Please suggest to me the latest research papers in which neural networks and other machine learning techniques are used for routing optimization and clustering in Wireless Sensor Networks and/or Internet of Things.
I did some basic work in this area using Elman NNs for traffic prediction. The objective is to deploy traffic prediction software to avoid congestion on the specific nodes by moving the traffic to other nodes before the actual congestion occurs. A sample of some of the references that I used at the time are:
Balamurugan, K., & PALANISAMY, V. (2011). Identifying network load balancing requirements on historical traffic flow using machine learning approach for flexible multipath networks. Indian Journal of Computer Science and Engineering, 1(1), 92-97.
Junsong, W., Jiukun, W., Maohua, Z., & Junjie, W. (2009, June). Prediction of internet traffic based on Elman neural network. In Control and Decision Conference, 2009. CCDC'09. Chinese (pp. 1248-1252). IEEE.
Gellman, M., & Liu, P. (2006). Random neural networks for the adaptive control of packet networks. In Artificial Neural Networks–ICANN 2006 (pp. 313-320). Springer Berlin Heidelberg.
Following the 'cited by' of these references can lead you to more up to date references
Take a look at my paper "Nature-Inspired Routing Algorithm for Wireless
Sensor Networks", AJEEE, 2011
Neural networks have solved a wide range of problems and have good learning capabilities. Their strengths include adaptation,ease of implementation, parallelization, speed, and flexibility.
There are two layers(input and competitive layer).
A two - layer feed forward neural network that implements the idea
of competitive learning ,the nodes in the input layer admit input patterns of sensor nodes competing for CH and are fully connected to the output nodes in the competitive layer. Each output node corresponds to a cluster and is associated with weight Wj , j = 1,2,...., m , where m is the number of clusters.
- Badges
- Science topic