CCIS 70 - Fuzzy Based Bandwidth Management for ... - Springer Link

Fuzzy Based Bandwidth Management for Wireless Multimedia Networks J.D. Mallapur1, Syed Abidhusain2, Soumya S. Vastrad1, and Ajaykumar C. Katageri1 1

Department of Electronics and Communications Engineering, Basaveshwar Engineering College, Bagalkot-587102, India [email protected] 2 Department of Electronics and Communications Engineering, BLDEA's college of Engineering and Technology, Bijapur-586101, India [email protected], [email protected], [email protected]

Abstract. After finding wide range of applications in the field of communications over decades, the wired networks are replaced by mobile and wireless networks and have become ubiquitous in the recent past. This is mainly due to the dynamic nature of these wireless networks. The interesting feature of the wireless communications is the need for connectivity at any place and at any time, which leads to frequent handoff. Due to larger bandwidth of wireless communications, the cell phones are not only used to communicate voice, but also to send and receive text, video and pictures. This requires some QoS criteria such as bandwidth utilization and time delay to be managed. In this paper, we present a novel approach for designing a high performance QoS management scheme that exploits attractive features of fuzzy logic and provide adaptation to dynamic cellular environment. Keywords: Fuzzy Logic, Wireless Multimedia Networks, Bandwidth.

1 Introduction Wireless/Mobile networking is one of the rapidly growing area in todays communication technology. Advancement in interactive multimedia applications, such as audio phone, movie/video on demand, video conference, video games, and so on has resulted in spectacular strides in the progress of wireless communication systems. In multimedia applications, data needs to be transmitted continuously thus demanding for larger bandwidth. Since bandwidth is the critical resource in wireless multimedia networks, it is necessary to employ mechanisms for efficient utilization of the available bandwidth. Due to the presence of inherent load variations in mobile multimedia network, there is a greater demand for efficient distribution of available bandwidth depending upon priority of calls, specially that for handoff call. Thus, there should be a mechanism to deal with these problems, which should not only deal with efficient distribution of bandwidth but should also take care of minimizing delay encountered V.V Das et al. (Eds.): BAIP 2010, CCIS 70, pp. 81–90, 2010. © Springer-Verlag Berlin Heidelberg 2010

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by accepted calls. In this paper we present one such mechanism for bandwidth allocation using fuzzy based techniques. In Fuzzy-based bandwidth allocation scheme, we assume that the multimedia applications can tolerate transient fluctuations in the QoS and allows for the temporary borrowing of bandwidth from existing connections in order to accommodate new and handoff call connections. Some of the users may move out of the cell thus releasing the allocated bandwidth for those specific users. The same can be redistributed with minimal disturbance to all of the existing users. This paper is structured into 6 major sections, where section 2 explains the previous works related to bandwidth management for wireless multimedia networks. Section 3 describes proposed scheme that highlights the Fuzzy based bandwidth management for wireless multimedia networks. Section 4 and 5 describes the simulations and results. Section 6 concludes the paper.

2 Related Work Previously published works relating to bandwidth management for wireless multimedia networks are briefly explained in this section. Developing a mobility and traffic model for multimedia mobile radio network according to measures from two considered services areas and analyzing by simulation is done in [1]. In [2], the problem of finding the reservation scheme that would minimize the amount of time for which bandwidth has to be allocated in a cell while meeting the QoS constraint is explored, hence an optimal time based bandwidth reservation and call admission scheme is proposed. The work in [3] examines QoS guarantees for bandwidth in mobile wireless networks, with a focus on reducing dropped connections on handoff. It develops a framework for analyzing issues relevant to handoff, and the main principal for this is the use of an arbitrary planar graph to model the adjacency relationships of cells in the network. The scheme in [4] investigates the issue of Optimal-Complete-Partitioning (OCP) policy, by introducing a conservative level of sharing into OCP, such that those sharing would have least adverse impact on the overall partitioning policy. The scheme in [5] proposes an on-line bandwidth reservation algorithm that adjusts bandwidth reservations adaptively based on existing network conditions. The major contribution of this work is an adaptive algorithm that is able to resolve conflicting performance criteriabandwidth utilization, call dropping and call blocking probabilities. In [6], the different aspects of handoff and handoff related features of cellular systems are discussed. Several system deployment scenarios that dictate specific handoff requirements are illustrated. The policy proposed in [7] is a threshold-based bandwidth reservation policy, which gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls according to their QoS constraints by reserving a maximum occupancy, i.e., a threshold, to each call class. The work in [8] proposes and evaluates a simple distributed adaptive bandwidth reservation scheme and a connection admission test for multimedia cellular networks that limit handover dropping probability to a prespecified target value.

