scholarly journals WiFi Transmit Power and its Effect on Co-Channel Interference

2021 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Zyanya Cordoba ◽  
Riddhi Rana ◽  
Giovanna Rendon ◽  
Justin Thunell ◽  
Abdelrahman Elleithy
Keyword(s):  
Video Streaming ◽  
Multiple Access ◽  
Power Level ◽  
Control Algorithms ◽  
Transmit Power ◽  
Cooperative Approach ◽  
Channel Interference ◽  
High Bandwidth ◽  
Wireless Environments ◽  
Multiple Clients

The mass adoption of WiFi (IEEE 802.11) technology has increased numbers of devices simultaneously attempting to use high-bandwidth applications such as video streaming in a finite portion of the frequency spectrum. These increasing numbers can be seen in the deployment of highly-dense wireless environments in which performance can be affected due to the intensification of challenges such as co-channel interference (CCI). There are mechanisms in place to try to avoid sources of interference from non-WiFi devices. Still, CCI caused by legitimate WiFi traffic can be equally or even more disruptive, and also though some tools and protocols try to address CCI, these are no longer sufficient for this type of environment. Therefore, this paper investigates the effect of transmit power and direction have on CCI in a high-density environment consisting of multiple access points (APs) and multiple clients. We suggest improvements on publicly- existing documented power control algorithms and techniques by proposing a cooperative approach consisting of the incorporation of feedback from the receiver to the transmitter to allow it to reduce power level where possible, which will minimize the range of CCI for near clients without compromising coverage for the most distant ones.

About Fully Homomorphic Encryption Improvement Techniques

2019 ◽  
Vol 10 (3) ◽  
pp. 1-20
Author(s):  
Ahmed El-Yahyaoui ◽  
Mohamed Daifr Ech-Cherif El Kettani
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Quality Of Services ◽  
End User ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Cloud Server ◽  
Encryption Schemes ◽  
Multiple Clients

Fully homomorphic encryption schemes (FHE) are a type of encryption algorithm dedicated to data security in cloud computing. It allows for performing computations over ciphertext. In addition to this characteristic, a verifiable FHE scheme has the capacity to allow an end user to verify the correctness of the computations done by a cloud server on his encrypted data. Since FHE schemes are known to be greedy in term of processing consumption and slow in terms of runtime execution, it is very useful to look for improvement techniques and tools to improve FHE performance. Parallelizing computations is among the best tools one can use for FHE improvement. Batching is a kind of parallelization of computations when applied to an FHE scheme, it gives it the capacity of encrypting and homomorphically processing a vector of plaintexts as a single ciphertext. This is used in the context of cloud computing to perform a known function on several ciphertexts for multiple clients at the same time. The advantage here is in optimizing resources on the cloud side and improving the quality of services provided by the cloud computing. In this article, the authors will present a detailed survey of different FHE improvement techniques in the literature and apply the batching technique to a promising verifiable FHE (VFHE) recently presented by the authors at the WINCOM17 conference.

scholarly journals Intelligent Load Balancing Techniques in Software Defined Networks: A Survey

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1091 ◽  
Author(s):  
Thabo Semong ◽  
Thabiso Maupong ◽  
Stephen Anokye ◽  
Kefalotse Kehulakae ◽  
Setso Dimakatso ◽  
...  
Keyword(s):  
Load Balancing ◽  
Performance Metrics ◽  
Denial Of Service ◽  
Internet Traffic ◽  
Future Research ◽  
Single Server ◽  
Multiple Servers ◽  
Future Research Directions ◽  
Load Balancer ◽  
Multiple Clients

In the current technology driven era, the use of devices that connect to the internet has increased significantly. Consequently, there has been a significant increase in internet traffic. Some of the challenges that arise from the increased traffic include, but are not limited to, multiple clients on a single server (which can result in denial of service (DoS)), difficulty in network scalability, and poor service availability. One of the solutions proposed in literature, to mitigate these, is the use of multiple servers with a load balancer. Despite their common use, load balancers, have shown to have some disadvantages, like being vendor specific and non-programmable. To address these disadvantages and improve internet traffic, there has been a paradigm shift which resulted in the introduction of software defined networking (SDN). SDN allows for load balancers that are programmable and provides the flexibility for one to design and implement own load balancing strategies. In this survey, we highlight the key elements of SDN and OpenFlow technology and their effect on load balancing. We provide an overview of the various load balancing schemes in SDN. The overview is based on research challenges, existing solutions, and we give possible future research directions. A summary of emulators/mathematical tools commonly used in the design of intelligent load balancing SDN algorithms is provided. Finally, we outline the performance metrics used to evaluate the algorithms.

scholarly journals Analysis of Multiple Clients’ Behaviors in Edge Computing Environment

2018 ◽  
Vol 67 (9) ◽  
pp. 9052-9055 ◽  
Author(s):  
Peiyuan Guan ◽  
Xiaoheng Deng ◽  
Yajun Liu ◽  
Honggang Zhang
Keyword(s):  
Edge Computing ◽  
Computing Environment ◽  
Multiple Clients

Who's First? Prioritizing Nursing Care in Simulation with Multiple Clients

2009 ◽  
Vol 5 (3) ◽  
pp. e132
Author(s):  
Kathleen Blust ◽  
Susan Kamath ◽  
Joyce Sizemore
Keyword(s):  
Nursing Care ◽  
Multiple Clients

scholarly journals Federated Learning: A Distributed Shared Machine Learning Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Kai Hu ◽  
Yaogen Li ◽  
Min Xia ◽  
Jiasheng Wu ◽  
Meixia Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Development Process ◽  
Machine Learning Algorithms ◽  
Process Definition ◽  
Future Developments ◽  
Private Data ◽  
Multiple Clients ◽  
Distributed Machine Learning

Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machine learning and deep learning. First of all, this paper introduces the development process, definition, architecture, and classification of FL and explains the concept of FL by comparing it with traditional distributed learning. Then, it describes typical problems of FL that need to be solved. On the basis of classical FL algorithms, several federated machine learning algorithms are briefly introduced, with emphasis on deep learning and classification and comparisons of those algorithms are carried out. Finally, this paper discusses possible future developments of FL based on deep learning.

Sign in / Sign up

Export Citation Format

Share Document