Middleware Vertical Handoff Manager: A Neural ... - Semantic Scholar

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.

Middleware Vertical Handoff Manager: A Neural Network-based Solution Nidal Nasser1, Sghaier Guizani2 and Eyhab Al-Masri1 1

2

Department of Computing & Information Science University of Guelph Guelph, ON, Canada N1G 2W1 {nasser, ealmasri}@cis.uoguelph.ca

Abstract— – Major research challenges in the next generation of wireless networks include the provisioning of worldwide seamless mobility across heterogeneous wireless networks, the improvement of end-to-end Quality of Service (QoS), supporting high data rates over wide area and enabling users to specify their personal preferences. The integration and interoperability of this multitude of available networks will lead to the emergence of the fourth generation (4G) of wireless technologies. 4G wireless technologies have the potential to provide these features and many more, which at the end will change the way we use mobile devices and provide a wide variety of new applications. However, such technology does not come without its challenges. One of these challenges is the user’s ability to control and manage handoffs across heterogeneous wireless networks. This paper proposes a solution to this problem using Artificial Neural Networks (ANNs). The proposed method is capable of distinguishing the best existing wireless network that matches predefined user preferences set on a mobile device when performing a vertical handoff. The overall performance of the proposed method shows 87.0 % success rate in finding the best available wireless network. To test for the robustness and effectiveness of the neural network algorithm, some of the features were removed from the training set and results showed a significant impact on the overall performance of the system. Hence, managing vertical handoffs through user preferences can be significantly affected with the selection of features used to provide the closest match of the available wireless networks.

I. INTRODUCTION The emergence and development of mobile devices continues to expand and reshape our living standards. In the recent years, advances in miniaturization, low-power circuit design, development in radio access technologies, and the increase in user demand for high speed Internet access are some of the main aspects leading to the deployment of a wide array of wireless and mobile networks and systems. The varying wireless technologies are driving today’s wireless networks to become heterogeneous and provide a wide array of new applications that eases and smoothes the transition across multiple wireless network interfaces. Fourth Generation (4G) wireless communication system is the promising solution for heterogeneous wireless networks. The 4G wireless system has the potential to provide many of the requirements other previous systems did not achieve such

Qatar University Foundation Program Unit P.O.Box 2713 Doha, Qatar

as high data transfer rates, effectives user control, seamless mobility, and others which will potentially change the way users utilize mobile devices [1-6]. 4G will integrate a multitude of different heterogeneous networks including cellular (1G, 2G and 3G), WLANs, satellite systems, 802.16 and Bluetooth just to mention a few. However, roaming across these types of wireless technologies creates many challenges which must be taken into consideration. These challenges include: vertical handoff and mobility management, resource coordination and allocation, provisioning quality of service, security, and pricing and billing. In addition to that, 4G mobile devices will enable users to selectively choose through a combination of features of an available wireless network. This adds to the existing complexity of heterogeneity in the sense that there must exist a way to enable mobile devices to find the closest match to a predefined set of user preferences and seamlessly connect to that best match when vertical handoff is applied. Criterion of a vertical handoff is one of the chief challenges for seamless mobility. Traditional handoff detection operations and policies, decision metrics, radio link transfer and channel assignment are not able to acclimatize to dynamic vertical handoff conditions or varying network availabilities. Furthermore, traditional handoff does not allow for device selection of networks since it assumes that there is only one type of network. In a mixed networking environment, user choice is a desirable enhancement. Related works for managing the vertical handoff have been recently discussed in the literature. However, more work need to be done. In recent studies [2, 3], we define a vertical handoff decision function which provides handover decisions when roaming across heterogeneous wireless networks. In [2], we described the diverse factors and qualities that aid in vertical handoff decision. We also proposed a vertical handoff decision function in [3] which allows the user to strategically prioritize the different network characteristics such as network performance, user preference and monetary cost. This function is simple and can be easily applied to any vertical handoff approach. New optimizations for vertical handoff decision algorithms developed in [4] to maximize the benefit of the handoff for both the user and the network. In [5], the performance of horizontal handoff and vertical handoff

