The rapid evolution in mobile wireless communication networks has generated Heterogeneous Wireless Networks (HWNs), which cover a diverse range of networks (e.g., 2G, 3G, and LTE-A). In HWNs, a mobile device supports multiple network... more
The rapid evolution in mobile wireless communication networks has generated Heterogeneous Wireless Networks (HWNs), which cover a diverse range of networks (e.g., 2G, 3G, and LTE-A). In HWNs, a mobile device supports multiple network interfaces that use different access methods for wireless links. In such an environment, the main challenge is Always Best Connected (ABC), which means that the mobile nodes rank the network interfaces and select the best one at anytime and anywhere according to multiple criteria (application-related criteria, network-related criteria, terminal-related criteria, user-related criteria). In this context, Multi Attribute Decision Making (MADM) techniques present a promising solution for the network interface selection problem. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one widely adopted MADM method. TOPSIS suffers from ranking abnormalities, e.g., if a low-ranking network (alternative) is disconnected or a new network i...
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In the last few years, evidence theory, also known as Dempster-Shafer theory or belief functions theory, have received growing attention in many fields such as artificial intelligence, computer vision, telecommunications and networks,... more
In the last few years, evidence theory, also known as Dempster-Shafer theory or belief functions theory, have received growing attention in many fields such as artificial intelligence, computer vision, telecommunications and networks, robotics, and finance. This is due to the fact that imperfect information permeates the real-world applications , and as a result, it must be incorporated into any information system that aims to provide a complete and accurate model of the real world. Although, it is in an early stage of development relative to classical probability theory , evidence theory has proved to be particularly useful to represent and reason with imperfect information in a wide range of real-world applications. In such cases, evidence theory provides a flexible framework for handling and mining uncertainty and imprecision as well as combining evidence obtained from multiple sources and modeling the conflict between them. The purpose of this paper is threefold. First, it introduces the basics of the belief functions theory with emphasis on the transferable belief model. Second , it provides a practical case study to show how the belief functions theory was used in a real network application, thereby providing guidelines for how the evidence theory may be used in telecommunications and networks. Lastly, it surveys and discusses a number of examples of applications of the evidence theory in telecommunications and network technologies.
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—When several networks (e.g., Wi-Fi, UMTS, and LTE) cover the same region, the mobile terminals that are equipped with multiple network interfaces provide the possibility for mobile end-users to select their believed best network. This is... more
—When several networks (e.g., Wi-Fi, UMTS, and LTE) cover the same region, the mobile terminals that are equipped with multiple network interfaces provide the possibility for mobile end-users to select their believed best network. This is known as the network selection problem, which is a decision making problem with multiple criteria (network conditions, service requirements, terminal characteristics, and user needs). Many network selection solutions using different mathematical theories have been proposed in the literature to allow the best connectivity for applications, users, and terminals. Unfortunately, most approaches for the network selection do not make effective selection decisions, since they are vulnerable to the uncertainty and imprecision related to network state information. In this paper, we investigate the belief functions theory to devise an efficient lightweight uncertainty-aware network interface selection scheme. We provide analytical studies and simulation experiments to demonstrate the efficiency of the proposed solution.
Research Interests:
—In Heterogeneous Wireless Networks (HWNs), the mobile terminals are equipped with multiple access network interfaces (GSM, UMTS, LTE, WiFi, Bluetooth, etc.), to provide the possibility for mobile end-users to rank the networks and... more
—In Heterogeneous Wireless Networks (HWNs), the mobile terminals are equipped with multiple access network interfaces (GSM, UMTS, LTE, WiFi, Bluetooth, etc.), to provide the possibility for mobile end-users to rank the networks and dynamically select the best one at anytime and anywhere, which is well known as Always Best Connected (ABC). In such environment, the major issue is network interface selection, which is a decision making problem with multiple alternatives (networks) and attributes (network characteristics, application requirements, terminal capacities, and user needs). In this context, many approaches have been proposed. Multi Attribute Decision Making (MADM) algorithms present a promising solution for multi-criteria decision making problems. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of MADM algorithms, which is widely adopted. TOPSIS ranks the available networks based on their scores, with the highest being the best. TOPSIS suffers from couple limitations. First is the ranking abnormality, e.g. if a low ranking network is disconnected then the order of higher ranking networks changes, which results in the selection of a less desirable network. Second is the selection strategy, where TOPSIS simply selects the network with highest score regardless of whether or not it satisfies the user and/or application needs. In this paper, we propose a new strategy based on utility function to remedy these shortcomings. The effectiveness of our strategy is evaluated through simulations. Obtained results show clearly that our strategy eliminates the rank reversal (ranking abnormality) phenomenon, and enhances the ranking quality by considering application and/or user needs.
Research Interests:
Network interface selection Multi attribute decision making (MADM) Technique for order preference by similarity to ideal solution (TOPSIS) Ranking abnormality Rank reversal a b s t r a c t The rapid evolution in mobile wireless... more
Network interface selection Multi attribute decision making (MADM) Technique for order preference by similarity to ideal solution (TOPSIS) Ranking abnormality Rank reversal a b s t r a c t The rapid evolution in mobile wireless communication networks has generated Heterogeneous Wireless Networks (HWNs), which cover a diverse range of networks (e.g., 2G, 3G, and LTE-A). In HWNs, a mobile device supports multiple network interfaces that use different access methods for wireless links. In such an environment, the main challenge is Always Best Connected (ABC), which means that the mobile nodes rank the network interfaces and select the best one at anytime and anywhere according to multiple criteria (application-related criteria, network-related criteria, terminal-related criteria, user-related criteria). In this context, Multi Attribute Decision Making (MADM) techniques present a promising solution for the network interface selection problem. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one widely adopted MADM method. TOPSIS suffers from ranking abnormalities, e.g., if a low-ranking network (alternative) is disconnected or a new network is discovered, then the order of the higher-ranking networks will change abnormally. These abnormalities can potentially decrease the quality of the results. In this paper, we propose new TOPSIS-based approaches for network interface selection that efficiently tackle the ranking abnormality problem in HWNs. The performance of our methods is evaluated through simulations. The results show that the proposed approaches reduce or completely eliminate the rank reversal, either when networks are disconnected or new networks are connected.