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The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose a Water-filling Based Dynamic Task Scheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness.

For Internet and cloud computing service providers, running massive geo-distributed data centers incurs prodigious electricity cost and water consumption as well as carbon emission rooted in electricity generation. Thus, it is critical significant for providers to lower down the operation cost of data centers. In this paper, we investigate the problem of energy management for geo-distributed data centers with renewable resources and energy storages. We aim to minimize the long-term operation cost including electricity cost, water consumption and carbon emission by leveraging the spatiotemporal diversity of these system states. To this end, we first formulate the cost minimization problem as a stochastic optimization problem, then we adopt the Lyapunov optimization technique to design a close-to-optimal online algorithm which only needs the current system information and achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. To reduce the computational complexity and unnecessary communication, we further propose a distributed algorithm based on the distributed computing framework alternating direction method of multipliers (ADMM), which enables each data center to make their own control decisions. Based on the real-world traces and extensive simulations, we demonstrate the effectiveness of our proposed algorithms.

This article elucidates a new real-time optimization framework for advanced energy-efficient management of active radial distribution networks. The proposed energy management process leverages the benefits of simultaneous deployment of online direct load control (DLC) and conservation voltage reduction (CVR) for decreasing peak energy demand. Initially the proposed problem is designed as a time-coupled stochastic mixed integer nonconvex programming (MINCP) to accommodate long-term offline beneficial aspects in real-time optimization framework, which is later simplified using merger of Queueing theory and Lyapunov optimization. A successive mixed integer linear programming (s-MILP) solution approach is proposed for accurate and fast convergence of the revised MINCP framework. The efficacy of the developed strategy is evaluated after comparing with two-benchmark energy management models (viz. offline and online greedy algorithm) by demonstrating on modified IEEE 69-bus test system. Further scalability of the proposed approach is validated by testing on large distribution networks.

Mobile edge computing (MEC) is a promising paradigm to meet the low latency requirement of applications like virtual/augmented reality, which moves computation work-loads from remote cloud to network edge. Task offloading is a crucial problem in a MEC system, which significantly concerns with the achieved latency of tasks and the energy consumption on edge nodes. Although many efforts have been paid on the problem, most of them ignore the hierarchical architecture of MEC and the heterogeneity of tasks generated by different applications. In this paper, we consider a three-layer MEC system with tasks of various types stochastically arrive in real time, in which each task can be executed on mobile smart devices, offloaded to the edge server, or offloaded to the remote cloud. The task offloading problem in such a MEC system with the objective to minimize the averaged latency over time is NP-hard. To solve it, we employ Lyapunov optimization and duality theory to reformulate the problem and decompose it into a set of subproblems. Each subproblem can be distributedly solved by a mobile device or the edge server individually. Extensive simulations are also performed to verify the feasibility and superiority of our approach.

Bike Sharing Systems (BSSs) have emerged as an economical and eco-friendly solution to alleviate the last-mile problem in intelligent transport systems. As an exclusive route selection problem in BSSs, Bike Trip Planning (BTP) has a clear goal: to select the available routes with minimum time cost for bike users, while satisfying their basic needs, e.g., the bounded longest walking distance. In this paper, we propose a real-time Lyapunov-based Bike Trip Planning (LBTP) policy that considers the users' waiting at stations, which has not been sufficiently studied in the literature. Both the total time cost and the service rate of all users are the core criteria of a BSS, so we make use of the Lyapunov optimization theory to make a tradeoff between them. Our policy can achieve self-organizing bike redistribution without extra redistribution budget required. The evaluation results show the superiority of our policy on the both system utility and user service rate compared with the existing BTP policies, and reveal the extra travel time fairness degree among different types of users under our policy.

In this paper, we study Space-Air-Ground integrated Vehicular Network (SAGVN), and propose an online control framework to dynamically slice the SAG spectrum resource for isolated vehicular services provisioning. In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which falls into the scope of Lyapunov optimization theory. By bounding the drift-plus-penalty, the original problem can be decoupled into four independent subproblems, each of which is readily solved. The merits of our control framework are three-fold: 1) the system is able to admit and process as many requests as possible (i.e., maximizing the time-averaged throughput); 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues are stabilized in the long-term. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning. Compared with the fixed slicing, our dynamic slicing can react to the vehicular environment rapidly and achieve an average 26% of throughput improvement.

