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
The actual learning process in a school, college or university should take full advantage of the digital transformation. Computers, mobile phones, tablets or other electronic devices can be used in learning environments to improve learning experience and students performance. However, in a university campus, there are some activities where the use of connected devices, might be discouraged or even forbidden. Students should be discouraged to use their own devices in classes where they may become alienated or when their devices may cause any disturbance. Ultimately, their own devices should be forbidden in activities such as closed-book exams. This paper proposes a system architecture to detect or block unwanted wireless signals by students' mobile phones in a classroom. This architecture focuses on specific wireless signals from Wi-Fi and Bluetooth interfaces, and it is based on Software-Defined Radio (SDR) modules and a set of antennas with two configuration modes: detection mode and blocking mode. When in the detection mode, the architecture processes signals from the antennas, detects if there is any signal from Wi-Fi or Bluetooth interfaces and infers a position of the unwanted mobile device. In the blocking mode, the architecture generates noise in the same frequency range of Wi-Fi or Bluetooth interfaces, blocking any possible connection. The proposed architecture is designed to be used by professors to detect or block unwanted wireless signals from student devices when supervising closed-book exams, during specific periods of time. © Daniel Barros, Paulo Barros, Emanuel Lomba, Vítor Ferreira, and Pedro Pinto; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).

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
Software-Defined Networking (SDN) is one of the promising and effective approaches to establishing network virtualization by providing a central controller to monitor network bandwidth and transmission devices. This paper studies resource allocation in SDN by mapping virtual networks on the infrastructure network. Considering mapping as a way to distribute tasks through the network, proper mapping methodologies will directly influence the efficiency of infrastructure resource management. Our proposed method is called Effective Initial Mapping in SDN (EIMSDN), and it suggests writing a module in the controller to initialize mapping by arriving at a new request if a sufficient number of resources are available. This would prevent rewriting the rules on the switches when remapping is necessary for an n-time window. We have also considered optimizing resource allocation in network virtualization with dynamic infrastructure resources management. We have done it by writing a module in OpenFlow controller to initialize mapping when there are sufficient resources. EIMSDN is compared with SDN-nR, SSPSM, and SDN-VN in criteria such as acceptance rates, cost, average switches resource utilization, and average link resource utilization.

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.