Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Reinforcement Learning
Networking with Cognitive Packets
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Scalable Performance Signalling and Congestion Avoidance
Scalable Performance Signalling and Congestion Avoidance
Design and application of hybrid intelligent systems
A Swarm Intelligent Scheme for Routing in Mobile Ad Hoc Networks
ICW '05 Proceedings of the 2005 Systems Communications
Policy-Based Network Management: Solutions for the Next Generation (The Morgan Kaufmann Series in Networking)
Ants and reinforcement learning: a case study in routing in dynamic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
K-Shortest paths q-routing: a new QoS routing algorithm in telecommunication networks
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part II
Determining the optimal configuration for the zone routing protocol
IEEE Journal on Selected Areas in Communications
QoS dynamic routing for wireless sensor networks
Proceedings of the 2nd ACM international workshop on Quality of service & security for wireless and mobile networks
Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics
Computer Networks: The International Journal of Computer and Telecommunications Networking
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In the last few years, the advance of multimedia applications has prompted researchers to undertake the task of routing multimedia data through Manet. This task is rather difficult due to the highly dynamic topology of mobile ad hoc networks and their limited bandwidth. Actually, different routing algorithms are proposed in order to route various kinds of sources (such as voice, video, or data) with diverse traffic characteristics and Quality of Service Requirements (QoS). These algorithms must take into account significant traffic problems such as packet losses, transmission delays, delay variations, etc, caused mainly by congestion in the networks. The prediction of these problems in real time is quite difficult, making the effectiveness of "traditional" protocols based on analytical models questionable. We propose in this paper a solution based on swarm intelligence paradigm that we find more adapted for this kind of problems.