agent assisted mobility and load aware fast handoff scheme in wireless mesh networks

26 Int. J. Internet Protocol Technology, Vol. 7, No. 1, 2012

Copyright © 2012 Inderscience Enterprises Ltd.

Agent assisted mobility and load aware fast handoff scheme in wireless mesh networks

Neeraj Kumar* Department of Computer Science and Engineering, Thapar University, Patiala, Punjab – 147004, India E-mail: [email protected] *Corresponding author

Naveen Chilamkurti Department of Computer Science and Computer Engineering, Latrobe University, Melbourne, Australia E-mail: [email protected]

Abstract: Wireless mesh networks (WMNs) have emerged as a leading technology for providing various cost effective services to the end users in recent times. In this paper, we propose a new agent assisted mobility and load aware fast handoff (AMLFH) scheme in WMNs. As mesh clients (MCs) cross different boundaries, the respective agent in that domain calculates the load on mesh gateways (MGs) and guides the incoming MCs to the suitable MGs for handoff. This mechanism reduces the handoff latency. Agents exchange the gateway load index (GLI) and handoff latency values (HLV) in their respective regions before starting the handoff procedure. The performance of the proposed AMLFH scheme is evaluated by extensive simulation using various metrics. The results obtained show that the proposed scheme is quite effective than the existing schemes with respect to the metrics defined above

Keywords: wireless mesh networks; WMNs; handoff latency; agents; load.

Reference to this paper should be made as follows: Kumar, N. and Chilamkurti, N. (2012) ‘Agent assisted mobility and load aware fast handoff scheme in wireless mesh networks’, Int. J. Internet Protocol Technology, Vol. 7, No. 1, pp.26–38.

Biographical notes: Neeraj Kumar received his PhD in CSE from Shri Mata Vaishno Devi University, Katra, India and MTech in CSE from Kurukshetra University, Kurukshetra, Haryana. He has more than ten years of experience in teaching and research in the area of theoretical computer science and mobile computing addressing the issues such as routing, security, QoS, optimisation, cache consistency and management, handoff mechanisms and learning algorithms. He has more than 30 publications in reputed peer reviewed journals and conferences including IEEE, Elsevier, Springer, Taylor & Francis, and Inderscience. He is a reviewer of many international journals of repute. He has edited/editing special issue of more than five journals of repute.

Naveen Chilamkurti is currently working as a Senior Lecturer at Department of Computer Science and Computer Engineering, La Trobe University, Australia. He received his PhD from La Trobe University. He is also the Inaugural Editor-in-Chief for International Journal of Wireless Networks and Broadband Technologies launched in July 2011. He has published about 105 journal and conference papers. His current research areas include intelligent transport systems (ITS), wireless multimedia, wireless sensor networks, vehicle to infrastructure, vehicle to vehicle communications, health informatics, mobile communications, WiMAX, mobile security, mobile handover, and RFID. He currently serves on editorial boards on several international journals. He is a senior member of IEEE. He is also an Associate Editor for Wiley IJCS, SCN, Inderscience JETWI, and IJIPT.

This paper is a revised and expanded version of a paper entitled ‘A fast handoff scheme in wireless mesh networks using agent technology’ presented at Australasian Telecommunication Networks and Applications Conference 2011 (ATNAC 2011), Melbourne, Australia, 9–11 November 2011.

Agent assisted mobility and load aware fast handoff scheme in wireless mesh networks 27

