Wireless Sensor Network, 2010, 2, 74-84
doi:10.4236/wsn.2010.21011 anuary 2010 (http://www.SciRP.org/journal/wsn/).
Published Online J
Agent-Oriented Architecture for Ubiquitous Computing in
Smart Hyperspace
Yichao JIN1, Ruchuan WANG 1,2,3, Haiping HUANG1, Lijuan SUN 1,3
1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China
2State Key Laboratory of Information Security, Graduate School of Ch inese Academy of Sciences, Beijing, China
3Institute of Computer Technology, Nanjing University of Posts and Telecommunications, Nanjing, China
Received September 3, 2009; revised Septe mber 23, 2009; accepted September 24, 2009
Abstract
Agent-oriented approach is increasingly showing its magic power in a diversity of fields, specifically, ubiq-
uitous computing and smart environment. Meanwhile, it is considered the next creative issue is to intercon-
nect and integrate isolated smart spaces in real world together into a higher level space known as a hyper-
space. In this paper, an agent-oriented architecture, which involves the techniques of mobile agents, middle-
ware, and embedded artificial intelligence, is proposed. Detailed implementations describe our efforts on the
design of terminal device, user interface, agents, and AI computing module to combine two single smart
spaces, UbiLab and UbiDorm, into a practical smart hyperspace.
Keywords: Agent-Oriented, Ubiquitous Computing, Smart Hyperspace, Middleware, Embedded Artificial
Intelligence
1. Introduction
The progressive advances in computer system together
with the simultaneous improvement in Wireless Sensor
Networks (WSNs) [1] and other related fields, contribute
to the ubiquitous c omputing era, when our p hy si cal space
will be filled with different kinds of smart devices, which
possess the capability of computing and communication.
At the same time, pervasive network, which are formed
by ubiquitous devices interconnected with each other
through wireless communications, internet and other
medium, supply services and useful information to us
instantly and constantly? Thus, Mark Weiser’s ubiqui-
tous computing, “services can be provided to users any-
time and anywhere with any devices” [2], which was
first propounded in 1991, is approaching. And we name
this kind of physical space as smart environment, where
the knowledge about its users and the surroundings could
all be acquired and applied based on the ubiquitous
computing, in order to adapt to users and meet the goals
of convenience and efficiency.
However, the tendency that a variety of smart devices
such as laptops, handhold PDAs, tiny sensor nodes etc.
are becoming common facilities in people’s daily life,
not only offers us with the fundamental platforms, but
also raises issues on how to take advantage of these het-
erogeneous devices to realize ubiquitous computing. Be-
sides, in order to deploy such smart environment, context,
which is defined as “any information that can be used to
characterize the situation of entities (i.e. whether a per-
son, place or object) that are considered relevant to the
interaction between a user and an application, including
the user and applications themselves” [3], is of para-
mount importance. Meanwhile, with the emergence of a
number of context-aware systems and smart environment
projects, the increasing natural demand on interconnect-
ing and integrating isolated smart spaces in real world
together into a higher level space known as a hyperspace,
also raises essential difficulties on how to implement
context-aware systems in large-scale intelligent envi-
ronment and how to expand context-awareness to dy-
namic, open systems.
As a feasible solution to those problems, agent-ori-
ented approach provides our research with the key fea-
ture of autonomy, collaboration and especially intelli-
gence. Furthermore, a novel architecture with multi-
agents to realize a smart hyperspace (including two sin-
gle physical intelligent environments, UbiLab and Ubi-
Dorm) is adopted by us.
The rest of this paper is organized as follows. Section

Y. C. JIN ET AL. 75
2 contains relevant researches on the fields of ubiquitous
computing, smart environment and agent-based approac-
hes. Section 3 explains the overview of our paradigm’s
framework and two essential components, namely, mid-
dleware and AI computing model. Section 4 details the
design and implementation of our experimental smart
hyperspace. Section 5 discusses the results and the
analysis of this work. And finally the conclusion and
future works are given in Section 6.
