Advances in Wireless Sensor Networks: 6th China Conference, by Hanzhen Cao, Weijun Qin, Jiadi Zhang, Qiang Li (auth.),

By Hanzhen Cao, Weijun Qin, Jiadi Zhang, Qiang Li (auth.), Ruchuan Wang, Fu Xiao (eds.)

This publication constitutes the refereed lawsuits of the sixth China convention on Advances in instant Sensor Networks, held in Huangshan, China, in October 2012.
The 70 revised complete papers have been rigorously reviewed and chosen from 458 submissions. The papers conceal a variety of issues together with within the instant sensor community fields nodes platforms, infrastructures, conversation protocols, and knowledge management.

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Additional resources for Advances in Wireless Sensor Networks: 6th China Conference, CWSN 2012, Huangshan, China, October 25-27, 2012, Revised Selected Papers

Example text

To generate the ith token in sever j, it needs the following approach: (i) 1. Random challenge value αi = fkchal within the GF (2p ), and one replacement (i) kprp key based on the KP RP . 2. Calculate the r index selected randomly: {Iq ∈ [1, · · · , l] | 1 ≤ q ≤ r}, where Iq = φk(i) (q). prp 3. Get the token r (j) vi αqi × G(j) [Iq ], where = (j) G(j) [Iq ] = gIq q=1 (j) where vi is not only the element of GF (2p ), but also the server’s response value the user expect when he challenges a specific data block.

For example, the ith response process of the review of the n servers challenge is as follows: 1. The user will send αi and the ith replacement key to each server. 2. r row index in the storage vector G(j) is arranged in a linear combination in sever: r (j) Ri αqi × G(j) [φk(i) (q)] = prp q=1 (j) (j) 3. With Ri received from all the servers, users get the hidden value of Ri , (j ∈ {m + 1, · · · , n}) by using the formula: r (j) Ri (j) fkj (S(Iq , j)) · αqi , where ← Ri − Iq = φk(i) (q). prp q=1 4.

This kind of storage mechanism can be only used for one-dimensional data storage management. While for multiple heterogeneous data stream in the Internet of Things, such as temperature, humidity, pressure and wind strength etc, this one-dimensional storage scheme will result in low storage efficiency and indexing efficiency. Therein we propose a multi-dimension B+ tree structure, which is applicable for multi-dimensional heterogeneous data stream storage. 1 J. Chen, T. Liu, and L. Li Storage Mechanism for Heterogeneous Data Stream Data Stream Sampling In order to improve the storage efficiency, we prefer to retaining the data which users are interested in, namely mutational data rather than storing the whole incoming data.

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