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With the increasing popularity of the internet of things (IoT) and 5th generation mobile communication technology (5G), mobile edge computing (MEC) has emerged as an innovative approach to support smart devices (SDs) in performing computational tasks. Nevertheless, the process of offloading can be energy-intensive. Traditional battery-powered SDs often encounter the challenge of battery depletion when offloading tasks. However, with the advancements in wireless power transfer technology, SDs can now achieve a sustainable power supply by harvesting ambient radio frequency energy. This paper studies the computation offloading in wireless-powered MEC networks with device-to-device (D2D) assistance. The SDs are categorized into near and far SDs based on their proximity to the MEC server. With the support of near SDs, far SDs can reduce transmission energy consumption and overall latency. In this paper, we comprehensively consider the allocation of energy harvesting time, transmission power, computation resources, and offloading decisions for SDs, establishing a mathematical model aimed at minimizing long-term average delay under energy constraints. To address the time-varying stochastic nature resulting from dynamic task arrivals and varying battery levels, we transform the long-term problem into a deterministic one for each time slot by introducing a queue and leveraging Lyapunov optimization theory. We then solve the transformed problem using deep reinforcement learning. Simulation results demonstrate that the proposed algorithm performs effectively in reducing delay and enhancing task completion rates. [ABSTRACT FROM AUTHOR]

Efficient delivery of content to areas where terrestrial Internet service is unavailable can be possible via content caching at low earth orbit (LEO) satellites. Cached content in several LEO satellites must be delivered via inter-satellite links (ISLs) with appropriate routing techniques. Until now, content caching and routing techniques have been optimized independently. To tackle this issue, the optimization of selecting a content-cached satellite and routing is jointly performed, using the example of Earth observation data cached across multiple satellites. In this paper, we first formulate a dynamic power minimization problem constrained by the queue stability of all LEO satellites, where the control variables are the selection of content-cached satellite and routing in every satellite. To solve this long-term time-averaged problem, we leverage Lyapunov optimization framework to transform the original problem into a series of slot-by-slot problems. Moreover, we prove that the average power consumption and the average queue backlog by the proposed algorithm can be upper-bounded via theoretical analysis. Finally, through extensive simulations, we demonstrate that our proposed algorithm surpasses existing independent content-retrieval algorithms in terms of power consumption, queue backlog, and fairness.

Despite the rapid growth of the Internet industry, the provision of full Internet service to remote regions is still challenging. As a solution, the combination of Low Earth Orbit (LEO) satellite communication and Mobile Edge Computing (MEC) is gaining attention. However, considering the high speed of LEO satellites in network environments remains a significant challenge. To this end, this paper introduces a dynamic computation offloading and resource allocation framework in the LEO satellite MEC architecture. Using Lyapunov optimization, we propose an efficient DCOOL algorithm to minimize average power consumption and propagation delay constrained by queue stability. Finally, comparative analysis and simulations demonstrate the superior performance of DCOOL while achieving lower power consumption and stable workload processing.

To address the problem that unmanned aerial vehicle (UAV) face complex time-varying fading channels, which could affect wireless transmission, a joint optimization problem of UAV's trajectory, reconfigurable intelligent surface (RIS) phase shift, offloading slot allocation, CPU frequency allocation, and user equipment transmission power was constructed. In order to solve the constructed problem, the stability constraints of UE and UAV task queues were transformed, and the multi-timeslot stochastic optimization problem was transformed into a deterministic optimization problem for each time slot. A JORL optimization method based on Lyapunov optimization and block coordinate descent (BCD) method was proposed. Firstly, the phase shift of RIS was solved based on the triangle inequality and the closure expression was obtained. Then the technology of transforming the non-convex into the convex problem was used to solve the offloading slot allocation, CPU frequency allocation and user equipment transmission power. Finally, the trajectory of UAV was solved based on successive convex approximation (SCA) method. Simulation results show that JORL has better performance in ensuring queue stability and reducing energy consumption.

