Wireless Smooth Data Streaming on Application Layer

2011 ◽  
Author(s):  
Chia Jung Chen ◽  
Rong Guey Chang ◽  
Chih Wen Hsueh
Keyword(s):  
Data Streaming ◽  
Application Layer ◽  
Smooth Data

scholarly journals Data Transmission Model with Lost Fragments Recovery Based on Application Layer ARQ

2021 ◽  
Vol 24 (3) ◽  
pp. 104-111
Author(s):  
A.V. Abilov ◽  
A.V. Chunaev ◽  
A.I. Nistyuk ◽  
I.A. Kaisina
Keyword(s):  
Mathematical Model ◽  
Wireless Networks ◽  
Wireless Communication ◽  
Data Transmission ◽  
Data Streaming ◽  
Data Loss ◽  
Application Layer ◽  
Loss Recovery ◽  
Osi Model ◽  
High Level

Wireless networks in difficult conditions of signal receiving are characterized by a high level of burst data losses, at which a large number of data fragments can be lost in a row. In this case, to recover the lost data, the use of forward error correction methods (FEC) in most cases does not give a sufficient effect. The use of standard data loss recovery methods based on automatic retransmission request (ARQ) at the data link and transport layers of the OSI model can lead to significant delays, which is often unacceptable for real-time streaming services. In such a case, it may be preferable to skip the piece of data rather than delay waiting for the piece to be delivered on retransmissions. The use of ARQ-based techniques on application layer of OSI model for data streaming allows for a more efficient recovery of lost data chunks in wireless networks with a high level of burst losses. The known models of a discrete channel for wireless networks allow for analytically assessing the probability of data loss, however, they do not take into account cases with retransmission of lost data. The study proposes a mathematical model of data transmission in a wireless communication channel based on the Gilbert model, which takes into account the loss recovery by the ARQ method and allows you to calculate the data loss ratio. To check the adequacy of the proposed model, a software was developed that ensures the transmission of data streaming in a wireless communication network with recovery of fragment losses at the application level, and a corresponding experimental study was carried out. It is shown that the mathematical model takes into account the burstiness of transmitted data losses and their recovery by the ARQ method.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

HULK and DDoS Attacks in Web Applications with Detection Mechanism

2018 ◽  
Vol 6 (6) ◽  
pp. 192
Author(s):  
Amit Sharma
Keyword(s):  
Web Applications ◽  
Denial Of Service ◽  
Single Server ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Detection Mechanism ◽  
Plan Execution ◽  
Social Affair ◽  
Server Application

Distributed Denial of Service attacks are significant dangers these days over web applications and web administrations. These assaults pushing ahead towards application layer to procure furthermore, squander most extreme CPU cycles. By asking for assets from web benefits in gigantic sum utilizing quick fire of solicitations, assailant robotized programs use all the capacity of handling of single server application or circulated environment application. The periods of the plan execution is client conduct checking and identification. In to beginning with stage by social affair the data of client conduct and computing individual user’s trust score will happen and Entropy of a similar client will be ascertained. HTTP Unbearable Load King (HULK) attacks are also evaluated. In light of first stage, in recognition stage, variety in entropy will be watched and malevolent clients will be recognized. Rate limiter is additionally acquainted with stop or downsize serving the noxious clients. This paper introduces the FAÇADE layer for discovery also, hindering the unapproved client from assaulting the framework.

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