mobile ims integration of the internet of things

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Mobile IMS Integration of the Internet of Things

in Ecosystem

Han-Chuan Hsieh, Jiann-Liang Chen, Ing-Yi Chen, Sy-Yen Kuo

Department of Electrical Engineering,

National Taiwan University of Science & Technology, Taipei, TaiwanDepartment of Computer Science and Information Engineering,

National Taipei University of Technology, Taipei, TaiwanDepartment of Electrical Engineering, National Taiwan University Dean,

College of Electrical Engineering and Computer Science

E-mail: [email protected]

AbstractA high-quality ecosystem plan may signify a mile-stone in the development of smart living. The Internet of

Things (IoT) is the key to establishing a high quality ecosystem.This study developed an IP Multimedia Subsystem (IMS) IoTarchitecture for ecosystem applications. The proposed mechanismgroups attach request signaling by agent-based operations toreduce network congestion and to enhance service quality. TheElectronic Product Code Information Services (EPCIS) platformis implemented and evaluated in a field trial. Performanceanalyses of the proposed architecture confirm that it can achievea high-quality ecosystem in terms of power consumption andnetwork performance with minimal loss of communication power.

Index TermsEcosystem, Internet of Things (IoT), IP Multi-media Subsystem (IMS), Smart Living, Electronic Product CodeInformation Services (EPCIS), Quality of Service (QoS)

I. INTRODUCTION

Ashton gave an apt definition about IoT If we had com-

puters that knew everything there was to know about things

using data they gathered without any help from us - we would

be able to track and count everything, and greatly reduce

waste, loss and cost. We would know when things needed

replacing, repairing or recalling, and whether they were fresh

or past their best. The IoT has the potential to change the

world, just as the Internet did. [1]. The vision is spreading out

research and development throughout the world [2, 3]. Now

one has foreseen the current advancement of IoT technology

in developing the identification and sensing technologies to

define a high-level interface that inferred form ecosystem.

Tansley defined an ecosystem as The whole system in-

cludes not only the organism-complex, but also the wholecomplex of physical factors forming what we call the envi-

ronment. [4]. Many ecosystems now developing worldwide

apply this concept to capture environmental information and

to interact with the environment by using identification and

sensing technologies [5, 6]. The IoT is an emerging architec-

ture that generally includes an ecosystem in which objects are

embedded with sensors and RFID tags that have the ability to

sense and identify environmental things.

An important problem is improving Quality of Service

(QoS) in ecosystems with IoT infrastructures. Since IoT is

still an emerging complex infrastructure, unexpected data rates

are inevitable. To manage data bursts, a suitable mechanismis needed for root cause finding; it must be able to handle

numerous data bursts caused by ecosystem applications. The

IMS was originally designed to evolve mobile networks to

deliver Internet Protocol (IP) multimedia to mobile users. The

IMS has become the core component within next generation

networks [7, 8]. This study proposes a tentative architecture

that combines IMS network and IoT to provide an infrastruc-

ture for high-quality ecosystem applications.

I I . FUNDAMENTAL C ONSIDERATIONS

This section gives some supporting considerations and back-

ground information for each component in the test-bed design.

A. Proposed IoT-IMS Architecture

This study effort is motivated according to the following

reasons:

The number of mobile device users has grown exponen-

tially for several years. Therefore, ecosystem applications

must be embedded in mobile communication frameworks,

and mobile communication operators are the object of this

proposed approach for improving service capability when

using ecosystem applications.

The most advanced technologies have been supported in

mobile devices. For instance, the latest smart phone in the

market can now use short range communication platforms

such as RFID, Near Field Communication (NFC) andBluetooth. Some smart phones are also equipped with

movement sensors, which enable them to complement

human activities.

For these two reasons, IoT technology should be imple-

mented in mobile communications infrastructures. We also

envision the use of the proposed framework as a research

test-bed. Aggregating those reasons the IoT infrastructure

framework namely IoT-IMS communication platform. The

purpose of the platform is to provide mobile devices with IoT

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber,

Physical and Social Computing

978-0-7695-5046-6/13 $26.00 2013 IEEE

DOI 10.1109/GreenCom-iThings-CPSCom.2013.347

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Fig. 1. IoT-IMS Communication Platform

Fig. 2. IoT-IMS Operation

gateway functionality and to enable ecosystem providers to

use network resources optimally and efficiently.