2. Related Works
2.1. Ubiquitous Computing
Industries as well as academia have advanced lots of ub-
iquitous projects, over the last two decades. SAFE-RD
[4], MARKS [5 ] and ETS [6 ] characterize the works car-
ried out by M. Sharmin, S. Ahmed, and S. I. Ahamed in
the field of ubiquitous computing, in which security, ad a-
ptability, efficiency, middleware, resource discovery and
self-healing are mainly investigated. MIT’s Oxygen pro-
ject [7], Carnegie Mellon University’s Aura project [8],
UC. Berkeley’s Endeavour Project [9] also devote their
efforts to a number of different aspects in realizing ubiq-
uitous computing (i.e. large-scale computing, QoS, task
scheduling, context awareness) in some particular condit-
ions, significantly accelerating the growth of smart
spaces.
2.2. Smart Environment
So far, in spite of no existing explicit definition of what
smart environment exactly is, massive endeavors towards
proposing such prototypes are already ongoing. Accord-
ing to Mark Weiser, smart environment is “a physical
world that is richly and invisibly interwoven with sen sors,
actuators, displays, and computational elements, embed-
ded seamlessly in the everyday objects of our lives, and
connected through a continuous network” [10]. The Aw-
are Home Research Initiative at Georgia Tech, which is
viewed as one of the first living laboratories, aimed at
multidisciplinary exploration of emerging technologies
and services based in smart home [11]. Another relevant
research launched by The University of Texas at Arling-
ton which was known as MavH ome (Manag ing an Ad ap-
tive Versatile Home) project, focus on the creation of an
intelligent and versatile home environment with state-of-
the-art algorithms and protocols used to provide a cus-
tomized, personal environment to the users of this space
[12]. Besides, the increased interests of industrial labs in
constructing smart environments are evidenced by Micr-
osoft’s Easy Living project [13], IBM’s BlueEyes project
[14], and the Speakeasy project at Xerox PARC [15], etc.
2.3. Agent-Oriented Solution
An agent is a software entity that has some properties of
a human such as autonomy, reasoning, learning, and
knowledge level communication, etc. [16]. Agent-ori-
ented approach, which is highly expected to play a vital
role in achieving smart space in high level, has already
expressed energetic effects on ubiquitous computing and
smart environments in numerous applications. Essex’s
iDorm project targets to realize the vision of ambient
intelligence in health care environments by combining
the use of unobtrusive sensors and effectors with intelli-
gent embedded-agents [17]. A novel type-2 fuzzy sys-
tems based adaptive architecture for agents embedded in
ambient intelligent environments, a hierarchical fuzzy
genetic multi-agent architecture for building learning
mechanism, together with another novel life-long learn-
ing approach based on intelligent agents are addressed by
Essex’s group in [18–20], respectively. Other Contem-
porary researches also cover the following areas: using a
neural networks agent based approach to recognize dif-
ferent high level activities [21], systematic and useful
methodology to develop agent-based system [22], intel-
ligent agents involving case-based reasoning (CBR) and
Bayesian Network [23] etc.
Based on these aforementioned technologies and theo-
ries, a foundation for our work had been laid. However,
dissimilar with the related projects, we introduce the
mobile agent to our intelligent hyperspace, in which im-
proved operation efficiency, minimal manual interaction,
seamless context exchange between multiple smart
spaces, optimal control strategy for a large and dynamic
environment are all ensured by us.
3. Framework
3.1. System Architecture
Figure 1 shows the configuration of our experimental
smart hyperspace, which involves the UbiLab and Ubi-
Dorm. We select laboratory and dormitory to build our
prototype, because they are the most common places a
researcher’s daily activities may cover. In our research,
both of them are furnished with many various devices
ranging from small ones like RFID and embedded nodes
suited wireless sensor networks (WSNs), to middle ones
such as PDAs, mobile phones and gateway, and to large
ones represented by laptops and PCs. These either mo-
bile or immobile devices are distributed over their local
physical space, constituting a smart network, which is
capable of obtaining context, computing, and providing
services to users. These two physical separate spaces co-
nnect with each other by telecommunication net such as
GPRS and GSM, Internet, etc. So they can be regarded
as the conceptual smart large environment as a whole.
