The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloud-fog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.

Nowadays, the usage of resource constrained devices is increasing and these devices are primarily working with sensitive data. Consequently, data security has become crucial for both producers and users. Limitation of resources is deemed as the major issue that makes these devices vulnerable. Attackers might exploit these limitations to get access to the valuable data. Therefore, carefully chosen and practically tested encryption algorithm must be applied to increase the device efficiency and mitigate the risk of sensitive data loss. This study will compare elliptic curve cryptography (ECC) algorithm with Key size of 160-bit and Rivest-Shamir-Adleman (RSA) algorithm with Key size of 1024-bit. As a result of this study utilizing ECC in resource constrained devices has advantages over RSA but ECC needs continues enhancement to satisfy the limitations of newly designed chips.

The Wireless Sensor Network in the Internet of Things (WSN-IoT) has been flourishing as another global breakthrough over the past few years. The WSN-IoT is reforming the way we live today by spreading through all areas of life, including the dangerous demographic aging crisis and the subsequent decline of jobs. For a company to increase revenues and cost-effectiveness growth should be customer-centered and agile within an organization. WSN-IoT networks have simultaneously faced threats, such as sniffing, spoofing, and intruders. However, WSN-IoT networks are often made up of multiple embedded devices (sensors and actuators) with limited resources that are joined via various connections in a low-power and lossy manner. However, to our knowledge, no research has yet been conducted into the security methods. Recently, a Contiki operating system’s partial implementation of Routing Protocol for Low Power & Lossy Network RPL’s security mechanisms was published, allowing us to evaluate RPL’s security methods. This paper presents a critical analysis of security issues in the WSN-IoT and applications of WSN-IoT, along with network management details using machine learning. The paper gives insights into the Internet of Things in Low Power Networks (IoT-LPN) architecture, research challenges of the Internet of Things in Low Power Networks, network attacks in WSN-IoT infrastructures, and the significant WSN-IoT objectives that need to be accompanied by current WSN-IoT frameworks. Several applied WSN-IoT security mechanisms and recent contributions have been considered, and their boundaries have been stated to be a significant research area in the future. Moreover, various low-powered IoT protocols have been further discussed and evaluated, along with their limitations. Finally, a comparative analysis is performed to assess the proposed work’s performance. The study shows that the proposed work covers a wide range of factors, whereas the rest of the research in the literature is limited.

Recently, with the technological and digital revolution, the security of data is very crucial as a massive amount of data is generated from various networks. Intrusion Detection System (IDS) has been observed to be perhaps the best solution because of its capability to distinguish between attacks that originate within or outside a corporate network. In this study, the most significant features for enhancing the IDS efficiency and creating a smaller dataset in order to reduce the execution time for detecting attacks are selected from the sizeable network dataset. This research designed an anomaly-based detection, by adopting the modified Cuckoo Search Algorithm (CSA), called Mutation Cuckoo Fuzzy (MCF) for feature selection and Evolutionary Neural Network (ENN) for classification. The proposed search algorithm uses mutation to more accurately examine the search space, to allow candidates to escape local minima. Moreover, the value of the solution is evaluated based on the objective function and the Fuzzy C Means (FCM) clustering method used to provide the best results for the overlapping dataset and create the fuzzy membership search domain which includes all possible compromise solutions. A proposed model has been practically used to the problem of intrusion detection as well as been validated using the NSL-KDD dataset. The experimental results reveal that reducing features by selecting and utilizing the most relevant features can improve execution time and at the same time enhance the efficiency and performance of IDS.

Vehicular ad hoc networks (VANETs) are a special type of wireless ad hoc network that requires highly scalable routing strategies to establishing reliable end-to-end communication. Because of the high dynamic of VANETs, the mobility of vehicle nodes increases the control traffic overhead. Accordingly, establishing reliable end-to-end communication paths depends entirely on the routing mechanism and the type of nodes mobility information. In this paper, we propose a new improvement to the mechanism of the Optimized Link State Routing Protocol(OLSR) protocol, named Cluster-based Adept Cooperative Algorithm (CACA), where each vehicle estimate a reliable low-overhead path using the cluster-based QoS algorithm. The CACA algorithm is introduced to improve the ability of the MPR scheme for maintaining long-lived routes. Moreover, the network scalability is enhanced by adaptively selecting most sustainable paths based on a signal strength beacon and the mobility degree of a node, which reduces significantly minimizes the size of control messages overhead as well the routing tables recalculation process. Simulation experiments using the network simulator are presented to demonstrate the effectiveness of our solution. The results show that the proposed algorithm can improve network performance effectively relative to other algorithms.