Mobile edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computation tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of MEC system will significantly affect the time delay of computation tasks. However, when combined them together, energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and energy harvesting process. Subsequently, we formulate a non-convex optimization problem with minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on Lyapunov optimization to achieve optimal solution, that is, LOMUCO. Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics, and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including LCA, OCA, RPCO, and LODCO, we can demonstrate the superiority of LOMUCO.

This paper considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For DTM, we characterize a throughput-backlog trade-off of [O}(1/V), O(V)] to indicate that the system throughput goes up as the queue backlog increases. However, as V goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog. To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of ${[O(1/V), O((\log(V))^2)]}$ between the system throughput and the queue backlog.

The accelerating convergence of artificial intelligence (AI) and Internet-of-Things (IoT) has sparked a recent wave of interest in Artificial-Intelligence-of-Things (AIoT). By exploiting the novel paradigm of edge intelligence, emerging computational intensive and resource demanding AIoT applications can be efficiently supported at the network edge. However, due to the limited resource capacity and/or power budget of the edge node, AIoT applications typically deploy compressed AI models to achieve the goal of low-latency and energy-efficient model inference. However, compressed models inherently suffer from the curse of data drift, i.e., the inference data at the deployment stage diverges from the training data at the training stage, leading to reduced model inference accuracy. To handle this issue, continuous learning has been proposed to periodically retrain the AI models on new data in an incremental manner. In this paper, we investigate how to coordinate the edge and the cloud resources to perform cost-efficient continuous learning, with the goal of simultaneously optimize the model performance (in terms of accuracy and robustness) and resource cost. Leveraging Lyapunov optimization theory, we design and analyze a cost-efficient optimization framework for making online decisions upon admission control, transmission scheduling, and resource provisioning, for the dynamically arrived new data samples of various AIoT applications. We examine the effectiveness of the proposed framework on navigating the performance-cost tradeoff theoretically and empirically through trace-driven simulations.

With the emergence of Industrial Internet of Things (IIoT), intensive computation workload will be imposed to Industrial End Units (IEUs). By leveraging Mobile Edge Computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This paper proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel Joint Task Scheduling and Resource Allocation (JTSRA) approach comprises two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power, and an online multi-agent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared ‘intuition’ and ‘cost optimal’ approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multi-access-node MEC systems.

The advent of vehicular edge computing (VEC) has generated enormous attention in recent years. It pushes the computational resources in close proximity to the data sources and thus caters for the explosive growth of vehicular applications. Owing to the high mobility of vehicles, these applications are of latency-sensitive requirements in most cases. Accordingly, such requirements still pose a great challenge to the computing capabilities of VEC, when these applications are outsourced and executed in VEC. Against this backdrop, we propose a new mathematical model which respectively generalizes the computation and communication models, and applies application oriented caching into VEC in this paper. Based on this model, a new strategy is further proposed to optimize the average response time of applications over an infinite time-slotted horizon for VEC. A long-term energy consumption constraint is imposed to guarantee the stability of VEC system, and the Lyapunov optimization technology is adopted to tackle this constraint issue. Two greedy heuristics are put forward to help find the approximate optimal solution in the drift-plus-penalty based algorithm. Extensive experiments have been conducted to evaluate the response time and energy consumption in the caching assisted VEC. The simulation results have shown that the proposed strategy can dramatically optimize the average response time while satisfying the long-term energy consumption constraint.

Recently, increasing investments in satellite-related technologies make the Low Earth Orbit (LEO) satellite constellation a strong complement to terrestrial networks. To mitigate the limitations of the traditional satellite constellation "bent-pipe" architecture, Satellite Edge Computing (SEC) has been proposed by placing computing resources at the LEO satellite constellation. Most existing works focus on space-air-ground integrated network architecture and SEC computing framework. Beyond these works, we are the first to investigate how to efficiently deploy services on the SEC nodes to realize robustness aware service coverage with constrained resources. Facing the challenges of spatial-temporal system dynamics and service coverage-robustness conflict, we propose a novel online service placement algorithm with a theoretical performance guarantee by leveraging Lyapunov optimization and Gibbs sampling. Extensive simulation results show that our algorithm can improve the service coverage by 4.3× compared with the baseline.