1 Introduction An increase in internet users has led to the development of applications which can run in heterogeneous environments and provide the interrupted access to the end users at any time. These services may be located at some centralised server or they may be at some distributed sites. To access these services, different types of networks are used which may be wired or wireless. In this direction, special types of networks are emerging as a new powerful technology called as wireless mesh networks (WMNs). These networks are a special type of network having multiple hops and are self-configured, self-healing and cost effective, greater coverage, low up-front costs and ease of maintenance and deployment (Akyildiz et al., 2005; Zhang et al., 2006; Subramanian et al., 2008; Kumar et al., 2011). WMNs consist of mesh clients (MCs)/nodes, mesh routers (MRs), and mesh gateways (MGs) in which MRs provide connectivity to a set of MCs and MGs provide connectivity to the internet as shown in Figure 1. MCs may act as both relays, forwarding traffic to or from other MCs, or providing localised connectivity to mobile or pervasive wireless devices, such as laptops, desktops and other MCs (Akyildiz et al., 2005). Figure 1 is a three tier architecture in which bottom layer consists of MCs, the middle layer forms the mesh backbone in which various routers and associated links exist while at the top layer MGs exist which is connected to the internet directly to provide the un interrupted services to the end users. The MCs may reside in different domains and share the valuable information with each other during mobility.

The MCs in WMNs can roam in different network domains to access the services provided by the underlying network which provides seamless connectivity to all its clients. A WMN forms a wireless backbone which is integrated with the internet using MGs which act as the internet attachment point for MRs. The wireless link capacity of MGs could be a bottleneck in a WMN as the MCs roam in different domains to access a particular service since the traffic from the MCs in the WMN is directed between the MRs and the internet (Xie et al., 2008). Hence for efficient use of the services in WMNs, a handoff mechanism is required as the MCs enter into different domain. A handoff is a mechanism in which MCs move from one network domain to another network domain. It can be classified into two broad categories as: homogeneous and heterogeneous. In case of homogeneous handoff an uninterrupted service is provided to the end users whenever MCs move between different domains (Chen et al., 2004). In case of heterogeneous handoff, three processes are described namely as: handoff initiation, handoff decision and handoff execution (Kassar et al., 2008). The key phase to all these three processes is the hand-off decision phase in which MCs have to take a decision when and how to take the handoff be selecting the suitable access networks. Moreover, as WMNs is mainly used for internet applications, so handoff mechanism is very important issue for monitoring mobility management for roaming MCs in WMNs to provide end to end quality of service (QoS) (Buddhikot et al., 2005; Navda et al., 2005; Amir et al., 2006; Xie and Wang, 2008; Zhao and Xie, 2011).

Figure 1 Three tier architecture of WMN (see online version for colours)

28 N. Kumar and N. Chilamkurti

In this modern era, people want to use the services such a multimedia movies and songs, voice over IP (VoIP), video on demand, etc., from anywhere. But all these applications have stringent QoS requirements in terms of network latency and packet data rate. As the wireless network has limited range in a particular region and it suffers packet loss due to various factors such as interference, poor signal, and capacity of wireless channel (Subramanian et al., 2008; Kumar et al., 2011), hence a novel handoff mechanism is required whenever MCs cross the boundaries in different network regions. Although there exists many solutions for handoff mechanism in WMNs such as Chen et al. (2004), Kassar et al. (2008), Buddhikot et al. (2005), Amir et al. (2006) and Zhao and Xie (2011), but none of the existing solutions have considered mobility and load of the access points (APs) providing the services to MCs except Xie et al. (2008) and Xie and Wang (2008). Hence keeping in view of al these factors, in this paper we propose a new agent assisted mobility and load aware fast handoff (AMLFH) scheme in WMNs. In the proposed scheme, different agents are deployed at the APs in the respective regions. These agents communicate with each other and also keep track of load on that AP. As soon as MC changes its domain and requires a handoff, it passes the message for handoff to all the agents. The agents check their respective load and respond back the message to MC. The MC chooses the agent having minimum load for handoff. Moreover, the agent also keeps track of mobility of MC in different domains. This procedure reduces the handoff latency and packet loss during handoff which results an increase in the throughout and performance of the overall system.

The rest of the paper is organised as follows. Section 2 describes the background and related work. Section 3 describes the system model and problem formulation. The proposed AMLFH scheme is described in Section 4. Section 5 presents the simulation environment with results and discussions. Finally, Section 6 concludes the paper.

