a self-adaptive placement protocol for mobile directories in manets
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.
service discovery machine learning mobile computing