scholarly journals Cross Layer Design based Hybrid Error Control for Packet Aggregation over WSN

2018 ◽  
Vol 7 (4.6) ◽  
pp. 382
Author(s):  
Lakshmi. S ◽  
Dr. Selvakumar raja.S
Keyword(s):  
Error Rate ◽  
Error Control ◽  
Forward Error Correction ◽  
Control Technique ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Source Information ◽  
Packet Aggregation ◽  
Wide Range ◽  
Error Control Mechanism

In general sensor networks have a wide range of application and also play a major role in developing precision and timely information. Mostly it deals with real world applications and in many cases the data sensed by the nodes should deliver within the time constriction. Meeting the deadlines is mandate for the applications and the data should be processed as soon as possible and without major data losses. The objective of this scheme is to collect data with high accuracy and low latency. Collecting and processing the data by means of aggregation will greatly reduce the congestion data rate. Cross Layer Design based Hybrid Error Control (CLDHEC) mechanism is proposed for reducing error rate occurred in sensor network which includes formation of network, data aggregation and transmission. Error control technique named Adaptive Forward Error Correction is used to defend the video transmission by recovering source information losses. FEC mechanism along with packet range control greatly increases the FEC efficiency in wireless networks. The service quality for different kind of data can be improved by lessening error rate using adaptive error control mechanism with cross layered design during packet aggregation. 

scholarly journals Cross-layer performance control of wireless channels using active local profiles

2007 ◽  
Vol 3 (3) ◽  
pp. 148 ◽  
Author(s):  
D. Moltchanov
Keyword(s):  
Performance Optimization ◽  
Wireless Channels ◽  
Forward Error Correction ◽  
Wireless Channel ◽  
Distribution Functions ◽  
Wireless Access ◽  
Cross Layer ◽  
Performance Control ◽  
Adaptation Mechanisms ◽  
Wide Range

To optimize performance of applications running over wireless channels state-of-the-art wireless access technologies incorporate a number of channel adaptation mechanisms. While these mechanisms are expected to operate jointly providing the best possible performance for current wireless channel and traffic conditions, their joint effect is often difficult to predict. To control functionality of various channel adaptation mechanisms a new cross-layer performance optimization system is sought. This system should be responsible for exchange of control informationbetween different layers and further optimization of wireless channel performance. In this paper design of the cross-layer performance control system for wireless access technologies with dynamic adaptation of protocol parameters at different layers of the protocol stack is proposed. Functionalities of components of the system are isolated and described in detail. To determine the range of protocol parameters providing the best possible performance for a wide range of channel and arrival statistics the proposed system is analytically analyzed. Particularly, probability distribution functions of the number of lost frames and delay of a frame as functions of first- and second-order wireless channel and arrival statistics, automatic repeat request, forward error correction functionality, protocol data unit size at different layers are derived. Numerical examples illustrating performance of the whole system and its elements are provided. Obtainedresults demonstrate that the proposed system provide significant performance gains compared to static configuration of protocols.

scholarly journals Literature Review on High Definition Image Error Concealment

2018 ◽  
Vol 7 (3.12) ◽  
pp. 165
Author(s):  
Ghouse Ahamed Z ◽  
Anuj Jain
Keyword(s):  
Error Correction ◽  
Error Detection ◽  
Error Control ◽  
Forward Error Correction ◽  
Error Concealment ◽  
Control Method ◽  
Error Resilience ◽  
Control Mechanisms ◽  
High Definition ◽  
Error Control Mechanism

This paper is give us a overview of Error control method used in image or video transmission. Data in transmission is lost due to link failure or due to congestion and loss in packets, so the aim of this method is to protect data from these errors. Error detection coding and Error correction coding are two types of error control mechanism. Some of the error control mechanisms are Retransmission, Forward error correction, error concealment and error resilience. We are discussing a summary of three methods and Error Concealment in details.  

Cross layer design with extensive virtual MIMO: FS-MUP optimization model for wireless sensor network

2020 ◽  
pp. 1-16
Author(s):  
Monali Prajapati ◽  
Dr. Jay Joshi
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Multiple Input Multiple Output ◽  
Wireless Sensor ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Virtual Mimo ◽  
Proposed Model ◽  
Multiple Input ◽  
Input Multiple Output

In the wireless sensor network (WSN), wireless communication is said to be the dominant power-consuming operation and it is a challenging one. Virtual Multiple-Input–Multiple-Output (V-MIMO) technology is considered to be the energy-saving method in the WSN. In this paper, a novel multihop virtual MIMO communication protocol is designed in the WSN via cross-layer design to enhance the energy efficiency, reliability, and end-to-end (ETE) and Quality of Service (QoS) provisioning. On the basis of the proposed protocol, the optimal set of parameters concerning the transmission and the overall consumed energy by each of the packets is found. Furthermore, the modeling of ETE latency and throughput of the protocol takes place with respect to the bit-error-rate (BER). A novel hybrid optimization algorithm referred as Flight Straight Moth Updated Particle Swarm Optimization (FS-MUP) is introduced to find the optimal BER that meets the QoS, ETE requirements of each link with lower power consumption. Finally, the performance of the proposed model is evaluated over the extant models in terms of Energy Consumption and BER as well.

scholarly journals Zero Average Surface Controlled Boost-Flyback Converter

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 57
Author(s):  
Juan-Guillermo Muñoz ◽  
Fabiola Angulo ◽  
David Angulo-Garcia
Keyword(s):  
Duty Cycle ◽  
Period Doubling ◽  
Power Converter ◽  
Control Technique ◽  
Average Surface ◽  
Flyback Converter ◽  
Stable Period ◽  
Period 2 ◽  
Wide Range ◽  
The Right

The boost-flyback converter is a DC-DC step-up power converter with a wide range of technological applications. In this paper, we analyze the boost-flyback dynamics when controlled via a modified Zero-Average-Dynamics control technique, hereby named Zero-Average-Surface (ZAS). While using the ZAS strategy, it is possible to calculate the duty cycle at each PWM cycle that guarantees a desired stable period-1 solution, by forcing the system to evolve in such way that a function that is constructed with strategical combination of the states over the PWM period has a zero average. We show, by means of bifurcation diagrams, that the period-1 orbit coexists with a stable period-2 orbit with a saturated duty cycle. While using linear stability analysis, we demonstrate that the period-1 orbit is stable over a wide range of parameters and it loses stability at high gains and low loads via a period doubling bifurcation. Finally, we show that, under the right choice of parameters, the period-1 orbit controller with ZAS strategy satisfactorily rejects a wide range of disturbances.

scholarly journals The Romano–Wolf multiple-hypothesis correction in Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 812-843
Author(s):  
Damian Clarke ◽  
Joseph P. Romano ◽  
Michael Wolf
Keyword(s):  
Error Rate ◽  
Multiple Testing ◽  
Original Data ◽  
Dependence Structure ◽  
Familywise Error Rate ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Multiple Hypothesis ◽  
Wide Range ◽  
Performance Gains

When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano–Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.

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