Wireless sensor networks are often deployed in public or otherwise untrusted and even hostile environments, which prompt a number of security issues. Although security is a necessity in other types of networks, it is much more so in sensor networks due to the resource-constraint, susceptibility to physical capture, and wireless nature. Till now, most of the security approaches proposed for sensor networks present single solution for particular and single problem. Therefore, to address the special security needs of sensor networks as a whole we introduce a security framework. In their framework, the authors emphasize the following areas: (1) secure communication infrastructure, (2) secure scheduling, and (3) a secure data aggregation algorithm. Due to resource constraints, specific strategies are often necessary to preserve the network's lifetime and its quality of service. For instance, to reduce communication costs, data can be aggregated through the network, or nodes can go to sleep mode periodically (nodes scheduling). These strategies must be proven as secure, but protocols used to guarantee this security must be compatible with the resource preservation requirement. To achieve this goal, secure communications in such networks will be defined, together with the notions of secure scheduling and secure aggregation. The concepts of indistinguability, nonmalleability, and message detection resistance will thus be adapted to communications in wireless sensor networks. Finally, some of these security properties will be evaluated in concrete case studies.
Efficient data reduction methods are needed to minimize the power consumption in multivariate Wireless Sensor Network (WSN). In this paper, we proposed a distributed multivariate data reduction model for sensor nodes. It is based on reducing data matrices during two phases: in-network data aggregation and polynomial regression. To evaluate the performance of the proposed technique, experiments on real sensor data have been conducted. The obtained results show that our proposed technique outperforms the existing ones in terms of the size of data transmitted over the network and in terms of the energy consumption.
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