Figure 1 shows the IoT-IMS communication platform. The

lowest layer in the platform is the IoT Perception Layer,

which captures object information, including Things [ID] and

Sensing [Characteristic]. The highest layer is the IoT Appli-cation Layer, which provides the ecosystem applications. Be-

tween the Perception Layer and Application Layer is the IoT

Computation Layer, which enables the ecosystem to achieve

high-quality services over huge data sets. The main goal of

the IoT Computation Layer is to provide high-quality service

for IoT-based ecosystem applications. The IoT architecture

proposed in this study supports the EPCIS platform and the

IMS platform in providing high QoS for mobile ecosystem

subscribers. The two platforms are described further below.

EPCIS platform: The Fosstrak EPCIS platform is an

open source RFID platform developed by Christian Flo-

erkemeier, Matthias Lampe and Christof Roduner of the

Distributed Systems Group and the Auto-ID Lab at ETH

Zurich. Here, Fosstrak EPCIS is used to enable IoT

system managers to implement EPCIS Query and Capture

interfaces, which allows users to turn their MySQL

database into an EPCIS Repository.

IMS platform: The IMS platform, which usually com-

prises many different Call Session Control Functions

(CSCFs), has three main functions in a packet switching

core network: providing QoS for services, providing

extensible charging mechanisms to multimedia services,

and integrating All-IP services. The IMS architecture,

which is based on the 3GPP (3rd Generation Partnership

Project) system, defines the QoS policy module, which

consists of Policy and Charging Rules Function (PCRF),

Policy and Charging Enforcement Function (PCEF), pol-

icy repository and web management interface. The PCRF

sets the QoS policy rules for synchronizing and linkingthe signaling and transport layers. The PCEF in the trans-

port layer receives the QoS policy rules for transmitting

information for different configurations.

The EPCIS platform and IMS platform comprise the IoT

Computation Layer. The two platforms are integrated in this

layer to provide mobile IoT ecosystem applications. The

unique framework designed for this platform extends the use

of IMS functions to identifying IoT objects in a standardized

operation. The platform allows QoS-distinguished treatment

for each IoT application. Figure 2 shows that, technically, the

IoT-IMS platform is implemented in a cloud computing system

by a middleware service in the Home Subscriber Service

(HSS) module of the IMS and by the Relational DataBaseManagement System (RDBMS) module of the IoT.

B. IoT-IMS Operation

To perform the identification processes on the IMS frame-

work, the HSS database must coordinate with the EPCIS

framework. In the generic IMS platform, the HSS contains the

user database required for the application negotiation process.

When a mobile device identifies a particular object, it obtains

the Universally Unique Identifier (UUID) for the object and

then queries the UUID in the EPCIS RDBMS. The HSS

associated with the user account gateway concurrently evokes

the related object UUID to the HSS table. Generally, the HSS

table stores the user profiles according to the Application

Server (AS) profiles. In the IoT-IMS platform, however, theRDBMS (MSSQL, MySQL) queries the Object Name Service

(ONS) to obtain the related service and then posts the URI to

the HSS database at the field of application service. Therefore,

whenever the IoT gateway discovers and pairs its connection

to particular objects, the related services are updated in the

HSS database.

The platform shown in Fig. 2 is the IMS capability exten-

sion, which enables the IoT object identification system to

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Object(s) ONSRDBMS (EPCIS)HSSUE(IoT GW)

Pairing Connection

POST Object UUID

GET UUID

At gateway identification

GET UUID

GET URI

PUT URI

Fig. 3. IoT-IMS System Operation Procedures

Fig. 4. NetFPGA Platform Board

provide standardized operation and management of IoT net-

works. Eventually, it can provide QoS treatment for each IoT

application. The platform is implemented by integrating the

EPCIS middleware service into the HSS module of the IMS.

Therefore, the IoT gateway can discover any object at any

event, identify them and post their service to IoT application

layer. The identified object then triggers the PCRF function

based on the event classification from the IoT perception layer

to manage the IoT traffic priority based on the application

scenario. The IoT gateway functionality is embedded in the

mobile device to enable the event signature function to obtain

the ID and characteristics of the object.

