A MIH-based Framework for Network Selection

in Future HetNets

Amina Gharsallah

, Nouri Omheni

, Faouzi Zarai

and Mahmoud Neji

NTS’Com Research Unit, University of Sfax, Tunisia

MIRACL Laboratory, University of Sfax, Tunisia

Keywords: Next Generation Wireless Networks, HetNets, Vertical Handover, MIH, Network Selection, RRM, MADM.

Abstract: The heterogeneity is one of the key concepts of the Next Generation Wireless Networks (NGWNs). How to

provide mobile users with the best connection anytime and anywhere become more important. In this paper,

we propose an efficient vertical handover framework based on Media Independent Handover (MIH)

technology, to address network selection in Heterogeneous Networks (HetNets) environments.

A performance analysis is done and results are compared with existing algorithms for vertical handover.

Results demonstrate a significant improvement with our developed approach.

1 INTRODUCTION

The Next Generation Wireless Networks (NGWNs)

are composed of multiple Radio Access

Technologies (RATs) that differ in bandwidth,

operating frequency, cost, coverage area, and

latencies. In this Heterogeneous Networks (HetNets)

environments, new Radio Resource Management

(RRM) schemes and mechanisms are necessary to

benefit from the individual characteristics of each

RAT.

An important RRM consideration for overall

NGWNs stability, resource utilization, user

satisfaction, and Quality of Service (QoS)

provisioning is the selection of the most appropriate

access network for a handover request. However,

choosing the best RAT is not an easy task and there

are many criteria to take into account when selecting

the access network.

The Media Independent Handover (MIH)

defined by the IEEE 802.21 standard was developed,

especially to facilitate interoperability and handover

among HetNets. This standard is in charge of the

handover initiation and preparation stage (Omheni,

2014). It introduces a logical entity called MIH

Function (MIHF). This entity hides the specificities

of different link layer technologies from the upper

layer entities. The upper layers entities communicate

with the MIHF to get information about the lower

layers. MIHF provides three main services: MIH

Event Service (MIES), MIH Command Service

(MICS), and MIH Information Service (MIIS).

The Media Independent Event Service (MIES)

provides services to the upper layers by reporting

events corresponding to dynamic changes in link

characteristics, status, and quality. The Media

Independent Command Service (MICS) enables the

upper layers to manage and control the functions of

the lower layers (physical and link) related to

handovers and mobility. A command to scan for

newly available links or to switch between available

links are typical examples of MIH commands.

The Media Independent Information Service (MIIS)

provides information about the characteristics and

services of the serving and neighbouring networks

while a Mobile Node (MN) moves.

However, no handover decision is made within

MIH, “the implementation of the decision algorithm

is out of the scope of MIH” (Lampropoulos, 2008).

Indeed, if within a single technology, the horizontal

handover can rely on the Received Signal Strength

Indicator (RSSI), with heterogeneous wireless

technologies, the vertical handover should consider a

multitude of parameters. The main difficulties for

such systems (HetNets) include the complexity and

the efficiency of the decision algorithm but also the

availability of the different decision criteria and

parameters.

In this research work, we propose an enhanced

IEEE 802.21 MIH based framework that integrates a

Vertical Handover Management Layer (VHML) for

Gharsallah, A., Omheni, N., Zarai, F. and Neji, M.

A MIH-based Framework for Network Selection in Future HetNets.

DOI: 10.5220/0006405500670074

In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) - Volume 6: WINSYS, pages 67-74

ISBN: 978-989-758-261-5

optimal network selection based on multi-criteria

metrics.

The rest of the paper is organized as follows:

In section 2, we describe the related work focused

on vertical handover and network selection in

HetNets. In section 3, we present the proposed

framework. In section 4, we present the multi-

criteria network selection algorithm. Section 5

shows the performance analysis of the new

approach. Finally, section 6 concludes the work and

suggests future work.

