Provisioning Fog Services to 3GPP Subscribers: Authentication and Application Mobility

Ali, Asad; Mallick, Tushin; Sakib, Sadman; Hossain, Md. Shohrab; Lin, Ying-Dar

doi:10.1109/icc45855.2022.9838904

ICC 2022 - IEEE International Conference on Communications 2022

DOI: 10.1109/icc45855.2022.9838904

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Provisioning Fog Services to 3GPP Subscribers: Authentication and Application Mobility

Asad Ali

Tushin Mallick

Sadman Sakib

et al.

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2024

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Cited by 2 publications

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“…Various HNTN structures can be exploited according to the characteristics of the corresponding environment and use case. Emerging technologies like Mobile Edge Computing (MEC) [22]- [25] enable partial or complete processing of user data. Furthermore, the integration of collaborative data computing (CDC), combining MEC [26], fog [27], and cloud computing in diverse configurations, presents a promising approach to address prevalent data processing challenges in wireless communication.…”

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confidence: 99%

Resilient Disaster Relief in Industrial IoT: UAV Trajectory Design and Resource Allocation in 6G Non-Terrestrial Networks

Mohammadisarab,

Nouruzi,

Khalili

et al. 2024

IEEE Open J. Commun. Soc.

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This research explores strategies to augment the connectivity among users within Hierarchical Non-Terrestrial Networks (HNTNs) dedicated to Disaster Relief Services (DRS). The primary goal is to optimize radio resources, computing capacities, and the trajectories of Unmanned Aerial Vehicles (UAVs) at each time slot, aiming to maximize the number of satisfactory connections (NSCs). UAVs function as aerial base stations (ABSs), establishing links for reduced capability (RedCap) user equipment (UE) using power domain non-orthogonal multiple access (PD-NOMA). Given the potential inoperability of terrestrial networks during disasters, swift data transmission is critical for mission-critical (MC) UEs. Therefore, end-to-end (E2E) delay is a crucial quality of service (QoS) constraint. The proposed problem is solved using a multi-agent recurrent deterministic policy gradient (MARDPG) algorithm, where the ABSs collaborate to maximize the NSCs and determine their optimal policy by interacting with the environment. Additionally, a sharing experience module (SEM) is incorporated, enabling agents to encode actions and observations using long short-term memory (LSTM), allowing each agent to utilize the historical actions and observations of other agents. To demonstrate the superiority of MARDPG, three algorithmic benchmarks and four different system models are implemented. The numerical results demonstrate the impact of various parameters, such as the number of subcarriers, users, and the maximum tolerable E2E delay on the NSCs. Furthermore, different scenarios indicate that MARDPG outperforms the benchmarks, achieving approximately a 6 percent optimality gap and a 91 percent fairness for achievable rate among users. HNTNs, DRS, UAVs, PD-NOMA, MARDPG I. INTRODUCTION A. Motivations and State-of-the-Art W ireless communication technologies have undergone significant advancements in recent years, emerging as a critical facilitator for upcoming consumer applications and services [2], [3]. The transition from the fourth-generation (4G) to the fifth generation leads to a categorizing of quality of services (QoSs), which are named ultra-reliable low-latency communications (URLLC), massive machine-type communication (mMTC), and enhanced mobile broadband

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mentioning

confidence: 99%

Resilient Disaster Relief in Industrial IoT: UAV Trajectory Design and Resource Allocation in 6G Non-Terrestrial Networks

Mohammadisarab,

Nouruzi,

Khalili

et al. 2024

IEEE Open J. Commun. Soc.

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MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

Lotfimahyari,

De Giovanni,

Gadia

et al. 2024

IEEE Access

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Online gaming has seen a significant surge in popularity, becoming a dominant form of entertainment worldwide. This growth has necessitated the evolution of game servers from centralized to distributed models, leading to the emergence of distributed game engines. These engines allow for the distribution of game engine modules (GEMs) across multiple servers, improving scalability and performance. However, this distribution presents a new challenge: the game engine module placement problem. This problem involves strategically placing GEMs to maximize the number of accepted placement requests while minimizing the delay experienced by players, a critical factor in enhancing the gaming experience. The problem can be formulated as an Integer Linear Programming (ILP) model, which provides an optimal solution but suffers from high computational complexity, making it impractical for real-world applications. To address this challenge, this paper introduces two novel heuristic algorithms, MAP-MIND and MAP-MIND*. The MAP-MIND algorithm demonstrates superior performance, achieving near-optimal delay and more than 92% GEM request acceptance in the worst heterogeneous scenarios. The MAP-MIND* algorithm, while slightly under-performing MAP-MIND in terms of delay, proves to be significantly faster, making it a viable alternative for real-world applications with equal GEM request acceptance. The tradeoff between the two algorithms offers a flexible approach to GEM placement, balancing performance and computational efficiency.INDEX TERMS Cloud gaming, distributed game engines, placement algorithm

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Provisioning Fog Services to 3GPP Subscribers: Authentication and Application Mobility

Cited by 2 publications

References 15 publications

Resilient Disaster Relief in Industrial IoT: UAV Trajectory Design and Resource Allocation in 6G Non-Terrestrial Networks

Resilient Disaster Relief in Industrial IoT: UAV Trajectory Design and Resource Allocation in 6G Non-Terrestrial Networks

MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

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