Efficient rate and power allocation in wireless sensor networks with big data constraint

Wireless sensor networks(WSNs) have attracted a lot of interest and been applied to various fields because of it’s Low cost and low power character. The applications position sensor nodes and collect their data for a long period with real-time data flow, which is considered as big data. Big data may be structured or unstructured, and then it needs to be stored for further processing and analyzing. But with the increasing of the environment and the lack of the spectrum, the efficiency of the network has become very difficult to improve. The resource allocation problem with big data model in WSNs is investigated in this paper, where the objective is to maximize the received rate and minimize the consumed power with big data. The rate constraint is time-varying, which is more suitable for the real sensor networks. The projection gradient method is used to solve the NP-complete problem, which leads the proposed algorithm easy to implement. The convergence analysis is also given in this paper. Numerical examples show that our algorithm can guarantee fast convergence within a few iterations and outperform the existing approaches in terms of throughput and energy efficiency.