Energy optimization for full-duplex wireless-powered IoT networks using rotary-wing UAV with multiple antennas

This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios.

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT). UAV can not only act as a relay, but also as an energy source to provide information and energy transmission for ground sensor nodes (SNs). This paper studies the efficient multi-UAV-assisted data collection problem in wireless powered IoT. Specifically multiple UAVs wirelessly charge SNs using radio frequency (RF) energy transfer, and the SNs then use the harvested energy to upload the updates of the sensed information to the UAVs, thus improving the freshness of collected data and extending the service time of the SNs. The problem is modeled as a partially observed Markov decision process (POMDP) with a large observation and action space, where each UAV acts as an intelligent agent to learn the environment and make decisions independently. The value-decomposition network (VDN) algorithm is employed to find the optimal strategy in the multi-agent deep reinforcement learning framework. Simulation results validate the effectiveness of the proposed data collection approach compared to two baseline policies.

This letter studies the use of Unmanned Aerial Vehicles (UAVs) in Internet-of-Things (IoT) networks, where the UAV with microwave power transfer (MPT) capability is employed to hover over the area of interest, charging IoT nodes remotely and collecting their data. Scheduling MPT and data transmission is critical to reduce the data packet loss resulting from buffer overflows and channel fading. In practice, the prior knowledge of the battery level and data queue length of the IoT nodes is not available at the UAV. A new onboard double Q-learning scheduling algorithm is proposed to optimally select the IoT node to be interrogated for data collection and MPT along the flight trajectory of the UAV, thereby minimizing asymptotically the packet loss of the IoT networks. Simulations confirm the superiority of our algorithm to Q-learning based alternatives in terms of packet loss and learning efficiency/speed.

Thanks to smart manufacturing and artificial intelligence technologies, unmanned aerial vehicles (UAVs) are envisioned to play a critical role in future Internet of things (IoT) networks to execute data collection tasks. In this article, we leverage age of information (AoI) to measure the freshness of data packets received by the UAV from IoT sensors. Considering the heterogeneity of IoT devices, we aim to minimize the weighted sum AoI by jointly optimizing the UAV's trajectory and IoT devices association in UAV-assisted IoT networks, where the UAV's cumulative propulsion energy cost is limited by the battery capacity. Since the optimization object is confined by a set of short-term constraints and a long-term constraint, this problem is modeled as a constrained Markov decision process (CMDP). We leverage safe actor-critic (Safe-AC) to solve the CMDP. To satisfy the mixed constraints, the safe policy set of Safe-AC is induced by a Lyapunov function, thereafter, a policy distillation technology is leveraged to search the optimal policy. Experimental results indicate that our proposed scheme can strictly satisfy the propulsion energy cost budget requirement at the expense of around 2% loss of the reward compared to baseline methods.

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

URLLC in UAV-enabled multicasting systems: A dual time and energy minimization problem using UAV speed, altitude and beamwidth

This paper studies an unmanned aerial vehicle (UAV)-enabled multiple access channel (MAC), in which multiple ground users transmit individual messages to a mobile UAV in the sky. We consider a linear topology scenario, where these users locate in a straight line and the UAV flies at a fixed altitude above the line connecting them. Under this setup, we jointly optimize the one-dimensional (1D) UAV trajectory and wireless resource allocation to reveal the fundamental rate limits of the UAV-enabled MAC, under the users' individual maximum power constraints and the UAV's maximum flight speed constraints. First, we consider the capacity-achieving non-orthogonal multiple access (NOMA) transmission with successive interference cancellation (SIC) at the UAV receiver. In this case, we characterize the capacity region by maximizing the average sum-rate of users subject to rate profile constraints. To optimally solve this highly non-convex problem, we transform the original speed-constrained trajectory optimization problem into a speed-free problem that is optimally solvable via the Lagrange dual decomposition. It is rigorously proved that the optimal 1D trajectory solution follows the successive hover-and-fly (SHF) structure. Next, we consider two orthogonal multiple access (OMA) transmission schemes, i.e., frequency-division multiple access (FDMA) and time-division multiple access (TDMA). We maximize the achievable rate regions in the two cases by jointly optimizing the 1D trajectory design and wireless resource (frequency/time) allocation. It is shown that the optimal trajectory solutions still follow the SHF structure but with different hovering locations. Finally, numerical results show that the proposed optimal trajectory designs achieve considerable rate gains over other benchmark schemes, and the capacity region achieved by NOMA significantly outperforms the rate regions by FDMA and TDMA.