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network's sustainable operation and growth. In this paper, we consider a WPT-MEC network with user cooperation to migrate the double near–far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability. Rigorous mathematical analysis and experimental results show that our algorithm achieves  O (1 / V) , O (V)  trade-off between computation rate and queue stability. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). However, maximizing the long-term energy efficiency (EE) of a user-cooperative WPT-MEC system presents significant challenges due to uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. In this paper, we propose an online control algorithm to maximize the long-term EE of a WPT-MEC system by making decisions on time allocations and transmission powers of mobile devices (MDs) for a three-node network. We formulate a stochastic programming problem considering the stability of network queues and time-coupled battery levels. By leveraging Dinkelbach's method, we transform the fractional optimal problem into a more manageable form and then use the Lyapunov optimization technique to decouple the problem into a deterministic optimization problem for each time slot. For the sub-problem in each time slot, we use the variable substitution technique and convex optimization theory to convert the non-convex problem into a convex problem, which can be solved efficiently. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline algorithms, achieving a 20% improvement in energy efficiency. Moreover, our algorithm achieves an [ O (1 / V) , O (V) ] trade-off between EE and network queue stability. [ABSTRACT FROM AUTHOR]

Providing the completely automated public test to tell computers and humans apart (CAPTCHA) services that are not vulnerable to learning is an important issue. Image-based CAPTCHA services have strengthened authentication by taking advantage of the fact that it is more difficult for computers to understand images, but the rapid growth of deep learning has made it possible to break and attack authentication. Image-based CAPTCHA uses pre-stored data, which enables learning and results in vulnerability. This paper presents an adaptive generative adversarial network (GAN) selection scheme using time-average image quality maximization subject to system/buffer stability. By using GAN, it can generate and provide new images for CAPTCHA authentication every time, preventing deep learning from learning images and enhancing security. In the image generation process, the trade-off exists between image quality and generation time, and in consideration of this trade-off, delay aware image-based authentication Lyapunov-based algorithm is proposed for stable and maximized performance. Moreover, through the performance evaluation, we investigate and show the existence of trade-off between generation time and generated image quality in the image generation process in both quantitative and qualitative manner. [ABSTRACT FROM AUTHOR]

The edge computing paradigm is evolving to provide time-sensitive computing services for energy-constrained mobile IoT devices, with the purpose of decreasing latency. In this paper, we investigate a sustainable edge offloading system that is composed of one green mobile IoT device and multiple access points (APs), which considers minimizing the execution latency, meanwhile adapting to the mobile awareness and dynamic energy harvesting (EH) process. The IoT device is able to reduce computing latency by offloading its generated tasks to the APs. We propose a deep deterministic policy gradient (DDPG) based optimal placement strategy (DBOP) algorithm to take advantage of computation resources and improve consumer experience quality. The DBOP first applies the Lyapunov optimization to decompose the constraint problem into subproblems in each time slot. Then, the DBOP makes optimal offloading decisions by predicting the computation resources and solving the per-slot subproblems with low computational complexity. Finally, the performance bounds of the proposed algorithm are discussed and the simulation results demonstrate the effectiveness of the DBOP. [ABSTRACT FROM AUTHOR]

To address the issue where the resource limitations of wireless devices caused state extraction delays that cannot meet the freshness requirements of real-time applications, considering the limited processing capacity of edge nodes, a scheduling method that jointly considered information freshness and real-time performance was proposed. This method initially characterized the task delay before computation and the information freshness after computation by utilizing the system time of the queue and the age of information, respectively. Simultaneously, reasonable deadlines were assigned to each offloaded task to ensure their validity before entering the computation process. Then, the minimum processing rate constraint method was employed to restrict the processing rate during task scheduling, thereby ensuring the real-time nature of task scheduling. Finally, the objective of optimizing long-term task scheduling decisions was achieved based on Lyapunov optimization techniques. Simulation results demonstrate the good performance of the proposed method in both scheduling timeliness and system information freshness.

