Joint Optimization Problem Research Articles

Lane-free signal-free intersection crossing via model predictive control

The operation of signal-free intersections, where Connected Automated Vehicles (CAVs) cross simultaneously for all Origin-Destination (OD) movements, has the potential to greatly increase throughput and reduce fuel consumption. Since the intersection crossing areas naturally include no lanes, an extended crossing area, appropriately delineated, can be considered as a lane-free infrastructure so as to enable further efficiency benefits. This paper presents two Model Predictive Control (MPC) schemes to manage CAVs in signal-free and lane-free intersections. In fact, the control inputs of all vehicles are optimized over a time-horizon by online solving of a joint Optimal Control Problem (OCP) that minimizes a cost function including proper terms to ensure smooth and collision-free vehicle motion, while also considering fuel consumption and desired-speed tracking, when possible. Additionally, appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. A fast Feasible Direction Algorithm (FDA) is employed for the numerical solution of the introduced OCP. Multiple simulations are carried out to assess the efficiency and practicality of the proposed methods. A comparison with signalized intersection operation is provided.

Joint Optimization of Empty Container Repositioning and Inventory Control Applying Dynamic Programming and Simulated Annealing

Based on the division of public and exclusive hinterlands, this paper studies the joint optimization problem of empty container repositioning and inventory control under non-stationary demand within the port cluster. This paper established a stochastic mixed integer programming model using distributed robust optimization methods, combined with quantitative and periodic inventory control strategies. After conducting a deterministic transformation, this paper designed a hybrid algorithm of dynamic programming and simulated annealing to solve the model, and compared the various costs under stationary and non-stationary scenarios. The results show that the joint optimization of empty container inventory control and repositioning can always reduce the total cost of empty container management for shipping companies. Periodic inventory control strategies are more suitable for shipping companies to apply to empty container management in non-stationary situations. Sensitivity analysis shows that there is a positive correlation between uncertain demand parameters and the total cost of shipping companies. The superiority of the empty container repositioning mode proposed in this paper under sea-land coordination has also been demonstrated by changing the accessibility parameters between terminals.

Joint optimization of recyclable inventory routing problem under uncertainties in an incentive-based recycling system

Due to the value of resource recovery and the development of a circular economy, waste recycling has gathered global attention. Recently, many emerging cities designed new systems like an incentive-based recycling system (IBRS). In such systems, recyclables are collected through community recycling nodes by offering incentives, then transported to street recycling stations and sorted before being finally recycled. The increased recycling nodes and the incentives enhance the convenience and residents’ enthusiasm for waste recycling, but also intensify the uncertainty of recycling quantities and the complexity of the recycling operation management. Poor recycling operation management may result in increased recycling costs or greater loss of recyclables, which discourages residents from participating in recycling. Based on an existing IBRS, this study investigates the joint optimization problem of the recyclable inventory management at each community recycling node and the vehicle routing from the recycling nodes to the recycling station. A two-stage dual-objective multi-period stochastic programming model is established to minimize the loss of recyclables and logistics costs, which is further reformulated using the weighting method and transportation cost approximation parameters. To solve the reformulated model, a three-phase iterative algorithm is designed by combining the progressive hedging algorithm and route splitting algorithm based on the Lin-Kernighan heuristic. A case study is conducted using data from Shanghai’s IBRS. The proposed joint decision model is superior to separate decisions and the three-phase iterative algorithm can reduce the average total cost by up to 42.12% compared to the genetic algorithm and the Iteration-Move-Search method in the literature. Additionally, a sensitivity analysis is conducted to provide managerial insights.

Partial Multi-Label Learning via Exploiting Instance and Label Correlations

The goal of partial multi-label learning is to induce a multi-label classifier from partial multi-label data where each instance is annotated with a number of candidate labels but only a subset of them are valid. Many of the existing studies either fail to fully utilize instance and label correlations to eliminate noisy labels or build an oversimplified multi-label classifier, both of which are unfavorable for the improvement of generalization performance. In this article, we put forward a novel model named Pml-ilc to learn a multi-label classifier from partial multi-label data. Specifically, Pml-ilc first encodes instances and labels into a compact semantic space and takes full advantage of instance and label correlations to eliminate noisy labels. Then, it induces a linear mapping from the feature space to the label space while exploiting label-specific features and instance correlations to facilitate the multi-label classifier learning process. Finally, the above two steps are combined into a joint optimization problem and an efficient alternating optimization procedure is developed to find a satisfactory solution. Extensive experiments show that Pml-ilc achieves superior performance on both real-world and synthetic partial multi-label datasets in terms of different evaluation metrics.

