Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An introduction to genetic algorithms
An introduction to genetic algorithms
Fair end-to-end window-based congestion control
IEEE/ACM Transactions on Networking (TON)
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
The Vision of Autonomic Computing
Computer
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
From Autonomic Computing to Autonomic Networking: An Architectural Perspective
EASE '08 Proceedings of the Fifth IEEE Workshop on Engineering of Autonomic and Autonomous Systems
Hop-by-hop toward future mobile broadband IP
IEEE Communications Magazine
Fundamental design issues for the future Internet
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
Self-Configuration and Optimization for Cognitive Networked Devices
Wireless Personal Communications: An International Journal
Knowledge-based design space exploration of wireless sensor networks
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
A novel energy-saving management mechanism in cellular networks
Proceedings of the 8th International Conference on Network and Service Management
| Hi-index | 0.00 |
In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selecting appropriate protocols and carrying out cross-layer optimization on the resulting stack is a key functionality of any CWN. We take a "black box" approach to the problem and study the use of simulated annealing for solving it. To improve the convergence rate of the basic algorithm we apply machine learning techniques to construct graphical models on the perceived relations between network stack parameters and application-specific network utilities. We test our optimizer design both in a simulation environment as well as on a network testbed with low-power radios. Our results show that even basic simulated annealing works well, but simple graphical models can further increase the convergence rate. However, use of sophisticated models such as Bayesian networks does not always lead to substantially better performance. The results indicate that enhanced simulated annealing indeed appears to be a promising foundation for future cognitive engine designs.