As an attractive alternative to traditional and large satellite, small satellite cluster (SSC) with low cost and short development time will play an important role in Earth observation. Generally, SSC networks for Earth observation are energy-limited, the design of the joint data downloading and resource management scheme remains challenging. On the one hand, with the ever-increasing amount of sensing data, multiple gateway stations and relay satellites have been deployed to receive the sensing data from SSC. The problem that where to download the sensing data should be addressed. On the other hand, due to the limited communication, storage, computing and power supply capacities of small satellite, an efficient multi-dimensional resource management scheme should be adopted. In this paper, the joint data downloading scheduling and multi-dimensional resource management problem for satellite cluster networks with relay satellites is investigated, which is formulated as a long-term optimization problem to minimize the energy consumption and guarantee the data drop rate performance. Then, this problem is decomposed into a series of mixed-integer programming problems by Lyapunov optimization theory. Leveraging many-to-many matching with empty node and the simplex method, an online joint data downloading and resource management (OJDDRM) scheme is proposed. Simulation results validate the proposed OJDDRM scheme's performance and demonstrate that there exists a balance point for the proposed OJDDRM scheme. Under the balance point, the data drop rate is zero and the energy consumption can be reduced compared with the existed schemes.

Computation offloading is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capability of mobile devices. In this paper, we study the task offloading and resource purchasing problems in an edge-cloud collaborative system. The purpose of this system is to minimize the cost of task offloading while ensuring that the tasks can be served before their maximum acceptable delays. Due to the uncertainty of both the task arrival rates and the prices of the computing resources, it is impossible to make an optimal decision online for a long-running time. Therefore, we propose a two-timescale Lyapunov optimization algorithm to overcome the uncertainty of the system's future information and make the optimal decisions only based on the system's current states. By purchasing computation resources in different timescales from the public cloud and making online decisions on where and how many requests should be offloaded, we can achieve an efficient outcome such that the system performance can approach the offline optimum without requiring a priori knowledge of system statistics. Rigorous theoretical analysis confirms the effectiveness of the proposed two-timescale Lyapunov optimization algorithm and extensive trace-driven experimental results show that the algorithm achieves outstanding performance gains over existing benchmarks.

We consider a system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problems versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than $60~\%$ saving in the average total transmit power compared to a baseline policy., 31 pages, 12 figures. arXiv admin note: text overlap with arXiv:1912.02421

Federated learning (FL) is a promising solution to privacy preservation for data-driven deep learning approaches. However, enabling FL in unmanned aerial vehicle (UAV)-assisted wireless networks is still challenging due to limited resources and battery capacity in the UAV and user devices. In this regard, we propose a deep reinforcement learning (DRL)-based framework for joint UAV placement and resource allocation to enable sustainable FL with energy harvesting user devices. We aim to maximize the long-term FL performance considering the limited resources in the network, such as harvested energy, bandwidth resources, and UAV’s energy budget. To reduce the complexity of the original problem, we leverage the Lyapunov optimization technique to transform a long-term energy constraint into a deterministic problem. We reformulate the optimization problem as the framework of a Markov decision process (MDP) and design a DRL-based algorithm to solve the MDP. The proposed solution can guarantee the sustainable operation of UAV-aided wireless networks by improving energy conservation of the network in the long run.

The integration of the fifth-generation (5G) wireless communication with power line communication (PLC) provides an efficient solution to accommodate the connection demands of massive industrial internet of things (IIoT) devices. Moreover, edge computing can provide sufficient computing services for IIoT devices. By empowering PLC-wireless integrated networks with edge computing, devices can offload task data to adjacent edge servers through either PLC or 5G subchannels by leveraging orthogonal frequency division multiplexing (OFDM). However, task offloading in the edge computing-empowered PLC-wireless integrated network faces several challenges such as channel access conflict, dynamic network state, as well as long-term queue stability assurance. In this article, we construct a one-to-many task offloading architecture to maximize the total throughput. Lyapunov optimization is adopted to convert the NP-hard task offloading problem. Then, a pricing-based matching with quota task offloading algorithm is proposed to handle channel access conflicts among IIoT devices. Simulation evaluation demonstrate that the proposed algorithm realizes excellent performances in throughput, energy consumption, and queuing backlog.

