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A Distributed Mechanism for Handling of Adaptive/Intelligent Selfish Misbehaviour at MAC Layer in Mobile Ad Hoc Networks

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Journal of Computer Science & Technology 2009年第3期24卷 472-481页

作者： raja gunasekaran Vaidheyanathan Rhymend Uthariaraj Uamapathy Yamini rajagopalan Sudharsan Selvaraj Sujitha Priyadarshini Department of Information Technology Anna University Ramanujan Computing Centre Anna University

Medium access control （MAC） protocols such as IEEE 802.11 are used in wireless networks for sharing of the wireless medium. The random nature of the protocol operation together with the inherent difficulty of monitoring in the open poses significant challenges. All nodes are expected to comply with the protocol rules. But, some nodes in order to gain greater benefits misbehave by not complying with the rules. One such selfish misbehavior is waiting for smaller back-off intervals when compared to the other nodes in the same subnet. Such selfish misbehavior is being tackled in this paper. A diagnosis scheme and a penalty scheme are being proposed for overcoming such selfish-misbehavior at MAC layer of mobile ad hoc networks which could be extended to other types of networks also.

关键词： WLANs MAC layer DCF contention backoff

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SDN assisted Stackelberg Game model for LTE-WiFi offloading in 5G networks

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Digital Communications and Networks 2019年第4期5卷 268-275页

作者： Sudha Anbalagan Dhananjay Kumar gunasekaran raja Alkondan Balaji Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathurIndia Department of Information Technology Anna UniversityChennaiIndia Department of Computer Technology Anna UniversityChennaiIndia

The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile *** MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected *** the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is ***,the APs should choose an optimal traffic to admit load for the price that it gets from *** balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is *** this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated *** MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and *** player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.

关键词： LTE WiFi Aggregation Software Defined Networking Stackelberg Game

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Editorial: special issue on “Evolution of information and communication technology towards 5G:New Emerging Areas, technologies, protocols, and applications”

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Digital Communications and Networks 2019年第04期5卷 266-267页

作者： gunasekaran raja Ali Kashif Bashir Anwer Al-Dulaimi Thamarai Selvi Somasundaram Shahid Mumtaz the Department of Computer Technology Anna University NGNLab the University of California Davis CA USA the Department of Computing and Mathematics Manchester Metropolitan University United Kingdom the Center of Excellence at EXFO Toronto Canada Thiruvalluvar University GS Portugal

The evolution of information and communication technology towards5G is promising, but there is still a lot of space for more inventions as it evolves based on the user requirement, which changes from time to *** there is also plenty of room for Artificial Intelligence and machine learning to do a better and smarter job as we go to 5G, and anticipate traffic as well, not just react. Learning and Analysis mechanisms play a vital role in the next generation of emerging technologies like IoT and Machine to Machine. Moreover, green convergence,

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