A survey on mobility management vertical handover decisions in heterogeneous wireless networks by IJRTER

A Survey On Mobility Management Vertical Handover Decisions in Heterogeneous Wireless Networks Ms Sonal D Agrawal1, Prof C.M Mankar2 PG Student 1, Guide 2 SSGMCE College of Engineering Shegaon

Abstractâ&#x20AC;&#x201D; In this paper we present an approach that uses autonomic management principles to provide personalized handover decisions for customized mobility management in heterogeneous wireless networks. Our algorithm uses a combination of functional and non-functional metrics to select the access point that best meets the needs of the user. Our algorithm supports the best access point (horizontal handover decisions) as well as the best access network (vertical handover decisions) to use, based on the current set of user preferences, application requirements, and context information.In this thesis, our main contributions are: (1) a novel decision making algorithm for personalized handover, (2) an enhanced autonomic architecture to apply to personalized mobility management, and (3) an extensible and user-friendly test platform for implementing and evaluating handover decision algorithms. Keyword: Mobile Device Management, Mobility Management, Heterogeneous Mobile Networks I. INTRODUCTION Mobile wireless technology has gained tremendous popularity due to its ability to provide ubiquitous information access to users on the move. However, presently, there is no single wireless network technology that is capable of simultaneously providing a low latency, high bandwidth, and wide area data service to a large number of mobile users. In new generation wireless network, mobile users are connected to the best available networks that suit their service requirements and switch between different networks based on their service needs. Efficient mobility management protocols are required to support mobility across heterogeneous. Access networks [5]. This next-generation of wireless systems represents a heterogeneous environment with different access networks technologies that differ in bandwidth, latency or cost. In this kind of environment, mobility management is the essential issue that supports the roaming of users from one system to another. Handover management, one of the mobility management components, controls the change of the MTâ&#x20AC;&#x2122;s point of attachment during active communication Mobility management contains two components: location management and handover management. Location management enables the system to track the locations of mobile users between consecutive communications. In mobile networks, first three generations evolved to contribute in increasing data rates and enriched communication experiences, achieving its peak in the third generation (3G) cellular networks. The next evolutionary steps after the 3rd generation aimed at providing extended mobility features with optimized and enhanced data rates and services. These systems are generally named as Beyond 3G (B3G) or Fourth Generation (4G) networks. They make heavy use of heterogeneous networking technologies in order to deliver mobile users more flexibility when using multi-service networks that provide diverse range of services [1] like seamless connection to the Internet by means of heterogeneous wireless networks, navigation services, location-aware services and IP based real-time multimedia. @IJRTER-2016, All Rights Reserved

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

II. LITERATURE SURVEY J.Domingo-Ferrer et al. (2011) have developed a paper for rule protection for the indirect discrimination prevention in data mining. The datasets are trained and developed to make the classification rules to be extracted. Indirect discrimination rules cannot be extracted from the trained dataset. (i.e.) the trained datasets are free from indirect discrimination. Datasets are modified if any indirect discrimination occurs. Standard data mining algorithms are used to prevent the indirect discrimination from the training dataset. Mykola Pechenizkiy et al. (2010) have developed a paper for discrimination aware decision tree learning. The decision tree models leads to the lower discrimination than the other models but with a little loss in the accuracy. The decision tree models are effective at removing the discrimination from the original datasets. The problem is the datasets are cleaned away for discrimination before the discovery of the classifier in the dataset. Sara Hajian et al. (2011) have developed a paper for prevention of discrimination in data mining for intrusion and crime detection. Data mining algorithm are used to prevent the direct and indirect discrimination. The data set obtained is free from the discrimination. In addition to detect the discrimination intrusion fraud and crime is also detected in the given dataset. III. RELATED WORK Objective Our approach proposes a handover decision process based on a three-phased process to find the network that can best full the user's requirements. The three phases are Network Detection, Network Evaluation, and Handover Execution [1, 2]. Network detection is used to discover available access networks and collect appropriate metrics to evaluate them. IV.

PROPOSED METHODOLOGY

Fig. 1. An illustration of the handover types

Horizontal Handover: Generally, the handover process has been considered and studied among wireless networks using the same access technology. When a mobile node (MN) moves between two cells using the same technology, then this kind of handover process is defined as horizontal handover. It is also known as Intra-cell (Intra-domain) handover and sustaining the running services is done by masquerading the change of IP address like in Mobile IP or dynamically bringing. Vertical Handover: A handover between two different access technologies is referred to as a vertical handover.It is also known as Inter-cell (Inter-domain, Inter-RAT1) handover as it occurs when the user moves into an adjacent cell and all of the terminalsâ&#x20AC;&#x2122; connections must be transferred to a new base station (BS) [8]. The main concern of vertical handover is to uphold running services despite the alteration of IP addresses, but also the modification of network interfaces and QoS features of different networks accordingly.