2 RELATED WORK

In this section, we review previous research that has

been done by other researchers on vertical handover

and network selection in HetNets. (Kumbalavati,

2015) summarized the current works in the vertical

handover on all three fronts: initiation, decision and

execution. The survey revealed the need for new

solutions to enable seamless handover with least

handover latency and reduced call drop ratio.

The proposed mechanism in (Pahal, 2014) makes

use of signal strength and residual time to initiate

handover. In heterogeneous networks, handover

decision based on the signal strength and residual

time is not sufficient. Indeed, signal strength,

available bandwidth, load, speed and packet loss are

among the other parameters that affect the

requirement of mobile user in terms of QoS

guarantee.

To support vertical handovers across HetNets,

several projects have begun to exploit the IEEE

802.21 MIH standard. (Omheni, 2014), proposed an

MIH based approach for handover initiation and

preparation in HetNets. The proposed framework is

based on the concepts of IEEE 802.21 for context

information gathering and optimized handover

decision-making. In addition, they presented a

network selection architecture and scheme that

provide a resource efficient mobility management

that aims at selecting the most suitable network

interface for each application. In (Amali, 2014), the

authors described a new framework of improved

media independent handover to perform vertical

handover in the context of HetNets. New functional

entities are introduced in the terminal side to

optimize the handover decision-making in the

proposed framework. Authors in (Bae, 2011)

proposed a Data Rate based vertical Handover

Triggering Mechanism (DR-HTM) based on IEEE

802.21 standard in order to maximize capacity of

both WLANs and cellular networks. In DR-HTM,

whenever a mobile node discovers WLAN, it

obtains achievable data rate of the WLAN by using

remote MIH services. Based on the information, the

mobile node determines execution of vertical

handover. In (Buiati, 2014), the authors proposed a

zone-based media independent information

service using the IEEE 802.21 standard to

accelerate the neighbour discovery procedure.

In the proposed scheme, the access networks are

associated and grouped in mobility zones, through

an efficient set of rules, to minimize the amount of

control messages flowing in the core network.

A number of researchers have proposed different

network selection methods for HetNets

environments. A multi-criteria access network

selection algorithm is proposed in (Verma, 2013).

The proposed methodology combines the Analytical

Hierarchy Process (AHP) to decide the relative

weights of criteria set according to network’s

performance, as well as the Grey Relational

Analysis (GRA) to rank the network alternatives.

This method mathematically presents a complex

solution. Processing a large number of parameters

the computational time is increasing and the user

terminal and infrastructure network elements are

additionally loaded so it is problematically

interesting but not adequate for a direct

implementation. Similar to this approach,

a combination of two Multi Attribute Decision

Making (MADM) methods: Analytic Network

Process (ANP) method and the Technique for Order

Preference by Similarity to the Ideal Solution

(TOPSIS) method, was proposed in (Lahby, 2011) in

order to develop an intelligent network selection

strategy. The ANP method is used to find the

differentiate weights of available networks by

considering each criterion and the TOPSIS method

is applied to rank the alternatives. In (Ahuja, 2014)

authors proposed a network selection scheme based

on weight estimation of QoS parameters in HetNets.

In this proposed scheme, the weight estimation for

the set of the network attributes is computed using

entropy and TOPSIS approach. The numerical

results show that the proposed model can be

effectively implemented to select the desired

network in a heterogeneous environment employing

triple-play services.

In this paper we focus on vertical handover in

next generation wireless networks. Our objective is

to propose an efficient vertical handover framework

based on IEEE 802.21 MIH standard to address

network selection in future HetNets.

WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems

3 PROPOSED FRAMEWORK

IEEE 802.21 has been basically designed to

facilitate the handover between heterogeneous

networks, but the logic of selection is left without

implementation. Thus, we propose an IEEE 802.21

enhanced MIH framework that integrates a Vertical

Handover Management Layer (VHML) for network

selection algorithm. We propose to implement our

proposed VHML between the MIHF layer and the

upper layer. Our new architecture retains message

flow introduced by MIH and MIH function.

