Today, we could describe the Internet of Things (loT) as the pervasive and global network that provides a system for monitoring, controlling, processing, and analyzing the data generated by IoT devices. The huge amount of data generated by IoT devices when transported and routed through the internet presents several challenges. One of the common routing protocols in IoT networks is RPL (Routing Protocol for Low Power and Lossy Networks), but it is prone to security issues and attacks. Due to the presence of sensitive data in IoT and its exchange in the open network, issues of privacy and security in this network should be given special attention. In addition, the nodes in the Internet of Things have limited resources, and the symmetric encryption key is used to encrypt the data of all nodes, which has security weaknesses. Therefore, an efficient and secure authentication scheme is needed so that IoT nodes can authenticate each other and share a secure session key. In this article, we review security aspects of RPL protocols focusing on selective forwarding attacks. Further, we propose a key agreement and authentication mechanism based on ECDH (Elliptic-Curve Diffie–Hellman). We show that our design is very secure, that it meets security requirements, and that it can withstand known attacks while having low costs for computation and communication.

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance the performance and functionality of mobile networks. UAVs can act as flying base stations, relays, or users to provide wireless services to ground users or devices. However, the optimal placement and trajectory design of UAVs in mobile networks is a challenging problem, as it involves multiple objectives, constraints, and uncertainties. In this paper, we provide a comprehensive survey of the state-of-the-art research on UAV placement and trajectory optimization in cellular networks. We first introduce the main objectives and challenges of UAV placement and trajectory optimization, such as maximizing coverage, throughput, energy efficiency, or reliability, while minimizing interference, delay, or cost. We also examine the primary models and assumptions employed for UAV placement and trajectory optimization, including channel models, mobility models, network architectures, and constraints. Additionally, we discuss the main methods and algorithms employed for UAV placement and trajectory optimization. These include optimization techniques, heuristic algorithms, machine learning approaches, and distributed solutions. Analytical results, numerical simulations, or experimental tests are further discussed as the main performance metrics and evaluation methods used for UAV placement and trajectory optimization. We also highlight the main applications and scenarios of UAV placement and trajectory optimization, such as cellular offloading, emergency communications, or aerial base stations. Finally, we identify some open problems and future research directions on UAV placement and trajectory optimization in cellular networks.

The need for continuous coverage, as well as low-latency, and ultrareliable communication in 5G and beyond cellular networks encouraged the deployment of high-altitude platforms and low-altitude drones as flying base stations (FBSs) to provide last-mile communication where high cost or geographical restrictions hinder the installation of terrestrial base stations (BSs) or during the disasters where the BSs are damaged. The performance of unmanned aerial vehicle (UAV)-assisted cellular systems in terms of coverage and quality of service offered for terrestrial users depends on the number of deployed FBSs, their 3-D location as well as trajectory. While several recent works have studied the 3-D positioning in UAV-assisted 5G networks, the problem of jointly addressing coverage and user data rate has not been addressed yet. In this article, we propose a solution for joint 3-D positioning and trajectory planning of FBSs with the objectives of the total distance between users and FBSs and minimizing the sum of FBSs flight distance by developing a fuzzy candidate points selection method.

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.

The advent of 5G Vehicle-to-Everything (5G-V2X) technology has revolutionized daily life and the economy. However, the complexity of testing 5G-V2X systems in lab and field settings along with the development cost is increasingly challenging. To overcome these issues, the paper proposes the use of Digital Twin technology, which offers a precise, accurate, and controllable lab-based representation of real-world test conditions. The main idea is to design an open-ended digital twin architecture specifically tailored for 5G-V2X, with the aim of fostering innovation in various aspects of autonomous driving. Considering the recent improvement in Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) technologies in the proposed architecture, it not only facilitates the development and testing of diverse and sophisticated network and communication layers solutions and applications, but also provides a real-time environment to evaluate new artificial intelligence (AI) methods, data and model sharing, and progress measurement in the field of 5G-V2X.