Research On Energv-Efficient Algorithms For Software-Defined Wireless Sensor Networks

Driven by the 100 billion sensor market of the Internet of Things,wireless sensor networks are penetrating into all walks of life.However,with the expansion of the network scale,the vertical coupling between the data plane and the control plane of the traditional architecture makes network management more difficult,and the energy efficiency problems caused by the limited battery resources of sensor nodes are becoming more serious.This paper studies the principle of software-defined wireless sensor networks and designs energy-efficient algorithms using sleep technology and routing technology to strengthen network management and achieve energy saving.The main contributions are as follows.This paper designs an energy-efficient algorithm based on sleep-wake scheduling,which solves the problems of connectivity,coverage,and sense blind spots when using sleep technology.The core of the algorithm is to propose a redundant node identification strategy,which selects redundant nodes for dormancy based on the redundant coverage rate,boundary coverage area,and the residual energy of exponential decay to meet network coverage requirements and implement energy saving.The simulation results show that by adjusting the threshold of the redundant coverage rate,the algorithm can improve the dormant rate,extend the network life,and reduce the network energy consumption under the premise of satisfying the network coverage.This paper proposes a routing programming algorithm based on an improved Q-learning algorithm to find an optimal route for the node to forward the packet to the sink node for the purpose of energy saving.The core work includes:(1)Model the routing decision process of the node as a Markov decision process;(2)Design a reward function based on the energy consumption,residual energy and distance of nodes;(3)Modify the iterative update formula of the Q-learning action value function,and introduce an early stopping mechanism to accelerate convergence.The simulation results show that the algorithm can dynamically adjust the routing forwarding strategy,is suitable for a variety of randomly distributed scenarios,achieves energy saving and has excellent convergence.It has been verified that combining the two energy-efficient algorithms proposed in this paper can maximize the energy saving effect.