C opyright © 2010 SciRes. WSN

Y. C. JIN ET AL. 76
Figure 1. Smart hyperspac e configuration.
Figure 2. Agent-based architecture for smart hyperspace.
As depicted in Figure 2, the architecture for smart
hyperspace is a hierarchy of rational agents, which are
able to accomplish their specific tasks to meet the overall
goal, and a set of concrete functional layers, which co-
operate to realize our system. The following details the
interpretation of each layer.
a) Device Layer (DL): The Device Layer contains the
very basic hardware including daily appliances (i.e. fan,
light, alarm, etc.), wireless sensor nodes, network hard-
ware such as gateway, and user interface devices involv-
ing PCs, PDAs, and mobile phones. This layer takes
charge of gathering context directly from surroundings
through sensors installed in tiny wireless nodes or from
user interface devices and other sources, controlling ap-
pliances according to the upper layer’s commands, ex-
changing physical bytes throughout heterogeneous net-
works.
b) Logical Interface Layer (LIL): The main task of this
Layer, whose crucial part is middleware, is formatting
and extracting the data either from the lower layer, DL,
or the higher layer, LCM. It is also responsible for man-
aging some necessary information between the agents
and users.
c) Local Context Management Layer (LCM): This
layer takes the responsibility of gathering, storing, and
generating useful knowledge to make our experimental
prototype operating as a smart entity. Besides, its duties
also consist of integrating both contextual information
and the message came from Space Interconnection Layer,
mining data, making proper decisions, and coordinating
the tasks allocated to different agents. In particular, a
database and a computing module with sufficient artifi-
cial intelligence play the most important role in this
layer.
d) Space Interconnection Layer (SIL): In order to re-
alize a hyperspace, we lay this layer in the top of our
hierarchical framework. Internet, GSM, and some other
widespread nets provide basic platforms to this layer.

Y. C. JIN ET AL. 77
Due to its existence, it is possible to build up the effec-
tive and efficient links between two physical separate
spaces (or further, among more than two spaces).
3.2. Middleware
Figure 3 shows the architecture of DisWare developed
by us [24]. This mobile agent-based middleware, which
chiefly contains interface for instructions, agent man-
agement module, system management module, and in-
terface for network communication, mediates between
mobile agents and OS layer (and device layer). It sepa-
rates applications from the specific design of system.
Consequently, the flexibility and availability of the
whole system are greatly improved. There are four major
parts com po si ng our Dis Wa re.
a) Instructions Interface (II): This interface provides
the instruction set for mobile agent, so that we could re-
alize different types of agents by simply modifying the
particular code of agent.
b) Agent Management (AM): Agent Resource Man-
agement, Agent Transfer Control, and Execution Man-
agement constitute the Agent Management module. This
module views agent as a certain class, management of
agent’s resource, dispatch and retraction of agent, man-
agement of execution queue etc. as functions in this class.
Some characteristics such as modular, encapsulation etc.,
which exist in object-oriented programming, are also
emphasized here to benefit the design of agent.
c) System Management (SM): This System Manage-
ment comprises Network Management, Device Man-
agement, and Memory Management. Information man-
Figure 3. Architecture of DisWare.
Figure 4. The construction of DisWare agent.
agement of neighbor related with nodes and sensors, al-
location of both local and remote memory are involved
in this module.
d) Network Communication Interface (NCI): This in-
terface takes charge of the contact with OS layer, device
layer and SM. The differences among heterogeneous
operating systems (we choose TinyOS and MantisOS for
WSNs, WinCE for PDA and smart phone, and WinXP
for PCs in our DisWare) are eliminated thanks to this
interface as well. As a result, System Management mod-
ule could call some corresponding functions directly to
configure the wired or wi r el ess commu ni c at ion.
