【摘要】 针对水声无线传感器网络的节能问题,提出了一种基于时隙CSMA的强化学习算法;该算法利用强化学习协议自适应水下环境,根据数据传输的距离调整发射功率,从而减少能量消耗,再结合时隙CSMA协议,使用随机退避算法减少信道中的数据碰撞,使得重发的数据包数量减少,节省能量;为验证算法的有效性,仿真实现了该算法,结果表明,该算法能够有效减少能耗,并延长网络寿命。更多还原

【Abstract】 In order to solve the problem of energy issues in underwater acoustic wireless sensor networks,we propose a reinforcement learning algorithm based on slotted CSMA.The proposed algorithm adopts reinforcement learning algorithm to self-adapt to underwater environment,and adjusts the transmit power according to the distance of data transmission.Therefore,it reduces energy consumption.Combined with slotted CSMA protocol,the proposed algorithm utilizes random backoff algorithm to reduce the data collisions in the channel,which reduces the number of retransmitted packets and saves energy.To verify the effectiveness of the proposed algorithm,we simulate the proposed algorithm.The simulation results show that the proposed algorithm can effectively reduce energy consumption and prolong the lifetime of the whole network.更多还原