2011 International Conference on Advanced Technologies for Communications (ATC 2011)

Similarity based Optimization Context Awareness In

Remote Healthy Service

Thi Hien Pham, Tien Trung Do, Thi Vu Anh Ha, Luong Pham Van

School of Information and Communication Engineering

Sungkyunkwan University, South Korea

phamhien@skku.edu, trungdt.ript@vnpt.com.vn, hathivuanh@skku.edu, pvluong@skku.edu

Abstract-Context awareness service is one of the key features in ubiquitous computing system. In heterogeneous pervasive computing system, effective context modeling and reasoning are important to enable the collaboration and distributed reasoning among the agents. The effectiveness of the previous approaches for distributed reasoning significantly degrades when a large number of agents are involved. In order to solve this problem we propose a layered context model facilitating distributed rea­soning. We also propose an similarity based context reasoning to optimize system performance. The performance of the proposed scheme is verified in remote health service, and it shows that the trust of reasoning outcome is greatly enhanced and the data traffic is effectively reduced.

Index Terms-context awareness; distributed reasoning; lay­ered context modeling; multiagents system; similarity.

I. INTRODUCTION

In open heterogeneous enviromnent, context awareness is

the key of the ubiquitous system. The main challenge in

such context-aware system is to make appropriate decisions

by promptly taking into account the user's context, the data

used to characterize the user's current situation. The task of

manipulating the context data in an intelligent way is a crucial

contemporary research, which is often referred to as context­

reasoning [10].

The recent research in similarity based context reasoning

tends to distributed collaboration employing multi-agent sys­

tem MAS. Here two problems need to be dealt with; how to

model and reason the context.

The existing approach using Bayesian network [6, 14] or

Petri net and fuzzy logic [7] for modeling the context and

apply distributed reasoning using the agents. An existing

solution of reasoning context employs a uniform context model

to merge all the agents' perspectives in the reasoning process

[I]. Each agent holds only a partial view of the context,

therefore, the perspectives of other agents need to be merged

with its own view for proper reasoning. This becomes even

more challenging and complicated if a large number of agents

are involved. Moreover, although the geometric model use for

context-situation is used in paper [I], there is no assumption

about the relation between points, especially their geometric

distance. Since discrete states of context can be mapped to real

values, no relation can be guaranteed for neighboring points

[9].

An important requirement for context-aware service in ubiq­

uitous computing enviromnent is efficient context modeling

978-1-4577-1207-4/11/$26.00 ©2011 IEEE 165

required for supporting the collaboration among the agents

considering individual perspectives. In this paper we propose

a layered context model approach in which higher level

context is inferred from lower level context models. Moreover,

based on Mahalanobis distance, the similarity based context

reasoning can take the correlation between context models

into account. Therefore, the proposed approach increases the

confidence of reasoning outcomes and maximizes the system

performance.

The rest of the paper is organized as follows. Section II

discusses the work related similarity based reasoning. Section

III introduces the proposed context modeling and similarity

based reasoning approach. Section IV deploys the proposed

context model and tests its effectiveness. Section V concludes

the paper and identifies the future work.

II. RELATED WORK

In an environment typified by complexity, openness, hetero­

geneity and context uncertainty, modeling and reasoning of the

contexts in a distributed manner are essential.

Similarity based reasoning is proposed in [3, 12]. In the

proposed approaches, fuzzy logic and ontology is used for ap­

proximate reasoning about the situation similarity. Fuzzy logic

is well suited for describing subjective contexts, performing

multi-sensor fusion of these subjective contexts and resolving

potential conflicts between different contexts [4]. However, in

the large scale agents system, the logic-based rules specity

become complex and difficult to gain overall picture of context

model [1].

III. PROPOSED SCHEME

In similarity based reasoning for multi-agent context aware

system, context modeling and the distribution of reasoning

process among the agents are the two key issues. Context

modeling deals with how context information is collected,

organized and represented. In this paper we propose a layered

context modeling based on multidimensional space which is

proposed in [1]. This model is proposed in the paper [13]. In

layer 1 (the lowest layer), a context model level 1 is inferred

from the original context information, while in each higher

layer, the context model is inferred from the adjacent lower

layer context without using the original context information.

A. Layered Context Modeling

• Definition 1 (Context): Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves [7].