3.3. Mobile Agent
Mobile agents are programs that can migrate from host to
host in a network, at times and to places of their own
choosing. The state of the running program is saved,
transported to the new host, and restored, allowing the
program to continue where it left off [25].
To meet this requirement, our DisWare agent pos-
sesses these three components: static code, context stor-
age, and state management as demonstrated in Figure 4.
Our DisWare agent runs static code which contains some
static functions to fulfill the particular missions. The
code size is usually much smaller than data size in mo-
bile agent. Context storage takes charge of storing all
kinds of useful context information including sensor data
gathered from physical environment, agent ID, program
counter, operation pointer and the address of different
kinds of static codes etc. Recording the agent’s state
(running states, migration states etc.) is achieved by state
management.
3.4. Embedded Artificial Intelligence
Automation was initially targeted as soon as the concept
of pervasive computing and smart spaces first came into
our view. It is widely accepted that the development of
artificial intelligence (AI) is the k ey so lution to th is pro b-
lem. In our research, we combine the benefits of diverse
AI algorithms into our UbiLab and UbiDorm environ-
ment. Owing to the DisWare, real-time context gained by
hardware devices can be easily obtained by agents. Be-
C opyright © 2010 SciRes. WSN

Y. C. JIN ET AL. 78
sides the communication between different agents be-
come convenient, and other functions like sensor recon-
figuration, dynamic reprogramming, and remote task
assignment which are difficult to achieve by some tradi-
tional methods are all available. Based on these advan-
tages, we import a data mining module to clarify the
useful information from historical records, and a fuzzy
module [20] to fuzz the real-time context to reduce the
computing complexity. The neural network [21], which
has the ability of learning, adapting, predicting, making
suitable decisions, is considered as the essential part in
AI module, and genetic algorithm [19] is used to tun e the
neural networks. A rule management is set here to store
both the general knowledge and the correct rules. Even-
tually, a fault tolerance module filters the actions gener-
ated by the Decision Module. And then the behaviors
which are carried out finally, feed back to the History
Module. Figure 5 illustrates how these components col-
laborate with each other to improve the overall perform-
ance in our smart hyperspace.
The fuzzy module extracts information by categoriz-
ing the real-time context into a set of fuzzy membership
functions, so that a simple but effective approach is
formed to build models at a certain level of information
granularity. Once the agent has extracted the member-
ship functions and the set of rules from the user input
data, the fuzzy module has learnt how to fuzz those con-
texts. The sample rule is as follows:
IF Temperature is X1(It) and Light is X2(Il) and
Humidity is X3(Ih) THEN O is Y(CF). (1)
Where X1, X2, X3 are conditions of fuzzy logic member-
ships function. It, Il, Ih, are the sense data, representing
the exact values of temperature, light and humidity re-
spectively. Y is the fuzzy output, and CF is the confi-
dence factor attached to consequent part of this rule.
Our neural network is a multi-input multi-output con-
nection ist feed forwar d architecture with two hidd en lay-
ers. The conditions about surroundings, current time, and
the state of various kinds of home appliances are all con-
sidered as inputs, while the outputs are the related com-
mands taking charge of controlling corresponding effec-
tors. As soon as the users change the context, the neural
network is triggered to enter a new training. Iterations of
each training are set at 2000.
And the genetic algorithm’s experimental setup is as
follows:
Probability of crossovers: 90%; Probability of muta-
tion: 0.1%; Population: 20; Generation: 2000.
Fitness function: Fitness =
2
[]
n
desiredoutputactual output
 (2)
Where n represents the total amount of outputs.
4. Implementation
4.1. Terminal Device
To implement our experimental smart hyperspace, de-
signing terminal devices, which have the first contact
with users or surroundings, are expected as the primary
step. Figure 6(a) exhibits the UbiCell node with a sen-
sor board installed on it, which was designed by us be-
fore especially for such applications to perceive humid-
ity, temperature, and luminance information at one time.