1-4244-0353-7/07/$25.00 ©2007 IEEE

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.

between WLAN and GPRS networks are studied and an optimization scheme for performing vertical handoff is presented. However, none of these studies consider the control and management of seamless vertical handoff between heterogeneous wireless networks that take into consideration user’s input from within a mobile device. The decision of when to handoff will no longer be the responsibility of the network operators, however, the mobile device will greatly contribute to that decision. Therefore, a solution at the application level that takes care of the communication between the mobile device and the available networks is not efficient due to the fact that mobile devices have minimal resources (e.g. power) to be able to perform such tasks. A middleware that can act as the middleman between available wireless networks and mobile devices is needed to provide users with the ability to interact more effectively between mobile devices and registered networks. In this paper we design and develop a middleware solution which we called Vertical Handoff Manager (VHM). VHM middleware is based on the use of Artificial Neural Networks (ANN). VHM will collect various mobile user preferences parameters and network information as inputs and utilizes the ANN to find the best match between what user want and what is available across these wireless networks. Once a match is found, the middleware will initiate a message between the mobile device and the chosen wireless network to execute the vertical handoff procedure. The remainder of this paper is organized as follows. Section II presents our middleware system architecture, Vertical Handoff Manager. Section III provides the data which are used in this paper to test the proposed solution. As well, it presents the obtained results followed by a discussion. Section IV concludes the work by referring to some future research directions.

Network Handling Manager Request

Response

Network 2 Feature Collector

ANN Training

Wireless Device

Network 1

ANN Selector

Vertical Handoff Manager

Network N Available Wireless Networks

Figure 2. Vertical Handoff Manager (VHM) Architecture

handles requests made by wireless devices when initiating a handoff, contacts available wireless networks to begin feature collection process. Once the ANN Selector defines the best available wireless network of choice, the NHM begins connecting the wireless device to the selected wireless network. B. Feature Collector Feature Collector (FC) module retrieves features that are used as attributes to feed to the neural network to be able to select the most appropriate wireless network. These features can be divided into three categories: network-specific, user – specific, and device specific. Network-specific features are those that describe the wireless network capabilities. These features are collected from available wireless networks which can be detected by the wireless device through VHM. Network-specific features include, but not limited to: (1) Network Usage Cost: how much it costs per min (in cents), (2) Network Security: On a scale of 1-10, how secure is the network (with 1 being low and 10 being high), (3) Network Transmission range: how wide is the network coverage (in km), and (4) Network Capacity: Data rate or speed of the connection (in Mbps).

VERTICAL HANDOFF MANAGER ARCHITECTURE In this section, we present the architecture of the Vertical Handoff Manager (VHM) middleware solution and explain the functions of all of its components. The aim of our approach is to design an intelligent system that has the ability to select the best available wireless network by taking advantage of user preferences, device capabilities, and wireless network features. The architecture of the VHM proposed solution is shown in Figure 1.

The device-specific features provide information about the mobile’s device. The device-specific features are used for compatibility issues and other technical information necessary when making a decision for a handoff. The user-specific features can be obtained through an application or saved locally on the wireless device. For example, a user can enter conditions such as cost per minute, specify security levels, power consumption, etc… These features are user-specific and could not be obtained without being allocated by the user. The user-specific features are used as a measure to find matches between user needs and the available wireless networks.

The Vertical Handoff Manager (VHM) is composed of three main components: (1) Network Handling Manager (NHM), (2) Feature Collector (FC), and (3) Artificial Neural Networks (ANN) Training/Selector. VHM interacts with available wireless networks and the wireless device. Each component within the VHM has a specific task. The following sections describe in detail the functionality for each of these components.

Handoff decision is based on the user preferences chosen and the available wireless network that best matches a given function. We proposed a Vertical Handoff Decision Function (VHDF) in [3]. VHDF is used to measure the improvement gained by handing over to a particular network i.

II.