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this paper, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and energy harvesting. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the CPU frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of user mobility, the EH model, and the channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

An integration of nonorthogonal multiple access (NOMA) in device-to-device (D2D) mobile groups (DMGs) generates a large amount of energy and delay due to sharing the same subcarrier. In this article, we investigate a distributed energy-efficient and delay tradeoff scheme for NOMA-based DMGs in wireless powered communication networks. The stochastic optimization problem is formulated to maximize the energy efficiency and minimize the delay. Many-to-one matching-based subcarrier allocation, weighted sum method, and Lyapunov optimization scheme are used to investigate the possible solution. Further, to obtain the closed-form expression of power and to optimize the time for wireless powered communication networks, we use Lagrangian function. Simulation results show that the proposed scheme achieves 2.6% and 15.78% superior performance than the existing NOMA and orthogonal frequency division multiple access (OFDMA) schemes under the condition of constant circuit power consumption.

Ultra-dense edge computing is expected to provide delay-sensitive and computational-intensive services for mobile devices. Due to the complexity and unpredictability of the network environment, it is challenging to ensure the continuity and security of computing offloading services in the process of user movement. Most existing works consider the decisions of communication handover and computational offloading simultaneously while ignoring the security on offloading tasks. In light of this, we propose a secure mobility management framework for blockchain-based ultra-dense edge computing, where blockchain reduces duplicate authentication between edge servers. We jointly optimize the wireless handover and service migration decisions between base stations, which is translated into a multi-objective dynamic optimization problem using the Lyapunov optimization. The optimization problem is solved by deep reinforcement learning approach based on the Actor – Critic method. Finally, we use simulation studies to evaluate the performance of the proposed scheme. The results show that, compared with other existing schemes, the proposed scheme can reduce the average delay of computing tasks, the rate of tasks failure and the rate of handover.

We consider a power system with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve their customers. All the entities in the system are foresighted : each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g., energy generation costs and the aggregators’ costs) by dispatching the energy production among the generators. The decision making of the foresighted entities is complicated for two reasons. First, the information is decentralized among the entities, namely each entity does not know the others’ states. Second, an aggregator’s current decision affects its future costs due to the coupling introduced by the energy storage. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its locally-available information. We prove that the proposed framework can achieve the social optimum despite being decentralized and involving complex coupling. Simulation results show that the proposed foresighted demand side management achieves significant reduction in the total cost, compared to the optimal myopic demand side management (up to 60% reduction), and the foresighted demand side management based on the Lyapunov optimization framework (up to 30% reduction).

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays’ beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes.

Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing future channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems. By doing so, it guarantees to satisfy all the long-term constraints by solving the per-frame subproblems that are much smaller in size. Then, LyDROO integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low execution latency that is particularly suitable for real-time implementation in fast fading environments., Comment: The paper has been accepted for publication by IEEE Trans. Wireless Communications, the source codes associated with the paper are available at https://github.com/revenol/LyDROO. arXiv admin note: text overlap with arXiv:2102.03286

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. However, as a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes’ operation states: working or recharging, to achieve a desirable network utility. In our design, we first consider a single-hop special case of small-scale networks. We transform the operation state scheduling problem into a linear programming problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. The multi-hop operation state scheduling problem is proved to be NP-hard. We show that the spatiotemporal coupling caused by time-varying network topology makes the problem quite challenging. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85 percent of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.

The advent of vehicle-to-everything communication facilitates the emergence of vehicular sensing networks, where vehicles equipped with advanced sensors continuously sample informative status updates of its surroundings and forward the sampled data to roadside infrastructure based on a certain routing strategy. The collected data is analyzed to obtain real-time situational awareness to impose certain behaviors on the vehicles. In such networked control systems, the timeliness of collected data is of critical importance to system performance, which can be quantified by the concept of Age of Information. Note that to obtain timely perception of its surroundings, each vehicle tends to sample status updates at the maximum frequency, which may congest the network due to limited communication resource. Moreover, the highly dynamic nature of vehicular network poses a great challenge in finding a reliable route for timely data forwarding. Therefore, the data collection scheme should be carefully designed to balance the timeliness of collected information and network stability. In this article, we study an age optimization problem by jointly considering the data sampling at source vehicles and the data forwarding process for multiple information flows across the network. We employ the Lyapunov optimization technique to develop a distributed age-aware data collection scheme consists of a threshold-based sampling strategy at source vehicles and a learning-based data forwarding strategy. Simulation results show that our proposed scheme outperforms existing strategies in collecting status updates in a timely manner.