Fuzzy Based Bandwidth Management for Wireless Multimedia Networks

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3 Proposed Work Mobile multimedia services have got enormous potential in the recent communication scenario. Since bandwidth and other resources like buffer, power consumption, etc are extremely valuable and scarce resources in these wireless multimedia networks, effective management of these resources is necessary to provide high quality service to users with different requirements. If the bandwidth required by each application is not available then the call dropping increases. Hence for efficient utilization of the bandwidth in order to avoid dropping of calls, we propose a fuzzy based bandwidth management scheme. In this section, we present the network environment, fuzzy based bandwidth allocation, dropping scheme and the algorithm for bandwidth management. 3.1 Network Environment A cell is considered to have several users operating in it. There is a chance of additional users joining the existing ones from adjacent cells due to handoff. New calls can also be generated within the cell by the existing users. Bandwidth allocation scheme exists at the base station. Which allocates bandwidth to new/handoff call according to certain criteria. We consider a borrowing-based bandwidth allocation scheme. Here we assume that the multimedia applications can tolerate transient fluctuations in the QoS and allows for the bandwidth to be borrowed temporarily from the existing connections in order to accommodate new/handoff connections. In other situation where the user moves out of the cell, the bandwidth is released. This bandwidth can be redistributed to all the existing users with minimal disturbance. 3.2 Bandwidth Allocation The difference between the required and expected amount of bandwidth of a connection is the actual borrowable bandwidth (shown in figure 1) and the cell may borrow some of these bandwidth from an existing connection in order to accommodate other incoming connections. The proposed fuzzy based bandwidth management scheme shown in figure 2 is located at the base station. This scheme comprises of QOS manager, fuzzy based dropper, bandwidth allocator, application database and fuzzy based allocator. The functions of each block are given below. •

QoS manager: It receives the request for the application connection from handoff/new calls with required specifications such as bandwidth, delay, etc. The fuzzy scheme computes the Dropping Factor (DF) and sends back to the QoS manager. QoS manager checks if DF=99, then it drops the call, otherwise it gives the output to Bandwidth Allocator. It allocates the bandwidth for requested applications, considering the input from QoS manager and also access allocation factor from the fuzzy allocator. If the bandwidth of requested application is less than the amount of bandwidth available or less than the total bandwidth borrowable from the pool of all the requests already being served, then the application will be accepted, otherwise rejected. It updates the application database with call information (bandwidth allocated, time delay).

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Fig. 1. Bandwidth borrowing

Fig. 2. Fuzzy based bandwidth management

•

• •

Fuzzy dropper: It accesses the parameters from database and calculates the dropping factor. The calculated dropping factor is used as an input to the QoS manager. Since handoff calls are given the highest priority, dropping factor for these calls is zero. Fuzzy allocation: It calculates the allocation factor for requesting application and decides whether to accept or reject calls by the help of time of entry of call request from the database. Application database: It is a database of all existing calls, containing information like bandwidth required, bandwidth allocated, delay associated with each call, type of call, bandwidth borrowable from each call, and so on. This information is given to bandwidth allocator, fuzzy dropper and fuzzy allocator.

3.3 Fuzzy Based Bandwidth Allocation Fuzzy controlled bandwidth allocation scheme consists of Fuzzification, Inference and Defuzzification steps, as shown in figure 3.


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Fig. 3. Fuzzy based dropping factor estimator

Fuzzy inputs like priority, packet size and rate of flow are considered in our proposed work for computing allocation factor. A single crisp value can take more than one linguistic values if the membership values overlap. In the Inference step, a set of rules called rule-base, which emulates the decision-making process of a human expert is applied to the linguistic values of the inputs to infer the output sets which represents the actual control signal for the process. Here we give the membership functions [G (BW), G (D) and G (AF)] for each of the considered fuzzy parameter and their range of linguistic values as depicted in figure 4.