A. Network Handling Manager Network Handling Manager (NHM) is mainly responsible for handling interactivity between VHM and available wireless networks as well as between VHM and wireless devices. NHM

Handoff decision parameters help determine which of the available networks is best suitable for data transfer. Because of their importance, we choose the following network parameters for VHDF: Cost of service (C): the cost of the different services to the user is a major issue, and can sometimes be the decisive factor in the choice of a network. Security (S): when the information being exchanged is confidential a network with

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.

high encryption is preferred. Power consumption (P): vertically handing off to a high power consuming network is not desirable if the mobile terminal’s battery is nearly exhausted or if the battery’s lifetime is relatively short. Network conditions (D): available bandwidth is used to indicate network conditions and is a major factor especially for voice and video traffic. The four preferences are summarized in Table 1. TABLE I.

USER PREFERENCES THAT CAN BE SET FROM A MOBILE DEVICE

Preference ID

Preference Name

1 2 3 4

Cost per minute Security Power consumption Network conditions

use of Artificial Neural Networks (ANN) can significantly enhance the chances of having a higher success rate for finding an appropriate wireless network when performing a handoff across heterogeneous networks. There are many reasons for using artificial neural networks as a possible solution to the selection problem: (1) Artificial neural networks can provide a simple representation for a physical implementation, and (2) ANN is capable of producing accurate results for inputs that are not present during the training process. In this paper, a neural network is investigated for network selector using a backpropagation neural (BPNN) algorithm. A multilayer feedforward neural with an input layer, a hidden layer, and an output shown on Figure 2. ah

As the mobile roams across different networks, VHDF is evaluated for all accessible networks. The network with the highest calculated value for VHDF is the most desirable for the user based on his specified preferences. The network quality Qi, which provides a measure of the appropriateness of a certain network i is measured via the function:

Qi = f

 1   Ci

, Si ,

1 Pi

, Di

  ω c 

1 Ci

, ω s Si , ω p

  

1 Pi

, ω d Di

bi

c b1

a1

Figure 2. Topology of the backpropagation ANN (a: input nodes, b: hidden nodes and c: output node)

(1)

In order to allow for different circumstances, there is an apparent necessity to weigh each factor relative to the magnitude it endows upon the vertical handoff decision. Therefore, a different weight is introduced as follows: Qi = f

wireless network network layer is

  

(2)

where ωc, ωs, ωp, ωd, ωf are weights for each of the network parameters. The values of these weights are fractions i.e. they range from 0 to 1. Furthermore all five weights add up to 1.0. Each weight is proportional to the significance of a parameter to the vertical handoff decision. The larger the weight of a specific factor, the more important that factor is to the user and vice versa. These weights are obtained from the user via a user interface. Due to the fact that each of the preferences chosen by the user has an associated unit that is different from the other (i.e. power is measured in Watts, cost is measured in cents, and others), it is necessary to find a way for Equation 2 to generate an optimized output using the associated weights. An Artificial Neural Network (ANN) is used as a possible solution to this problem. The next section demonstrates the proposed ANN solution and implementation. C. Artificial Neural Network (ANN) The ability to accurately select the appropriate features in a physical process in this study is a challenging task specially when associating it with a classification and/or selection process. There is no unique feature extraction technique that can satisfy the requirements of all types of data. Therefore, the

Choosing the best wireless network is not a simple selection problem. In order to solve the network selection problem, one hidden layer is used in the feedforward neural network. In this study, all neurons use sigmoid activation function. Random values, which serve as weights, are generated for all connections from input to hidden (referenced by vhi) and from hidden to output layers (reference by wij). In addition, biases are assigned random values at the hidden nodes (θi) and the output node (τi). The activation functions at the hidden layer (referenced by bi ) are calculated using the following equation: n

bi = f (∑ ah * vhi + θ i )

(3)

i=1

The activation values at the output layer (referenced by cj) are calculated as follows: n

c j = f (∑ ah * wij + τ j )

(4)

i =1

In both Equations (3) and (4) f(x) denotes the logistic sigmoid threshold function, f ( x) = 1 /(1 + e− x ) . The Backpropagation neural network has been trained with moderate values for the learning rate (α) and momentum (µ). The weights are recalculated every time a training vector is presented to the network. The exit strategy or the termination

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.

