Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks

Directed diffusion is a novel data-centric, data disemmination paradigm for sensor networks. Directed diffusion has some novel features: data-centric dissemination, reinforcement-based adaptation to the empirically best path, and in-network data aggregation and caching. These features can enable highly energy-efficient and robust dissemination in dynamic sensor networks, while at the same time minimizing the per-node configuration that is characteristic of today's networks.

Directed diffusion consists of several elements. Data is named using attribute-value pairs. A sensing task (or a subtask thereof) is disseminated throughout the sensor network as an interest for named data. This dissemination sets up gradients within the network designed to "draw" events (i.e., data matching the interest). Events start flowing towards the originators of interests along multiple paths. The sensor network reinforces one, or a small number of these paths.

More details can be found in the publications on directed diffusion. Directed diffusion simulation code is available in ns-2. The latest release of the diffusion routing software is available on the Testbeds and Software page.




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