New radio in unlicensed spectrum (NR-U) and network slicing have been emerged as two promising technologies to meet with the demand of enormous traffic volume, extreme transmission speed, and highly heterogeneous service requirements in future 5 G networks. Additionally, spurred by both economic considerations and environmental concerns, green communication becomes a new prominent figure of merit in the design of future 5 G networks. In this paper, we investigate an energy consumption scheduling for unlicensed heterogeneous networks, namely NR-U and WiFi, by leveraging the network slicing technology. First, we introduce a network slice broker, which enables virtual heterogeneous network operators to request and lease resources cooperatively via signaling means. Then, we investigate a joint network selection and resource allocation scheduling to minimize the network energy consumption in real time environment, taking into account various user data rate and delay requirements. An online Lyapunov optimization algorithm has been introduced to solve the proposed problem with low computational complexity and rapid convergence. We show that there exists a tradeoff between energy consumption and traffic delay, which can be adjusted through control parameters. Numerical results are presented to confirm our analyses and demonstrate the effectiveness of the proposed algorithms.

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), computing-intensive and delay-sensitive IoT applications (APPs) are proliferating with an unprecedented speed in recent years. Mobile edge computing (MEC) and energy harvesting (EH) technologies can significantly improve the user experience by offloading computation tasks to edge-cloud servers as well as achieving green and durable operation. Traditional centralized strategies require precise information of system states, which may not be feasible in the era of big data and artificial intelligence. To this end, how to allocate limited edge-cloud computing resource on demand, and how to develop heterogeneous task offloading strategies with EH in a more flexible manner are remaining challenges. In this paper, we investigate an EH-enabled MEC offloading system, and propose an online distributed optimization algorithm based on game theory and perturbed Lyapunov optimization theory. The proposed algorithm works online and jointly determines heterogeneous task offloading, on-demand computing resource allocation, and battery energy management. Furthermore, to reduce the unnecessary communication overhead and improve the processing efficiency, an offloading pre-screening criterion is designed by balancing battery energy level, latency, and revenue. Extensive simulations are carried out to validate the effectiveness and rationality of the proposed approach.

Integrating edge computing and non-orthogonal multiple access (NOMA) into the power Internet of Things (PIoT) provides a promising way to meet the stringent demands of communication delay, energy consumption, and massive connectivity. The service tasks can be partitioned into independent subtasks, and can be processed locally by PIoT devices or offloaded to the edge servers through reusing radio resource blocks (RBs). In this paper, we propose a Multi-timescale multi-dimEnsion Resource allocatIon and Task Splitting algorithm, namely MERITS, for NOMA-edge computing-based PIoT. First, Lyapunov optimization is exploited to decompose the long-term stochastic optimization problem into three short-term deterministic subproblems, i.e., RB allocation in a large timescale, task splitting and computation resource allocation in a small timescale. The first subproblem is modeled as a one-to-many matching problem with externalities, and solved by the proposed swap matching-based RB allocation algorithm. The second subproblem is relaxed into a continuous convex problem which is easily to be solved, and the third subproblem is solved by Lagrange dual decomposition. Simulation results demonstrate that MERITS outperforms the existing resource allocation and task splitting algorithms in terms of energy consumption, queuing delay, queue backlog, and connection success ratio in the massive connectivity scenario.

In this paper, we propose an online power allocation scheme to maximize the time average of the expected value of sum rate for multiple downlink receivers with energy harvesting transmitter. The transmitter employs non-orthogonal multiple access (NOMA) and/or orthogonal multiple access (OMA) to transmit data to multiple users. Additionally, we consider the scenario where each individual user has a quality of service constraint on its required instantaneous rate. The decisions of total transmit power and power allocation for different users in a given time slot are obtained with the help of Lyapunov optimization technique. The proposed schemes do not require any statistical information of the channel states and the harvested energy. The proposed power allocation schemes entail in small complexity based power allocation decisions. Therefore, the proposed schemes can provide solutions in real time and are more suited for online power allocation problems where the system state parameters (e.g., channel state, harvested energy etc) change quickly. The performances of the proposed schemes are demonstrated with the help of simulation results.

Mobile edge computing (MEC) is a promising technique to alleviate the energy limitation of Internet of Things (IoT) devices, as it can offload local computing tasks to the edge server through a cellular network. By leveraging extreme value theory (EVT), this work proposes a priority-differentiated offloading strategy that takes into account the stringent quality of service (QoS) requirements of mission-critical services and green resource allocation. Particularly, Lyapunov optimization is first introduced to derive an upper-bound queue minimization problem with the consideration of energy consumption and task priority. The peaks-over-thresholds (POT) model is then applied to evaluate the stationery status and cooperate with Wolf-PHC learning to optimize resource allocation. Finally, simulation results verify that the proposed offloading policy performs well in terms of its energy-saving capability while satisfying different demands of mission-critical IoT services.

