ISSN 2383-630X(Print) / ISSN 2383-6296(Online)

Journal of KIISE, Vol. 45, No. 4, pp. 395-402, 2018. 4

https://doi.org/10.5626/JOK.2018.45.4.395

․This work was supported by Institute for Information & communications

Technology Promotion(IITP) grant funded by the Korea government

(MSIT) (No.2015-0-00557, Resilient/Fault-Tolerant Autonomic Networking

Based on Physicality, Relationship and Service Semantic of IoT Devices)

․이 논문은 2017 한국컴퓨터종합학술 회에서 ‘Smart Agent Based Data

Aggregation for Smart City’의 제목으로 발표된 논문을 확장한 것임

논문 수 : 2017년 7월 31일

(Received 31 July 2017)

심사완료 : 2018년 1월 25일

(Accepted 25 January 2018)

CopyrightⒸ2018 한국정보과학회ː개인 목 이나 교육 목 인 경우, 이 작물

의 체 는 일부에 한 복사본 혹은 디지털 사본의 제작을 허가합니다. 이 때,

사본은 상업 수단으로 사용할 수 없으며 첫 페이지에 본 문구와 출처를 반드시

명시해야 합니다. 이 외의 목 으로 복제, 배포, 출 , 송 등 모든 유형의 사용행

를 하는 경우에 하여는 사 에 허가를 얻고 비용을 지불해야 합니다.

정보과학회논문지 제45권 제4호(2018. 4)

†

††

†††

††††

학생회원

비 회 원

학생회원

종신회원

:

:

:

:

경희 학교 컴퓨터공학과

munir@khu.ac.kr

경희 학교 컴퓨터공학과

saab0015@khu.ac.kr

robi@khu.ac.kr

경희 학교 컴퓨터공학과

doma@khu.ac.kr

경희 학교 컴퓨터공학과 교수(Kyung Hee Univ.)

cshong@khu.ac.kr

(Corresponding author임)

지연에 민감한 스마트 시티 서비스를 한 스마트 에이 트 기반의

동 데이터 수집 기법 연구(Smart Agent based Dynamic Data Aggregation for

Delay Sensitive Smart City Services)

엠디 시라 무니르† 살더 크룰 아베딘

†† 엠디 골람 라비울 알람

††

(Md. Shirajum Munir) (Sarder Fakhrul Abedin) (Md. Golam Rabiul Alam)

김 도†††

홍 충 선††††

(Do Hyeon Kim) (Choong Seon Hong)

요 약 스마트시티는 녹색기술과 더불어 지속가능한 사회 발 을 한 의 지능형 기술의 비 이

다. IoT 기반의 스마트서비스, 를 들어 스마트 운송, 스마트 건강, 스마트 홈, 스마트그리드, 스마트 보안

등의 응용 로그램은 질높은 삶과 웰빙생활을 보장하는 스마트시티의 핵심 원동력이다. 이러한 서비스들을

가능하게 하기 해, 애 리 이션은 다수의 IoT노드로부터 데이터를 수집해야한다. 이 경우, 스마트 시티

의 앙 집 식 네트워크에서 거 한 네트워크 트래픽을 리하는 것이 더 어려워진다. 본 논문에서는 스

마트시티의 분산된 고 도 네트워크에서 Dynamic Data Aggregation기반의 스마트에이 트를 도입하여

여러 서비스 제공 업체 명한 시민들의 서비스 요청을 충족시킴으로써 이 문제를 해결하 다. 마지막

으로 제안하는 스마트 에이 트 기반 동 데이터 집 모델의 결과를 시뮬 이션 한 결과, 서비스 이행

시간과 컨버 스와 련하여 제안된 근법에 한 성능이 향상되었다.

