IoUT: Modelling and simulation of Edge-Drone-based Software-Defined smart Internet of Underwater Things

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.

Automated summary

The research explores the importance of IoUT in monitoring underwater environments and proposes an intelligent IoUT model based on drones and edge computing. By selecting efficient cluster head nodes to reduce energy consumption, the efficiency of data transmission is improved, and the performance of the new model is compared with traditional models.