An adaptive epidemic information dissemination model for wireless sensor networks

We propose an adaptive bio-inspired information dissemination model that exploits the specific characteristics of the sampled/generated data stream (DS) in a wireless sensor network. Our model extends the basic epidemic algorithm by adapting key operational parameters (i.e., the forwarding probability and validity period) of the data dissemination process. The main idea is that the forwarding probability is tuned according to the variability of the involved DS. Our findings from the introduction of this adaptive epidemic are quite promising. Our scheme supersedes conventional probabilistic information dissemination algorithms in terms of efficiency and reliability.

► We propose an adaptive epidemic dissemination model.
► It is based on a data-aware epidemic spreading mechanism.
► It tunes the probabilistic nature of the epidemic spreading.
► It supersedes conventional epidemic algorithm in terms of efficiency and reliability.