Fig. 4. Membership function for input and output linguistic parameters (dropping)

• • •

Bandwidth: For bandwidth BW, its linguistic values are low (bw0 to bw2), medium (bw1 to bw3) and high (bw2 to bw4). Delay: For delay D, its linguistic values are low (d0 to d2), medium (d1 to d3) and high (d2 to d4). Dropping factor: For dropping factor DF, its linguistic values are low (df0 to df2), medium (df1 to df3) and high (df2 to df4).

The fuzzy bandwidth allocation scheme forms a fuzzy set of dimension G(BW) * G(D) * G(AF). The membership values of the assigned fuzzy variables depends on the network administrator, i.e., he/she can assign the different values at different instants of time depending upon the network conditions. To decide an appropriate output membership function, the strength of each rule must be considered. For this reason, the output membership function is a complicated function and hence center of area method is used for defuzzification. This method finds the center point of the fuzzy output membership function which is used for allocating buffer for requesting application. The fuzzy rule base table shown in figure 5 considers 9 different rules.

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In Defuzzification, the defuzzified output parameter gives flexibility to the network administrator to perform soft buffer allocation. The defuzzification method used here is center of area method. Bandwidth H H H M M M L L L

Delay L M H L M H L M H

Dropping factor L L L L L H H H H

Fig. 5. Fuzzy rule base table for dropping factor computation

Algorithm 1: Bandwidth allocation in a cell Begin • Receive application request from new calls and handoff calls. • if(bandwidth available), allocate the bandwidth to application request, calculate the remaining bandwidth after allocation. • if(bandwidth not available), call algorithm 2. Borrow from existing applications depending on borrowing factor. • Compute the bandwidth for requeted application. • Inform the source about the allocation and rejection. • go to step 1. • Stop. End. Algorithm 2: Computation of dropping factor Begin • Intialize fuzzy controller with delay of application, bandwidth allotted to the connection. • Find the membership function of each delay and bandwidth allocatted. • Find the dropping factor membership from above information. • Inform to bandwidth allocator. • Go to Algorithm 1. • Stop. End.

4 Simulation 4.1 Simulation Model A single cell environment with an area of (x,y) meters is considered. N number of users are generated in a cell comprising of both handoff and new calls. Maximum


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bandwidth of a cell is assumed to be BWmax Mbps, Maximum delay bound T msec. Bandwidth requests for each call generated randomly in the range BWreq1 to BWreq2 Mbps. The application requests may be from either new or handoff calls. Each application is having a priority p having linguistic values low (p0 to p2), medium (p1 to p3) and high (p2 to p4) that are randomly selected at the time of connection. Allocation factor is considered in the range (af0 to af2) for low, (af1 to af3) for medium and (af2 to af4) for high. 4.2 Simulation Procedure The simulation procedure consists of following steps, • • • •

Generate a cellular network. Generate the application/call requests. Apply the proposed scheme. Compute the performance of the end system.

4.3 Simulation Inputs Single cell environment with an area of (x,y) sq.Kms. Each cell is divided into grids of size z sq.Km., z = 1 sq.Kms. f % grids are in the blocking range, f = 10 %. The mobile nodes are randomly placed in any of the grids within the cell. Mobile nodes can move in any of eight directions: N, S, E, W, NE, NW, SE, SW. Speed of the mobile is randomly selected among the following: low(1 mt/sec.,pedestrian), medium(50 mts/sec., medium vehicle speed), and high(100 mts/sec., speedy vehicle). Bandwidth of the cell M = 50Mbps. Application request types from mobile nodes: Handoff and new calls. Minimum, maximum bandwidth requests for each call are generated randomly between [0.6 max, max], where max is randomly generated between 0.5 to 1 Mbps, 1 to 2 Mbps, 1.5 to 3 Mbps for low, medium and high delay of applications respectively. Maximum of n = 150 users are considered in a cell. Among the active users a % move within the cell and b % users are handoff from neighboring cells. Among the generated call requests 75 % are new calls and the rest are handoff calls. An accepted call lives upto its duration or gets dropped if moves into the blocking range or if bandwidth borrowed causes its share of bandwidth to be less than its minimum requirement. 4.4 Performance Parameters The performance parameters measured are as follows: • • • •

Dropping probability: it is defined as the total Number of call request dropped at the base station. Bandwidth utilization: it is defined as the ratio of bandwidth utilized to the maximum size of bandwidth available at base station. Calls accepted: it is defined as the ratio of calls accepted to the total Number of calls arrived. Time of new calls accepted: It is defined as the ratio of time of new calls accepted to the total time of all calls generated.

