基于近似l_0重构算法的传感网数据收集方案

Approximate l_0 recovery algorithm-based data collection scheme in wireless sensor networks

针对无线传感器网络中的数据收集问题,基于压缩感知理论设计并实现了一种高效节能的数据收集方案.数据采集时利用矩阵投影对传感器节点感知的数据进行压缩,数据重构时利用指数函数族对l0范数进行逼近,从而将带约束条件的l0范数最小化问题转化为无约束条件的优化问题,同时还设计了相应的加权函数,从而进一步提高重构算法的收敛速度.实验结果表明:所设计的基于近似l0范数重构算法的传感网数据收集方案在数据收集过程中具有较高的运行效率,其对无线传感器网络的带宽、能量等资源消耗较低;在数据重构过程中能够在适当的重构时间内进一步提高压缩数据的重构成功率.

To solve the data collection problem in wireless sensor network,an efficient and energysaving data collection scheme based on compressive sampling theory was proposed.The matrix projection method was adopted to compress the sensors′sensed data in the data collection phase.Meanwhile,in order to convert the constrained l0 norm minimization problem into an unconstrained optimization problem,a family of exponential functions was utilized to approximate the l0 norm of the original signal in the data recovery phase.Furthermore,a series of weighting functions were also designed to accelerate the convergence speed of the recovery algorithm.The experiment results have shown that the proposed scheme is efficient and low cost in terms of bandwidth and energy in the data collection phase and also provides a higher recovery rate than the existed recovery schemes within an appropriate reconstruction time in the data recovery phase.