condition for the network is based on the sum square error until it reaches a certain threshold assigned prior to running the network. The network should be able to choose from a given set of user preferences with the best available wireless network (selected from Table 2). III. DATA, RESULTS AND DISCUSSION Data used in this paper was customized in a way to suit the purpose of this method. A program was written to generate a random numbers that represent a set of user preferences or weights defined in Equation 2. Each preference is represented by a percentage and then converted to a scale of 0 to 1 to be later used as weights. The weights assigned in a real-time example entered through a mobile device. However, in this paper these weights are generated through a program written in Active Server Pages (ASP). Four preferences were used as shown in Table 1. The preferences shown on Table 1 are used as inputs to the neural network. The numbers generated represent the priority set by the user on a mobile device and the sum of the four parameters equals 1 (numbers were initially entered as percentages and then normalized between 0 and 1 to be compatible with the ANN). There were ninety samples each containing four features representing user preferences and a fifth feature representing the type of wireless network. The data takes into consideration a variation of at least five different wireless networks, each with different network parameters. Note that for the purpose of testing our solution we limit the number of available networks to 5. In order to select a network, there exists a list of networks in which the neural network must be able to select or propose. Table 2 presents a sample list of possible wireless network types used for the data selection for each of the samples. In addition, it outlines sample network parameters that are used to propose a network for each of the samples in a dataset. These network features (parameters) are used to define the Network Definition Language (NDL) earlier discussed. The middleware determines the available wireless networks and retrieves the network parameters through the NDL. TABLE II. Network ID Network 1 Network 2 Network 3 Network 4 Network 5

Cost (cents) per minute 10.00 25.00 30.00 10.00 100.00

SAMPLE WIRELESS NETWORKS PARAMETERS Network Security

Network Transmission Range

Network Capacity

Classification

5.00 8.00 10.00 0.00 10.00

0.01 0.10 0.09 0.05 1.00

0.80 11.00 5.00 2.00 100.00

1 2 3 4 5

Each sample in the data is classified into one of the given networks shown in Table 2. For instance, each sample in the data consists of five parameters; the first four parameters represent the user preferences and the fifth represents the type of network to select. The classification for each sample was based on an intuitive algorithm that determines the best matching network. For example, if a user gives more priority to preference id 2 (Security) 100% priority, then the algorithm

would choose the network with the highest network security from Table 2 (i.e. Network 3 or 5). If the user selects 70% security and 30% for cost per minute, the algorithm would classify the sample to Network 3 since the user this time takes into consideration the security as well as the cost per minute. Therefore, the choice of Network 3 is more suitable than Network 5 since is more cost effective. In order to measure the performance of the network, an acceptable error value is used to determine if the neural network converges to a solution or not. Since the neural network uses a logistic function to detect the error for each iteration, the input must be in the range of 0 to 1 (since the sigmoid will make sure the nodes at the output layer will never be 1 or 0 but either 1-e or e). For all samples, the inputs must be normalized. To achieve this task, the first step is to (center) subtract its average, and the second step is to (scale) divide by its standard deviation. This way, the input can still be fed into the standard logistic function and work with the backpropagation algorithm implemented. The dataset used in this paper is extracted from 90 generated samples consisting of five types of wireless networks (as shown in Table 2). Each sample represents single user selection preferences for a wireless network with vector dimension of 5 elements. The last item in each sample is the class label or the proposed network type. In order to measure the performance of the wireless network selector defined as the performance rate, the following formula is used: PR = 100 x (Correctly Selected / Total Samples)

(4)

where PR represents the performance rate. Due to the fact that the number of hidden nodes when using the backpropagation algorithm must be defined before the training, the structure is not determined by the training algorithm. Therefore, a sub set of the data with equal number of samples from each network types is used to find the network configuration with highest performance. Table 3 summarizes the constants used with different network configuration. TABLE III.