With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users' long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is an emerging technology for beyond fifth-generation (B5G) networks to support significant features, such as spectrum efficiency, high data rate, and reduced power consumption. Efficient spectrum allocation and optimum power control in D2D networks is one of the major issues that arise due to dynamic traffic arrivals. This article proposes a novel approach to maximize the spectral efficiency of the D2D network. The formulated problem is a mixed-integer nonlinear programming problem (MINLP). However, due to its complexity, the global optimal solution is difficult to solve directly. A two-stage optimization algorithm is presented: optimal resource allocation algorithm (ORAA) by utilizing the concept of queue dynamics and optimal power control algorithm by adapting the Lyapunov stability method. The proposed method achieves higher spectral efficiency up to 21.6% and latency is minimized up to 39.89% over the benchmark schemes. The proposed method shall find immense use in smart traffic management to support Intelligent transportation systems. [ABSTRACT FROM AUTHOR]

In response to the construction of green data centers for sustainable development, reducing data center power consumption and carbon dioxide emissions has become an urgent issue. A data center model considering cooling systems was established. The model transforms cost functions of data center power costs, bandwidth costs, carbon emission costs, and cooling costs into a cost minimization problem. The data center operational costs are reduced by effective workload scheduling and rational servers on-off strategies. The electricity prices and carbon emission rates are difficult to predict accurately due to the fluctuation. A feature of the Lyapunov optimization framework was utilized to propose an online control strategy. The strategy only requires knowledge of the current system information. An alternating direction method of multipliers (ADMM) is introduced to enable the control center to coordinate workload distribution among various data centers. Data centers only need to exchange workload decisions, effectively reducing computational complexity and safeguarding user privacy. Simulation results based on real-world data demonstrate that the proposed algorithms can effectively reduce data center operational costs. [ABSTRACT FROM AUTHOR]

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks.

Recently, as computing-intensive services such as object recognition for autonomous vehicles have increased, power consumption and computational loads of vehicles have been prominent. To tackle this issue, there have been growing interests in edge computing technology, which offloads workloads of services to nearby vehicle edge computing (VEC) servers. However, the existing offloading technologies in the VEC server made independent offloading decisions for each service, and they did not consider shared workloads of multimodal vision applications. In this paper, we first aim to capture the intricate workload relationships among multimodal applications in modeling an integrated process-network load balancing for a VEC-assisted autonomous vehicle system. To this end, we formulate an energy minimization problem of a vehicle constrained by outage probability of service requests where the decision variables are (i) dynamic offloading policy between vehicle and VEC server and (ii) onboard CPU clock frequency of a vehicle every time slot. To solve this problem, we leverage Lyapunov optimization to transform the long-term average problem into a slot-by-slot problem. Then, by minimizing the slot-by-slot objective function every time slot, we develop a latency-sensitive energy minimization (LEMON) algorithm. Finally, we evaluate the performance of the proposed algorithm in realistic vehicular network environment, and show that the proposed LEMON algorithm which captures the shared workloads reduces 57% of average queue backlog and 37% of average power consumption compared to the existing algorithm which does not consider shared workload characteristics.

Synchronous federated learning (FL) over wireless networks often suffers from the straggler effect, when the time required for training local models and uploading trained parameters varies significantly across heterogeneous wireless devices. This disparity prolongs the duration needed for model aggregation at the data center and slows down the convergence of synchronous FL, posing a significant challenge for FL over wireless networks. In this paper, we propose a novel buffer-aided FL scheme to mitigate the straggler effect. A buffer with sufficiently large storage is deployed at each wireless device to temporarily store the collected training data and adaptively outputs it during local training, according to the computational capabilities and communication data rates of the wireless devices. Consequently, all local models can be synchronously aggregated at the data center to reduce the number of rounds required for model aggregation in FL. To ensure timely information updates, a staleness function is further introduced to characterize the freshness of the data used to train local models. Additionally, the entropic value-at-risk (EVaR) of the data queues is introduced to eliminate the impact of discarded data at the buffers and improve the accuracy of trained local models. We formulate a delay-aware online stochastic optimization problem to minimize the long-term average staleness of all wireless devices for buffer-aided FL. Our problem formulation simultaneously guarantees the stability of data queues at the wireless devices and reduces the risk of data loss. By employing the Lyapunov optimization technique, we transform the problem into instantaneous deterministic optimization subproblems and further solve each subproblem online via utilizing its hidden convexity. Simulation results demonstrate that the proposed buffer-aided synchronous FL scheme can effectively improve the convergence rate of FL and, at the same time, ensure timely synchronization of heterogeneous wireless devices.