The introduction of VHML between MIHF and the

upper layers must preserve the continuity of message

flow (commands and events) between local and

remote MIH-entities. Figure 1 shows the overall

proposed architecture.

Figure 1: Proposed Vertical Handover Framework.

 Lower Layers

The lower layers (Physical and Link) are responsible

for effective interface switching and handover

trigger generation through MIES. It gathers link

quality information and provides measurements.

 The MIHF Module

This layer is responsible for different tasks related to

the vertical handover initiation and links control.

It allows communication in both directions between

lower and upper layers through three services: event,

command and information services.

- The Media Independent Event Service (MIES):

It detects events and delivers triggers corresponding

to dynamic changes in link characteristics, status and

quality to the Multi-criteria Selection Module

(MSM) in the proposed Vertical Handover

Management layer (VHML).

- The Media Independent Command Service

(MICS): It provides a set of commands to the

VHML to control handover relevant link states.

The VHML is able to control the physical and the

link layer through the MICS.

- The Media Independent Information Service

(MIIS): It provides the information model for query

and response on network resources and capabilities.

It allows the MN to discover and obtain network

information within a neighbouring area.

 The Vertical Handover Management Layer

This additional layer is responsible for vertical

handover management. VHML is composed of two

main functional entities responsible for context

gathering, intelligent handover decision-making and

accurate handover triggering: Multi-layer Sniffer

(MLS) and Multi-criteria Selection Module (MSM).

Each functional entity has specific roles in the

architecture, as follows:

- The Multi-layer Sniffer (MLS): It detects, subtracts

and gathers information from several sources in

different protocol layers. For instance, from the

application layer, it determines the application-level

QoS and user preferences. At network layer and via

IEEE 802.21 MIH, MLS collects information about

the available Points of Attachment (PoAs) and sends

them its QoS requirements based on its context and

preferences. From lower layers, MLS gathers

physical and link layers information such as RSS,

packet delay and packet loss rate via MIES.

- The Multi-criteria Selection Module (MSM): It is

responsible for network selection. It gets information

about users and application requirements from the

MLS. It communicates with the MIIS to get

information about the characteristics and services of

neighbouring networks.

Based on the trigger events provided by the

MIES, on neighbour networks information provided

by the MIIS and users and application requirements

from MLS, this block checks for available networks,

A MIH-based Framework for Network Selection in Future HetNets

and selects the most appropriate as a target network.

If this latter provides sufficient QoS, the mobile

node hands over to this network. When a vertical

handover is required, the MSM sends decision

notification to the MICS to activate the lower layers

handover, and a notification to the upper layers to

activate the IP layer handover.

 Upper Layers

When a handover is required, the upper layers

handle the handover execution. Several IP protocols

at the network layer, such as Mobile IPv4 (MIPv4),

Mobile IPv6 (MIPv6), Proxy Mobile IPv6

(PMIPv6), Hierarchical MobileIPv6 (HMIPv6), are

able to perform the handover according to various

strategies.

4 NETWORK SELECTION

ALGORITHM

Currently, the implementation of the IEEE 802.21

standard considers only the radio signal strength

indicator as a parameter to determine the best

network. In next generation wireless networks,

handover decision based only on RSSI is not

sufficient to satisfy users’ need. In fact, radio signal

strength, available bandwidth, delay, and packet loss

are among other parameters that have an impact on

the mobile user in terms of QoS. For example, a bad

QoS, when using a real time application, may be due

to the poor support for bandwidth allocation and

data rate because of high load in the serving network

while the radio signal strength is good.

In this section, we propose a new access network

selection algorithm, based on additional parameters.

The considered factors are defined as:

F = (R,V,E,B,D,L,C,S) (1)

Where R, V, and E denote the RSS, the user velocity

and battery status respectively. B, D, and L denote

the available bandwidth, delay, packet loss rate of

the network respectively. C, S denote the cost of the

network in monetary units per bit, and the security

level respectively.

