Based on the deployment knowledge and the irreversibility of some hash chains, a novel pairwise key distribution scheme (DKH-KD) for wireless sensor networks is proposed. In DKH-KD scheme, before the nodes in the network are deployed, the offline server constructs a number of hash chains and uses the values from a pair of reverse hash chains to establish their pairwise keys among the nodes in the same region, while, among the neighbor nodes in the different regions, some pairs of the hash chains based on the deployment knowledge are employed to establish the pairwise keys. These procedures make the attackers hard to break the network and ensure that the probability of the pairwise key establishment is close to 1. Compared with the Dai scheme and the q-composite's scheme, our analyses show that DKH-KD scheme can improve the probability of the pairwise key establishment and the invulnerability more efficiently.
Abstract:We investigate machine learning for the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernels. We provide some estimates for the learning raters of both regression and classification when the hypothesis spaces are sample dependent. Under a weak condition on the kernels we derive learning error by estimating the rate of some K-functional when the target functions belong to the range of some Hilbert-Schmidt integral operator.
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