키워드: 스마트시티, 동 데이터 수집, 스마트 에이 트, 분산 토폴로지, 사물인터넷

Abstract Smart city is the vision of modern intelligent technology toward the sustainable

development of green technology and social development. Smart services e.g. smart transportation,

smart health, smart home, smart grid, smart security, and IoT based applications are the key enablers

of smart city, that ensure the quality life and well-being. In a bid to ensure the functionalities of those

services, the IoT applications gather data from numerous IoT nodes. In such a case, it becomes more

396 정보과학회논문지 제45권 제4호(2018. 4)

challenging to managing huge network traffic in the centralized network of smart city. Therefore, in this

research, we have focused on the resolution of this problem through the introduction of of smart

agent-based dynamic data aggregation (DDA) from distributed dense smart city network for city service

fulfillment. In this research study, we purposed to model a peer to peer fully distributed system using

distributed hash table chord protocol. We also proposed an algorithm for the IoT network and designed

smart agent based IoT node searching algorithm for crowd sourcing. Finally, we simulated the result of

the proposed smart agent based dynamic data aggregation model in an effort to achieve a higher

performance gain for the proposed approach in respect to service fulfillment time and convergence.

Keywords: smart city, dynamic data aggregation, smart agent, distributed topology, internet of things

1. Introduction

Smart city shall be able to integrate understandably

and seamlessly an enormous number of heterogeneous

sensors and actuators with microcontroller based IoT

devices. The existing research on IoT based smart

city includes defining IoT architectures, protocols

along with a wide variety of enabling technologies

[1-3]. The data center in the cloud is linked to a set of

services, such as electrical energy, water, and gas supply

provided in smart cities. Additionally, the smart agents

are goal oriented, autonomous, adaptive, and coopera-

tive to achieve the application specified targets [4].

Smart city like environment is comprised of more

than a billion of IoT nodes. In Figure 1, we consider

a typical smart city geographic area with densely

scattered access points (APs) (red diamond) and IoT

nodes (blue dot) where 51 access points (AP) and

2601 IoT nodes. It becomes challenging to provide

quick response for a single point controlled network

when the request are a massive amount. In a generic

system model of a smart city, first, all the IoT nodes

transmit data to the corresponding cluster head in a

certain geographic area. Then, the cluster head takes

a decision for sending the IoT sensor data to a

single point receiver based on the data priority [5].

However, the major problem of a centralized network

is, if the network somehow collapses, the whole

smart city network clamps down.

Fig. 1 APs (Diamond) and IoT Nodes (Dot)

For the smart city security system, there are many

surveillance cameras in the city which are connected

with some APs. In a typical IoT network, if the

security manager needs to monitor the real-time data

feed of 10 surveillance cameras, the manager needs

to send a request to the server or central AP. As a

result, there is an additional traveling cost for the

APs with a substantial amount of queuing delay.

Furthermore, the IoT network also depends on a

single point AP. Now the research question is if the

centralized AP is collapsed due to a large volume of

data request, how the AP will provide services for

the requester.

In this paper, we have proposed a fully distributed

peer to peer chord network for the smart city and a

smart agent-based dynamic data aggregation technique

for the smart city services. When there is a service

fulfillment or data collection request, the smart agent

is responsible for finding the AP list for the corre-

sponding IoT nodes from the chord lookup table and

it calculates the minimum traverse cost between the

APs with the individual delay cost. Finally, the smart

agent travels through the minimum cost APs for the

request fulfillment. This research work is extended

from our previous paper [6].

The rest of the paper is organized as follows:

Section 2 presents the background and current

research of smart city services whereas Section 3

provides the system model of the proposed model.

In Section 4 depicts the problem formulation and

algorithm design. Simulation and performance ana-

lysis are included in Section 5. Finally, we provide

the concluding remarks in Section 6.

2. Background

Mobility, transportation, public services, supply

chain management, security management are the

지연에 민감한 스마트 시티 서비스를 한 스마트 에이 트 기반의 동 데이터 수집 기법 연구 397

Fig. 2 System Model

basic components of a smart city like urban environ-

ment. In the smart cities, it is absolutely essential to

managing the natural resources for smart recycling

and usage. By the increasing number of smart city

application, the volume of multi-dimension IoT

sensor data needs to be processed in a very short

time period for ensuring better Quality of Service

(QoS) [7]. Previously, most of the smart city networks

are being managed in a centralized manner or based

on cluster head.

