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
One of the technologies based on information technology used today is the VANET network used for inter-road communication. Today, many developed countries use this technology to optimize travel times, queue lengths, number of vehicle stops, and overall traffic network efficiency. In this research, we investigate the critical and necessary factors to increase the quality of VANET networks. This paper focuses on increasing the quality of service using multi-agent learning methods. The innovation of this study is using artificial intelligence to improve the network's quality of service, which uses a mechanism and algorithm to find the optimal behavior of agents in the VANET. The result indicates that the proposed method is more optimal in the evaluation criteria of packet delivery ratio (PDR), transaction success rate, phase duration, and queue length than the previous ones. According to the evaluation criteria, TSR 6.342%, PDR 9.105%, QL 7.143%, and PD 6.783% are more efficient than previous works.

Abstract
Recent studies in information computation technology (ICT) are focusing on Next-generation networks, SDN (Software-defined networking), 5G, and 6G. Optimal working mode for device-to-device (D2D) communication is aimed at improving the quality of service with the frequency spectrum structure is of research areas in 5G. D2D communication working modes are selected to meet both the predefined system conditions and provide maximum throughput for the network. Due to the complexity of the direct solutions, we formulated the problem as an optimization problem and found the optimal working modes under different parameters of the system through extensive simulations. After determining the links' optimal modes, we calculated the network throughput; because of selecting the best working modes, we obtained the highest throughput. A major finding from this research is that D2D communication pairs are more inclined to use full-duplex (FD) mode in short distances to meet system requirements, and so most communications take place in FD mode at these distances. According to these results, using FD communication at short distances offers better conditions and Quality of service (QoS) than QoS-D2D method.

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
This research aims to represent a novel approach to detect malicious nodes in Ad-hoc On-demand Distance Vector (AODV) within the next-generation smart cities. Smart city applications have a critical role in improving public services quality, and security is their main weakness. Hence, a systematic multidimensional approach is required for data storage and security. Routing attacks, especially sinkholes, can direct the network data to an attacker and can also disrupt the network equipment. Communications need to be with integrity, confidentiality, and authentication. So, the smart city and urban Internet of Things (IoT) network, must be secure, and the data exchanged across the network must be encrypted. To solve these challenges, a new protocol using CLustering Multi-Layer Security Protocol (CL-MLSP) with AODV has been proposed. The Advanced Encryption Standard (AES) algorithm is aligned with the proposed protocol for encryption and decryption. The shortest path is obtained by the clustering method based on energy, mobility, and distribution for each node. Ns2 is used to evaluate the CL-MLSP performance, and the parameters are network lifetime, latency, packet loss, and security. We have compared CL-MLPS with ECP-AODV, Probe, and Multi-Path. The proposed method superiority rates in energy consumption, drop rate, delay, throughput, and security performance are 6.54%, 12.87%, 8.12%, 9.46%, respectively.

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.