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Abstract This paper applies a quantum machine learning technique to predict mobile users' trajectories in mobile wireless networks by using an approach called quantum reservoir computing (QRC). Mobile users' trajectories prediction belongs to the task of temporal information processing, and it is a mobility management problem that is essential for self‐organising and autonomous 6G networks. Our aim is to accurately predict the future positions of mobile users in wireless networks using QRC. To do so, the authors use a real‐world time series dataset to model mobile users' trajectories. The QRC approach has two components: reservoir computing (RC) and quantum computing (QC). In RC, the training is more computational‐efficient than the training of simple recurrent neural networks since, in RC, only the weights of the output layer are trainable. The internal part of RC is what is called the reservoir. For the RC to perform well, the weights of the reservoir should be chosen carefully to create highly complex and non‐linear dynamics. The QC is used to create such dynamical reservoir that maps the input time series into higher dimensional computational space composed of dynamical states. After obtaining the high‐dimensional dynamical states, a simple linear regression is performed to train the output weights and, thus, the prediction of the mobile users' trajectories can be performed efficiently. In this study, we apply a QRC approach based on the Hamiltonian time evolution of a quantum system. The authors simulate the time evolution using IBM gate‐based quantum computers, and they show in the experimental results that the use of QRC to predict the mobile users' trajectories with only a few qubits is efficient and can outperform the classical approaches such as the long short‐term memory approach and the echo‐state networks approach.

Recent advances in 5G and beyond have further expanded the potential of IoT applications, bringing unprecedented levels of connectivity, speed, and low latency. However, these advances come with significant security threats that can cause widespread damage. An effective approach to addressing these issues involves the integration of cutting-edge technologies like machine learning (ML), particularly deep reinforcement learning (DRL). DRL is a specialized area of ML that integrates the concepts of deep learning and reinforcement learning to create effective solutions for various tasks. In particular, DRL can facilitate the creation of intelligent security systems that can adapt to dynamic and intricate IoT applications connected to 5G and beyond networks. However, effectively implementing DRL-based intrusion detection frameworks in IoT applications connected to 5G networks poses significant challenges due to bandwidth utilization and device behavior. The data generated by IoT devices is often limited, and malicious behavior may be infrequent, making it difficult to accurately identify and train the algorithm to detect such behavior. Moreover, DRL algorithms pose a significant challenge for IoT devices constrained by limited bandwidth, as communicating large amounts of data required by DRL algorithms can cause network congestion and delay critical communications. In this article, we introduce a novel approach to improving the security of IoT applications in the 5G and beyond era by developing an intrusion detection system that employs DRL algorithms. Our approach involves a distributed Q-learning algorithm that observes the behavior of connected devices and predicts anomalous actions. Additionally, to overcome the challenges associated with bandwidth utilization and device behavior, we introduce a bandwidth allocation problem based on a reputation mechanism that allocates bandwidth to only trustworthy devices. Finally, we evaluate our proposed intrusion detection system on the selected indicators. The numerical results demonstrate that our proposed approach outperforms the referenced solutions on the selected indicators.

The theory and operations of data dissemination in wireless scenarios have endless applications based on the characteristics of underlying networks. The wireless networks that do not rely on any pre-existing infrastructure such as wireless sensor networks (WSNs), wireless mesh networks (WMNs), wireless body area networks (WBANs), or Mobile Ad hoc Networks have gained much attention due to their smart applications. Such types of wireless networks are categorized under wireless ad hoc networks with no central administration. With the rapid development in the applications of such types of ad hoc networks, Flying Ad hoc networks, commonly known as unmanned aerial vehicles (UAVs), have recently captured the attention of vendors and investors due to the flying nature of the entities. The UAV is officially called drone, which has the ability to fly without a human pilot aboard. The network of drones or UAVs has commercially gained popularity because of its applications such as surveillance, agriculture, and photography. For instance, the new applications that are being developed for Flying Ad hoc Network (FANET) bring up new challenges such as multipath propagations, severe shadowing, traffic load balancing, mobility, congestion, and high error rates, which usually result in degradation of the network. On the contrary, the applications developed and enabled in FANET may also result in collision with commercial flights due to the highlighted challenges.

