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 for call admission control and routing in integrated service networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Mobility prediction and routing in ad hoc wireless networks
International Journal of Network Management
Geography-informed energy conservation for Ad Hoc routing
Proceedings of the 7th annual international conference on Mobile computing and networking
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
LQ-Routing Protocol for Mobile Ad-Hoc Networks
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
Adaptive Routing for Sensor Networks using Reinforcement Learning
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Energy-aware topology control for wireless sensor networks using memetic algorithms
Computer Communications
Widest K-Shortest Paths Q-Routing: A New QoS Routing Algorithm in Telecommunication Networks
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
HotPower'10 Proceedings of the 2010 international conference on Power aware computing and systems
Energy Efficient and Scalable Routing Protocol for Extreme Emergency Ad Hoc Communications
Mobile Networks and Applications
A Survey of Routing Protocols that Support QoS in Mobile Ad Hoc Networks
IEEE Network: The Magazine of Global Internetworking
Optimal planning of sensor networks for asset tracking in hospital environments
Decision Support Systems
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Mobile-Ad-Hoc-Networks (MANETs) are self-configuring networks of mobile nodes, which communicate through wireless links. The main issues in MANETs include the mobility of the network nodes, the scarcity of computational, bandwidth and energy resources. Thus, MANET routing protocols should explicitly consider network changes and node changes into the algorithm design. MANETs are particularly suited to guarantee connectivity in disaster relief scenarios, which are often impaired by the absence of network infrastructures. Moreover, such scenarios entail strict requirements on the lifetime of the device batteries and on the reactivity to possibly frequent link failures. This work proposes a proactive routing protocol, named MQ-Routing, aimed at maximizing the minimum node lifetime and at rapidly adapting to network topology changes. The proposed protocol modifies the Q-Routing algorithm, developed via Reinforcement Learning (RL) techniques, by introducing: (i) new metrics, which account for the paths availability and the energy in the path nodes, and which are dynamically combined and adapted to the changing network topologies and resources; (ii) a fully proactive approach to assure the protocol usage and reactivity in mobile scenarios. Extensive simulations validate the effectiveness of the proposed protocol, through comparisons with both the standard Q-Routing and the Optimized Link State Routing (OLSR) protocols.