Nearby in the stack

Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks · arXivDesk

1004.4610Apr 26, 2010Heni Kaaniche and Farouk Kamoun, "Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks", Journal of Telecommunications, Volume 2, Issue 1, p95-101, April 2010

Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks

Heni Kaaniche, Farouk Kamoun

Neural and Evolutionary Computing

Abstract

Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. This method consists of a multi-layer and recurrent neural network using back propagation through time algorithm for training.

Briefing

What this paper is actually doing

This is a heuristic sketch from the abstract. Sign in for a live briefing.

Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions.

GraduateNovelty 3/5

Researchers working in cs.NE and adjacent areas.

Claims

The paper addresses: Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks.
Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks.

Methods

Method details are not extracted without an AI key — open the PDF or HTML version.
Author comment: Heni Kaaniche and Farouk Kamoun, "Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks", Journal of Telecommunications, Volume 2, Issue 1, p95-101, April 2010

Limitations

This is a heuristic briefing generated without a language model. Treat it as a sketch, not an analysis.

Why it matters

Newly listed under cs.NE. Connect your OpenRouter key to unlock a full arXivDesk briefing.

Open questions

What is the strongest empirical result, and on which baseline?
Which assumption would most change the conclusion if it failed?

Socratic margin

Ask the abstract

Questions are grounded in the title and abstract. If the PDF is required, arXivDesk will say so.

Sign in to ask questions about this paper.

Nearby in the stack

2212.06370

over 3 years ago

Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation

Giorgio Morales, John W. Sheppard

Machine Learning Machine Learning

2306.14753

about 3 years ago

The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory

Sergey Oladyshkin, Timothy Praditia, Ilja Kröker +3

Neural and Evolutionary Computing Machine Learning

2307.05639

about 3 years ago

Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks

Danny D'Agostino, Ilija Ilievski, Christine Annette Shoemaker

Machine Learning Artificial Intelligence Neural and Evolutionary Computing

1905.05918

over 7 years ago

A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems

David Laredo, Zhaoyin Chen, Oliver Schütze +1

Machine Learning Neural and Evolutionary Computing Machine Learning

2205.13273

about 4 years ago

Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks

Guilherme Vieira, Marcos Eduardo Valle

Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing

2608.12772

3 days ago

Insights from Multi-tasking the EAX Algorithm for the Travelling Salesperson Problem

Liam Wigney, Aneta Neumann, Yew-Soon Ong +1

Neural and Evolutionary Computing