CONSTANT USED FOR THE BPNN CONFIGURATION

Learning Rate Momentum Acceptable Error Range Number of Epochs Network 1 Training Samples Network 2 Training Samples Network 3 Training Samples Network 4 Training Samples Network 5 Training Samples

0.1 0.1 0.005 [-0.7 0.7] 700 15 15 15 15 15

Testing the network with the above parameters yields the results shown in Table 4. Using different network configurations, the highest performance rate is shown to be using the network configuration with 5 input nodes, 10 hidden nodes, and 1 output node yielding 99.215 % performance rate. Based on the highest performance rate of 99.215%, the network configuration of 5x10x1 was applied to training of all samples

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.

in the dataset, the 120 samples. Only 75% of the training samples were considered for the testing mode with a total number of 90 different samples which yields a performance rate of 87.0%. Table 5 summarizes the results generated by the network.

In order to test the accuracy of the system, another trial with a different error convergence for the neural network was conducted. Figure 5 shows the obtained results. Repeating the same parameters as in trial 2 except with a lower learning rate yields the results presented in Figure 6.

TABLE IV.

To effectively test the accuracy, a test for significantly lowering the error rate yields the graph in Figure 7.

RESULTS FROM RUNNING VARIOUS CONFIGURATIONS OF THE BPNN Configuration Hidden Nodes

Output Nodes

5 5 5 5 5 5

4 6 8 10 12 14

1 1 1 1 1 1

Trial 2: Learning Rate: 0.6, Error: 0.005

Performance Rate 85.367 91.257 96.158 99.215 95.658 94.245

0.30 0.25 0.20 Error

Input Nodes

0.15 0.10 0.05 0.00

TABLE V.

1

RESULTS FROM RUNNING BPNN WITH THE PERFORMANCE RATE (FROM T RIAL 1)

Type

Correctly Selected

# of Incorrect Selected

Network 1 Network 2 Network 3 Network 4

21 19 15 23

4 2 3 3

2

3 4

5

6 7

8

9 10 11 12 13 14 15 16 17 18 19 Epochs

Figure 5. Number of epochs versus the error (Trial 2) Trial 3: Learning Rate: 0.2, Error: 0.005 0.30 0.25

A correct selection distribution for the BPNN is presented in Figure 3 (From Trial 1)

Error

0.20 0.15 0.10 0.05 0.00

BPNN Correctly Classified Networks

1

4

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 Epochs

Network 1

Network 5

Figure 6. Number of epochs versus the error (Trial 3) Network 2

Network 4

Trial 4: Learning Rate:0.1 , Error:0.00005

Network 3

0.30 0.25

Network 2

Network 3

Network 4

Network 5

Figure 3. Distribution of correctly selecting wireless networks using BPNN

The convergence rate based on the number of epochs and iterative decrease in the error is shown in Figure 4 (from Trial 1). Note that in Figures 4-7, the solid line represents backpropagation without momentum and the dashed line represents backpropagation with momentum. Trial 1: Learning Rate: 0.5, Error: .05 0.40 0.35

Error

0.30 0.25 0.20 0.15 0.10 0.05 0.00 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 Epochs

Figure 4. Number of epochs versus the error (Trial 1)

0.20 Error

Network 1

0.15 0.10 0.05 0.00 1

11

21

31 41

51

61

71

81

91 101 111 121 131

Epochs

Figure 7. Figure 7: Number of epochs versus error (Trial 4)

Figure 5 represents the convergence of the BPNN to a solution within an acceptable error value (Error = 0.005). Due to the fact that using momentum in a backpropagation algorithm takes into consideration the previous delta wij of the previous inter-connection, the system converges much faster than running the network without momentum. It was noted varying the learning rate has a considerable impact on the system as well as the number of nodes. The higher the learning rate, the faster the system converges; however, there is a decrease in the performance of the system. This is due to the fact that higher learning rates allow the BPNN to learn quickly and thus does not have the enough time to adapt or learn

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.

enough to make accurate distinctions. As shown on Figure 5, the lower the acceptable error, the longer the network will converge. In addition, Figure 6 shows that learning rates affects the speed and processing time of the network in which the network will converge. In addition, Figure 7 shows that a very small acceptable error such as 0.00005 will take longer for the network to converge and find the best wireless network match. This means the smaller the acceptable error, the longer the network will take to converge. Therefore, when choosing a neural network as an acceptable solution for selecting the best wireless network match across heterogeneous wireless networks, a use of combination of learning rates and acceptable error values must be selected with caution specially when running such system in real-time environment. Finding the correct feature set for the selection of wireless networks can be very challenging. The four features extracted a set of possible user preferences on a mobile device appear to have solid results with a reasonable performance. However, it is important to test the effectiveness of each of the features selected for the neural network. In order to test the effectiveness for all the features, elements were removed from the neural network and tested to evaluate the performance and the selection rate success. Table 6 presents the results obtained from removing elements and testing for feature effectiveness on the overall performance of the neural network: TABLE VI.

