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A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called "autoscaling Bloom filters", which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate. [ABSTRACT FROM AUTHOR]

This article addresses a problem of the severe unfairness between multiple TCP sessions in a wireless context also known as "TCP capture" phenomenon. We present an adapted max-min fairness framework to the specifics of MANETs. We introduce a practically implementable cross-layer network architecture which enforces our formal model. We have verified with simulations and real world experiments that under our adaptive rate limiting scheme unfair behavior virtually vanishes. The direct consequence of this work guaranteed stable services for TCP-based applications in MANETs, including traditional FTP, web as well as for UDP-based sessions. [ABSTRACT FROM AUTHOR]