With the explosive growth of user data, AI applications are increasingly affecting people’s lives. To tackle the problems of data privacy and network congestion, people raise their interest in the Federated Learning (FL) framework, which enables Edge Nodes (ENs) to learn a global model without data sharing. However, FL also brings some challenges, including energy consumption restrictions, EN heterogeneity, data imbalance, and so on. These problems may lead to poor convergence accuracy, slow convergence speed, and high energy consumption during FL model training. In this paper, we focus on the performance of the FL model under energy-limited training devices, heterogeneous hardware and unbalanced data, while taking into account the social relationships between the devices. We utilize the Lyapunov optimization technique to convert the original problem into an online optimization problem, and introduce two algorithms to address this online problem. Through our analysis, we demonstrate that the optimal solution to the online problem can approximate the optimal solution to the original problem. Our simulation results validate that our proposed algorithms can achieve great performance while satisfying the energy constraints and outperforms the benchmark algorithms.

Multi-energy systems are one of the key technologies to tackle energy crisis and environmental pollution. An energy hub (EH) is a minimum multi-energy system. Interconnection of multiple EHs through energy routers (ERs) can realize mutual energy assistance. This paper proposes a peer-to-peer (P2P) energy sharing strategy between EHs including ERs in an interconnected system, which is divided into two levels. In the lower level, a method of determining the charging/discharging constraints of energy storage devices is proposed. Based on the Lyapunov optimization method, virtual queues are used to model the energy storage devices and flexible loads in the system. The objective is to minimize the overall operating cost of the interconnected system. In the upper level, a non-cooperative game model is introduced to minimize the cost of purchasing power from other EHs for each EH. A best response-based method is adapted to find the Nash equilibrium. The simulation outcomes demonstrate that application of the proposed strategy can reduce operating costs of an interconnected system and each EH. On basis of a real-world dataset of interconnected EHs, both analytical and numerical results show the effectiveness of the proposed strategy.

Although federated learning (FL) represents a distributed machine learning paradigm that ensures privacy protection, the failure of stragglers to upload local models in a timely manner results in an overall degradation of the global model’s performance, and the difficulty of accurately predicting whether clients will succeed in uploading a local model makes client selection still a challenge. To address this issue, existing works mainly focus on increasing the number of clients who participate in training within a fixed time, however, the fact is that the performance of a global model depends on the data used for training. Therefore, increasing the clients’ data contribution to the global model can effectively enhance the global model’s performance. To this end, we propose a Bayesian estimation based FL framework, named FedBoost, to enhance the performance of the model when straggler problem exists. Specifically, we formulate a long-term problem aimed at maximizing clients’ cumulative effective data contributions, while satisfying a long-term fairness constraints, which ensure a minimum selection frequency for clients. By analyzing the stability of virtual queues, we transform the long-term problem into a stepwise one via Lyapunov optimization, reducing its computational complexity. Due to the inability of the server to predict whether clients successfully upload the local model before receiving the actual upload, we use Bayesian estimation based on the observed frequency of successful uploads to estimate this probability. Last, extensive experimental results indicate that the average test accuracy of our FedBoost is up to 5.59% higher than both FedAvg and FedCS on three real-world datasets, and achieves test loss that are at most 0.1646 below the two baselines. Furthermore, the value of Lapunov function remains lower than 1.4, and at least 85% of the estimation of probabilities are in a reasonable range.