Cache-assisted computation offloading for workflow applications in industrial internet of things

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Optimizing point-of-sale services in MEC enabled near field wireless communications using multi-agent reinforcement learning

In the next-generation communication system, near-field communication (NFC) is a key enabler of contactless transactions, including mobile payments, ticketing, and access control. With the growing demand for contactless solutions, NFC technology will play a pivotal role in enabling secure and convenient payment experiences across various sectors. In contrast, Internet of Things (IoT) devices such as phones’ Point of Sale (PoS) constitute limited battery life and finite computational resources that act as a bottleneck to doing the authentication in a minimal amount of time. Because of this, it garnered considerable attention in both academic and industrial realms. To overcome this, in this work we consider the Multiple Mobile Edge Computing (MEC) as an effective solution that provides extensive computation to PoS connected to it. To address the above, this work considers the PoS-enabled multi-MEC network to guarantee NFC communication reliably and effectively. For this, we formulate the joint optimization problem to maximize the probability of successful authentication while minimizing the queueing delay by jointly optimizing the computation and communication resources by utilizing a multi-agent reinforcement learning optimization approach. Through extensive simulations based on real-world scenarios, the effectiveness of the proposed approach was demonstrated. The results demonstrate that adjusting the complexity and learning rates of the model, coupled with strategic allocation of edge resources, significantly increased authentication success rates. Furthermore, the optimal allocation strategy was found to be crucial in reducing latency and improving authentication success by approximately 9.75%, surpassing other approaches. This study highlights the importance of resource management in optimizing MEC systems, paving the way for advancements in establishing secure, efficient, and dependable systems within the Internet of Things framework.

Optimization of the age of correlated information in V2X networks with edge computing

Vehicle-to-Everything (V2X) communication has the potential to revolutionize the travel experience in intelligent transportation, which has received the great attention recently. However, ensuring the freshness of information from multiple sources is critical for the real-time and reliable communication in vehicular networks, especially for timely updates of service centers. To address this issue, we use a promising metric called Age of Correlated Information (AoCI), which can characterize the freshness of multi-source information. Therefore, we propose a novel model that can dynamically regulate the channel activation matching and edge computing collaboration strategy to minimize AoCI in V2X vehicular networks. Firstly, we describe the system model of a V2X network with edge computing, including definitions and assumptions for freshness of information, edge co-computing, etc. Secondly, we formulate the joint optimization problem as a source-related age minimization (SRAM) problem, which is NP-complete. A heuristic algorithm is proposed to solve it under fast-fading channel. Finally, since traditional graph models cannot capture the changing correlation between nodes in dynamic networks, we use graph convolutional networks(GCN) to extract the features of multi-source correlation. The features extracted by GCN include relevant attributes of the sources and its communication links. The features are provided as input to a double deep Q network (DDQN) for training the model that can adapt to a dynamic network environment. Extensive simulation experiments in different network scenarios validate that our proposed method can effectively and efficiently reduce the average AoCI and the computational resources.

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

Robust joint multiple resources allocation algorithm for cooperative underwater acoustic communication networks

AbstractIn this article, a joint relay selection, power control and time allocation problem is studied to maximize the energy efficiency for the cooperative underwater acoustic communication networks. The joint optimization problem is full of challenges due to the strong coupling of multiple resources and the uncertain characteristics of the underwater acoustic communication scenario. To address this issue, the worst‐case method is employed to transform an original uncertain problem into a deterministic problem. Furthermore, we propose the block coordinate descent‐based method to decouple the strongly coupling multi‐resource allocation problem into three relatively independent sub‐problems. The coupling of multiple resources is completely decoupled, thereby greatly reducing the solving difficulty. In addition, given that the sub‐problems with the fractional objective function are still non‐convex and hard to solve, the Dinkelbach‐based method is proposed to transform the fractional objective function into a subtractive form. At last, the relay selection sub‐problem is transformed into a integer programming problem, and the time allocation sub‐problem is transformed into a linear programming problem, whose optimal solutions can be obtained by some well‐established solution methods. The power allocation problem is transformed into a convex optimization problem, which can be solved by the Lagrangian dual method. Finally, in the proposed iteration structure, the three sub‐problems are alternatingly solved until convergence. Simulation results are presented to demonstrate the efficiency and robustness of the proposed algorithm.

A cost-efficient content distribution optimization model for fog-based content delivery networks

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

A deep reinforcement learning framework and its implementation for UAV-aided covert communication

In this work, we consider an Unmanned Aerial Vehicle (UAV)-aided covert transmission network, which adopts the uplink transmission of Communication Nodes (CNs) as a cover to facilitate covert transmission to a Primary Communication Node (PCN). Specifically, all nodes transmit to the UAV exploiting uplink non-Orthogonal Multiple Access (NOMA), while the UAV performs covert transmission to the PCN at the same frequency. To minimize the average age of covert information, we formulate a joint optimization problem of UAV trajectory and power allocation designing subject to multi-dimensional constraints including covertness demand, communication quality requirement, maximum flying speed, and the maximum available resources. To address this problem, we embed Signomial Programming (SP) into Deep Reinforcement Learning (DRL) and propose a DRL framework capable of handling the constrained Markov decision processes, named SP embedded Soft Actor-Critic (SSAC). By adopting SSAC, we achieve the joint optimization of UAV trajectory and power allocation. Our simulations show the optimized UAV trajectory and verify the superiority of SSAC compared with various existing baseline schemes. The results of this study suggest that by maintaining appropriate distances from both the PCN and CNs, one can effectively enhance the performance of covert communication by reducing the detection probability of the CNs.

