Networks II

B5L-A: Networks II

Session Type: Lecture
Session Code: B5L-A
Location: Room 1
Date & Time: Thursday March 23, 2023 (15:20-16:20)
Chair: Lei Cao
Track: 6

Paper ID	Paper Title	Authors	Abstract
3101	An ALOHA Multi-User Game with Tradeoff Between Throughput and Transmission Proportional Fairness	Andrey Garnaev, Wade Trappe	The paper considers an ALOHA type communication, where several users send data, and investigates the problem in a game-theoretic framework. We show that in such ALOHA type communication, the throughput metric leads to a continuum of equilibria. Moreover, its traditional extension via adding a transmission cost, which might collapse such a continuum to a finite number of equilibria, actually remains a multi equilibria situation, and that this might potentially destabilize users communication. Also we show that in contrast to the flat-fading multiple access communication (MAC) networks, in an ALOHA network, users\' communication might not be stabilized via switching from throughput to latency communication utility. This phenomena puts forward a question of which metric might lead to a unique equilibrium, and thus to stability in multi user ALOHA type communication. Here for multi user ALOHA type communication we suggest an advanced communication metric reflecting tradeoff between throughput and the transmission\'s fairness. Such metric might be also interpreted as a trade-off for a user between its selfish and cooperative behavior. We prove that, in contrast to the throughput metric, such an advanced metric always lead to a unique equilibrium, and thus to communication stability under the corresponding transmission protocol. The other proven advantage is that it supports uninterrupted communication between the users. The equilibrium is derived in closed form. Finally, it is shown how to optimize weights of throughput and transmission\'s fairness in the suggested trade-off metric.
3191	Strategic Multi-Task Coordination Over Regular Networks of Robots with Limited Computation and Communication Capabilities	Yi Wei{2}, Marcos Vasconcelos{1}	Coordination is a desirable feature in multi-agent systems, allowing the execution of tasks that would be impossible by individual agents. We study coordination by a team of strategic agents choosing to undertake one of the multiple tasks. We adopt a stochastic framework where the agents decide between two distinct tasks whose difficulty is randomly distributed and partially observed. We show that a Nash equilibrium with a simple and intuitive linear structure exists for diffuse prior distributions on the task difficulties. Additionally, we show that the best response of any agent to an affine strategy profile can be nonlinear when the prior distribution is not diffuse. Finally, we state an algorithm that allows us to efficiently compute a data-driven Nash equilibrium within the class of affine policies.
3178	Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks	Xi Zhang{2}, Qixuan Zhu{2}, H. Vincent Poor{1}	Due to the explosively increasing number of mobile users and the new types of their data demands over the fifth generation (5G) mobile wireless network, wireless network researches have been shifted toward the sixth generation (6G) wireless network. Although the research for software-defined network (SDN) architecture in 5G mainly focuses on the dynamic programming for internet backbone, these software programming techniques can be also applied in the network edge, to support the exponentially increasing resources demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a power tool to show how fast we can tolerate the levels of network imperfections to increase with the number of mobile users. In this paper, we investigate the scaling behavior of software-defined architectures over the 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses.
3200	A Novel Feature Selection Technique for Intrusion Detection System Using RF-RFE and Bio-Inspired Optimization	Zinia Anzum Tonni, Rashed Mazumder	A massive change has been brought about by the constantly expanding usage of technology and the internet, which has substantially enhanced the accessibility to services that might drastically alter people\'s lives. However, this every time-on connection has also enabled malicious actors to exploit vulnerabilities in hardware and software, leading to potential damage to network infrastructure. With the high volume of network traffic, cyber analysts struggle to quickly monitor, detect, and respond to such attacks. To ensure the integrity, availability, and confidentiality of the network infrastructure and digital assets, Intrusion Detection Systems must be put in place. Intrusion Detection Systems (IDS) is a key component in securing networks, but the complexity of large datasets used to build them can lead to time-consuming computations. To address this issue, a two-layer feature selection technique is proposed in this study. Results show the usefulness of the suggested feature selection strategy in lowering the complexity of IDS while enhancing accuracy. This approach is tested on a significant dataset called CSE-CIDS-2018. Finally, Random Forest Classification is used to verify the model. The outcomes of this strategy show how the suggested feature selection technique works to make IDS less difficult while improving accuracy.