In mobile edge computing research, the edge server can effectively save computing resources by caching task data. But how to allocate cache resources to solve the competitive relationship between edge servers, as well as energy consumption and efficiency issues, and achieve optimal system performance is an NP difficult problem. Therefore, this paper proposes an online potential game resource allocation strategy OPSCO (online potential game strategy based on cache optimization) based on cache optimization, using a new cache replacement strategy CASCU (cache allocation strategy based on cache utility) to maximize the utility of the cache. By optimizing the efficiency indicator function of edge servers, combining factors such as cache replacement cost with Lyapunov optimization, potential game, and EWA (exponential weighting algorithm) algorithm, the competitive relationship of edge servers is modeled, and potential game related proofs and analyses are conducted. The simulation results show that compared with other resource allocation strategies, OPSCO can significantly improve the task completion rate and cache utility, reduce equipment energy consumption and time overhead, and solve the resource allocation and data cache problems in mobile edge computing online cache scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we study a buffer-aided TDMA uplink network, where multiple status-update devices and throughput-demand devices are supposed to upload their data to one information access point (AP), and all devices are assumed to be provisioned with a data buffer to temporarily store the randomly generated data from either the installed sensor or upper-layer applications. To fulfill the communication requirements using two types of devices, the average Age of Information (AoI) is utilized to characterize the data freshness of the status-update devices, while the average sum rate is employed to capture the average transmission performance of the throughput-demand devices. On this basis, a joint-optimization problem was formulated to minimize the average AoI for status-update devices and to maximize the average sum rate for the throughput-demand devices. Lyapunov optimization framework was used to solve the problem of obtaining an AoI-aware adaptive TDMA uplink scheme. Numerical results are presented to show that an AoI-aware adaptive TDMA uplink scheme can effectively fulfill the heterogeneous service requirements using status-update devices and throughput-demand devices. [ABSTRACT FROM AUTHOR]

Sixth-generation wireless systems not only have more demanding communication requirements, they are also expected to have high-precision sensing capabilities and sufficient computing power. Integrated sensing, communication, and computation (ISCC) can meet the above system requirements and save spectrum resources. In this paper, we build a resource allocation and offloading decision problem in an ISCC scenario that makes considerations for user mobility and partial offloading policies. The established problem minimizes the average task cost when given constraints such as the typical sensing failure rate and task completion delay. We use Lyapunov optimization theory to transform the proposed problem and propose a two-level optimization algorithm based on matching theory to offer a solution for the transformed problem. The inner layer obtains the task offloading ratio through theoretical derivation, and the outer layer determines the base station access and channel assignment based on the inner layer results. The simulation results show that the average task cost can be effectively reduced while also guaranteeing high-quality sensing performance. [ABSTRACT FROM AUTHOR]

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks.

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network with user cooperation to migrate the double near–far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability. Rigorous mathematical analysis and experimental results show that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queue stability.

Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). However, maximizing the long-term energy efficiency (EE) of a user-cooperative WPT-MEC system presents significant challenges due to uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. In this paper, we propose an online control algorithm to maximize the long-term EE of a WPT-MEC system by making decisions on time allocations and transmission powers of mobile devices (MDs) for a three-node network. We formulate a stochastic programming problem considering the stability of network queues and time-coupled battery levels. By leveraging Dinkelbach’s method, we transform the fractional optimal problem into a more manageable form and then use the Lyapunov optimization technique to decouple the problem into a deterministic optimization problem for each time slot. For the sub-problem in each time slot, we use the variable substitution technique and convex optimization theory to convert the non-convex problem into a convex problem, which can be solved efficiently. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline algorithms, achieving a 20% improvement in energy efficiency. Moreover, our algorithm achieves an [O(1/V),O(V)] trade-off between EE and network queue stability.

