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 are not able to handle route discovery sessions by themselves; they can only store information about the most recent zone state. Moving sensors find lots of applications in robotics where robots continue to move from one room to another to sensing the environment. A huge amount of energy can be saved in these kinds of networks if transmission range control is applied. The 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 action, the next states of the concerned sender node as well as the communication session are re-determined while the router receives a reward. In order to decide the optimum power level in the next iteration, the Epsilon-greedy algorithm is applied in this study. 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 high network throughput.

中文翻译：

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具有移动传感器的软件定义无线传感器网络中基于增强的传输范围控制

软件定义无线传感器网络 (SD-WSN) 中的路由可以是单跳或多跳，而网络则是静态的或动态的。在静态 SD-WSN 中，从源到目的地的最佳路由的选择是由 SDN 控制器完成的。另一方面，如果存在移动传感器，则区域的 SDN 控制器无法自行处理路由发现会话；它们只能存储有关最新区域状态的信息。移动传感器在机器人技术中有很多应用，其中机器人继续从一个房间移动到另一个房间以感知环境。如果应用传输范围控制，可以在这些类型的网络中节省大量能源。每个节点存在多个功率等级，并且在潜在的发送方节点决定传输/转发消息后，这些级别中的每一个都采取可能的行动。基于每个这样的动作，当路由器收到奖励时，相关发送方节点的下一个状态以及通信会话被重新确定。为了决定下一次迭代的最佳功率水平，本研究采用了 Epsilon-greedy 算法。根据当前的网络场景重新确定。仿真结果表明，我们提出的工作通过降低能耗和保持高网络吞吐量使网络达到平衡。为了决定下一次迭代的最佳功率水平，本研究采用了 Epsilon-greedy 算法。根据当前的网络场景重新确定。仿真结果表明，我们提出的工作通过降低能耗和保持高网络吞吐量使网络达到平衡。为了决定下一次迭代的最佳功率水平，本研究采用了 Epsilon-greedy 算法。根据当前的网络场景重新确定。仿真结果表明，我们提出的工作通过降低能耗和保持高网络吞吐量使网络达到平衡。