Since network selection in an environment of

heterogeneous RATs depends on several factors, we

focus on the MADM approach to realize a dynamic

interface selection. The main objective of MADM

approach is to determine the optimal network from a

set of candidate networks. Each alternative is

defined by a set of attributes. In our algorithm,

alternatives represent the candidate access networks

and attributes represent the candidate access

networks characteristics.

The MADM essentially consists of four steps:

identification of alternatives and attributes,

development of the attributes, weight determination,

and selection of the best alternative.

4.1 Identification of Alternatives and

Attributes

In this step, all alternatives and attributes involved in

the choice of the best RAT will be determined.

The application scenario for our proposed scheme

for the best network selection is a heterogeneous

network consisting of 3GPP LTE and WLAN

networks.

Network metrics are: RSS (R), MN velocity (V),

battery status (E), available bandwidth (B), delay

(D), packet loss rate (L), cost (C) and security

level (S). Some of these parameters can be provided

by the IEEE 802.21 standard such as the link quality,

security level, and cost. While others must be

estimated such as the available bandwidth and the

energy associated with the MN battery consumption.

The proposed MLS entity is responsible for

gathering all information about the attributes of each

alternative.

4.2 Attributes Development

Since the process MADM uses different attributes

with different units of measurement, it is necessary

to use the normalized vector of process when the

input matrix X becomes normalized matrix R.











∑













,



∈[01]

(2)

Where, i

∈

{1,…,n} is an alternative (i.e. access

network) among the set n of the alternatives, and

∈

{1,2,3,4,5,6,7,8} is an attribute (selection

parameter) from the set F of attributes involved in

the choice of the best RAT.

4.3 Weight Initialization using Entropy

Method

We have used entropy method [12] for weight

initialization. As it is more accurate, more

appropriate and is convenient to implement.

The entropy method consists of the following

algorithm.

WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems

Step 1: It is necessary to transform the model such

that all the attributes must be maximized.

Step 2: Determine the entropy of the attributes based

on the relation:





=

−1



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

×

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

∈



1,2,3,4,5,6,7,8



(3)

Step 3: Determine the deviation within each

criterion:

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=1−

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∈



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

(4)

Step 4: Determine the weight coefficients: If the

user equally prefers all of the parameters, then:









∑











(5)

If the user determines the subjective weights, then:





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



∑













(6)

Where w

is the weight selected according to the

service. After determining the initial weight

coefficients using the entropy method, the weights

are computed for the requested service using the

following equation.







ℎ



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

ℎℎ

 



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







(7)

4.4 Network Selection using TOPSIS

Method

TOPSIS is one of MADM techniques based on the

concept that the chosen alternative should have the

shortest Euclidean distance from the ideal solution

and the longest from the non-ideal solution.

The algorithm calculates perceived positive and

negative ideal solutions based on the range of

attribute values available for the alternatives.

The premise of the algorithm is that the best solution

is the one with the shortest distance to the positive

ideal solution and longest distance from the negative

ideal solution, where distances are measured in

Euclidean terms. This method is simple and it gives

an indisputable sequence of solution preference.

The following steps are involved in the application

of TOPSIS to the network selection problem.

Step 1: After criteria weights estimation, in general,

normalized matrix moves into the weighted matrix:

V = W.R (8)

Here, V represents the updated weight matrix, i.e.,

the multiplication of the weight coefficients matrix

generated by the entropy method and the proposed

weight assignment algorithm with the normalized

weight matrix.

Step 2: The ideal solution is the set:



=max



|j∈



,min



|j∈



′

(9)

Where J is the set of criteria that is being maximized

and J’ is the set of criteria that is being minimized.

This model is transformed while finding the weight

coefficients such that all of the parameters are

maximized and are based on that relation as well.