To address the information collection and aggregation issue in emergency scenarios characterized by the lack of public infrastructure and unstable satellite signals, an information timeliness-oriented information collection and transmission scheduling mechanism was proposed for emergency unmanned aerial vehicle (UAV) networks where information collection and transmission capabilities were constrained by energy consumption.Considering the age of information (AoI) as the metric and constraint of information timeliness, a stochastics optimization problem was constructed with the objective of minimizing UAV information collection and transmission energy consumption.By resorting to the Lyapunov optimization technique, virtual queues were established to impose information timeliness constraints on queue lengths, and the original problem was decoupled into two sub-problems, information collection and transmission scheduling, with the premise of ensuring system stability.Corresponding heuristic algorithms were proposed for each sub-problem.Simulation results demonstrate that the proposed algorithm outperforms conventional queue scheduling approaches in convergence rates and system energy consumption with guaranteed information timeliness.

Traditional network architectures cannot meet user needs for content caching, and there is a conflict between low latency requirements and high communication costs in content delivery. To address these problems, an online content caching and delivery algorithm based on Lyapunov optimization and branch and bound method is proposed under the scenario of horizontal collaboration between edge nodes to balance delivery delay and cost and to make efficient decisions on content caching and content delivery. The proposed algorithm decomposes the continuous problem into a single slot online optimization problem based on Lyapunov optimization theory, and solves them using branch and bound algorithms. Simulation experiments showed that the proposed algorithm can achieve lower average content delivery delay and higher content hit rate under a limited content delivery cost budget. It can also adaptively balance content delivery delay and delivery cost. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study an edge caching and blockchain enabled space-air-ground integrated networking (SAGIN) network, where a low-earth-orbit (LEO) satellite serves as the content provider, and multiple edge caching enabled unmanned aerial vehicles (UAVs) will cache some contents to provide user equipments (UEs) with satisfactory content access services together with the satellite. Moreover, there’s a blockchain system that is deployed on UAVs, to provide the network with trust mechanism without requiring a centralized authority. From the standpoint of the operator, we intend to maximize the long-term averaged economical revenue by providing UEs with satisfactory and secure content access services. To achieve this purpose, we will jointly optimize the content placement of each UAV, content replacement when each UAV is full, the access control of each UE, and the blockchain deployment strategy about each UAV. the concept of queues in Lyapunov optimization is utilized to represent the backlog of edge equipment, ensuring the stability of virtual queues on UAVs and satellites, while satisfying the caching capacity constraints for content caching and blockchain deployment. Due to the tight coupling of optimization in each time slot and the variables within each time slot, our problem, which involves stochastic optimization and binary integer programming, is challenging to solve. To address this issue, we initially employ Lyapunov optimization theory to transform and decouple the problem into individual time-slot optimization problems. Subsequently, we utilize an effective heuristic algorithm called the fireworks algorithm to solve these individual optimization problems. However, the original fireworks algorithm cannot be directly applied to our problem due to its binary characteristics and inter-coupling constraints. Therefore, we have redesigned the explosion and mutation operations to adapt them to our specific problem. Simulation results demonstrate that our proposed algorithm outperforms other baseline algorithms.

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.

In recent years, solar power technology and energy storage technology have advanced, leading to the increased use of solar power devices and energy storage systems in residential areas. Carbon management has become an important method to help the community manager guide energy consumption in a timely manner, effectively reduce the carbon emissions of the community, and reduce the substantial harm to the environment. This paper aims to study the issue of carbon management and resource allocation in an intelligent community with combined heat and power (CHP) systems and solar power. The presence of heterogeneous load demands in the power grid was considered. The main objective was to minimize the average system cost over time, which included the costs associated with the power grid and gas. The Lyapunov optimization theory was employed to solve the non-convex optimization problem of carbon management and resource allocation without energy sharing. To solve the energy-sharing problem, we designed an energy-sharing algorithm based on the Q-learning algorithm. Lastly, we conducted extensive simulations using actual trace data to validate the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a long-term power minimization problem of cell-free massive multiple-input multiple-output (MIMO) systems. To address this issue and to ensure the system queue stability, we formulate a dynamic optimization problem aiming to minimize the average total power cost in a time-varying system under imperfect channel conditions. The problem is then converted into a real-time weighted sum rate maximization problem for each time slot using the Lyapunov optimization technique. We employ approximation techniques to design robust sparse beamforming, which enables energy savings of the network and mitigates channel uncertainty. By applying direct fractional programming (DFP) and alternating optimization, we can obtain a locally optimal solution. Our DFP-based algorithm minimizes the average total power consumption of the network while satisfying the quality of service requirements for each user. Simulation results demonstrate the rapid convergence of the proposed algorithm and illustrate the tradeoff between average network power consumption and queue latency. [ABSTRACT FROM AUTHOR]