Nowadays, a large number of civilian unmanned aerial vehicles (UAVs) are increasingly being used in many of our daily applications. However, there are many kinds of UAVs that need to enhance their endurance due to their limited resources. In order to make the UAVs operate efficiently, it is necessary to schedule UAVs with charging requirements. In this paper, renewable energy production and storage equipment on the basis of traditional charging stations is adopted to reduce the power purchase from the distribution network as much as possible. An online algorithm based on Lyapunov optimization is proposed to schedule the charging of UAVs and the energy management of the charging station. Meanwhile, contract theory is used to design the optimal charging strategy in the case of information asymmetry. Hence, local renewable energy can be utilized to the greatest extent, and electricity purchase costs can be reduced. Through the incentive system, users can spontaneously charge at low peak times and avoid the risk of grid overload and high energy cost. The simulation results show that the algorithm can improve the efficiency of charging station operators, allowing users to avoid charging at peak times, and only use real-time information to schedule UAVs. Compared to other algorithms, the proposed scheme can bring good revenue to operators while ensuring long-lasting operations of charging stations.

In this paper, the problem of energy-efficient data gathering in an Internet of Things (IoT) based remote area surveillance application is addressed by designing a suitable MAC layer uplink solution. We follow the 3GPP specified scheduled access scheme for narrowband IoT (NB-IoT) and consider presence of both delay sensitive and delay tolerant traffic in the network. First, we present a mixed integer non-linear programming (MINLP) based optimization framework to minimize the IoT devices’ cumulative transmission energy per uplink frame under the constraints of finite resource blocks, mean queue stability and residual energy levels of IoT nodes. A cross layer approach has been adopted. Precisely, using Lyapunov optimization, we propose a dynamic, distributed transmit power allocation for IoT nodes and a centralized node scheduling scheme. Further, assuming Poisson model for traffic generation at delay tolerant traffic generating nodes, we suggest a distributed probabilistic sleep scheduling scheme to improve the average delay experience of delay sensitive traffic while improving the overall energy conservation. Simulation results suggest impressive performance of our proposed solution over the existing uplink solution for NB-IoT, in terms of delay experience of delay sensitive traffic, buffer length requirement and nodes’ total energy consumption at high traffic load.

To increase the feasibility of wireless powered communication networks (WPCNs), the synergy of efficient multiple access protocols and diverse types of resources, e.g., multiple antennas, memories, and energy storage devices is of paramount importance. To this end, in this paper, we introduce the use of buffered sources in WPCNs with uplink non-orthogonal multiple access, in which a multi-antenna hybrid access point can both transmit energy and receive information. The proposed scheme aims at maximizing the long-term utility function, which is determined by the achieved average data rate and fairness among sources. The formulated long-term time-average optimization problem is converted into instantaneous ones by exploiting the Lyapunov optimization framework. The derived analytical solutions reveal the impact of data buffers’ length on the decoding order of sources’ messages. Finally, it is shown that the proposed scheme achieves superior long-term utility compared to the considered baseline schemes with non-buffered sources and buffered sources with orthogonal multiple access. Additionally, it is demonstrated that our proposed scheme can ensure fairness without reducing the average data rate.

This article aims to develop a multiobjective optimization portfolio for real-time energy management in a smart home equipped with battery associated rooftop solar panels, lighting loads, air conditioners, and other smart home appliances. The energy management problem is framed for simultaneous minimization of the monetary energy cost and total dissatisfaction due to regulation in power consumption. The entire optimization portfolio is designed as a time average stochastic problem, which is simplified by the combination of queueing theory and Lyapunov optimization. The revised problem takes form of a mixed integer convex nonlinear programming, which is further solved using outer approximation approach. The proposed real-time home energy management framework needs only the current data regarding the random input parameters like renewable generation, energy price, and aggregated load demand, and does not call for their probabilistic estimation. Case study is carried out on a practical home data to proof efficacy of the proposed strategy and also the simulation outcomes are compared with one of the popular real-time energy management process named greedy algorithm.