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5 Results The results that we obtained as a consequence of using the above stated approach one can clearly demonstrate the worthfulness of adopting fuzzy approach for bandwidth allocation. Further we show that by adopting the fuzzy approach, various QOS parameter viz., Bandwidth Utilization, number of call acceptance, etc increases as compared to non fuzzy approach. By observing the figure 6 we can notice that better bandwidth utilization is achieved through fuzzy approach. Figure 7 shows how number of calls accepted increases if we use fuzzy approach, as compared to non fuzzy. Figure 8 shows how the number of calls dropped decreases if we use fuzzy approach. In fuzzy approach the time required to accept the call is less as compared to non fuzzy approach and is as depicted in Figure 9. Bandwidth Utilization 50

Bandwidth Utilized

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20 Fuzzy scheme Non fuzzy scheme

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0

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20 30 No. of nodes

Fig. 6. Bandwidth Utilization

Fig. 7. Acceptance of Calls

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Fig. 8. Dropping of Calls

Fig. 9. Call Acceptance Time

6 Conclusions In this paper we proposed a scheme for Bandwidth allocation for multimedia applications by using fuzzy logic. The main objective is to use the available Bandwidth efficiently and decrease the rejection and drop of calls. One important characteristic of this bandwidth allocation scheme is that dropping & allocation is done looking at some fuzzy parameters of each application. Parameters considered are amount of bandwidth required and time delay. Extensive simulation results reveal that the scheme proposed features very low call dropping probability, low call rejecting probability, and good bandwidth utilization as compared to a traditional bandwidth allocation scheme.

References [1] Rejeb, S.B., Tabbane1, S., Choukairieure, Z.: Mobility model used for QoS management for wireless multimedia network. International Journal of Wireless and Mobile Computing 2(4), 350–361 (2007)

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[2] Ganguly, S., Nath, B., Goyal, N.: Optimal bandwidth reservation schedule in cellular networks. In: Proc. of 22nd Annual Joint Conference of the IEEE Computer and Communications Societies, March 30-April 3, vol. 3 (2003) [3] Hutchens, R., Singh, S.: Bandwidth reservation strategies for mobility support of wireless connections with QoS guarantees. In: Proc of the 25th Australian Conference on Computer Science, Melbourne, Victoria, Australia, vol. 4, pp. 119–128 (2002) [4] Li, L., Chigan, C.: Effects of bandwidth sharing on optimal complete partitioning policy. In: Proc. of Advanced Simulation Technologies Conference, Hyatt Regency, Crystal City, Arlington Virginia, USA, April 18-22 (2004) [5] Kim, S., Varshney, P.K.: An adaptive bandwidth reservation algorithm for QoS sensitive multimedia cellular networks. In: 2002 IEEE 56th Vehicular Technology Conference, Proc of VTC 2002-Fall, vol. 3, pp. 1475–1479 (2002) [6] Tripathi, N.D., Reed, N.H., VanLandingham, H.F.: MPRG, Virginia Tech, Handoff in cellular systems. In: 11th WSEAS International Conference on Communications, Agios Nikolaos, Crete Island, Greece, vol. 11, pp. 366–370 (2007) [7] Nasser, N., Hassanein, H.: Bandwidth reservation policy for multimedia wireless cellular networks and its analysis. International Journal of High Performance Computing and Networking 4(1/2), 3–12 (2006) [8] Kim, H.B.: An adaptive bandwidth reservation scheme for multimedia mobile cellular networks. In: Proc. of IEEE International Conference, May 2005, vol. 5, pp. 3088–3094 (2005)