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

Unmanned Aerial Vehicles (UAVs) have been widely employed in the Internet of Things (IoT) networks due to their high mobility and high probability of line-of-sight (LoS) propagation. Equipped with certain payloads, UAVs are able to gather data from sensors located in a particular area where no ground base station is available for transmitting data, such as oceans and mountains. However, for a time-sensitive network, the latency has to be minimized, especially in heterogeneous scenarios where each sensor has its own latency tolerance, which emphasizes the importance of trajectory design of UAVs. In this paper, we propose a priority-oriented trajectory planning problem for a UAV-aided time-sensitive heterogeneous IoT network, based on which we provide a solution for satisfying the latency tolerance of the network within a given period of time. Aiming at optimizing trajectories, we employ continuous Deep Q-Learning Network (DQN) which is proven to be capable of identifying a relatively optimal trajectory compared to the benchmarks through a large number of experiments. Simulation results are provided for demonstrating that the proposed DQN-based algorithm outperforms the benchmarks. More particularly, the proposed DQN-based algorithm is capable of achieving in excess of 49% and 10% improvements in system costs over the greedy algorithm and Q-Learning algorithm, respectively.

New technologies and communication standards have changed traditional network processes. Internet of Things (IoT) is one of the emerging technologies where devices are connected to facilitate the users. When the networks are more congested due to a large number of users then the existing routing protocol and communication channels suffer from congestion, disconnection, overhead, and packet drop issues. Unmanned Aerial Vehicles (UAVs) are adopted to support the ground networks for more feasible data communication. These networks provide coverage facilities to IoT networks and provide smooth data dissemination services. Through the use of relay and cooperative communication technologies, UAVs could enlarge the communication space for IoT networks. Traditional network routing protocols have been adopted for data communication in these networks. However, the adopted protocols are not able to handle mobility and uncertain network conditions. This paper proposes a Decision-based Routing for Unmanned Aerial Vehicles and Internet of Things (DR-UAVIoT) network. The proposed protocol is useful for UAV-to-IoT and UAV-to-UAV data communication. The performance of the proposed solution is evaluated with the existing protocols in terms of data delivery, delay, and network overhead. The experimental results indicate the better performance of the proposed protocol in terms of less delay, less overhead, and better data delivery ratio as compared with existing routing protocols.

\n Unmanned Aerial Vehicles (UAVs) are being employed in many fields such as precision agriculture, environmental monitoring, disaster response support, and others. Following the technological development, these vehicles are now being connected to other devices and systems such as the Internet of Things (IoT), allowing them to provide and consume services as one of the things in the environment. The IoT is believed to bring new unprecedented development to our society and will impact and enable new, more powerful applications in many areas. This integration does not come without challenges. The intrinsic IoT heterogeneity regarding devices, platforms, and communication protocols makes the task of service provision and management much harder to be done in the application layer. Security is one of the most critical challenges in IoT-enabled applications, being sometimes put in after the main development is done, if at all. This work demonstrates how to leverage UAVs to be connected in the IoT using SEMU SErvice Management for UAVs. SEMU comprises (a) communication security, including contextaware security; (b) service management (registration, discovery, request and consumption) for different types and data requirements and (c) autonomous task management that allows the UAVs to perform their mission without human intervention. Its functionality and feasibility is demonstrated by its use in different applications simulating real-life scenarios such as a UAV-enabled delivery service and a UAV-enabled data collection from IoT sensors. Future research will be able to leverage on SEMU and its prototype to further investigate and advance the state-of-art regarding IoT-enabled UAV applications and their security mechanisms.\n

We propose a time division multiple access (TDMA) scheme for the BPSK modulated single carrier block transmission with frequency domain equalization (SC-FDE) over ultra-wideband (UWB) communications. The transmitter and receiver matrices for the proposed TDMA SC-FDE system are derived to achieve the multiple access interference (MAI) free and interblock-interference (IBI) free transmission without the insertion of cyclic prefix (CP) between blocks, circumventing the need for long CP and large FFT size in high speed UWB communications. The performance of the proposed TDMA SC-FDE system over UWB channels is investigated and compared with that of the block spread code division multiple access (CDMA) scheme. The effects of oversampling diversity and imperfect channel estimation are also investigated. Results show that the proposed TDMA scheme can effectively eliminate the error floor in CDMA due to insufficient CP, and it is more efficient to accommodate larger number of users than CDMA in high data rate transmission

Unmanned Aerial Vehicles (UAVs) have emerged as crucial components of 5G Internet of Things (IoT) networks, expanding coverage and capacity in various applications. While prior research has extensively examined the impact of power control factor adjustments on UAV uplink transmission, limited attention has been devoted to UAV downlink transmission, which is vital for seamless connectivity and quality of service. In this study, we address this research gap by investigating the effects of reduced power control factors on UAV downlink transmission. We use MATLAB simulations to model how user equipment (UE) is randomly placed in a UAV network. We then check the coverage probability and spectral efficiency for UAV altitudes between 200 and 500 m, taking into account different power control factors in the downlink transmission. Our findings show that a certain threshold of 0.05 for the power control factor keeps coverage probabilities at different heights, making sure that served UEs have enough signal strength. Surprisingly, there is no significant change in spectral efficiency when compared to the baseline results. This means that lowering the power control factor does not hurt spectral efficiency in UAV downlink transmission. These results help us figure out how to make UAV communication work better in 5G IoT networks. They also help us make smart choices about how to change the power to make up for lost downlink signals while keeping the quality of service high. Moreover, our study lays the groundwork for future research in UAV deployment and communication optimization within advanced wireless networks.

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.