Two-Way Relay Strategies With a Multiaccess Uplink and Queue Stability Constraints

2016 ◽  
Vol 65 (10) ◽  
pp. 8003-8013 ◽  
Author(s):  
Zhi Chen ◽  
Teng Joon Lim ◽  
Mehul Motani
Keyword(s):  
Queue Stability

Routing Optimization with Efficient Second Order Distributed Approach Using Congestion Control Rules

2017 ◽  
Vol 7 (7) ◽  
pp. 363
Author(s):  
Jaya Pratha Sebastiyar ◽  
Martin Sahayaraj Joseph
Keyword(s):  
Congestion Control ◽  
Second Order ◽  
Back Pressure ◽  
Order Method ◽  
Delay Performance ◽  
Routing Optimization ◽  
Distributed Approach ◽  
Control Rules ◽  
Primal Dual ◽  
Queue Stability

Distributed joint congestion control and routing optimization has received a significant amount of attention recently. To date, however, most of the existing schemes follow a key idea called the back-pressure algorithm. Despite having many salient features, the first-order sub gradient nature of the back-pressure based schemes results in slow convergence and poor delay performance. To overcome these limitations, the present study was made as first attempt at developing a second-order joint congestion control and routing optimization framework that offers utility-optimality, queue-stability, fast convergence, and low delay.  Contributions in this project are three-fold. The present study propose a new second-order joint congestion control and routing framework based on a primal-dual interior-point approach and established utility-optimality and queue-stability of the proposed second-order method. The results of present study showed that how to implement the proposed second-order method in a distributed fashion.

Heterogeneous Spectrum Aggregation: Coexistence From a Queue Stability Perspective

2018 ◽  
Vol 17 (4) ◽  
pp. 2471-2485 ◽  
Author(s):  
Yitu Wang ◽  
Wei Wang ◽  
Vincent K. N. Lau ◽  
Lin Chen ◽  
Zhaoyang Zhang
Keyword(s):  
Spectrum Aggregation ◽  
Queue Stability

Novel approach to the queue stability analysis of polling models

1998 ◽  
Author(s):  
Sum Lam ◽  
Rocky K. C. Chang
Keyword(s):  
Stability Analysis ◽  
Polling Models ◽  
Novel Approach ◽  
Queue Stability

scholarly journals A Privacy-Preserving Incentive Mechanism for Data Offloading in Satellite-Terrestrial Crowdsensing

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Boxiang Zhu ◽  
Jiarui Li ◽  
Zhongkai Liu ◽  
Yang Liu
Keyword(s):  
Privacy Protection ◽  
Task Assignment ◽  
Privacy Preserving ◽  
Data Offloading ◽  
Remote Areas ◽  
Lyapunov Optimization ◽  
Optimization Theorem ◽  
Queue Stability ◽  
Total Payment

Data offloading algorithm is the foundation of urban Internet of Things, which has gained attention for its large size of user engagement, low cost, and wide range of data sources, replacing traditional crowdsensing in areas such as intelligent vehicles, spectrum sensing, and environmental surveillance. In data offloading tasks, users’ location information is usually required for optimal task assignment, while some users in remote areas are unable to access base station signals, making them incapable of performing sensing tasks, and at the same time, there are serious concerns about users’ privacy leakage about their locations. Until today, location protection for task assignment in data offloading has not been well explored. In addition, existing privacy protection algorithms and data offloading task assignment mechanisms cannot provide personalized protection for different users’ privacy protection needs. To this end, we propose an algorithm known as differential private long-term privacy-preserving auction with Lyapunov stochastic theory (DP-LAL) for data offloading based on satellite-terrestrial architecture that minimizes the total payment. This not only gives an approximate optimal total payment in polynomial time but also improves the issue of poor signal in remote areas. Meanwhile, satellite-terrestrial data offloading architecture integrates wireless sensor networks and cloud computing to provide real-time data processing. What is more, we have considered long-term privacy protection goals. We employ reverse combinatorial auction and Lyapunov optimization theorem to jointly optimize queue stability and total payment. More importantly, we use Lyapunov optimization theorem to jointly optimize queue stability and total payment. We prove that our algorithm is of high efficiency in computing and has good performance in various economic attributes. For example, our algorithms are personally rational, budget-balanced, and true to the buyer and seller. We use large-scale simulations to evaluate the proposed algorithm, and compare our algorithm with existing algorithms, our algorithm shows higher efficiency and better economic properties.

scholarly journals Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1359
Author(s):  
Joohyung Jeon ◽  
Soohyun Park ◽  
Minseok Choi ◽  
Joongheon Kim ◽  
Young-Bin Kwon ◽  
...  
Keyword(s):  
Energy Efficient ◽  
High Performance ◽  
Control Algorithm ◽  
Global Model ◽  
Network Congestion ◽  
Local Data ◽  
Learning Platforms ◽  
Selection For ◽  
Resource Status ◽  
Queue Stability

Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges; these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction; however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.

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