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

1) Handover Decision Management: Handover is the process of maintaining a user's active session(s) when a mobile device changes its connection point to an access network, (e.g., a base station or an access point).Depending on the access network that each point of attachment belongs to, the handover can be either horizontal or vertical [45]. A horizontal handover takes place between points of attachment supporting the same network technology, (e.g., between two neighboring base stations of a cellular network). On the other hand, a vertical handover occurs between points of attachment supporting different network technologies,(e.g., between an IEEE 802.11 access point and a cellular network base station). a)Cost Function-based Approaches A vertical handover decision cost function is a measurement of the benefit obtainedby handing over to a particular network. an optimized cost function is used to evaluate the target network (based on QoS factor) establishing a tradeoff between user satisfaction (gains in QoS) and network efficiency. The cost function is applied on two vertical handover policies, one for all the user's active sessions collectively (i.e., all are handed over to the same target network) and one for each of the user's active sessions independently (with prioritization). b) AI-based Approaches The concepts of Fuzzy Logic (FL), Neural Networks (NN), Expert Systems, and Genetic Algorithms (GA) from AI can be used to choose when handover occurs and which network to choose among different available access networks. These AI mechanisms are combined with multiple criteria or attribute concepts in order to develop advanced decision algorithms for both non-real-time and realtime applications. 2)Context Information for Handover Decision a)User Preferences Network values: Bandwidth, Network Type, Power Consumption, Security, Received Signal Strength (RSS), Power Network-independent values: Quality, Lifetime, Cost, Subscription Information, User Profile, User's Current Status (mobility, location, etc.) b) Application Requirements Bandwidth ,Packet Error Rate (PER), Delay, Jitter, Packet Loss Ratio (PLR). V. PROPOSED REQUIREMENTS In this section, we present requirements for developing our HMN Tool Suite. We surveyed the characteristics of mobile devices and HMNs and general functions of emulators and simulators. We also surveyed management issues related to mobile devices in HMNs, especially mobility management. We summarized the requirements as follows: Heterogeneous Networks Modeling: The tool should provide to create, modify, and delete multiple kinds of mobile networks, mobile nodes, and network servers. It should provide to specify their characteristics. Mobile Device: The tool should provide to create, modify, and delete mobile devices with their own functions.

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Network Traffic: The tool should provide a time-series network traffic for each application between a mobile node and an application server via mobile networks. Handover Decisions: The tool should provide a framework for implementing handover decision algorithms for mobility management in mobile nodes and show how to use context information. Scalability: The tool should provide to run simulations with a large number of nodes in a reasonable amount of time. Flexibility: The user should provide to specify relevant simulation parameters, policies, network statuses, and services in a human readable configuration file or a GUI based configuration manager. Reuse of Simulation Code: The provided implementation of handover decisions should be reusable for real network applications enabling researchers to validate the simulation results by comparing them to the results from real-world test networks. Statistics: The tool should collect statistical data. The output should be in a format that is easy to post-process (e.g., for generating gnuplot-based graphs). Interactive Visualizer: In order to validate and debug new or existing management functions, the tool should provide a GUI, which can visualize the network map, access networks, servers, and mobile nodes in a customizable way. VI. DESIGN AND IMPLEMENTATION In this section, we present the design and implementation of the HMN Tool Suite. First, we describe the overall structure of our tool. Then, we present the design of mobile devices. Finally, we present the implementation details.

Figure 1: Overall architecture of the HMN Tool Suite

Above overall architecture of the HMN Tool- Suite which is composed of Network Map Editor, HMN Emulator, HMN Simulator, and HMN Simulator CLI. Network Map Editor provides to create a network map for emulating and simulating. We create mobile networks, mobile nodes, and network servers with customized parameters. We also specify the moving path and velocity of each mobile node and time-series network traffic information of each application traffic between a network server and a mobile node via a mobile network. A network map is composed of mobile networks, mobile nodes, and network servers. It includes co ordinations and time-series parameters of mobile networks and mobile nodes for testing mobility. HMN Tool The HMN Emulator supports several different views of the network map, including HMN Emulator GUI, HMN Network Simulator, Monitor View, and CLI view. The HMN Emulator GUI shows the current emulating environment. We also create emulating scenarios using this GUI. It has Server