Nowadays, there are active research for deep learning applications to smart cities, e.g., smart factory, smart and micro grids, and smart logistics. Among them, for industrial smart harbor and logistics platforms, this paper proposes a novel two-stage algorithm for large-scale surveillance. For the purpose, this paper utilizes drones for flexible localization, and thus, the algorithm for scheduling between multiple drones and multiple multi-access edge computing (MEC) systems is proposed under the consideration of stability in this first-stage. After the scheduling, each drone transmits its own data to its associated MEC for enhancing the quality and then eventually the data will be used for surveillance. For improving the quality, super-resolution is used. In the second-stage algorithm, the self-adaptive super-resolution control is proposed for time-average performance maximization subject to stability, inspired by Lyapunov optimization. Based on data-intensive simulation results, it has been verified that the proposed algorithm achieves desired performance.

Mobile-edge computing is a promising computing paradigm with the advantages of reduced delay and relieved outsourcing traffic to the core network. In mobile-edge computing, reducing the computation offloading cost of mobile users and maintaining fresh information at edge nodes are two critical while conflicted objectives, as both consume the limited wireless bandwidth of edge nodes. Although extensive efforts have been devoted to optimizing computation offloading decisions and some works have investigated freshness-aware channel allocation issues recently, no prior works have considered the above conflict. This article is the first work to jointly optimize the channel allocation and computation offloading decisions, aiming at reducing the computation offloading cost within freshness requirements of sensors. We analyze the recursiveness of Age of Information (AoI) in analogy to the evolvement of a queue and formulate the problem as a nonlinear integer dynamic optimization problem. To overcome the challenges of AoI-computation cost tradeoff, AoI time dependency and high complexity caused by the heterogeneity of users, we propose an algorithm to solve the problem with reduced computation complexity. Specifically, we first transform the original problem into a static optimization problem in each time slot (which is NP-hard) based on Lyapunov optimization techniques. To reduce the computation complexity, we exploit the finite improvement property of potential games and further enforce centralized control to reduce the number of improvement iterations. Simulations have been conducted and the results demonstrate that the proposed algorithm shows good effectiveness and scalability.

The age of information (AoI) is a key performance metric for data freshness in real-time systems, measuring the time elapsed between status updates received at a remote destination. This paper addresses the analysis of both the AoI and the delay for a system demanding timely status updates, consisting of an internet of things (IoT) community, and a multi-core edge computing node (EN). The IoT devices perform channel contention according to a slotted aloha non-orthogonal multiple access scheme with two transmission power levels to access the EN. The objective of the paper is the dynamic selection of both the EN computation capabilities and the suitable access probability for the two power levels, aiming at maximizing the time-average system utility, while guaranteeing the queues stability. The device end-to-end delay is analyzed by resorting to the martingale theoretical approximation under the condition that the Lyapunov optimization framework is performed on a time slot basis, hence leading to a novel instantaneous mixed integer problem formulation. Furthermore, the generalized benders decomposition method is employed to provide a suboptimal solution for the formulated problem. Finally, the accuracy of the proposed framework is validated by comparing the obtained analytical predictions with simulation results.

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX).

In this paper, we investigate the power allocation problem in downlink orthogonal frequency-division multiple access (OFDMA) networks. Different from previous researches on power allocation, we take into account various practical factors, such as the stochastic traffic arrival, the time-varying channel, the queue stability requirements of all users, the channel estimation cost and the corresponding effect of imperfect channel state information (CSI) on data transmission rate. The power allocation problem is formulated as maximizing the time-averaged data transmission rate by optimizing pilot and data power allocation subject to the queue stability and the maximum transmit power constraints. The data transmission rate is defined in terms of the pilot transmit power, the data transmit power and the channel estimation error, which is non-concave. To solve the non-concave and stochastic optimization problem, a dynamic pilot and data power allocation (DPDPA) algorithm is proposed with the aids of approximate transformation, Lyapunov optimization and Lagrange dual formulation. Moreover, we derive the bounds of performances, in terms of the time-averaged data transmission rate and queue length.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services, which require low delay and high accuracy. Sampling rate adaption, which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this article, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading, and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.