RESULTS FROM RUNNING BPNN WITH THE PERFORMANCE RATE (FROM T RIAL 1)

Feature Removed All Features Feature 1 Feature 2 Feature 3 Feature 4 Feature 1,3 Feature 2,4

Classification Rate 87.0 72.5 68.3 65.8 74.4 69.5 60.7

Results from Table 6 show that reducing the number of user preferences (parameters) in the data fed to the neural network significantly impact the overall performance of the system. For example, when removing one feature (one of the four user preferences shown in Table 1), the network performs in the 66%-74% range. However, reducing more than one preference (i.e. removing two features at the same time and thus having two features remaining), it significantly reduces the performance between the 61%-70% range. Therefore, the more user preferences used, the better the performance of the network. Table 6 shows the effectiveness of the features used (Table 1) to test the neural network for selecting the best available wireless network.

IV.

CONCLUSIONS AND FUTURE WORK

A backpropagation based neural network has been presented in this paper for the purpose of selecting the best available wireless network during handoffs based on a set of predefined user preferences on a mobile device. The features used from the data generated has been carefully selected and used as inputs for the neural network in order to have a high performance rate. The feature set is mainly dependent on several factors such as power consumption, cost of service, network security, and network capacity. Results show that there exists a relationship between possible user preferences although having non-uniform metrics. The use of neural networks provides a way to optimize the selection of the best available wireless network in a heterogeneous environment during a handoff process. The average performance of the neural network selection is 87%. In all of the four trials ran for testing the system, the neural network always converged to a solution which implies that the use of neural networks in selecting the best wireless network positively can be used. The proposed method is capable of selecting the best available wireless network with a reasonable performance rate, however, there still room for improvement. It is observed that the BPNN takes long time during the training mode due to the large data size which could become an issue when implementing such system in real-time. In addition, the ability of the system configuration to adapt to current data being fed into it might become infeasible since the proposed method defined the number of hidden nodes prior in the training mode. However, results show that the proposed method can be used for applications in user control during handoff process as a starting point in which can serve as basis for other types of neural networks. The ability to use other types of neural networks such as ARTMAPs, Fuzzy ARTs, or Self Organized Map is a solution that can be explored in the future and compared against the BPNN. REFERENCES [1]

[2]

[3]

[4]

[5]

Nidal Nasser, Bader Al-Manthari and Hossam Hassanein, “A Performance Comparison of Class-based Scheduling Algorithms in Future UMTS Access”, Proc. Of the IEEE IPCCC, Phoenix, Arizona, April 2005, pp. 437-441. Nidal Nasser, Ahmed Hasswa and Hossam Hassanein, “Handoffs in Fourth Generation Heterogeneous Networks”, IEEE Communications Magazine, Vol. 44, No. 10, October 2006, pp. 96-103. Ahmed Hasswa, Nidal Nasser and Hossam Hassanein, “Tramcar: A Context-Aware, Cross-layer Architecture for Next Generation Heterogeneous Wireless Networks”, IEEE International Conference on Communications (ICC), Istanbul, Turkey, June 2006, pp. 240-245. F. Zhu and J. McNair, "Optimizations for Vertical Handoff Decision Algorithms," IEEE Wireless Communications and Networking Conference (WCNC), March 2004, pp. 867-872. M. Ylianttila, J. Makela, and P. Mahonen, " Optimization scheme for mobile users performing vertical handoffs between IEEE 802.11 and GPRS/EDGE networks, " IEEE PIMRC, vol.1, Sep. 2002, pp. 15-18