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Components, Network Components, Mobile Node Components, a Network Map View, a Network Editor, a Path Editor, and a Monitor View. The Server Component, Network Component, and Mobile Node Component have their specific icons and information defined using the Server Info, Network Device Info, and Network Mobile Node Info information classes, respectively. The Network Map View shows the current network map and all components. The Path Editor enables us to create and modify paths for each mobile node. The Monitor View provides a current snapshot of all components by monitoring them periodically and displaying them on demand. Finally, the Network Player generates network events for transferring packets between network devices, mobile devices, and servers in the network map. Hardware Requirements Hard disk RAM Processor speed

: : :

500 GB 2 GB i3

Software Requirements Operating System Technology Used Development IDE Database Server

: : : :

Windows 7/8 JSP, Servlet, Database Connectivity : Net beans 7.1 SQLYOG

JDBC

Experiment We implemented our HMN Tool Suite using the Java programming language (Java platform standard edition, JDK 6). We used an XML parser in the javax. xml. parsers package for creating and editing configuration files of a network map, a network, a mobile node, and a network server. We also used for displaying performance result graphs. Finally, we used Java Swing for constructing a GUI

Figure 2: A screenshot of the HMN Tool Suite

VII. CONCLUSION In this paper, we have presented a novel tool suite for emulating and simulating mobile devices in heterogeneous mobile networks for testing management methods. Our HMN Tool- Suite allowed the user to create multiple types of wireless access networks, mobile nodes, and network servers for creating simulation scenarios. In the case study, we presented context aware handover decision management which is a management issue for mobile devices. We also deployed this tool as an open source project to encourage other researchers to test and improve it.

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International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

VIII. FUTURE CHALLENGES For future work, we will integrate our tool into existing network simulators by importing network traces from them. Currently, time-series packet traffic data is manually configured for simulation. It is important to note that this tool is not an independent simulator designed to replace traditional simulators. We will also apply our tool suite to other useful case studies such as configuration, fault, and performance management. REFERENCES S.Hajain, J.Domingo Ferrer, and A.Martinez Balleste,”Rule protection for Indirect Discrimination Prevention in Data Mining”, Proc.Eighth Int’l Conf.Modeling Decisions for Artificial Intelligence (MDAI’11).pp.211-222, and 2011. 2. S. Shivashankar and B. Ravindran, “Multi Grain Sentiment Analysis using Collective Classification”, Proceedings of the European Conference on Artificial Intelligence, pp. 823-828, 2010. 3. George Stylios, Dimitris Christodoulakis, Jeries Besharat, Maria-Alexandra Vonitsanou, Ioanis Kotrotsos, Athanasia Koumpouri and Sofia Stamou, “Public Opinion Mining for Governmental Decisions”, Electronic Journal of eGovernment, Vol. 8, No. 2, pp. 203-214, 2010. 4. AnindyaGhose and Panagiotis G. Ipeirotis, “Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 10, pp. 1498-1512, 2011. 5. JanyceWiebe and Ellen Riloff, “Finding Mutual Benefit between Subjectivity Analysis and Information Extraction”, IEEE Transactions on Affective Computing, Vol. 2, No. 4, pp. 175-191, 2011. 6. Huifeng Tang, Songbo Tan and Xueqi Cheng, “A survey on sentiment detection of reviews”, Expert Systems with Applications, Vol. 36, pp. 10760–10773, 2009. 7. Ahmed Abbasi, Stephen France, Zhu Zhang and Hsinchun Chen, “Selecting Attributes for Sentiment Classification Using Feature Relation Networks”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 3, pp. 447-462, 2011. 8. Michael Wiegand and Alexandra Balahur, “A Survey on the Role of Negation in Sentiment Analysis”, Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, 2010. 9. A. Abbasi, H. Chen and A. Salem, “Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums”, ACM Transactions on Information Systems, Vol. 26, No. 3, Article No. 12, 2008. 10. SugeWang, Deyu Li , Xiaolei Song , Yingjie Wei and Hongxia Li, “A feature selection method based on improved fisher’s discriminant ratio for text Sentiment Classification”, Expert Systems with Applications, Vol. 38, No. 7, pp. 8696–8702, 2011. 11. Q. Ye, Z. Zhang and R. Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches”, Expert Systems with Applications, Vol. 36, No. 3, part 2, pp. 6527–6535, 2009. 12. Shoushan Li, ShengfengJu, Guodong Zhou and Xiaojun Li, “Active Learning for Imbalanced Sentiment Classification”, Proceedings of the International Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 139–148, 2012. 1.

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