As the utilization of complex and heavy applications increases in autonomous driving, research on using mobile edge computing and task offloading for autonomous driving is being actively conducted. Recently, researchers have been studying task offloading algorithms using artificial intelligence, such as reinforcement learning or partial offloading. However, these methods require a lot of training data and critical deadlines and are weakly adaptive to complex and dynamically changing environments. To overcome this weakness, in this paper, we propose a novel task offloading algorithm based on Lyapunov optimization to maintain the system stability and minimize task processing delay. First, a real-time monitoring system is built to utilize distributed computing resources in an autonomous driving environment efficiently. Second, the computational complexity and memory access rate are analyzed to reflect the characteristics of the deep learning applications to the task offloading algorithm. Third, Lyapunov and Lagrange optimization solves the trade-off issues between system stability and user requirements. The experimental results show that the system queue backlog remains stable, and the tasks are completed within an average of 0.4231 s, 0.7095 s, and 0.9017 s for object detection, driver profiling, and image recognition, respectively. Therefore, we ensure that the proposed task offloading algorithm enables the deep learning application to be processed within the deadline and keeps the system stable. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy resources, power systems are facing increasing challenges in terms of flexibility and regulation capability. To address these, energy storage systems (ESSs) have been deployed on both transmission systems and distribution systems. However, it is hard to coordinate these ESSs with a single centralized optimization, and the time-domain coupling constraints of ESSs lead to high optimization complexity and a time-consuming calculation process. In this regard, this paper proposes a hierarchical transmission and distribution systems coordinative optimization framework, considering the ESSs at both ends of the systems. The decoupling of the time-domain coupling constraints of ESSs is realized by the Lyapunov optimization. Furthermore, the decoupling mechanism is embedded in the iterative process of analytical target cascading (ATC). In addition, an ATC-based Lyapunov optimization (ATC-L) approach is proposed to solve the co-optimization problem of the operations of the transmission system with multiple connected distribution systems. Through a case study, it is verified that the proposed framework and the ATC-L approach can effectively reduce the system's operational cost and improve the consumption rate of renewable energy. [ABSTRACT FROM AUTHOR]

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Based on the idea of crowdsourcing,mobile crowdsensing(MCS) collects mobile sensing devices to sense the surroun-ding environment,which can make environment sensing and information collection more flexible,convenient and efficient.Whe-ther the task allocation strategy is reasonable or not directly affects the success of the sensing task.Therefore,formulating a reasonable task allocation strategy is a hotspot and focus in the research of MCS.At present,most of the task allocation methods in MCS systems are offline and targeted at single type tasks.However,in practice,online multi-type task allocation is more common.Therefore,this paper studies the task allocation method in MCS for multiple types of tasks,and proposes an online task allocation strategy oriented to system benefits combined with the characteristics of MCS technology in the military field.In this paper,a long-term,dynamic online task allocation system model is established,and the problem is solved based on Lyapunov optimization theory with the system benefit as the optimization goal,so that the online dynamic control of task admission strategy and task allocation scheme is realized.Experiment shows that the online task allocation algorithm proposed in this paper is effective and feasible.It can reasonably allocate the tasks arriving at the MCS system online,ensure the stability of the task queue,and increase the system utility by adjusting the parameter value.