2 Background and related work The switching of MCs from one domain to another is called as handoff mechanism. During the handoff across different domains, the handoff latency caused by load balancing and mobility are the two key factors to be considered. Both of these two factors affect the overall performance of any handoff decision. The handoff latency should be minimised especially for real time delay sensitive applications such as VoIP, video on demand, etc. Over the years, a number of solutions have been proposed in this field keeping in view of handoff latency due to load balancing and mobility of the MCs. These are broadly classified into following two categories.

2.1 handoff mechanisms

Shin et al. (2004) have improved the latency of 802.11 handoff using neighbour graphs. The authors have described a novel and efficient discovery method using neighbour and

non overlap graphs. The proposed scheme reduces the total time spent in waiting for accessing a channel for handoff mechanism. Jooris et al. (2007) have proposed the use of a virtual AP with which a mobile station is constantly connected, to enable a fast handoff through cooperation from both APs and station. Huang et al. (2006) propose several architectures for better handoff performance base on 802.11-based protocols. Hasswa et al. (2005) have proposed a vertical handoff function for taking handoff decisions in wireless networks. The authors have discussed various factors and metrics which are key factors in taking the handoff decisions. Based upon these factors and metrics, they have defined a vertical handoff decision function (VHDF) which assign weights to different network factors such as QoS, power requirements, mobility, load balancing, etc. Onel at al. (2004) have proposed a multi criteria handoff decision scheme for the next generation communication systems. They have proposed a novel fuzzy logic-based handoff decision algorithm for the mobile communications systems. They have used a handoff decision metrics RSSI which is the ratio of the used capacity to the total capacity for the APs. They have also compared their algorithm with the received signal strength indicator (RSSI)-based handoff decision algorithm as well. Ezzouhairi et al. (2008) have proposed a fuzzy decision making strategy for vertical handoff mechanism. The author presents a review on the proposed vertical handoff management, and focuses on the decision making algorithms in vertical handoff. McNair and Zhu (2004) have proposed a vertical handoff mechanism for fourth generation multi network environment. Authors present a tutorial on the design and performance issues for vertical handoff in multi-network fourth generation environment. Nkansah-Gyekye and Agbinya (2006) have proposed a vertical handoff mechanism for wireless LAN. The authors give a fuzzy logic-based vertical handoff scheme involving some key parameters and the solution of the wireless network selection problem using a fuzzy multiple attribute decision making (FMADM) algorithm. Ceken et al. (2010) have proposed an interference aware vertical handoff decision algorithm for QoS support in heterogeneous wireless networks. The authors have proposed a fuzzy logic-based handoff decision algorithm for wireless heterogeneous networks. The parameters; data rate RSSI, and mobile speed are considered as inputs of the proposed fuzzy-based system in order to decide handoff initialisation process and select the best candidate AP around a smart mobile terminal. The proposed method takes interference power, which is referred to as interference rate, as another input to the decision process. Shi et al. (2010) have proposed a seamless handoff scheme in Wi-Fi and Wi-MAX heterogeneous networks. They have proposed a horizontal handoff scheme that reduces the horizontal handoff latency based on the location and movement pattern of a mobile node (MN). Also a vertical handoff scheme for providing seamless services between Wi-Fi and WiMAX networks is also presented. Xu et al. (2010) have proposed a faster and smoother handoff in AP-dense 802.11-based


wireless networks. The authors focus on improving the AP scan process, which is a bottleneck to handoffs mechanism and valuated critical handoff parameters through extensive analysis of the acquired data. Chen et al. (2011b) have proposed a cross layer protocol for mobility and handoff in LTE networks. Authors have present a cross-layer protocol of spectrum mobility (layer-2) and handover (layer-3) in cognitive LTE networks by considering the Poisson distribution model of spectrum resources. A cross-layer handoff protocol with the minimum expected transmission time is developed in cognitive LTE networks. Liu et al. (2011) have proposed a bidding model and cooperative game-based vertical handoff decision algorithm. The authors have formulated a multi-tenderee bidding model among heterogeneous access networks as a cooperative game process to seek for larger total payoff. Then, they have proposed algorithm to evaluate the network utility and standard deviation through simulation, and show that it is effective to achieve the load balancing and meet the QoS requirements of various applications using the proposed model.

