DESIGN AND IMPLEMENT ADAPTIVE NEURAL NETWORK SOFTWARE FOR ROUTING DATA PROCESS IN COMPUTER NETWORK

The data transmission in computer network is very important. Therefore, this issue always need a serious attention, especially in the middle and wide area networking which consist of many routers. This Adaptive Neural Network's software  for routing data in computer network (which is called JST Router later) is designed to solve a routing problem for choosing the best data routing path using Backpropagation Algorithm of Neural NetworkKeywords : routing table , routing algorithms, routing protocols , Artificial Neural Networks

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images. Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.Comment: ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video abstract at https://youtu.be/NHQsDaycwy

A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and Energy- Aware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

Routing in Optical Multistage Interconnection Networks: a Neural Network Solution

There has been much interest in using optics to implement computer interconnection networks. However, there has been little discussion of any routing methodologies besides those already used in electronics. In this paper, a neural network routing methodology is proposed that can generate control bits for an optical multistage interconnection network (OMIN). Though we present no optical implementation of this methodology, we illustrate its control for an optical interconnection network. These OMINs may be used as communication media for shared memory, distributed computing systems.The routing methodology makes use of an Artificial Neural Network (ANN) that functions as a parallel computer for generating the routes. The neural network routing scheme may be applied to electrical as well as optical interconnection networks.However, since the ANN can be implemented using optics, this routing approach is especially appealing for an optical computing environment. The parallel nature of the ANN computation may make this routing scheme faster than conventional routing approaches, especially for OMINs that are irregular. Furthermore, the neural network routing scheme is fault-tolerant. Results are shown for generating routes in a 16 times 16, 3 stage OMIN. (Also cross-referenced as UMIACS-TR-94-21.

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial