Internet of Underwater Things (IoUT) is growing an attractive zone of the research to explore the underwater scenario for surveillance. The autonomous manner of Unmanned Aerial Vehicles (UAV) or Drone makes search and surveillance of the target underwater region effectively. Drone gather, analyse, and transmit data at the time of flight by consuming battery energy. Underwater search and monitoring are carried out by IoUT. But battery energy is limited in drones, and elongation of the lifetime is an important requirement in IoUT. Energy consumption by being reduced by selecting an efficient cluster head in the Underwater Wireless Sensor Networks (UWSNs). Efficient data transmission makes a network efficient in terms of energy consumption, and delay. Software-Defined Networking (SDN) make UWSN more scalable. An Edge-Drone-based four-layer Software-Defined smart Internet of Underwater Things (EdgeIoUT) for efficient data transmission is proposed in this article. The architecture of the proposed EdgeIoUT model is based on edge computing and the drones act as an edge device in the proposed work. The proposed EdgeIoUT model works in four layers, (i) Underwater sensor and cluster head layer, (ii) Drone layer (iii) SDN switch, and (iv) SDN controller & data storage layer. The comparison of the proposed model with the existing traditional Software-Defined IoUT is performed using QualNet 7.1 simulator. The outcome of simulation shows that the proposed EdgeIoUT superior with regard to mean jitter by 63%, mean total energy consumption by 3%, and packet delivery ratio by 17% than traditional SD-IoUT in maximum data size.

水下物联网（IoUT）正在发展一个有吸引力的研究领域，以探索用于监视的水下场景。无人机或无人机的自主方式可以有效地搜索和监视目标水下区域。无人机在飞行时通过消耗电池能量来收集，分析和传输数据。水下搜索和监视由IoUT进行。但是电池能量受无人机限制，使用寿命的延长是IoUT的一项重要要求。通过在水下无线传感器网络（UWSN）中选择高效的簇头来降低能耗。高效的数据传输使网络在能耗和延迟方面都非常高效。软件定义网络（SDN）使UWSN更具可扩展性。本文提出了一种基于Edge-Drone的四层软件定义的水下事物智能互联网（EdgeIoUT），用于有效的数据传输。提出的EdgeIoUT模型的体系结构基于边缘计算，无人机在提出的工作中充当边缘设备。提出的EdgeIoUT模型在四个层中起作用，（i）水下传感器和群集头层，（ii）无人机层（iii）SDN交换机，以及（iv）SDN控制器和数据存储层。使用QualNet 7.1模拟器将建议的模型与现有的传统软件定义IoUT进行比较。仿真结果表明，与传统的SD-IoUT相比，EdgeIoUT在最大数据大小方面的平均抖动提高了63％，平均总能耗降低了3％，数据包传输率提高了17％。