Implementation of the platform in a cloud computing system

enables efficient resource allocation and platform scalability.

The resource allocation is subject to the QoS parameters used

for IoT applications and for object identification assessment

within the networks. When the identified object is classified

by the IoT perception layer, the PCRF functions of the IMS

are triggered. The PCRF parameter is then used to manageIoT traffic priority based on each application scenario. Figure

3 shows a system platform in which the mobile device is

operated as an IoT gateway to enable event signature to

identify the object ID and its characteristics.

C. OpenFlow-based NetFPGA Platform

This section proposes an OpenFlow-based NetFPGA plat-

form that applies the proposed QoS mechanism according to

Fig. 5. OpenFlow network topology

network conditions and then proposes an adaptive QoS strat-

egy forwarding routing flows. The Net Field Programmable

Gate Array (NetFPGA) is a low-cost open platform which is

proposed by Stanford University. Shows on Fig. 4, NetFPGA

contains an FPGA [9], four 1 Gigabit Ethernet ports, some

buffer memory (SRAM and DRAM), and a PCI interface.

The OpenFlow is an open standard which is based on an

Ethernet switch, with an internal flow-table, and a standardized

interface to add and remove flow entries in the network

environment. Imagine that if every developer wants to build

their own network in laboratory for experiments, much re-

source they would spend? As a result, campus network seems

to be the best solution. However, if the experiment affects

the original campus network, that may destroy the campus

network.

Network virtualization is the way to solve this problem

which not only can control the packets routing but also can

approach the load balance by sharing the load to other unused

wire, Figure 5 shows the OpenFlow implementation. Most of

the router, switch, access point (AP) are commercial products.

In order to keep their competitiveness, those companies try not

to release the inner part of the products. Although, it allows

users set the virtualized network function, but the performance

cannot reach what users estimate. The OpenFlow Consortium

proposed the concept of OpenFlow in 2008, which can easily

approach the goal of network virtualization allow researchers

to implement their creative ideas such as new protocols and

new applications on campus network and not affect the current

network. For this test-bed, OpenFlow is implemented with a

Capsulator function. The Capsulator mechanism enables IP

packets to be sent to other Capsulators. A Capsulator can

be linked through several networks. In each network, eth0 isused as a tunnel port to communicate with other Capsulator,

while links to the Internet. When the Border Port Ethernet

packet is received, it is added to the IP packet and then

transmitted to other Capsulators; when the Tunnel Port (eth0)

IP packet is received, the first IP packet header is removed.

After the Tag value is checked, the Ethernet packet is sent to

all Border Ports with the same Tag value. Since the operating

system is used in all segments in each terminal reorganization,

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Fig. 6. Capsulator OpenFlow network example

Fig. 7. Intelligent Agent Operation

Fig. 8. IoT-IMS Test-Bed

an Ethernet packet of any size can be given a tunnel. For

the above technique, Fig. 6 shows the numbers of links in

examples of Capsulator OpenFlow networks. Each network

has a Tunnel Port (eth0) and two Border Ports (eth1 and eth2).

The eth1 links the OpenFlow Switch and NOX Controller of

the Control Port, and eth2 links the OpenFlow Switch with

the Data Port, which may be LAN Root Switch the Data

Port. The NOX Controller can control the network in all

OpenFlow Switches; additionally, if network A and Network

B are directly linked to eth1 Port through two OpenFlow

Switches, they can communicate with each other.

Fig. 9. IoT-IMS Operation Procedure

III. PROPOSEDAGENT-BASEDM ECHANISM

Based on the proposed IoT-IMS platform, an agent-based

mechanism for enhancing QoS is proposed. The agent-based

mechanism includes an intelligent agent and a QoS mechanism

for cooperative QoS-awareness networking. By exchanging

agent messages, the objects are grouped and triggered only

a default bearer from first one, the reduced steps for the

remaining objects substantially reduces massive attach request

signaling.

Learning the behavior of an IoT-IMS network and its

patterns can improve the efficiency of an intelligent agent

in solving network problems. Agents are software modules

with cooperative capabilities such as assisting users and other

agents. Figure 7 compares the operations between an environ-

ment and intelligent agent. One learning mode is reinforcementlearning, which is based on the intelligent agent concept.