The traditional service fulfillment approach is to

find all-pairs shortest paths and construct a simp-

lified graph for the candidate nodes. By using a new

graph, the Traveling Salesman technique solves the

problem [8].

Distributed hash table (DHT) based peer to peer

chord topology is a well-known topology. It main-

tains a finger table for the whole network. For

managing this distributed network, it has used a

chord protocol so that it improves the scalability of

consistent hashing by avoiding the requirement of

having a global network knowledge at every node.

Moreover, in N-node network, the computational

complexity for traversing one node to another node

is O(log N), and also a lookup requires O(log N)

complexity to exchange messages [9].

The M/M/1 queuing model is a well-known and

easily tractable queuing model for the service fulfill-

ment [10]. In several distributed cloud computing

system, the M/M/1 queuing model is widely used which

can efficiently compute the waiting delay. Therefore,

in this paper we have used M/M/1 queuing model.1)

3. System Model

The proposed system model of smart agent-based

dynamic data aggregation for smart city IoT network

is presented in Figure 2. We have proposed a DHT

based peer to peer distributed network for smart city

where every AP is considered as a node and all

other IoT nodes are the elements of the APs. So, the

IoT devices are assigned with one hash key when

these devices connect to the APs. Therefore, we

maintain a finger table for the whole network where

the APs and IoT nodes are equipped with wireless

connectivity. The APs are connected with the BTS

and there is a backhaul connection between the base

transceiver station (BTS) and the core network. In

addition, the cloud is also connected to the core

1) However, in our system model, it is also possible to use M/G/1

queuing model. In that case, the following equation will be used

for calculating the queuing delay[11], m

398 정보과학회논문지 제45권 제4호(2018. 4)

network. The smart agent is a part of the distributed

system where there are multiple agents in the

network and every AP has a queuing model for

serving the request fulfillment.

4. Problem Formulation and Algorithm

We assume the APs are in a chord network and

all the APs of the smart city network follow the

M/M/1 queuing model for request fulfillment. In this

scenario, we focus on the QoS of service fulfillment

by improving the APs selection model for IoT node

and thus, minimizing the queuing delay for the pay-

load transfer. We have considered the APs selection

by using a modified finger table successor and

predecessor based value that takes the maximum

utility so that the network cost becomes minimum

between one AP to another AP. The minimum

network cost APs selection value generates a new

dynamic topology graph to find the minimum cost

APs for a service fulfillment request. We also consider

two types of delay in our model to minimizing the

network cost. First, we consider the AP to AP

traversing delay and second, the waiting delay for

the service fulfillment request from the APs.

Figure 3 depicts a simplified form of the smart city

network where, and are two service providers

with the smart agent and to are the APs. If

the smart agent from service provider requests to

AP for a service, the traverse time between

and becomes the traversing delay and also AP

has some waiting delay for the request fulfillment.

The queuing delay at each AP depends on the

average data transfer volume rate, service rate

and request rate from the service provider .

The summary of the notations is described in

Table 1.

We label the APs with the numbers 1, 2, …, n and

define as,

if

where ∀∈∀∈

is a binary indicator variable where

indicates the request is assigned to and

traverse through the AP to AP , and

otherwise.

Table 1 Summary of Notation

Notation Description

Set of the smart agent

Set of the APs

Requested service provider index

Destination AP index

Source AP index

Traverse delay from to

Processing time in

Request arrival rate from

Service rate from to

Upper bound for waiting time of requests

Data volume transfer rate

Distance cost from to

Total travel time from to per unit time

Total waiting time from to per unit time

For the n numbers of AP in the network, the

distance cost from AP to AP is . Where

… and ≠.

∀∈ ∀∈ ≠ (1)

Equation (1) is the total traversing cost for a

service fulfillment at AP for all possible active

APs form AP to AP .

