Ponder: Realising Enterprise Viewpoint Concepts
EDOC '00 Proceedings of the 4th International conference on Enterprise Distributed Object Computing
Attribute-based filtering for embedded systems
Proceedings of the 2nd international workshop on Distributed event-based systems
Finite State Transducers for Policy Evaluation and Conflict Resolution
POLICY '04 Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks
PICO: A Middleware Framework for Pervasive Computing
IEEE Pervasive Computing
Self-Managed Cell: A Middleware for Managing Body-Sensor Networks
MOBIQUITOUS '07 Proceedings of the 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking&Services (MobiQuitous)
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During the last years there has been a strong research effort on the autonomic communications and self-management paradigms. Following this impulse, the academic community and the industry have proposed several architectures and techniques to allow network devices to make their own configuration decisions. Those proposals often include resource-expensive technologies such as complex inference machines, ontological modeling and probabilistic prediction that may not be suitable for the most pervasive and inexpensive network-enabled devices. This paper addresses this facet of the autonomic systems introducing RAN. This system aims to be a complete rule-based, distributed system specially designed and implemented to enable autonomic behavior on very constrained devices, such as domestic wireless routers with resources as low as 16 MB of RAM and 4 MB of storage memory. The RAN system was developed to serve the objectives of Rural Ambient Networks, a project that targets the so-called Digital Divide deploying low-cost wireless mesh infrastructure in rural communities. In this context, RAN, in autonomic and distributed manners, optimizes the network configuration to minimize the monetary cost that the community has to pay for using the IT infrastructure. Finally, this work presents an evaluation of RAN that shows how it makes possible to perform sophisticated optimization decisions with a very small overhead in terms of CPU and memory.