MARE: resource discovery and configuration in ad hoc networks
Mobile Networks and Applications
D'Agents: applications and performance of a mobile-agent system
Software—Practice & Experience - Special issue: Mobile agent systems
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
LIME: A Middleware for Physical and Logical Mobility
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
KEPPAN: Knowledge exploitation for proactively-planned ad-hoc networks
Journal of Network and Computer Applications
Managing ad-hoc networks through the formal specification of service requirements
COORDINATION'06 Proceedings of the 8th international conference on Coordination Models and Languages
Hi-index | 0.00 |
Mobile Ad hoc Networks (MANETs) are dynamic environments where frequent changes in the network topology due to physical mobility of hosts result in unpredictable, sporadic and transient connectivity. Due to this high level of uncertainty, only limited guarantees can be given for interactions among agents that run on the mobile hosts. This is not desirable as any interaction among agents on different hosts is susceptible to interruption. In this paper, we explore means to alleviate the level of uncertainty in a MANET by having hosts and agents share knowledge of their non-functional attributes such as location, velocity, etc. with each other. This shared knowledge can be used to compute, for example, the points in space and time when two hosts are likely to be within communication range. This information can then be provided to individual agents, making them more aware of the constraints within which they operate and thereby giving them a chance to tailor their behavior so that they are less affected by unpredictable disconnections. The contributions of this paper are a minimalist formalism for knowledge exchange, a software architecture supporting knowledge exchange, and an empirical evaluation of the benefits of exploiting knowledge to increase the predictability of interactions.