The ideal solution in this case is the set:



max



|j∈



1,2,3,4,5,6,7,8





(10)

The non-ideal solution in this case is the set:



min



|j∈



1,2,3,4,5,6,7,8





(11)

Step 3: The distance between each alternative, from

the positive ideal 





and negative ideal solution





are defined as follows:













−













(12)













−













(13)

Step 4: Finally, the ranking of networks is

performed by considering the relative closeness to

the ideal solution, expressed as:

















+





(14)

Where the best network is the one with the largest

relative closeness to the ideal solution.

A MIH-based Framework for Network Selection in Future HetNets

5 PERFORMANCE EVALUATION

5.1 Simulation Model

To evaluate the performance of the proposed

enhanced MIH-based multi-criteria network selection

scheme, we used the open-source MATLAB simula-

tor. The simulated network is presented in Figure 2.

We consider a 3GPP LTE networks overlaying

WLAN networks. Mobile users are uniform randomly

distributed within the networks and may freely move

in accordance with the Random Walk point mobility

model. The used propagation model in this simulation

is the cost 231 indoor office model.

Figure 2: Scenario topology.

The simulation results are obtained using typical

values of simulation parameters from Table 1

(Chaari, 2017). We have measured the packet loss

rate and vertical handover blocking probability. We

compare our work to RSSI based algorithm and

adaptive threshold algorithm (Amali, 2014).

Table 1: Simulation parameters values.

Parameters Values

Number of Macro cells

Number of Small cells per Macro cell

eNodeB transmit power

3GPP LTE system bandwidth

WLAN IEEE 802.11n data rate

LTE coverage(m)

Wi-Fi coverage(m)

Total number of mobile nodes

Mobile nodes distribution model

Mobile nodes Mobility model

Propagation model

Mobile nodes traffic model

Mobile node speed

46 dBm

20MHz (100RB; 180 kHz/1 RB)

100 Mbps

1000

250

420

Uniform randomly

random walk model

cost 231 indoor office model

Video application

Varies from 3 to 120km/h

5.2 Simulation Results

5.2.1 Packet Loss Rate

Packet loss rate is the ratio of the number of lost

packets and the total number of sent packets.

Figure 3 presents the rate of packet loss, according

to the rate of arrival handover requests values.

We notice from the curves that our proposed

algorithm outperforms the RSSI based algorithm and

adaptive threshold algorithm. This is because our

proposed algorithm makes the best network

selection which could minimize the total number of

unnecessary handovers resulting in improving the

total packet loss rate of the whole network.

Figure 3: Scenario topology.

From the figure 3, in case of using our proposed

scheme we can observe that the growth of packet

loss rate for video traffic starts from a rate equal to 2

requests/s, and the probability begins to grow and

reaches a value of 1 % for a rate equal to 7 calls/s.

Whereas, for adaptive threshold algorithm and the

RSSI based algorithm, the probability of packet loss

can reach about 1.7 % and 2.3% respectively for a

rate equal to 7 calls/s. The packet loss rate is

comparatively high for the other two methods

because the high number of unnecessary handovers

during a session.

5.2.2 Handover Blocking Probability

Handover blocking probability is the ratio of the

number of dropped handover requests and the total

number of handover requests. Figure 4 shows the

performance comparison of handover blocking

probability for video service.

WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems

The handover blocking probability in case of

using our proposed algorithm is always the lowest,

followed by the adaptive threshold algorithm, and

the RSSI based algorithm.

Figure 4: VHO blocking probability.

By comparing the curves, we note that the

proposed network selection algorithm, gives a slight

increase in handover blocking rate. This rate can

reach the order of 2%. While the handover blocking

rate for the other solutions increases rapidly with the

increment of the total number of handover requests

arrival rate. It increases to 4.5% in the case of using

the adaptive threshold algorithm, and around of 6%

in the case of using and the RSSI based algorithm.

These results of handover blocking probability are

observed for simulation experiments involving more

than 7 requests / second.

6 CONCLUSIONS

In HetNets environments, architectural and

implementation schemes are of prime importance to

achieve ubiquitous access and seamless mobility.

In this paper, we present an architectural solution.

