Unmanned aerial vehicle (UAV)-based wireless networks have received increasing research interest in recent years and are gradually being utilized in various aspects of our society. The growing complexity of UAV applications such as disaster management, plant protection, and environment monitoring, has resulted in escalating and stringent requirements for UAV networks that a single UAV cannot fulfill. To address this, multi-UAV wireless networks (MUWNs) have emerged, offering enhanced resource-carrying capacity and enabling collaborative mission completion by multiple UAVs. However, the effective operation of MUWNs necessitates a higher level of autonomy and intelligence, particularly in decision-making and multi-objective optimization under diverse environmental conditions. Reinforcement Learning (RL), an intelligent and goal-oriented decision-making approach, has emerged as a promising solution for addressing the intricate tasks associated with MUWNs. As one may notice, the literature still lacks a comprehensive survey of recent advancements in RL-based MUWNs. Thus, this paper aims to bridge this gap by providing a comprehensive review of RL-based approaches in the context of autonomous MUWNs. We present an informative overview of RL and demonstrate its application within the framework of MUWNs. Specifically, we summarize various applications of RL in MUWNs, including data access, sensing and collection, resource allocation for wireless connectivity, UAV-assisted mobile edge computing, localization, trajectory planning, and network security. Furthermore, we identify and discuss several open challenges based on the insights gained from our review.

Innovations in industrial automation, information and communication technology (ICT), renewable energy as well as monitoring and sensing fields have been paving the way for smart devices, which can acquire and convey information to the Internet. Since there is an ever-increasing demand for large yet affordable production volumes for such devices, printed electronics has been attracting attention of both industry and academia. In order to understand the potential and future prospects of the printed electronics, the present paper summarizes the basic principles and conventional approaches while providing the recent progresses in the fabrication and material technologies, applications and environmental impacts.

Internet of Things (IoT) aims to create a vast network with billions of things that can seamlessly create and exchange data, establishing intelligent interactions between people and objects around them. It is characterized with openness, heterogeneity, and dynamicity, which inevitably introduce severe security, privacy, and trust issues that hinder the widespread application of IoT. Trust management (TM) holds great promise in identifying malicious nodes, maintaining trust relationships, and enhancing system security. Traditional TM systems (TMSs) can be classified into centralized, semi-centralized, and distributed ones, all three of which suffer from critical challenges and thus are not sufficient for facilitating IoT development. Blockchain, as a disruptive technology, can help addressing the challenges of TM in IoT, thanks to its advanced features, such as decentralization, consistency, and tamper-proofing. As a result, blockchain-based TM (BC-TM) has been extensively studied in recent years to achieve decentralized TM in IoT. However, it still lacks a comprehensive survey on the current state of the arts. To fill this gap, in this article, we conduct a serious survey on BC-TM in IoT. We first propose a set of evaluation criteria that should be met by a TMS in IoT. Then, we propose a taxonomy of TMSs and continue with a thorough review on BC-TM in IoT by employing the proposed criteria. In the end, based on the review, a series of open issues are identified, and future research directions are suggested.

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems, such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy OFDM signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higherorder modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the M-ary case. We corroborate our analysis using Monte- Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.

Non-orthogonal multiple access (NOMA) and backscatter communications are considered to be promising technologies for beyond the fifth-generation (5G) due to their applications in large-scale Internet-of-things networks for providing low-powered and spectral-efficient communication. NOMA also provides a new way to enable vehicle-to-everything (V2X) networks and improve the achievable rates through cooperation. Motivated by these developments, we provided a novel analysis for NOMA-enabled backscatter-based V2X networks. Specifically, we consider that several vehicles are connected to the base station (BS) via different roadside units (RSUs) and the backscatter tags along the road. These backscatter tags can be considered ultra low-powered safety sensors that communicate with the vehicles using the same spectrum resources. To optimize the performance of such networks, a joint problem of optimal power allocation at BS and RSUs has been formulated. Subsequently, the problem has been transformed into a convex problem and solved using KKT conditions and subgradient methods. To evaluate the performance of the proposed solution, Monte-Carlo simulations have been performed in MATLAB. The acquired results clearly demonstrate that the proposed approach performs better than the conventional suboptimal NOMA scheme and joint optimal TDMA scheme.