Figure 6(b) displays the UbiDot node equipped with
pulse, body temperature and blood oxygen sensors.
Similarly, it can acquire all the three types of physio-
logical information simultaneously [26]. Figure 6(c)
expresses the gateway, whose mission is exchanging
messages throughout wireless sensor networks, GSM/
GPRS, and Internet. The wireless multimedia sensor
node used to capture sound and images is described in
Figure 6(d).
Figure 5. Hybrid AI te chniques into smart hyper space.
Figure 6. Various terminal devices.

Y. C. JIN ET AL. 79
Figure 7. Snapshots of GUI in various devices.
Figure 8. The architecture of DisWare agent.
4.2. User Interface
In our smart hyperspace, services should be supplied to
users conveniently through efficient interfaces on the
human interaction devices. Hence, graphical user inter-
faces (GUIs) are designed to hit this target. Figure 7(a)
presents the GUI of real-time monitor on temperature,
humidity and luminance [27] in PC. Their values would
be updated periodically according to the information the
agents transmits, so the changes could be identified viv-
idly. Unlike the PC, even a notebook PC device, devoid
of mobility is no longer th e problem of some PDA based
or smart phone based solutions. Figure 7(b) indicates the
mobile phone wireless application interface. Figure 7(c)
shows the initial welcome screen with five choices, while
one instance of history query interface is portrayed by
Figure 7(d). In our experiment, the GUIs in PC are
achieved using Visual C# in Visual Studio 2005. And the
GUIs in PDA or smart phone are implemented in win-
dows CE.net framework 2.0.
4.3. Design of Agent
The programming framework and strategies involved in
JACK Intelligent Agent [28] are consulted here in order
to carry out the agent-based solution. The advantages of
BDI [28] and feedback structure are mixed with each
other in the agent based on our DisWare, which could
also be called as DisWare agent. Hence, DisWare agent
could perform either some event-driven reactions or
aim-oriented processes on its initiative. In detailed im-
plementation, we compile our DisWare agent language
code into pure nesC (a programming language for deeply
networked systems), and calls the nesC compiler to gen-
erate executions for the nodes in wireless sensor net-
works. And we use C# language to realize the same
function in PCs, PDAs or smart phones. Figure 8 de-
scribes the components of our DisWare agent [24].
4.4. Learning Phase
In our work, the learning is achieved through interaction
with the actual environment. During the learning phase,
every request offered by users, together with the corre-
sponding environment states and other related informa-
tion captured by the mobile agents will be viewed as an
input sample. And whenever the request is received, this
neural network with the assistance of other AI techniques
discussed before, are trained based on the new sample set.
In our implementation, the summation of samples is no
more than 2000 due to the limitation of computational
resource. Case there are already 2000 samples, which are
stored in our system, when the new sample is added, the
earliest sample would be deleted from the database si-
multaneously. Thus, an in cremental and lifelong learning
phase is formed.
4.5. Experimental Scenario
All of the components mentioned above are involved in
our experimental test bed, the UbiLab workplace envi-
ronment as shown in Figure 9(a) and the UbiDorm as
represented in Figure 9(b) and Figure 9(c). Custom
power line control automates all the lights, fans, air-
conditioner, and other appliances such as fire alarm, hu-
midifier, etc. Perception of light, humidity, and tempera-
ture, smoke, motion, and switch settings is performed
through wireless sensor networks or wireless multimedia
sensor networks. Identity check-up was accomplished b y
RFID techniques and motion sensors as soon as the users
entered either UbiLab or UbiDorm, not only to prevent
illegal persons from our smart hyperspace, but also to
record the context about when the every single valid user
reached there and how many authorized experimenters in
C opyright © 2010 SciRes. WSN

Y. C. JIN ET AL. 80
the UbiLab in any specific time etc. The security of our
smart hyperspace is also insured by the smoke sensors
and the fire alarm. A gateway fulfills the target of inter-
connection among isolated physical spaces. And Figure
9(d) demonstrates a base node also can integrate the in-
formation, and communicate with PCs and other terminal
devices.