5G technologies promise faster connections, lower latency, higher reliability, more capacity and wider coverage. We are looking to rely on these technologies to achieve Vehicle-to-Everything (V2X) communications, which increase the safety and autonomy of vehicles in addition to road safety, saving energy and costs. The integration of vehicular communication systems and 5G is the subject of many research. Nowadays, researchers address challenges such as automated and intelligent networks, cloud and edge data processing, network management, virtualization, security, privacy and finally interoperability. This paper provides a survey of the latest V2X use cases including requirements, and various 5G enabling technologies under consideration for vehicular communications. Subsequently, we first provide an interesting mapping between the three 5G pillars and V2X use case groups. Then, we present a summary of potential applications of enabling technologies for V2X use case groups. Finally, the open directions of research are discussed, and the challenges that await to be met are pointed out.

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

The operation of modern electronic communication devices relies on constant energy sources such as DC power, obtained from AC sources via AC to DC conversion, or small chargeable/replaceable batteries. However, in some cases, providing constant energy sources may not be feasible. Advanced energy harvesting technologies can be used to power up modern day electronic communication devices [item 1) in the Appendix]. These energy harvesting technologies rely on energy sources naturally present in the environment, such as solar energy, wind energy, and heat [item 2) in the Appendix]. Moreover, energy can also be acquired from the movement of different parts of the human body such as legs (walking), heart (beating) and arms (swinging), just to name a few. All these energy harvesting technologies and techniques may satisfy the need of energy for low power communication devices and may enable the charging of electronic mobile devices anywhere and at any time [item 3) in the Appendix].

ZigBee is often chosen as a technology to connect things because of characteristics, such as network resilience, interoperability, and low power consumption. In addition, Zigbee Pro, with its Green Power feature, allows low-power networking capable of supporting more than 64 000 devices on a single network, making it an excellent choice to connect things. However, in recent years, we have witnessed the proliferation of smart devices using either 802.11 or ZigBee technologies, which operate in the same frequency band. Proposing and developing techniques that may improve the fair operation and performance of these technologies in coexistence scenarios have been a major concern in industry and academia. In this paper, we propose the use of traffic prioritization for ZigBee nodes in order to improve their performance when coexisting with IEEE 802.11 nodes. We develop an analytical model based on Markov chains, which captures the behavior of channel access mechanisms for both 802.11 nodes and different ZigBee priority class nodes. Based on extensive simulations, we validate the accuracy of the proposed model, and demonstrate how traffic prioritization of ZigBee nodes effectively improves their performance when coexisting with 802.11 nodes. We also demonstrate that this improvement comes at the cost of negligible degradation in the performance of the 802.11 nodes.

In this paper, a vehicular cloud (VC) model is adopted where vehicles offer data as a service. We propose solutions for efficient data delivery based on transmission scheduling methods where vehicles gather data from their mounted sensors. This is done by first organizing vehicles into clusters, so that each cluster works as VC. A distributed D-hop cluster formation algorithm is presented to dynamically form vehicle clouds. The algorithm groups vehicles into non-overlapping clusters, which have adaptive sizes according to their mobility. VCs are created in such a way that each vehicle is at most D-hops away from a cloud coordinator (broker). Each vehicle chooses its broker based on relative mobility calculations within its D-hop neighbors. After cloud construction, a mathematical optimization scheduling algorithm is used to maximize throughput and minimize delay in delivering data from vehicles to their VC broker. Our proposed optimization model implements a contention-free-based medium access control where physical conditions of the channel are fully analyzed. Extensive simulations were performed for different scenarios to evaluate the performance of the proposed cloud formation and cloud-based transmission scheduling algorithms. Results show that VCs formed by our algorithms are more stable and provide higher data throughputs compared with others.

The plethora of research, standardization and developments in Internet of Things (IoT) has increased enormously in recent years. This is due to the vast scope of IoT. Internet of Things refers to the worldwide network of interconnected objects, which allow people or things to be connected anytime, anyplace, with anything and anyone, using any path, any network and any service. The objects have different characteristics: mobile or static, with or without energy constraint, different computation and storage capabilities, equipped with different communication technologies and sensors, etc.