2.2 Mobility management

Also there are many research proposals regarding the mobility in WMNs. Zhang et al. (2010) have proposed a hybrid routing protocol for mobility management in WMNs. The proposal consists of mobility management scheme for both link layer and network layer routing. Both intra-domain and inter domain mobility management have been designed to support seamless roaming in Wi-Fi-based WMNs. Wang et al. (2007) have proposed Ant-based solution which involves a network-based intra-domain fast handoff scheme. When a MC begins handoff, the new router sends a location update message to the location server. The former router sets up a temporary tunnel with the new router, and forwards the buffered packets. This former router then informs the correspondent node’s router to set up a data path with the new router for the MC. Huang et al. (2007) have proposed the hierarchically structured mesh mobility management scheme. In this proposal, three types of MRs are considered namely as gateways, superior routers, and access routers. When a handoff is activated, the prior router adds a temporary routing entry to forward the packets to the new router. Then, the location information of the MC is updated at the superior router. Navda et al. (2005) have proposed a cross-layer mobility management scheme called as iMesh. Every router in the mesh network has a routing table containing the paths to all MCs. Before handoff, a MC searches for new router candidates in the link layer. It broadcasts a probe request message to all routers in

its vicinity. Upon receiving the probe response messages, the MC selects the router providing the best link quality. Ren et al. (2007) have proposed mesh networks with mobility (MEMO) management which adopts a cross-layer mobility management solution. The IP address of a MC is assigned by a simple hash function, and it remains within the domain during client roaming. When a MC decides to change its router, the original router notifies the correspondent node’s router to initiate route discovery for the MC. Langar et al. (2009) have proposed mobility aware clustering algorithm with interference constraints in WMNs. The authors have proposed a new metric for clustering called as INX to improve the network throughput. Chen et al. (2011a) have proposed call admission control (CAC) and resource management by integrating SIP and QoS mechanisms for IEEE 802.11 e. The proposed resource management scheme can dynamically adjust the resource distribution among existing calls by controlling their supporting codecs and packetisation intervals. In this way, a multi-grade QoS is achieved with decreased blocking rate for new calls and less dropping rate for handoff calls in the proposed scheme.

3 System model Consider a WMN as consisting of MCs, MRs, and MGs. Each MR contains certain fixed number of radio interfaces. Let a WMN is represented by G = (V, E), where V = {v1, v2, …, vn} is the set of MRs and E = {e1, e2, …, en} is the set of edges/communication links in the network. MC can go from one network domain to another. Figure 2 contains four network domains (domain 1, domain 2, domain 3, domain 4) having APs, MRs which are serving the MCs in their respective domains. MCs send their requests to APs to MRs. MRs finally are connected to MGs. As shown in Figure 2, intra domain handoffs are controlled by respective MRs while in case of inter domain handoffs, MGs is the controller. In each case both the mobility and load on the controller is monitored before taking the decision of handoff to reduce the handoff latency. In each domain, one load monitoring agent (LMA) is deployed to keep track of load and mobility of MCs. Also one call transfer agent (CLA) is also deployed so as to transfer the call when the demand for a particular service can not be satisfied within the local domain. Both these agents are also communicating with each other to share the information with each other about the parameters such as load and mobility of the MCs.