• Definition 2 (Context attribute): A context attribute ai is defined as any type of data that is used in the process of reasoning about context, ai (t) is context attribute at time t. The attribute consists of name ani and value avi respectively. Therefore, it can be represented as (ani, avD. With each value avi, we give an acceptable region Ai- Table II is an example about the relation between context attribute noise level, attribute value and acceptable region.

• Definition 3 (levell Context State): Levell context state is a set of context attributes C1 = (aI, a2, ... , an). Level 1 context modeling is inferred from original context information.

• Definition 4 (Attribute weight): Given a set of context attributes (aI , a2, ... , an ) with associate weight (Wl, W2, ... , Wn ). A weight Wi E [0, 1] represents the importance of an attribute is in describing context.

n

L Wi = 1, (1 ::.; i ::.; n), Wi E [0, 1] (1) i=1

TABLE I LEVEL T CONTEXT MODELING

Level 1 context modeling Attribute Weight Temperature 0.3

Pulse rage 0.3 The patient is taking treatment Blood pressure 0.2

Amount of Activity 0.3 Stationary duration 0.2

TABLE II CONTEXT ATTRIBUTE - PULSE RATE

Attribute name Attribute val ue Acceptable range High pulse rate 90-120 BpM

Pulse Rate Medium pulse rate 70-90 BpM Low pulse rate 50-70 BpM

• Definition 5 (contribution): Given an acceptable re­gion A� with a contribution S of an element, reflects how well that element plays in inferring higher level context modeling. Table I describes the level I context state "professor is teaching" in studying class, in which represents relation of context level 1 modeling, context attribute, attribute weight.

• Definition 6 (Context state level (i+1)): A level i+1 f .. . CHI - (C

i C

i C

i) context state 0 source J IS. j - l,j, 2,j, ... , m,j where i � 1 to guarantee higher level context can be deduced from lower level contexts.

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• Definition 7 (Context weight): A set of level i con­text states from source j: (CLj' q,j' ... , C;:,,) are used

for inferring higher level context c;+ 1. Some contexts are important than the other [15]. Given associate context weights (wctj' wctj' ... , wc';,,) are assigned for relative importance of contexts in deducing the higher level context. Table III is an example when higher level context is inferred from the lower context.

m L w4,j = 1, (1 ::.; k ::.; m), WCk,j E [0, 1] (2) k=1

B. Similarity based Context Reasoning

Although the layered context modeling is proposed in our previous work [13], there is no criterion to classify the level of the context layer. Another proposal in the paper [I] also use the geometrical space to model context-situation, there is no assumption about the relation between the adjacent points.

A fundamental requirement for autonomic computing is to be able to automatically infer how human users react in similar contextual conditions [9]. In the paper [2], the degree of similarity is defined by Euclide distance. However, the two disadvantages of the Euclidean distance are: the Euclidean distance is extremely sensitive to the scales of the variables involved and blind to correlated variables. In this paper we propose Mahalanobis Distance to obtain the similarity of context models by taking the correlation between context models into account. Therefore, the confidence of reasoning process is increased.

• Definition 8 (The lvI ahalanobis Distance between context models): Given two context models CJ and

CrThe dissimilarity- Mahalanobis Distance measure of the same distribution with covariance matrix Sis:

where m is the total number of unique context attributes and qi is the sum of weight of overlap region between attribute .

• Definition 9 (The Degree of Similarity between two context models): The Degree of Similarity (DoS) between two context models CJ and Clk is:

DoSj1 = 1 - Dif fJf (4)

• Definition 10 (total Degree of Similarity): Given a set of known context models level k: Ch i = 1,2, ... , n). For a unknown context model leveli: Cj (i # k, I # j), the total Degree of Similarity between Cj and set of Clk

models (I = 1,2, ... , n) is:

m DoSj1 = L(1- Dif fJf)

1=1

IV. PERFORMANCE EVALUATION

(5)

In this section, we implement two simulations. In the first simulation is similarity-based context reasoning. Here Mahalanobis distance and Euclidian distance are compared.