Background With the steady growth of trade between China and Europe, the number of trains in CHINA RAILWAY Express has steadily increased under the Belt and Road Initiative. By the end of 2019, more than 21,000 trains have been operated. However, in the development of CHINA RAILWAY Express, the cost of empty container transportation and leasing remains high. So, it is necessary to put forward the strategy of container sharing. Aim Through the optimization of empty container transfer and rent under container sharing, this study aim to reduce the operation cost and put forward the strategy of container sharing. Method Considering the dynamic characteristics of empty container transportation in reality, this paper constructs a multi-period empty container transportation optimization model with the lowest total cost and the stability of the network, combining with the container sharing strategy. Lyapunov optimization method is used to transform the complex problem into a single period optimization problem, and then genetic algorithm is used to solve the model. Result Based on the analysis of 13 existing regular train companies, the results show that the model can effectively improve the utilization rate of containers and reduce the cost in the decision-making period. Based on the results and parameters analysis, some suggestions are put forward to optimize the empty container sharing and effectively realize of container sharing.

The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.

Driven by the rapid technological advances, industrial Internet of Things (IIoT) has recently been embraced to enhance autonomous industrial processes. Since a huge diverse traffic would be generated by IIoT, the industrial processes would meet the challenges of spectrum scarcity and on-demand service requirements. Millimeter wave (mmW) and pervasive edge computing (PEC) technologies in 5G communication are available to deal with these requirements. In this article, a novel dual-band framework that integrates both mmW and microwave ( $\mu$ W) networks in PEC environment has been proposed, which locally performs joint resource allocation and power assignment over mmW and $\mu$ W to meet IIoT devices’ specific requirements. To consider the new prominent figure of merit in IIoT scenario, the scheduling problem is formulated as an optimization problem to minimize the IIoT energy consumption in real-time environment. A Lyapunov optimization technique has been applied for the objective function with low complexity and rapid convergence. To solve the NP-hard Lyapunov algorithm, we introduce a block coordinate descent method that decompose the Lyapunov problem into two nested subproblems over the mmW and $\mu$ W networks. An initialization-free semidistributed scheme is proposed in mmW PECs, which not only requires little information exchange via the $\mu$ W network but also achieves the global optimal solution. Numerical results are shown to demonstrate the effectiveness of our proposed algorithms and confirm our theoretical analyses.

Airborne users are always dreaming of enjoying a good Internet access experience while in the air. However, due to long propagation delay and limited network coverage, the existing data communication methods utilized in space and ground communications not only fail to ensure the quality-of-service (QoS) of airborne users, but also incur significant energy consumption to process content requests. In this paper, we introduce the aeronautical ad hoc network (AANET) as a new method of network access and design an energy-efficient data access scheme in civil aircrafts-enabled space-air-ground integrated networks (CAE-SAGIN). In order to minimize the energy consumption, we propose a service selection scheme based on reinforcement learning and formulate a joint optimization problem of resource allocation and request distribution. Leveraged by the Lyapunov optimization method, the optimization problem can be solved by the proposed joint optimization algorithm. Extensive simulations are conducted to confirm the stability of the CAE-SAGIN, and demonstrate that the proposed data access scheme can effectively reduce both the energy consumption and the processing delay. Moreover, the advantages of using AANET are becoming more obvious when higher data rate is required.

The expensive cost and intermittent availability of renewable energy bring great challenges to its efficient utilization in green data centers. In this paper, we propose a new way to achieve an explicit trade-off between operational cost and carbon emission by dynamic storing off-site renewable energy in distributed data centers. We first formulate a constrained stochastic optimization problem for cost minimization of data centers. Then, by leveraging Lyapunov optimization theory, we design an online Carbon Capped Cost Minimization algorithm (CCCM) to achieve a near-optimal cost with rigorous mathematical proof. Specially, the decisions at each time slot are determined with an efficient iterative algorithm based on the Generalized Benders Decomposition (GBD) technique. Finally, extensive simulations are conducted to show the effectiveness of our algorithm. The results show that our algorithm can save about 6% total costs compared with the algorithm without offsite energy storage.

As the number of low Earth orbit satellites is increasing, new algorithms and high-performance computing resources are needed to handle the enormous real-time arrival data generated by satellites. Based on the requirements, this paper proposes a novel real-time adaptive speckle filtering selection algorithm for satellite synthetic aperture radar (SAR) images. The use of high-performance filters generates high-quality images whereas it introduces delays. Thus, a real-time adaptive algorithm which achieves time-average SAR image quality maximization subject to delays using Lyapunov optimization, where the delay is formulated with a queuing model. Evaluation results show that the proposed algorithm guarantees desired performance improvements.