Figure 2 Network model for fast handoff in different network domains (see online version for colours)

3.1 Problem formulation

Let S = {S1, S2, …, Sn} are the total number of services available in each domain and R = {R1, R2, …, Rn} re the MCs request received by LMA in a particular time interval. Let λ, μ, η are the request arrival, service and inter arrival time. Then the total load generated by all the requests from MCs is

1

ni

i

GLI LBI=

=∑ (1)

where LBIi, 1 ≤ i ≤ n is the load balancing index of each MC request which it generates during migration and arrival from one network domain to the new network domain where LBI for each request is defined by the Poisson distribution as follows:

( ), 1

R iii

SS eLBI i n

R

−= ≤ ≤ (2)

Define the handoff latency value (HLV) for the incoming request as

( )* _

1

1 11*

time stampn

i

eHLV RTT

n

λ

μ η−

=

⎛ ⎞⎛ ⎞− −⎜ ⎟⎜ ⎟

⎝ ⎠⎜ ⎟= ⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠

∑ (3)

where 1μ

is the mean service time for the request from

MCs, and RTT is the round trip time. It is the sum of request submitted to the acknowledgement received. time_stamp is the time during which the request from MCs

is received. 1η

is the mean inter arrival time.

Then the objective function can be defined as follow:

1

minn

i

i

Objective HLV=

= ∑ (4)

Subject to

min maxGLI≤ ≤ (5)


4 Proposed approach The proposed handoff scheme is divided in to two parts depending upon the mobility and type of request received by the MCs. These two categories are intra domain and inter domain handoff schemes. Each gateway has agents deployed to keep track of all the activities happening at his end. LMA is deployed to continuously check the load on the gateway. It calculates the value of gateway load index (GLI) for each incoming request and directed the request to intra domain or inter-domain handoffs. Both the schemes are explained in the coming sections:

4.1 Intra domain handoff mechanism

As soon as the MCs request come to the LMA, it will extract the information and check out if the load on the gateway lies between minimum and maximum value then extract the information from the header field of the received information. If the request can be satisfied within the same domain then service is provided to MCs otherwise it is handed over to CTA to locate the suitable service provider in other domain (inter domain handoff). LMA is the overall in charge monitoring the progress of handoff in a particular region. It will receive the clients request for handoff and handover them to the classifier which classify them according to their resource requirements. If the resource requirement is below a particular threshold then the services will be provide to the demanding MCs with in the same domain. The sequence of events are shown as below:

Definition: A classifier is a function which maps the incoming client requests to suitable domain as follows:

: ,f R θ→

where R = {R1, R2, …, Rn}and θ = {0, 1} depending upon whether the transfer is intra or inter domain.

4.2 Inter domain handoff mechanism

As shown in Figure 3, LMA transfer the handoff requests from MCs to the other gateway if the request can not be satisfied with the local resources. It transfer the request to CTA and start the timer. CTA replicates the agents and each is sent to other gateway where they contact the respective CTA for request satisfaction. During this process there exist considerable amount of handoff latency. Agents collect the data and respond back from their source and submit the data before the time expires. The minimum value of the latency is selected for the service to be handed off. The complete procedure to calculate the handoff latency is as follows.

Classifier classifies the clients requests according to their requirements for resource demands. Then from equation (3), the value of HLV for initial request can be calculated as follows:

( )* _

1

1 11*

time stampeHLV RTT

n

λ

μ η−⎛ ⎞

− −⎜ ⎟⎝ ⎠= (6)

where 1

1μ

is the mean service time for request1 from MCs

and t1 is the first time stamp for this request. In addition to this value there is a considerable delay for inter agent communication in transferring the request from one domain to another in case of inter domain migration. This will also depend upon the upward bandwidth available for the channel to communicate from one domain to another. Hence equation (6) can be modified as follows for inter domain migration.

( )* _

1

1 11*

* _

* _

time stamp

size

C

delay

eHLV RTT

nS time stampb

NAC time stamp

λ

μ η−⎛ ⎞

− −⎜ ⎟⎝ ⎠=

⎛ ⎞+ ⎜ ⎟⎜ ⎟

⎝ ⎠

+

(7)

where Ssize is the size of the request and bC is the bandwidth of the channel available for communication and NACdelay is the number of agents communication delay of the network. Then by taking the derivative of the HLV with respect to time_stamp will give the combined handoff latency for inter domain scenario.