The service rate from AP to AP is,

.

Here, and are the data volume transfer rate at

and processing time at respectively. Aggregated

service rate at is ∀∈ ≠

.

(2)

In equation (2), we calculate the expected waiting

time at. .

The total waiting time for the requests from the

per unit time is,

ϵ

ϵ

(3)

The total traverse time for requests from the

per unit time is,

ϵ

ϵ

(4)

So, the total delay is the summation of total waiting

time and total traverse time as follow,

지연에 민감한 스마트 시티 서비스를 한 스마트 에이 트 기반의 동 데이터 수집 기법 연구 399

ϵ

ϵ

(5)

The simplified equation of total delay for the

service fulfillment at AP is,

ϵ

ϵ

(6)

The network utility, which is the inverse function

of total cost as follows,

∀∈ ∀∈ ≠ (7)

The total utility gain, for the service request, is

given at equation (7).

ϵ

ϵ

(8)

Equation (8) represents the sum of total utility

for the service fulfillment request.

The objective of the optimization problem in (9) is

to maximize the total network utility.

max ∀∈ ∀∈ (9)

Subject to:

ϵ

ϵ

∀∈ ∀∈ (10)

ϵ

ϵ

≤ ∀ ϵ (11)

ϵ ∀ ϵ ∀ ϵ ≠ (12)

From equation (10) to equation (12) are the constraints

of the objective function (9). Equation (10) has the

guarantee that the request from a smart agent is

already sent to one AP and also every active AP

must have participated in the utility calculation from

the requested AP. Equation (11) determines the esti-

mated waiting time for any service request does not

exceed and in our formulation, we have assumed

that the value of is infinity. Finally, the last

constraint in equation (12) defined the binary decision

making of . The problem in (9) is an NP-hard

and integer linear programming problem. The centralized

solution of the problem (9) leads to large delay and

therefore, not applicable for the real-time IoT app-

lications.

In order to, solve the problem in (9), in this paper,

we propose a heuristic approach to solve this problem.

Firstly, a smart agent finds the APs list for candi-

date nodes from the chord lookup table and calculates

the all possible minimum traversing cost for APs by

using the assigned successor and predecessor APs.

Algorithm 1 Service Fulfillment

1: APList ← Finger table information for

requested APs

2: for each requested AP List

3: for each possible path

4: Initialize constraints using equation

(10),(11),(12)

5: Delay ← solving equation (6)

6: Utility ← solving equation (8)

7: Compute optimal APs using equation (9)

8: end for

9: Service fulfillment completion

10: end for

Besides, it calculates the minimum APs traversal cost

and at the same state smart agent also computes the

expected delay for those APs. Secondly, the smart

agent takes a decision about the maximum network

utility APs that leads to minimum cost for service

fulfillment. Finally, a smart agent fulfills the request

by traversing the minimum cost APs. Alg. 1 presents

the major steps of the proposed smart agent-based

service fulfillment algorithm.

5. Performance Evaluation

We have evaluated the performance of the proposed

approach using simulation in java platform. We have

also used python platform for the result analysis.

Figure. 4 represents the user interface that is deve-

loped and used to evaluate the performance of the

proposed smart agent algorithm.

In our simulation scenario, a chord network is

implemented by using open chord APIs’ [12], and the

smart agent minimum cost AP findings algorithm,

M/M/1 queuing model and the service fulfillment

module are implemented in java platform. We create

the Smart city peer to peer network where the number

of APs and IoT sensor nodes are 51 and 2601

respectively. We have simulated the result for 1 to

30 service fulfillment request parallelly. Figure 5 is a

typical visual description of a service fulfillment that

green line is the minimum costing APs of the service

by using proposed model.