TABLE III HIGHER LEVEL CONTEXT INFERED FROM LOWER LEVEL CONTEXT

Level 3 context modeling Level 2 context modeling wcL Level 1 context modeling wci

The patient is beling followed 0.2 The patient is sleeping 0.4

Patient's checking room The patient is doing exercise 0.6

The patient is being periodically healthy checked 0.2 The patient is taking a treatment 0.7

The doctor is checking 0.3

The patient is being in emergency situation 0.6 The patient is in sudden changing condition 0.5

The patient is in sudden changing activity 0.5

The system tries to adapt the new context by searching the distance is useful for predicting whether he or she has any

most similar model in the known context models. The second disease or not.

simulation is the effectiveness of proposed scheme in reducing

the data traffic.

Fig. 1. The architecture of context aware system.

A. Agent-based Context Aware System

The agent technology has been widely accepted as an

efficient paradigm for developing intelligent services when

distributed computing environment [8, 15]. In this paper we

implement a healthy remote service employ multi-agent con­

text aware system (figure 1). The system consists of context­

aware agent, aggregator, and service agent. In the low level,

the agent aggregator gather the context attributes by RF-ID

reader and then disperse them to infer the higher level context

model.

B. Similarity-based context reasoning

1) Degree of Similarity between People: In this part, we

assume there is a new person (Table I) who comes to the

hospital to check the healthy. Then, we can infer the confi­

dence of Mahalanobis distance and Euclidean distance when

they are used to predict the conditions of the patient. Note

that the criterions to define whether a person has any disease

or not was predetermined before. The first simulation tests a

normal person while the second simulation checks the diseased

person. In both of cases, Figure 2 and Figure 3 show that the

confidence obtained by Mahalanobis distance is much higher

than that of Euclidean distance. Therefore, the Mahalanobis

167

� SJ! 'E OJ '5 m

� m

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1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

1.0

0.9

0.8

0.7

..,. � 0.6

'E en 0.5 '5 � 0.4

al' o 0.3

0.2

0.1

0.0

0

---T- Euclidean Distance of Normal Person

� Mahalanobis Distance of Normal Person _ Euclidean Distance of Diseased Person -+- Mahalanobis Distance of Diseased Person

20 40 60 80 100

Time(ms) 120 140

Fig. 2. The predicted result of a normal person.

....

o

..... �

--T- Euclide:; Dislance of Normal Person

I � Mahalanobis Distance of Normal Person

_ Euclidean Distance of Diseased Person -+- Mahalanobis Distance of Diseased Person

.A'- '""-...,...

20 40

--

60 80 100

Time (ms)

�

120 140

Fig. 3. The predicted result of a diseased person.

2) Resource Consumption: In ubiquitous environment, the

context aware system updates information when the condition

of the environment changes. Therefore, the agent needs to

communicate with the other by exchanging the messages

between them. In this paper, we reference the hierarchical

P2P networking scheme which is proposed in the paper [16].

TABLE IV THE NORMAL AND DISEASED PERSON

Temperature (Celsius) Pulse rate (BmP) Blood pressure (mmHg) Amount of activity 36.5 - 37.5 80-120 38- 40 110-130

Here the sender agent compares the message with the previous

one before sending it. If the similarity between the messages

is high (Figure 4), the agent does not send the message

but saves it in the local store. This case happens because

of: the new context model is quite similar to the known

model (Mahalanobis distance based). As a result, the proposed

scheme decreases the amount of traffic when the context data

does not change frequently (Figure 5).

•

Compere

Fig. 4. Message composition in the sender agent.

4000 � Proposed Scheme _Scheme (1)

3500

3000

_ 2500

� � 2000

� � 1500 o

1000

80

Time(ms)

100 120 140

Fig. 5. Throughput of the proposed scheme compare to the scheme in (1).

V. CONCLUSION

In this paper, we propose a novel layered context model

in which higher level context model can be inferred from

lower level context models without any requirement of original

context information. The Mahanobis distance is used to define

the similarity between the unknown context model and the

known context model. The system tries to optimally adapt the

new model by finding the most similar known context model.

168

110-140 250-450 130-160 100-200

The proposed scheme is tested by computer simulations

to prove that our approach enhances the trust of reasoning

outcomes than the earlier approach. Moreover, a large amount

of data traffic can be saved in adapting two similar context

models. As a result, the proposed approach optimize the

system performance.

In the future work, we will research on how to apply

distributed reasoning autonomously to enhance personalize

intelligent service. We will extend the context model and

context adaption in stability and reliability for adapting context

aware pervasive environment.

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