Figure 3 Interaction between agents for load sharing for inter domain handoff mechanism (see online version for colours)

Hence ( ) 0,d HLVdt

= where t = time_stamp

( )* _ 1

* 0* _

time stampsize

delayC

eSRTT NAC

n time stamp b

λ

η−

⎛ ⎞+ + =⎜ ⎟⎜ ⎟⎝ ⎠

(8)

Equation (8) gives a rough estimation about the handoff latency during the migration of clients requests from one domain to another.


Figure 4 Sequence of activity during handoff mechanism in the proposed scheme

N

N

Start

LMA receive the client s request

Send requests to classifier

((resource_demand) < thr) Service is granted to clients

Transfer the request to CTA

Y

N

Start the timer with time stamp and launch CTA to multiple gateways

Calculate GLI for each gateway

Calculate HLV for each gateway

Choose gateway with minimum value of HLV and GLI

(( _ _ ) & &( ))

receive no replytime_stamp_expired

Y

Handoff request is accepted

End


Algorithm 1 Agent AMLFH algorithm

Input Parameters: N: Number of agents Queue_Size: Size of the input queue requests from MCs LMA: Load monitor agent CTA: Call transfer agent GLI: Gateway load index HLV: handoff latency value time_stamp: time to send the request for handoff Output: Fast handoff to MCs 1. MCs moves from one network domain to other network domain 2. If (MCs_enter_new_domain) then 3. Send the request to the nearest MG for service 4. Request is entered into the initial state 5. Request is sent to the gateway agent 6. Gateway agent check the load on the gateway 7. If (min ≤ load ≤ max) then 8. Receive the requests 9. Send the requests to classifier 10. Check the resource requirement of the request 11. Check the destination of the request by extracting the information from header field 12. If ((Request_resources)) < thr) then 13. Establish the connection in the same gateway and provide the service 14. Else 15. Call Procedure (LMA, CTA, HLV, time_stamp) 16. Procedure (LMA, CTA, HLV, time_stamp) 17. Start the timer with input as time_stamp 18. Launch CTA to multiple gateways 19. LMA on respective gateway accepts the request from CTA 20. Calculate the GLI on each gateway using equations (1) and (2) above 21 Calculate HLV value using equation (3) above 22. Request for the handoff is sent to the gateway having minimum value of GLI, 23. and HLV 24. Wait for the reply from the agent to which request is sent 25. If ((Receive_no_reply)) && (time_stamp_expired)) then 26. Drop the handoff request 27. Else 28. Handoff Request is transferred to the gateway from which acknowledgement is 29. received 30. End Procedure


5 Results and discussions To demonstrate the effectiveness of the proposed scheme, we have evaluated the proposed scheme in various environments with varying nobilities and load conditions. Various parameters are chosen to show the effectiveness of the proposed scheme under simulation environment. Following simulation environment are set up in the proposed scheme:

5.1 Simulation environment

We have studied the impact of the proposed AMLFH mechanism in WMNs by simulation using ns-2 (The Network Simulator NS-2, http://www.isi.edu/nsnam/ns/) in various scenarios. In the simulation study, we have considered the network of 512 nodes randomly placed in a 1,000 × 1,000 square metre area. Moreover, each MR is equipped with two interfaces: one is used for transmission and other is used for reception. We have compared the performance of the proposed scheme with Seamless handover in IPv6 (Oh et al., 2009), FHMIPv6 (Jung et al., 2005) and NDPR (Shen et al., 2005). The topology of the underlying network chosen is the grid topology as shown in Figure 5 in which a 16 × 8 topology grid with gateway is placed in the upper left corner and MRs in the corresponding square opposite corner, i.e., one gateway is deployed for 16 × 8 grid which may also sometime act as MR . We have considered four such grids each having 128 nodes in the network covering a total area of 1,000 × 1,000 square metre having 512 nodes which includes MCs, MRs, and MGs. We have considered 20 simulation iterations with each iteration is of 120 sec. The parameters chosen for evaluation of the proposed scheme are: handoff latency, packet loss, throughput, and end-to-end delay.

