This paper introduces an improved localization algorithm that bases on distance and local coordinate system which is built up by beacon nodes. This algorithm evaluates the differences of several independent localization information to determine whether upgrade this node as beacon nodes according to the size of the difference whether beyond the prescribed or not. It can effectively prevent and reduce the accumulated and spread error in localization process by the audit of beacon nodes, and improve the positioning accuracy of nodes in the network.
Sensor nodes may be deployed in hostile environments, and the sensed data is sent to the destination along the routing path, if a forwarding node in the routing path is compromised by the adversary, the data can not arrive at the destination. There are many studies on detection of malicious or compromised node, and remove the compromised nodes form the routing path, but, efficient, reliable, and secure broadcast are the major problems of the schemes. In this paper, we propose a novel secure data transport scheme for wireless sensor networks. The proposed scheme divide the secure data into n shares pair using Asmuth-Bloom threshold secret sharing scheme, and forwarded the n shares pair along the multi-path to the base station, the base station only receives k distinct shares pair from the n shares pair, he can obtain the secure data. The proposed protocol can resist selective forwarding attack, false data injection attack, replay attack, and even if there are compromised nodes in some routing paths, the base station can still get the correct secure data without removing the compromised nodes from the routing paths.
In order to solve the decision information fusion issues of resource-constrained wireless sensor network, several decision information fusion rules under exponential distribution fading channel are investigated in this paper. At first, optimal likelihood ratio rule is given. The detection performance of this fusion rule is best, however, this rule acquires channel information which is too costly for resource constrained sensor networks. To solve this problem, suboptimal likelihood ratio fusion rule is proposed which requires only the knowledge of channel statistics. In addition, the reduced forms of the suboptimal are also derived, in the case of extreme channel signal-to-noise ratio (SNR). Theoretical analysis and simulations show that suboptimal fusion rule needs much less computation and information, yet exhibits only slight performance degradation. Suboptimal fusion rules are practicable for resource constrained wireless sensor networks decision information fusion system working in ocean environment.
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