-living applications, we should provide technology

for better privacy protection. For example, users should be

able to understand and control how their personal infor-

mation is used. Tracking may only be triggered if urgent

events are detected or tracking should be stopped tempo-

rarily if the users pressed a button on the sensor. These

security and privacy issues will be addressed by SICS in

future.

With the wireless networks growing, how does the

electromagnetic emission impact human health becomes an

important question. Some researchers point out that there

are solid evidences of strong electromagnetic emission

leading to adverse biological effects [36]. To reduce the

electromagnetic emission while guaranteeing the wireless

service performance should be addressed by any large-

scale SICS deployment.

6 Conclusion

We have evaluated the image transmission quality in two

indoor testbeds deployed in a residential house and an

office building. The single-hop wireless network provides

reasonable image transmission quality. The multi-hop

wireless network, however, suffered unfairness of perfor-

mance across wireless nodes, leading to unacceptable

image transmission quality for the nodes that are more than

one hops away from the server. The multi-radio mechanism

showed great performance improvement in multi-hop

wireless network, which could be one solution to imple-

ment large-scale wireless camera networks.

We also demonstrated the feasibility of continuous

camera monitoring by integrating wireless sensor for

localization. In our experiment, the wireless sensor-based

approach achieved room-level accuracy with reasonable

delay time, which is good enough for the continuous

camera-tracking application.

To the best of our knowledge, SICS is the first experi-

mental system that implements continuous camera-based

monitoring using wireless mesh backbone, on-board image

processing, and wireless sensor integration. The evaluation

of the prototype system shows reasonable performance on

sensor localization, image transmission over a multi-hop

mesh network, and overall application quality. We plan to

extend the current office testbed hardware, to implement

continuous camera-based monitoring networks with multi-

radio mesh network backbone supported. We also plan to

study the cross-layer system management approach to tune

the system parameters based on real-time wireless

measurements.

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