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—To observe the complicate physical world by a WSN, the sensors in the WSN senses and samples the data from the physical world. Currently, most of the existing work use equi-frequency sampling methods (EFS) or EFS based sampling methods for data acquisition in sensor networks. However, the accuracies of EFS and EFS based sampling methods cannot be(More)
—The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks. The scale of the sensory data in many applications has already exceeds several petabytes annually, which is beyond the computation and transmission capabilities of the conventional WSNs. On the other hand, the information carried by big(More)
1 Proof of Theorem 1 Theorem 1. To satisfy that any r(x) (x ∈ S(t)) can be extracted with error O(M j), where M j ⊆ S(t) ⊆ M j+1 and 1 ≤ j ≤ log n, the condition |I| − µ ≤ |A| ≤ |I| should be ensured, where µ = 1 − 1 β · h · |M j |. Proof. As presented in Section IV.A, the querying process for r(x) in T is a traversal process over T. Assuming for each(More)
—Continuous aggregation is usually required in many sensor applications to obtain the temporal variation information of aggregates. However, in a hostile environment, the adversary could fabricate false temporal variation patterns of the aggregates by manipulating a series of aggregation results through compromised nodes. Existing secure aggregation schemes(More)