We propose an IEEE 802.21 enhanced MIH

framework that integrates a Vertical Handover

Management Layer for multi-criteria network

selection.

Simulation results show that the proposed

network selection algorithm performs better than

other existing algorithms. It significantly reduces

handover blocking probability rate and packet loss

rate.

Regarding our future plans, we mainly intend to

further elaborate on the handover management

particularly we will interest to handover in vehicular

communication in advanced 5G network.

REFERENCES

Omheni, N., Zarai, F., Obaidat, M. S., Hsiao, K., 2014. “A

Novel Vertical Handoff Decision Making Algorithm

Across Heterogeneous Wireless Networks”,

Computer, Information and Telecommunication

Systems (CITS), 2014 International Conference, Jeju,

South Korea, pp. 1-6, July 2014.

Lampropoulos, G., Salkintzis, A. K., Passas, N., 2008.

“Media Independent Handover for Seamless Service

Provision in Heterogeneous Networks,” IEEE

Communication Magazine, Vol. 46, No. 1, pp.64-71,

January 2008.

Kumbalavati, S. B, Mallapur, J. D. 2015. “A Survey on

Vertical Handover in Heterogeneous Networks”,

International Journal of Informative & Futuristic

Research (IJIFR), Vol. 2, No. 9, pp.3028-3033, May

2015.

Pahal. S, Singh. B, Arora, A., 2014. “Performance

Evaluation of Signal Strength and Residual Time

based Vertical Handover in Heterogeneous Wireless

Networks”, International Journal of Computing and

Network Technology, Vol.2, No.1, pp.25-31, January

2014.

Omheni, N., Zarai, F., Obaidat, M. S., Smaoui, I. Kamoun,

L., 2014. “A MIH-based approach for best network

selection in heterogeneous wireless networks”,

Journal of Systems and Software, Vol.92, pp. 143–

156, June 2014.

Amali, C., Dhanasree, J., Ramachandran, B., 2014.

“Enhanced Media Independent Network Selection for

Heterogeneous Wireless Networks”, Institution of

Electronics and Telecommunication Engineers (IETE)

Technical Review, Vol.31, No.5, pp. 392-401, October

2014.

Bae, S. J, Chung, M. Y., So, J. 2011.S. J. Bae, M. Y.

Chung and J. So, “Handover triggering mechanism

based on IEEE 802.21 in heterogeneous networks with

LTE and WLAN”, International Conference on

Information Networking (ICOIN), Barcelona, pp. 26 –

28, January 2011.

Buiati, F., Villalba, L.J.G., Cañas, D.R., Orozco, A.L.S.,

Kim, T., 2014. “A Zone-Based Media Independent

Information Service for IEEE 802.21 Networks”,

International Journal of Distributed Sensor Networks,

Vol. 2014, pp.1-5, March 2014.

Verma, R., Singh, N.P., 2013.“GRA Based Network

Selection in Heterogeneous Wireless Networks”,

Wireless Personal Communications, Vol.72, No.2, pp.

1437-1452, September 2013.

Lahby, M., Cherkaoui, L., Adib, A., 2012. “An Intelligent

Network Selection Strategy Based on MADM

Methods in Heterogeneous Networks”, International

A MIH-based Framework for Network Selection in Future HetNets

Journal of Wireless & Mobile Networks (IJWMN),

Vol. 4, No. 1, pp.83-96, February 2012.

Ahuja, K., Singh, B., Khanna, R., 2014. “Network

Selection Based on Weight Estimation of QoS

Parameters in Heterogeneous Wireless Multimedia

Networks,” International Journal of Wireless

Personal Communications, USA, Vol.77, No.4, pp.

3027-3040, August 2014.

Chaari, M. B., Rejeb, S. B., Tabbane, S. 2017. “SON

Handover Algorithm for Green LTE-A/5G HetNets”,

Wireless Personal Communications, March 2017.

WINSYS 2017 - 14th International Conference on Wireless Networks and Mobile Systems