Real-time volumetric contents streaming on augmented reality (AR) devices should necessitate a balance between end-users' quality of experience (QoE) and the latency requirements. Lowering the quality of the volumetric contents to diminish the latency hinders the user's QoE. Otherwise, setting the quality of volumetric contents relatively high to improve the users' QoE increases the latency, which can be challenging to meet user satisfaction in AR services. Based on this trade-off observation, our proposed method maximizes time-average AR quality under latency requirements, inspired by Lyapunov optimization framework. In order to control the AR quality depending on latency requirements, we control the point cloud rendering ratio in the volumetric contents under the concept of Lyapunov optimization. Our extensive evaluation demonstrates that our proposed method achieves desired performance improvements, i.e., avoiding latency growing while ensuring the high quality of the volumetric contents streaming in AR services.

To cope with unprecedented mobile traffic explosion, various state-of-the-art wireless technologies such as small cell, mmWave, and heterogeneous networks have been intensively studied. Especially, small cell enhances the network capacity at the same area by deploying more next-generation NodeBs (gNBs) with the universal frequency reuse; nevertheless, it increases interference due to the shorter distance between cells requiring more exchange of feedback message for interference management (IM). In this paper, we first propose a practical cellular network architecture, namely EdgeSON, that provides network operators the opportunity to strike a balance between centralized and distributed cellular managements for the enhancement of the network performance with a reasonable feedback information exchange and spatio-temporal power sharing. On top of EdgeSON, we formulate an optimization problem aiming to maximize the time-averaged utility of users constrained by the time average transmit power budget per cluster. Although we exploit Lyapunov optimization to induce slot-by-slot subproblems, one of the transformed subproblems to find a set of power allocation and user scheduling is known as NP-hard. To tackle this issue, we propose a low-complex and practical interference management algorithm, namely IMPowerShare by introducing a critical user and power sharing virtual queue concepts which maximally exploit structural characteristics of EdgeSON to efficiently solve the NP-hard problem. IMPowerShare definitely differs from the existing interference management or spatio-temporal power sharing works in perspectives of practical feedback information exchange in EdgeSON and optimization framework. Finally, via extensive simulations in real gNB topologies in Korea, we verify that IMPowerShare outperforms the existing interference management algorithms in small cell networks.

In disasters, unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) when terrestrial BSs are unavailable. Although most studies have focused on optimal UAV localization to provide efficient network connectivity for outdoor users, supporting indoor users is also of great importance. Additionally, fairly providing network connectivity to as many indoor users as possible is critical in emergencies. Therefore, we propose a table-based fair transmission algorithm based on carrier-sense multiple access with collision avoidance (CSMA/CA) that aims to provide as many equal opportunities as possible. Moreover, we propose a Lyapunov optimization-based optimal UAV positioning algorithm to satisfy various disaster requirements. To analyze the proposed algorithm’s performance, two building types were considered: standard- and factory-type buildings. We then compared the proposed table-based fair transmission algorithm with the conventional protocol according to various building types. Furthermore, via intensive simulations, we demonstrated the Lyapunov optimization-derived UAV movements according to situational requirement variations.

As the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead of smart devices. Additionally, regarding the execution of tasks in edge servers, smart devices could provide a low-latency environment to the end users. Within this paper, an artificial intelligence (AI)-empowered fast task execution method in heterogeneous IoT applications is proposed to reduce decision latency by taking into account different system parameters such as the execution deadline of the task, battery level of devices, channel conditions between mobile devices and edge servers, and edge server capacity. In edge computing scenarios, the number of task requests, resource constraints of edge servers, mobility of connected devices, and energy consumption are the main performance considerations. In this paper, the AI-empowered fast task decision method is proposed to solve the multi-device edge computing task execution problem by formulating it as a multi-class classification problem. The extensive simulation results demonstrate that the proposed framework is extremely fast and precise in decision-making for offloading computation tasks compared to the conventional Lyapunov optimization-based algorithm results by ensuring the guaranteed quality of experience.