As a special type of wireless P2P systems, sensor networks are often deployed for detecting events caused by disasters. The peer-to-peer mode of the sensor system itself gets challenged either directly by damages of the disaster or by unreliable wireless links. This work explores possible failure models and compares the performance of several gossip protocols corresponding to the failures models. With further refinement of the gossip protocols, the performance in the failure modes caused by disasters is improved. The evaluation of our simulation results shows that using refined gossip protocols in correspondence to the failure models, the information aggregation and dissemination speed, communication cost and the accuracy of the aggregated data can be improved.

Many applications in comparative genomics lend themselves to implementations that take advantage of common high-performance features in modern microprocessors. However, the common suggestion that a data-parallel, multithreaded, or high-throughput implementation is possible often ignores the complexity of actually creating such software. In this paper, we present two parallel algorithms for a classic comparative genomics algorithm, the dot plot. First, we describe a data-parallel algorithm that achieves speedups of up to 14.4x over the sequential version for large genomic comparisons. Then, we use the new algorithm as the base for a coarse-grained parallel version, suitable for multiprocessor and cluster environments, that scales linearly with the number of processors. These speedups introduce the opportunity to perform full pairwise comparisons on entire genomes on a much larger scale than previously possible. We also present the experimental, model-driven approach used to develop the algorithm that allowed us to carefully study and evaluate implementation options and to fully understand the parameters affecting its performance