Figure 10 and Figure 11 show both the sensor layout
and the actuator layout in UbiLab and UbiDorm, where
six kinds of sensors or devices, three sorts of physio-
logical sensors, and a variety of effectors are involved.
Some UbiCell nodes are pre-installed to monitor the en-
vironment, while others, which are connected with ef-
fectors, take charge of controlling them. DisWare is in-
stalled in every single terminal device in order to manage
mobile agents. Volunteers in UbiDorm are additional
required to wear the UbiDot node to sensor the physio-
logical index. Figure 9. The UbiLab and UbiDorm.
Figure 10. The UbiLab layout.
Figure 11. The UbiDorm layout.

Y. C. JIN ET AL. 81
Figure 12. The precision on test set against experimental time.
Finally, as an illustration of techniques used in our re-
search, a continuous evaluation on this prototype system,
which lasted three weeks, has been conducted by us.
During this period, volunteers who didn’t take part in the
development of our system executed their daily work in
our UbiLab in the daytime, and one of them occupied the
UbiDorm in the night time. Both the lights, fans, others
effectors in laboratory and the home appliance such as
air-conditioner were the pre-configured instruments,
which could be operated by users in accordance with
their own feeling via the interface we designed in all
kinds of devices. For instance, when the user felt hot, he
would probab ly turn on the air-cond itioner. To achieve a
higher level goal, the condition that the user certainly
wanted to enter his cool dorm during harsh summer, is
under consideration too. So he could turn on the Ubi-
Dorm’s air-conditioner when he was about to leave for
UbiDorm a few minutes later, but still stayed in UbiLab.
A more complex situation would be as follow, if the us er
felt nervous partly because of the high temperature and
low level of humidity, consequently, his pulse would
become higher than usual. At this time, the user would
turn on the air-conditioner and humidifier, and be likely
to enjoy some bright music. Subsequently, after the ini-
tial monitor phase, our system would try to predict user’s
action based on the trained embedded artificial intelligent
module, and then automate corresponding actuators.
Meantime, for users, they were hardly aware of the cycle
that new introduced samples brought modification and
adaptation to our system every now and then.
5. Results and Analysis
Each day in this experimental period, we estimated the
performance of this system twice per day (at 9:00AM
and 9:00PM), which used our agent-based solution, by
inputting 30 simulated data (not including multimedia
information), and then identified whether the outputs are
correct by human. Meanwhile, in order to prove the
availability of learning mechanism using h ybrid AI tech-
niques, we disable only Fuzzy Module, only Genetic
Algorithm, both Fuzzy Module and Genetic Algorithm,
respectively, under the same samples.
Figure 12 summarizes the experiment’s consequence.
The Y-value in Figure 12 presents the quotient which
was obtained by the number of outputs match the simu-
lative environmental con dition to user’s demand dividing
by the sum of input data, while the X-value illustrates the
experiment time. It is obvious that because of insufficient
samples, the first three days’ (that is, the initial monitor
phase’s) execution seems not so ideal. However, with the
increment of samples, the precision grows. Besides, Fig-
ure 12 also addresses that the AI computing module
without Fuzzy module act a little inferior to that of hy-
brid AI. And if there’s no Genetic Algorithm adding in-
telligence to our system, there are no apparent relation-
ships between the amount of samples and its precision,
under what condition, the prototype performed much
worse.
In addition, we also established the solution with no
fuzzy module under the same situation, and then we
C opyright © 2010 SciRes. WSN

Y. C. JIN ET AL. 82
Figure 13. The received packets against experimental time.
