Abstract
Internet of Things (IoT) and Wireless Sensor Networks (WSN) are a set of low-cost wireless sensors that can collect, process and send environment's data. WSN nodes are battery powered, therefore energy management is a key factor for long live network. One way to prolong lifetime of network is to utilize routing protocols to manage energy consumption. To have an energy efficient protocol in environment interactions, we can apply ZigBee protocols. Among these Artificial Intelligence Interactions routing methods, Tree Routing (TR) that acts in the tree network topology is considered a simple routing protocol with low overhead for ZigBee. In a tree topology, every nodes can be recognized as a parent or child of another node and in this regard, there is no circling. The most important problem of TR is increasing the number of steps to get data to the destination. To solve this problem several algorithms were proposed that its focus is on fewer steps. In this research we present an artificial Intelligence Tree Routing based on RNN and ZigBee protocol in IoT environment. Simulation results show that NEWTR improve the network lifetime by 5.549% and decreases the energy consumption (EC) of the network by 5.817% as compared with AODV routing protocol.

Abstract
Today, cloud platforms for Internet of Everything (IoE) are facilitating organizational and industrial growth, and have different requirements based on their different purposes. Usual task scheduling algorithms for distributed environments such as group of clusters, networks, and clouds, focus only on the shortest execution time, regardless of the power consumption. Network energy can be optimized if tasks are properly scheduled to be implemented in virtual machines, thus achieving green computing. In this research, Dynamic Voltage Frequency Dcaling (DVFS) is used in two different ways, to select a suitable candidate for scheduling the tasks with the help of an Artificial Intelligence (AI) approach. First, the GIoTDVFS_SFB method based on sorting processor elements in Cloud has been considered to handle Task Scheduling problem in the Clouds system. Alternatively, the GIoTDVFS_mGA microgenetic method has been used to select suitable candidates. The proposed mGA and SFB methods are compared with SLAbased suggested for Cloud environments, and it is shown that the Makespan and Gain in benchmarks 512 and 1024 are optimized in the proposed method. In addition, the Energy Consumption (EC) of Real PM (RPMs) against the numeral of Tasks has been considered with that of PAFogIoTDVFS and EnergyAwareDVFS methods in this area. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Abstract
Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art.

Abstract
The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.

Abstract
Short reaction times are among the most important factors in preventing casualties or providing first assistance to potential victims during large scale natural disasters. Consequently, first response teams must quickly gain a comprehensive overview and thus situation awareness of the disaster situation. To address this challenge, the ResponDrone-platform was developed within the scope of the ResponDrone project. A fleet of unmanned aerial vehicles provides critical information from the disaster site to the first response teams in real-time and can act as a communications relays in areas with disrupted communications infrastructure. The unmanned aerial vehicles are commanded via a web-based multi-mission control system. Data sharing between the individual components is realized via a web-based cloud platform. The ResponDrone platform's capabilities were successfully tested and validated within the scope of several flight and simulation trials. This paper describes the components that were developed, integrated into a system-of-systems and demonstrated during the ResponDrone project and explains how the components work together in order to execute task-based multi-UAV missions. Further, the results of the validation trials are presented and an outlook on the next steps for further exploitation of the ResponDrone platform is given. © 2022 IEEE.

Abstract
In the last few decades, there has been a growing interest in exploring the sea. The activities of the so-called blue economy can go from applications such as offshore maritime wind farms to ocean environment monitoring, which are supported by sensed platforms such Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs) that require the use of reliable underwater communications. Currently, there is no suitable solution that is able to combine long-range and broadband underwater communications. The integration of different technologies, namely acoustics, RF, and optical on a multimodal approach, has been considered a suitable solution to overcome the limitations caused by the water propagation medium. Since missions at the ocean are usually expensive and demand large human and technological resources, it is important to have accurate simulation platforms for these multimodal underwater wireless networks. This paper presents the first version of a novel simulation framework - MultiUWSim (Beta) -, built upon ns-3, which integrates multiple communications technologies (RF, acoustics and optical). The current version of the simulation platform offers the possibility of simulating acoustic-based and radio-based physical wireless interfaces in a single node in a ns-3 simulation environment, enabling fully-customizable underwater network simulations.