When a learning agent performs an action during a certain

status, the environment either rewards the learning agent with

a reinforcement value or punishes it.

IV. NETWORKI MPLEMENTATION

To enable the identification process and performance eval-

uation in the IMS framework, the network is implemented in

the vSphere cloud computing platform. The platform imple-

mentation consists of three Virtual Machines (VMs).

A. IoT-IMS Test-BedThe platform implementation in the IoT Medical application

scenario includes two sensor types: a blood pressure sensor

and a cardiology sensor. Both sensors are paired with the IoT

gateway through a Bluetooth network. Figure 8 shows that the

object is identified by the EPCIS mechanism, which registers

the AS (Medical Server) into IMS HSS database. When a

certain object is already identified, the AS receives data from

these sensors.

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B. Control Plane Grouping Mechanism

Figure 9 shows how the IoT-IMS test-bed achieves a high

quality ecosystem for IoT communication technology. The

first object of a specific group that the default bearer must

establish implies that the IoT-IMS operation procedure can

skip the default bearer grouping steps. Therefore, the test-

bed reduces handling, utilization, and attachment time. Afterthe flow analysis, the CPU utilization of the test-bed is

compared between the Conventional Flow and Proposed Flow.

The Proposed Flow is the result obtained by default bearers

grouping. Conventional Flow and Proposed Flow:

O(N, T) =

Nn=1

Pp=1

n,p (1)

where

O(N, T) is the complexity of service flows for Nusers

N is the number of users

T is the timing interval of the flown is thenth user

P is the total steps of service flow

p is thepth step

n,p is the delay of thepth step for thenth user

By grouping the common steps in the procedure into a single

user, the delays of the steps of the users are replaced by the

additional delays to accomplish the proposed flow. The time

consume by the Nusers by the proposed flow is expressed in

O(N, T) =Nn=1

Pp=1

n,p

=

P

p=1

1,p+

N

n=2

p

n,p+ n

(2)

where

O(N, T) is the complexity of service flows for Nusers

N is the number of users

T is the timing interval of the flow

n is thenth user

P is the total steps of service flow

p is thepth step

n,p is the delay of thepth step for thenth user

is the set of the necessary steps for theproposed flow

n is the delay of the proposed step for thenth user

V. PERFORMANCEA NALYSIS

Figure 10 depicts the signaling operation between IMS and

EPCIS when the object identification signals sent to applica-

tions are increased. Clearly, fluctuation in the CPU utilization

of the EPCIS server and IMS core is higher than that of the AS.

(a)

(b)

Fig. 10. (a) Platform Utilization in basic IMS scheme, (b) PlatformUtilization in signaling grouping mechanism

Moreover, the IMS core VM CPU utilization approximates

60%, AS VM approximates 25%, and EPCIS VM approx-

imates 70% (Fig. 10 (a)). Medical applications of the VM

CPU may increase the number of service requests from the

IMS client for IoT QoS management. The signaling grouping

mechanism reduces fluctuations in platform performance, and

the IMS and EPCIS VM achieve better performance (Fig. 10

(b)). Both VMs send signaling and information queries to the

service, which causes a higher system utilization compared to

other VMs. The final step is evaluating the CPU utilization

in IoT-IMS platform in cloud computing platform approach to

conduct general QoS management in IoT-IMS framework.

VI . CONCLUSION

A framework for an IoT test-bed over an IMS network is

presented to improve QoS in ecosystem applications. Con-

sidering the unique QoS parameters of IoT, which emphasizedata precision and accuracy, this framework provides an agent-

based scheme for managing ecosystem data according to net-

work layer behaviors. By using this test-bed for each behavior

in an IP core network, IoT-IMS network layer data gathered

by the network agent can be used to adjust specific QoS

requirements and optimize the platform utilization. The further

work will focus on middleware CPU loading optimization by

gateway tasks scheduling of IoT-IMS network layer.

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ACKNOWLEDGEMENT

The authors would like to thank the National Science

Council of the Republic of China, Taiwan for financially

supporting this research under Contract No. NSC 100-2219-

E-011-004 and NSC 101-2219-E-011-002.

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