Figure 5 Network topology used in the proposed scheme

5.2 Discussion on results obtained

5.2.1 Impact on handoff latency

Figure 6 shows the impact of the proposed scheme on handoff latency. As shown in figure the proposed scheme

has lowest handoff latency in comparison to all the other schemes. With an increase in the speed of MCs, the handoff latency also increases, but the proposed scheme has least increase in handoff latency than the other schemes. This is due to the fact that in the proposed scheme, agent receives the incoming request to transfer to the suitable MGs depending upon the value of LBI. It transfers the request for handoff to the classifier which classifies the incoming traffic to intra or inter domain region. For inter domain transfer, the calculation of HLV is done which will estimate the exact value of this parameter. The advantage of using the agent is its adaption to the environment and replicates itself to move multiple locations from the source and submit its result after return. This shows the effectiveness of the proposed scheme compared to other schemes of its category as shown in Figure 6.

Figure 6 Impact of the proposed scheme on handoff latency (see online version for colours)

MCs Speed (m/sec.)0 10 20 30 40 50 60

Han

off L

aten

cy (s

ec.)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Proposed Seamless hanover in IPv6NDPRFHMIPv6

5.2.2 Impact on packet loss rate

Figure 7 shows the impact of the proposed scheme on packet loss rate. As shown in figure with an increase in the speed of MCs, packet loss rate also increased. But this packet loss rate is minimum in the proposed scheme as compared to other schemes of its category. The packet loss in the proposed scheme is less due to the use of agents. Agents replicates themselves from their source and submit the results from their launcher and adaptive to take the decision at their own if some fault occurs. This mechanism reduces the delay and packet loss in the process and hence there is an increase in packet transfer as shown in Figure 7. This shows the effectiveness of the proposed scheme compared to other schemes.


Figure 7 Impact of the proposed scheme on packet loss rate (see online version for colours)

MCs Speed (m/sec.)0 10 20 30 40 50 60

Pack

et L

oss R

ate

0.02

0.04

0.06

0.08


5.2.3 Impact on throughput

Figure 8 shows the impact on throughput of the proposed scheme with varying the data size and number of users. As shown in figure, with an increase in data size and number of users, throughput decreases in all the schemes. But the decrease in the proposed scheme is less compared to other schemes of its category. This is due to the fact that with an increase in size of the data and number of users there is an increase of the load on the network, but this load is controlled by the LMA agent in the proposed scheme. LMA

agent transfers the incoming requests to the classifier for intra and inters domain migrations. The values of LBI and HLV are also calculated separately to control the multiple incoming requests from different clients having variable data size requests. Based upon the size of the data, the requests are transferred to their final destination. This shows the effectiveness of the proposed scheme compared to other schemes of its category.

5.2.4 Impact on end-to-end delay

Figure 9 shows the impact of the proposed scheme on end-to-end delay with varying data size and number of users. As shown in Figure 9, with an increase in data size and number of users, the end-to-end delay increases in all schemes, but this increase is less in the proposed scheme as compared with the other schemes of its category. This is due to the fact that the proposed scheme calculates the HLV value which takes into the account the mean inter arrival time, service request and transfer time. These parameters give good estimation to take a decision about the call transfer to its final destination. Moreover agent communication and round trip time is also considered. With an inclusion of these factors, a rough estimation about the end-to-end delay can be done which guides the agents to make an adaptive decision about the handoff transfer. Hence there is a decrease in this value in the proposed scheme in comparison to other schemes of its category.

