The monitoring of underwater aquatic habitats and pipeline leakages and disaster prevention are assisted by the construction of an underwater wireless sensor network (UWSN). The deployment of underwater sensors consumes energy and causes delay when transferring the gathered sensed data via multiple hops. The consumption of energy and delays are minimized by means of an autonomous unmanned vehicle (AUV). This work addresses the idea of reducing energy and delay by incorporating an AUVs-assisted, three-dimensional UWSN (3D-UWSN) called DEDG 3D-UWSN. Energy in the sensor nodes is saved by clustering and scheduling; on the other hand, the delay is minimized by the movement of the AUV and inter-cluster routing. In clustering, multi-objective spotted hyena optimization (MO-SHO) is applied for the selection of the best sensor for the cluster head, which is responsible for assigning sleep schedules for members. According to the total number of members, an equal half of the members is provided with sleep slots based on the energy and hop counts. The redundancy in the gathered data is eliminated by measuring the Hassanat distance. Then, the moving AUV is able to predict its movement by the di-factor actor–critic path prediction method. The mid-point among the four heads is determined so that the AUV can collect data from four heads at a time. In cases where the waiting time of the CH is exceeded, three-step, inter-cluster routing is executed. The three steps are the discovery of possible routes, ignoring the longest paths and validating the filtered path with a fuzzy–LeNet method. In this 3D-UWSN, the sensed data are not always normal, and, hence, a weighted method is presented to transfer emergency events by selecting forwarders. This work is implemented on Network Simulator version 3.26 to test the results. It achieves better efficiency in terms of data collection delay, end-to-end delay, AUV tour length, network lifetime, number of alive nodes and energy consumption.

In this work, we consider a UAV-assisted cell in a single user scenario. We consider the Quality of Experience (QoE) performance metric calculating it as a function of the packet loss ratio. In order to acquire this metric, a radio-channel emulation system was developed and tested under different conditions. The system consists of two independent blocks, separately emulating connections between the User Equipment (UE) and unmanned aerial vehicle (UAV) and between the UAV and Base station (BS). In order to estimate scenario usage constraints, an analytical model was developed. The results show that, in the described scenario, cell coverage can be enhanced with minimal impact on QoE.

Edge computing is the key to building 5G Networks and Future 2030 Networks. Edge computing extends the cloud computing paradigm by placing resources close to the network edges to cope with the upcoming growth of connected devices. Future applications: health monitoring and predictive services within the framework of the Smart City, Internet of things (IoT), vehicular ad hoc network, autonomous vehicles present a new set of strict requirements, such as low latency. In this paper, we develop a set of methods for managing and orchestrating new intelligent services in a new network and computing infrastructure. In addition, we consider a new prototype using an orchestration system for managing the autonomous vehicles’ resources in comparison with the existing approaches to the design of high-load networks. This orchestration platform is based on independent Docker containers that running the orchestration system. The main goal of our proposed system is to build an efficient network architecture with a minimum delay to process the information based on neural networks. Finally, simulation results proved that the proposed system can significantly not only reduce the overall network load but also increase the quality of the transmitted stream across the network in comparison with traditional network architectures.