400 정보과학회논문지 제45권 제4호(2018. 4)

Fig. 4 Smart Agent Application

Figure 6 describes the service fulfillment execution

time RTT (round-trip time) comparison for 1 to 30

service request at a time. The dot line (red) is an

existing sink node based centralized system, diamond

shape (blue) is for smart agent based peer to peer

system that is only considered the AP to AP traver-

sing minimum cost (maximizing utility) and rectangle

line (green) combined minimum cost (maximizing utility)

for AP to AP traversing and queuing delay. We get

Fig. 5 Service Fulfillment APs in Smart City Network

Fig. 6 Round-trip time (RTT) for Service Fulfillment

Fig. 7 Convergence Result Previous vs Proposed System

Fig. 8 Waiting Time vs. Utility

a better result when multiple service fulfillment request

s work together by using total utility.

Figure 7 shows the convergence time assessment

for three different cases where it shows that to fulfill

the 10 different service request our proposed model

converge with in 100 micro-second. However, others

two 1000 and 10000 micro-second respectively. We

can perceive that by the increasing number of request

it takes less convergence time compared with sink

node based model and travelling cost minimization

model.

Figure 8 presents the relation between the waiting

time and utility gain. Box plot from Figure 9

describes the mean value of the utility within a

range for three different analysis of the smart city

network.

The average time complexity of our solution is

O((2n)) and the optimal solution for a sink node

based solution is around O(2nn2) and in each

subproblem for this is O(2nn).

지연에 민감한 스마트 시티 서비스를 한 스마트 에이 트 기반의 동 데이터 수집 기법 연구 401

Fig. 9 Box Plot for Utility

6. Conclusion

Smart agent based DDA is a novel approach that

enables a wide variety of smart city services. The

proposed approach substantially helps to reduce the

network overheads by introducing decentralized net-

work management and utility-based AP selection.

Additionally, the computational and dynamic data

aggregation responsibilities are dispersed to multiple

APs instead of single AP. This method will signifi-

cantly reduce the risk of network failure in a dynamic

environment.

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open-chord(downloaded 2017, Mar. 04)

Md. Shirajum Munir received the

B.Sc. degree in Computer Science and

Engineering from Khulna University,

Bangladesh in 2010. He served as a

Lead Engineer at solution laboratory,

Samsung R&D Institute, Bangladesh

from 2010 to 2016. Since March 2017,

he is working for his Ph.D. in Computer Science and

Engineering at Kyung Hee University, South Korea. His

research interest includes internet of things, fog

computing, software defined networking, and machine

learning.

402 정보과학회논문지 제45권 제4호(2018. 4)

Sarder Fakhrul Abedin received the

B.S. degree in computer science from

Kristianstad University, Kristianstad,

Sweden, in 2013. Since March 2014, he

has been working toward the Ph.D.

degree in computer science and engi-

neering at Kyung Hee University, Seoul,

South Korea. His research interests include Healthcare

IoT network management, cloud computing, fog computing,

and wireless sensor network. Mr. Abedin is a Member of

the KIISE.

Md. Golam Rabiul Alam (M’17 , S’ 15)

received the B.Sc. degree in Computer

Science and Engineering from Khulna

University and M.S. degree in Inform-

ation Technology from University of

Dhaka respectively. He has been

awarded PhD in Computer Engineering

from Kyung Hee University in 2017. His research interest

includes machine learning, ambient intelligent technologies,

and internet of things.

Do Hyeon Kim received his B.S.

degree in communication engineering

from Jeju National University, in 2014

and his M.S. degree from Kyung Hee

University in 2017. He is currently

working toward his PhD degree at the

department of computer science and

engineering, Kyung Hee University. His research interests

include SDN/NFV.

Choong Seon Hong received the B.S.

and M.S. degrees in electronic enginee-

ring from Kyung Hee University,

Seoul, South Korea, in 1983 and 1985,

respectively, and the Ph.D. degree

from Keio University, Minato, Japan,

in 1997. He served Korea Telecom

Telecommunications Network Laboratory as a Director

of Networking Team from 1988 to 1999. Since 1999, he

has been Professor, Department of Computer Science

and Engineering, Kyung Hee University. His research

interests include future Internet, ad hoc networks,

network management, and network security.