An Asynchronous Anycast Cross-layer Protocol for WSN Suited to Noisy Environments

2021 ◽  
Author(s):  
Tales Heimfarth ◽  
João Carlos Giacomin ◽  
Bruno de Oliveira Schneider
Keyword(s):  
Cross Layer ◽  
Noisy Environments

WSN-HaDaS: A Cross-Layer Handoff Management Protocol for Wireless Sensor Networks, a Practical Approach to Mobility

2015 ◽  
Vol E98.B (7) ◽  
pp. 1333-1344
Author(s):  
Raymundo BUENROSTRO-MARISCAL ◽  
Maria COSIO-LEON ◽  
Juan-Ivan NIETO-HIPOLITO ◽  
Juan-Antonio GUERRERO-IBANEZ ◽  
Mabel VAZQUEZ-BRISENO ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Practical Approach ◽  
Wireless Sensor ◽  
Cross Layer ◽  
Handoff Management ◽  
Management Protocol

Prediction-Based Cross-Layer Resource Allocation for Wireless Multi-Hop Networks with Outdated CSI

2014 ◽  
Vol E97.B (4) ◽  
pp. 746-754 ◽  
Author(s):  
Wei FENG ◽  
Suili FENG ◽  
Yuehua DING ◽  
Yongzhong ZHANG
Keyword(s):  
Resource Allocation ◽  
Cross Layer ◽  
Outdated Csi

CROSS LAYER OPTIMIZATION WITH QOS FOR HETEROGENOUS AD-HOC NETWORK

2019 ◽  
Vol 7 (4) ◽  
pp. 1 ◽  
Author(s):  
SETHI ANITA ◽  
VIJAY SANDIP ◽  
KUMAR RAKESH ◽  
◽  
◽  
...  
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Cross Layer ◽  
Cross Layer Optimization

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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