Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
Service Discovery in Pervasive Computing Environments
IEEE Pervasive Computing
Toward Distributed Service Discovery in Pervasive Computing Environments
IEEE Transactions on Mobile Computing
A Mobile-Directory Approach to Service Discovery in Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
Service discovery in mobile ad hoc networks: A field theoretic approach
Pervasive and Mobile Computing
Hi-index | 0.00 |
We present our Service Directory Placement Protocol (SDPP), a multi-directory scheme for service discovery in Mobile Ad-hoc Networks (MANETs). SDPP promotes the use of both fixed and mobile directories co-existing in a hybrid setting comprised of devices with different memory availability. Our proposed system is based on a heuristic approach, whose performance is optimized by formulating the directory-placement problem as a Semi-Markov Decision Process solved by Q-Learning. Performance evaluations reveal typical performance gains ranging between 15% and 75% of SDPP compared with a default broadcast approach for MANETs comprised of hosts moving at pedestrian speeds. A two-step process for practical implementation based on "off-line" computer simulations is also described.