Abstract
Low Power Wide Area Networks (LPWAN) are used worldwide in several Internet of Things (IoT) applications that rely on large-scale deployments. Despite most of these networks include their own security mechanisms with built-in encryption, they are still vulnerable to a range of attacks that can be performed using low-cost Software Defined Radio (SDR) hardware and low-complexity techniques. This work provides an experimental setup implemented to exploit physical layer vulnerabilities with SDR techniques. Several attack vectors typically related to LPWAN within the IoT ecosystem are implemented and tested such as Global Positioning (GPS) Spoofing, Replay Attacks, Denial-of-Service (DoS) and Jamming, in environments that rely specifically on LoRaWAN networks. The results show that, in LoRAWAN networks, a set of vulnerabilities can be exploited leading to the incorrect functioning of the executed applications, and possible damage to the systems in which they operate. It was possible to verify that, depending on the type of activation method used between the devices and the LoRaWAN server, the communications and the devices can be compromised.

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
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
Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods.

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.