Unmanned aerial vehicles (UAVs) acting as aerial base stations are regarded as an effective solution for emergency communication, due to their high mobility and low cost. In this paper, we consider the differentiated communication requirements in disaster relief scenarios, where the uplink demands high throughput and the downlink requires low latency and high reliability. To simultaneously capture the timeliness and reliability requirements of downlink transmissions, we propose a novel QoS evaluation metric based on finite blocklength theory. Building on this, we formulate a system utility maximization problem and solve it by jointly optimizing UAV trajectory and ground node access control. To address this problem, we propose a novel algorithm named PW-QMIX, which integrates a priority-based heuristic access control policy with a QMIX-based trajectory optimization scheme, effectively tackling the challenges posed by the high-dimensional joint action space. Furthermore, to tackle the challenge of dynamic observation caused by UAV movement and sensing limitations, we design a weight generation network (WGN) and incorporate it into the input layer of QMIX. The WGN dynamically generates weight matrices based on observed nodes, enhancing UAV’s adaptability to local observation variations. Simulation results validate the significance of jointly optimizing uplink and downlink communications. Moreover, compared with state-of-the-art algorithms, the proposed PW-QMIX demonstrates significant advantages in convergence and scalability.

Adopting federated learning (FL) in unmanned aerial vehicle (UAV) networks is a promising paradigm, which can empower UAV networks with enhanced intelligence to support complex applications. Considering the imbalanced data properties, limited energy and unstable wireless connection of UAVs, an effective client scheduling scheme is critical for the design of efficient FL. In this paper, in order to properly consider the priority criteria, we first propose two importance metrics from the perspectives of data attributes and local updates, namely data importance measurement (DIM) and gradient importance measurement (GIM). Then, take into account DIM and GIM, an optimization problem is formulated to jointly optimize the client scheduling, computation and communication of UAVs. Due to the non-convex nature of this problem, we decompose it into two sub-problems and derive their optimal closed-form solutions. Simulations demonstrate that, compared to benchmark schemes, our proposal ensures better performance on test accuracy, convergence and energy saving.

Unmanned aerial vehicles (UAVs) significantly augment mobile edge computing (MEC) networks with their flexible deployment. In this paper, we investigate a priority-driven multi-UAV cooperative MEC system, in which the task priority are jointly determined by the task queue and task type. The system aims to maximize the task priority gain, subject to the constraints on offloading decision, UAV trajectory design and task scheduling. To solve this problem, we develop a priority scheduling insert based heterogeneous Q-mixing networks (PSI-HQMIX) framework, where the PSI scheme dynamically updates the position of tasks within the queues and the HQMIX algorithm is used to obtain the optimal offloading decisions and trajectories. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of the achieved average priority gains and convergence.

As a potential technique, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) can provide flexible coverage and computing services for real-time applications such as emergency search, traffic control and disaster rescue. In this paper, we investigate a freshness sensitive multi-UAV assisted MEC system where tasks arrive stochastically. The system aims to minimize the age of information (AoI), subject to the constraints on computation offloading, trajectory control and communication resource allocation. Due to the dynamic environment and the coupling of variables, we develop a multi-agent reinforcement learning (MARL) scheme, in which a federated updating method is introduced. Through our scheme, smart mobile devices, UAVs and cloud center can collaborate to learn interactive policies. Simulation results validate that our scheme outperforms local computing, remote computing, and centralized solutions in terms of both the average AoI and convergence.

The radio map has emerged as a promising tool for optimizing spectrum resource utilization and shaping the future landscape of intelligent wireless networks. However, the deployment of radio maps across the network introduces computational and latency challenges, restricting their real-time applications from the user’s perspective. In this paper, we introduce an innovative scheme for constructing and distributing radio maps in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks. Initially, we transform the distribution of radio maps into a collaborative process between UAV server and smart mobile devices (SMD), proposing four distribution modes tailored to different network conditions. This ensures that each SMD can access radio maps with the lowest cost. Additionally, our scheme integrates a deep reinforcement learning (DRL) framework, fostering seamless coordination between UAV server and SMD to enhance overall system performance and operational efficiency. Simulation results validate the efficiency and efficacy of our proposed scheme in optimizing radio map distribution strategies and resource allocation, further confirming the potential real-time applications of radio maps in future wireless networks.

Some Internet of Things (IoT) applications represented by vehicular networks, Internet of Medical Things (IoMT), and fire alarm systems have high requirements on the freshness of receiving information. Due to limited computing capability of IoT devices, mobile edge computing (MEC) is applied to reduce packet calculation time and improve packet freshness. In this paper, we investigate a MEC system for sharing vehicle status information and use the age-of-information (AoI) to define the freshness of information in the MEC system. The whole system is modeled as a two-stage tandem queue model with multi source-destination pairs. We derive the closed-form expression for the average AoI of partial computing and analyze the impact of system parameters on the average AoI, which provides guidance on how to set parameters to maximize the information freshness of the MEC system. As a more flexible scheme, partial computing we used reduces the AoI of the MEC system compared to remote computing. Numerical analysis validates our theory.

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing the computational burden when the edge is overloaded. In this article, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Especially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multiobjective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios, respectively, to reveal the advantages of the proposed framework.

With the widespread application of infotainment services in intelligent connected vehicles (ICVs), network traffic has grown exponentially, bringing huge burden and energy consumption to the ICV network. Edge caching, which enables edges [e.g., vehicles or roadside units (RSUs)] with cache storages, is a promising technology to alleviate this problem. In this article, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to RSU (V2R), an energy-aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and RSUs as well as the size of transmission content, the energy consumption model in the ICV network is formulated to implement the optimal selection of cache nodes. Then, the selection of the cache node in the ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy-efficiency cache node selection (OEECS). The simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Mobile edge computing (MEC) has risen as a promising paradigm to provide high quality of experience via relocating the cloud server in close proximity to smart mobile devices (SMDs). In MEC networks, the MEC server with computation capability and storage resource can jointly execute the latency-sensitive offloading tasks and cache the contents requested by SMDs. In order to minimize the total latency consumption of the computation tasks, we jointly consider computation offloading, content caching, and resource allocation as an integrated model, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We design an asymmetric search tree and improve the branch and bound method to obtain a set of accurate decisions and resource allocation strategies. Furthermore, we introduce the auxiliary variables to reformulate the proposed model and apply the modified generalized benders decomposition method to solve the MINLP problem in polynomial computation complexity time. Simulation results demonstrate the superiority of the proposed schemes.

Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices' battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.

People spend up to one-third of lives asleep, and healthy sleep habits can make a big difference in their quality of life. But in modern society, many people have unhealthy sleep diaries and suffer from various sleep disorders, which may result in irregular mood fluctuations or even mental health problems such as anxiety and depression. We propose the Emotion-Aware Smart Tips (EAST), a novel approach that could help to inform users about their irregular emotional states with smart tips to improve their sleep qualities. EAST aims at helping users keep healthy sleep schedules and emotional states by providing smart tips through a novel model that combines multivariate regression, random forest, and neural network to quantify the relations between sleep patterns and emotional states. Prototype implementation and initial experiments of EAST in mobile phones have demonstrated its desired functionality and practicality for real-world deployment.