Figure 14. The number of manual interaction with our system.
compared our system with that by counting the overall
received packets in every 12 hours. Figure 13 clarifies
the result of this comparison. Obviously, the non-fuzzy
solution’s overload is much higher than that of our full
functional system. And during our experimental time
both solutions perform stably in the every certain inter-
val.
Furthermore, the number of manual interaction with
our proposed system was recorded by us each day. Ow-
ing to the existence of the predicting and decision mod-
ule, the interactions were reduced apparently according
to Figure 14.
6. Conclusions and Future Work
In this paper, we have proposed an agent-oriented archi-
tecture for ubiquitous computing, as well as an actual
paradigm of smart hyperspace based on this novel struc-
ture, which is context-aware, capable of monitoring and
providing automation to researchers. It is proved that this
novel architecture involving DisWare, AI computing
module, multi-agents, a diversity of terminal devices and
user interfaces can be put together harmoniously and
successfully into practice. And the results also address
our propounded hybrid AI techniques perform availably,

Y. C. JIN ET AL. 83
effectively and efficiently.
Our future work experimental program includes the
plans about adding more physical spaces which can
cover almost each aspect of a person’s daily activities
such as classroom and car to our paradigm under this
architecture. In addition, we also aim to extend the types
of both sensors and actuators to complete a full func-
tional smart hyperspace, which really acts a unique test
bed for relevant further researches, and provides poten-
tial value to commercial activities.
7. Acknowledgements
We are grateful that the subject is sponsored by the Na-
tional Natural Science Foundation of P.R. China (No.
60773041), the Natural Science Foundation of Jiangsu
Province(BK2008451), National 863 High Technology
Research Program of P.R. China (No. 2006AA01Z219),
High Technology Research Pro gram of Nanjing City (N o.
2007RZ106, 2007RZ127), Foundation of National La-
boratory for Modern Communications (No. 9140C11050
40805), Jiangsu Provincial Research Program on Natural
Science for Higher Education Institutions (No. 07KJB
520083) and Special Fund for Software Technology of
Jiangsu Province. Postdoctoral Foundation of Jiangsu
Province(0801019C), Science & Technology Innovation
Fund for higher education institutions of Jiangsu Prov-
ince(CX08B-085Z、CX08B-086Z ), and the six kinds of
Top Talent of Jiangsu Province.
8. References
[1] F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E.
Cayirci, “Wireless sensor networks: A survey,” Comput.
Networks (Elsevier), Vol. 38–4, pp. 393–422, 2002.
[2] M. Weise, “The compute r for the 21st century,” Scientific
American, pp. 94–104, September 1991.
[3] A. K. Dey, D. Salber, and G. D. Abowd, “A conceptual
framework and a toolkit for supporting the rapid proto-
typing of context-aware applications,” Human-Computer
Interaction, Vol. 16, No. 2–4, pp. 97–166, 2001.
[4] M. Sharmin, S. Ahmed, and S. I. Ahamed, “SAFE-RD
(secure, adaptive, fault tolerant, and efficient resource
discovery) in pervasive computing environments,” IEEE
international Conference on Information Technology
(ITCC 2005), Las Vegas, NV, pp. 271–276, April 2005.
[5] M. Sharmin, S. Ahmed, and S. I. Ahamed. “MARKS
(middleware adaptability for resource discovery, knowl-
edge usability and self-healing) for mobile devices of
pervasive computing environments,” In Proceedings of
the Third International Conference on Information Tech-
nology: New Generations (ITNG 2006), Las Vegas, Ne-
vada, April 2006.
[6] S. Ahmed, M. Sharmin, and S. I. Ahamed. “ETS (effi-
cient, transparent, and secured) self-healing service for
pervasive computing applications,” International Journal
of Network Security, Vol. 4, pp. 271–281, 2007.
[7] The Oxygen Project,
http://oxygen.lcs.mit.edu/overvview. html.
[8] Project Aura, http://www.cs.cmu.edu/~aura/.
[9] The Endeavour Project, http://endeavour.cs.berkeley.edu/.
