论文标题
带有移动传感器的软件定义的无线传感器网络中基于增强学习的传输范围控制
Reinforcement Learning based Transmission Range Control in Software-Defined Wireless Sensor Networks with Moving Sensor
论文作者
论文摘要
软件定义的无线传感器网络(SD-WSN)中的路由可以是单个或多跳的路由,而网络是静态的或动态的。在静态SD-WSN中,从源到目标的最佳路由的选择是由SDN控制器完成的。另一方面,如果在那里移动传感器,则区域的SDN控制器无法自行处理路线发现会话;他们只能存储有关最新区域状态的信息。移动传感器可以找到许多机器人应用程序,这些应用程序继续从一个房间移动到另一个房间,从而感知环境。如果应用传输范围控制,则可以在这些网络中节省大量能量。每个节点中都存在多个功率级别,并且在潜在的发送者节点决定传输/转发消息后,每个级别都采取了可能的操作。基于每项此类活动,相关发件人节点的下一个状态和通信会话将在路由器收到奖励时重新确定。在本研究中使用Epsilon-Greedy算法来确定下一次迭代中的最佳功率水平。根据当前的网络方案,可以确定它。仿真结果表明,我们提出的工作通过减少能源消耗和维持网络吞吐量来导致网络达到平衡。
Routing in Software-Defined Wireless sensor networks (SD-WSNs) can be either single or multi-hop, whereas the network is either static or dynamic. In static SD-WSN, the selection of the optimum route from source to destination is accomplished by the SDN controller(s). On the other hand, if moving sensors are there, then SDN controllers of zones cannot handle route discovery sessions by themselves; they can only store information about the most recent zone state. Moving sensors find lots of robotics applications where robots continue to move from one room to another to sensing the environment. A huge amount of energy can be saved in these networks if transmission range control is applied. Multiple power levels exist in each node, and each of these levels takes possible actions after a potential sender node decides to transmit/forward a message. Based on each such activity, the next states of the concerned sender node and the communication session are re-determined while the router receives a reward. The Epsilon-greedy algorithm is applied in this study to decide the optimum power level in the next iteration. It is determined anew depending upon the present network scenario. Simulation results show that our proposed work leads the network to equilibrium by reducing energy consumption and maintaining network throughput.