Figure 8 Impact of the proposed scheme on throughput (a) with data size and (b) number of users (see online version for colours)

Data Size (Mb.)0 10 20 30 40 50 60

Thro

ughp

ut (M

bps)

0

2

4

6

8

10

12


Number of users 0 2 4 6 8 10 12

Thro

ughp

ut (M

bps)

0

2

4

6

8

10

12


(a) (b)


Figure 9 Impact of the proposed scheme on end-to-end delay (a) with data size and (b) number of users (see online version for colours)

Data Size (Mb)0 10 20 30 40 50 60

End-

to-e

nd d

elay

(ms)

10

20

30

40


Number of users 0 2 4 6 8 10 12

End-

to-e

nd d

elay

(ms)

10

20

30

40


(a) (b)

Figure 10 Impact of the proposed scheme on (a) call blocking rate and (b) call dropping rate (see online version for colours)

MCs Speed(m/sec.)0 10 20 30 40 50 60

Cal

l blo

ckin

g ra

te (%

)

20

40

60

80


MCs Speed(m/sec.)0 10 20 30 40 50 60

Cal

l dro

ppin

g ra

te (%

)

20

40

60

80


(a) (b)

5.2.5 Impact on call blocking rate and call dropping rate

Figure 10 shows the impact of the proposed scheme on call blocking rate and call dropping rate with increase in MCs speed. As shown in Figure 10, with an increase in MCs speed, both call blocking rate and call dropping rate increases. But there is small amount of increase in both the values in the proposed scheme as compared to other schemes. This is due to the fact that the proposed scheme have used agent assisted load balancing and mobility management scheme which takes care of load on individual

MG and select the best MG using load balancing if the new incoming request for handoff comes by dividing these requests in to intra and inter domain regions. Incoming calls are kept in separate queues depending upon the data size and available resources and transfer accordingly to their LBI and HLV values. Hence there is a decrease in call dropping rate and blocking rate.

5.2.6 Impact on data delivery cost

Figure 11 shows the impact of the proposed scheme on data delivery cost with data size. The data delivery cost is


calculated by measuring total time taken in the number of migrations made from one domain to other during handoff procedure, i.e., if we set the value of time to maximum value in equation (8),then we get the data delivery cost for migration from one domain to other. As shown in Figure 11, the proposed scheme has least data delivery cost than the other schemes with increased in the size of the data. With an increase in size of data, the cost in terms of network resource consumption to deliver that data to the destination also increases. The data delivery cost is measured in terms of resource consumption in delivery the data to the total number of resources in the network. For the sake of simplicity, we have assumed network bandwidth, and delay as the total number of network resources available for our use. There are separate metrics to calculate both these values before making any decision about the incoming call transfer. Hence there is a decrease in the value of overhead and data delivery in the proposed scheme in comparison to other schemes of its category as shown in Figure 11.

Figure 11 Impact of the proposed scheme on data delivery cost (see online version for colours)

Data Size(Mb)0 10 20 30 40 50 60

Dat

a D

eliv

ery

Cos

t

0.0

0.5

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6 Conclusions WMNs have emerged as a new technology to provide the QoS to various applications such as VoIP, video on demand, video conferencing, etc. In this paper, we have proposed an AMLFH scheme in WMNs. An agent is deployed in each region which will monitor the load and mobility of the MCs in the region. As soon as the new MC enters in to the different region from its own registration domain and demands for resource access, the corresponding agent guides it when and to whom the handoff should be delivered. The proposed handoff scheme is divided in to intra and inter domain regions and a classifier is used for the same to distinguish among these regions. An inter domain handoff latency is calculated analytically which gives a rough estimation about this value and can be used for future reference. Moreover, GLI is also calculated using load balancing index which the MCs generates during migration

from one domain to other. The performance of the proposed scheme is evaluated with respect to the metrics such as handoff latency, throughput, packet loss rate, end to end delay, call blocking rata and call dropping rate and data delivery cost. The results obtained show that the proposed scheme is quite effective in providing the fast handoff with respect to the above metrics in comparison to the existing schemes.

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