[10] C. Diane and D. Sajal, “Smart environments: Technology,
protocols and applications,” Wiley-Interscience, ISBN
0-471-54448-5, 2004.
[11] Essa, “Ubiquitous sensing for smart and aware environ-
ments: Technologies towards the building of an aware
home,” Position Paper for the DARPA/NSF/NIST Work-
shop on Smart Environments, July 1999.
[12] S. K. Das and D. J. Cook, “Designing smart home envi-
ronments: A paradigm based on learning and prediction,”
In: Mobile, Wireless, and Sensor Networks: Technology,
Applications and Future Directions (Eds. R. Shorey, et
al.), Chapter 13, pp. 337–356, John Wiley, 2006.
[13] The Easy Livi ng Project,
http://research.microsoft.com/ easyliving/.
[14] The BlueEyes Project,
http://www.almaden.ibm.com/cs/ BlueEyes/.
[15] W. K. Edwards, M. Newman, and J. Z. Sedivy, “The case
for recombinant networking,” Xerox PARC Technical
Report, 2001.
[16] M. Wooldridge, “Agent-based computing,” Interoperable
Comm. Networks, Vol. 1, No. 1, pp. 71–97, 1998.
[17] H. Hagras, V. Callaghan, M. Colley, G. Clarke, A.
Pounds-Cornish, and H. Duman, “Creating an ambi-
ent-intelligence environment using embedded agents,”
IEEE Intelligent Systems, Vol. 19, pp. 12–20, 2004.
[18] F. Doctor, H. Hagras, and V. Callaghan, “A fuzzy em-
bedded agent-based approach for realizing ambient intel-
ligence in intelligent inhabited environments,” IEEE
Transactions on Systems, Man and Cybernetics, Part A,
Vol. 35, No. 1, pp. 55–65, 2005.
[19] H. Hagras, et al., “A hierarchical fuzzy genetic
multi-agent architecture for intelligent buildings learning,
adaptation and control,” International Journal of Informa-
tion Sciences, Vol. 150, pp. 33-54, March 2003.
[20] H. Hagras, F. Doctor, V. Callaghan, and A. Lopez, “An
incremental adaptive life long learning approach for
type-2 fuzzy embedded agent in ambient intelligent envi-
ronments,” IEEE Transactions on Fuzzy Systems, Vol. 15,
No. 1, pp. 41–55, 2007.
[21] F. R. Illingworth, V. Callaghan, and H. Hagras, “A neural
network agent based approach to activity detection in
AmI environments,” In Proceedings of IEEE Interna-
tional Workshop on Intelligent Environments (IE05),
2005.
[22] S. Lee and T. Kim, “Generic engineering approach for
agent-based system development,” In Proceedings of
MATA 2002, LNCS, Vol. 2521, pp. 58–71, 2002.
C opyright © 2010 SciRes. WSN

Y. C. JIN ET AL. 84
[23] N. Kushwaha, M. Kim, D. Y. Kim, and W. D. Cho, “An
intelligent agent for ubiquitous computing environments:
smart home UT-AGENT,” In Proceedings of IEEE
Workshop on Software Technologies for Future Embed-
ded and Ubiquitous Systems, pp.157–159, 2004.
[24] W. F. Li, “Research on agent-oriented middleware key
technologies of wireless sensor networks,” Ph.D. disser-
tation, Nanjing University of Posts and Telecommunica-
tions, pp. 113–139, 2008.
[25] D. Kotz and R. Gray, “Mobile agents and the future of
the internet,” ACM SIGOPS Operating Systems Review,
Vol. 33, No. 3, pp. 7–13, July 1999.
[26] The UbiCell, UbiDot, http://www.wsns.net.cn/.
[27] R. C. Wang, L. J. Sun, et al., “Mobile agent-based wire-
less sensor network middleware,” Technical Report for
National 863 High Technology Research Program of
China, pp.188–249, December 2008.
[28] JACK Intelligent Agents,
http://www.agent-software. com/.