Joint optimization for energy efficient full-duplex UAV relaying with multiple user pairs

This paper investigates an unmanned aerial vehicle (UAV) assisted amplify-and-forward relaying, where a full-duplex (FD) fixed-wing UAV employs a time-division multiple access scheduling protocol to provide relay services for multiple source–destination user pairs. With the aim of maximizing energy efficiency (EE) of the system, a joint optimization problem is studied so as to jointly fulfill the communication scheduling of multiple user pairs, the transmit power control and the trajectory design of the UAV. Since the optimization variables of the problem are coupled, it is non-convex and hence hard to solve directly. To this end, the initial problem is decomposed into three subproblems corresponding to the optimization of communication scheduling, and transmit power and trajectory of the UAV, respectively. The three subproblems are solved by utilizing the linear programming, the successive convex approximation (SCA), and the Dinkelbach’s algorithm. Then an iterative algorithm based on the block coordinate descent technique is proposed to tackle the joint optimization problem by optimizing the three blocks of variables alternately. Simulation results demonstrate that the proposed algorithm converges efficiently, and the EE of the joint optimization scheme can be significantly improved compared to the benchmark schemes.

DELTA: Memory-Efficient Training via Dynamic Fine-Grained Recomputation and Swapping

To accommodate the increasingly large-scale models within limited-capacity GPU memory, various coarse-grained techniques, such as recomputation and swapping, have been proposed to optimize memory usage. However, these methods have encountered limitations, either in terms of inefficient memory reduction or diminished training performance. In response to this, our paper introduces DELTA, an innovative approach for memory-efficient large-scale model training that combines fine-grained memory optimization and prefetching technology to reduce memory usage while maintaining high training throughput concurrently. Initially, we formulate the problem of memory-throughput joint optimization as an easy-solving 0/1 Knapsack problem. Leveraging this formalization, we use an improving polynomial complexity heuristic algorithm to address the problem effectively. Furthermore, we introduce a novel bidirectional prefetching technology into dynamic memory management, which significantly accelerates the model training when compared to relying solely on recomputation or swapping. Finally, DELTA offers users an automated training execution library, eliminating the need for manual configuration or specialized expertise. Experimental results demonstrate the effectiveness of DELTA in reducing GPU memory consumption. Compared to state-of-the-art methods, DELTA achieves substantial memory savings ranging from 40% to 72%, while maintaining comparable convergence performance for various models, including ResNet-50, ResNet-101, and BERT-Large. Notably, DELTA enables the training of GPT2-Large and GPT2-XL with batch sizes increased by 5.5 × and 6 ×, respectively, showcasing its versatility and practicality in enabling large-scale model training on GPU hardware.

QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems

Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. Recently, QNN systems have been found to manifest robustness issues similar to classical DL systems. There is an urgent need for ways to test their correctness and security. However, QNN systems differ significantly from traditional quantum software and classical DL systems, posing critical challenges for QNN testing. These challenges include the inapplicability of traditional quantum software testing methods to QNN systems due to differences in programming paradigms and decision logic representations, the dependence of quantum test sample generation on perturbation operators, and the absence of effective information in quantum neurons. In this paper, we propose QuanTest, a quantum entanglement-guided adversarial testing framework to uncover potential erroneous behaviors in QNN systems. We design a quantum entanglement adequacy criterion to quantify the entanglement acquired by the input quantum states from the QNN system, along with two similarity metrics to measure the proximity of generated quantum adversarial examples to the original inputs. Subsequently, QuanTest formulates the problem of generating test inputs that maximize the quantum entanglement adequacy and capture incorrect behaviors of the QNN system as a joint optimization problem and solves it in a gradient-based manner to generate quantum adversarial examples. Experimental results demonstrate that QuanTest possesses the capability to capture erroneous behaviors in QNN systems (generating 67.48%-96.05% more high-quality test samples than the random noise under the same perturbation size constraints). The entanglement-guided approach proves effective in adversarial testing, generating more adversarial examples (maximum increase reached 21.32%).

Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement

To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.

Failure risk management: adaptive performance control and mission abort decisions.

The failure behavior of safety-critical systems typically depends on the system performance level, which offers opportunities to control system failure risk through dynamic performance adjustment. Moreover, mission abort serves as an intuitive way to mitigate safety hazards during mission execution. Our study focuses on systems that execute successive missions with random durations. To balance mission completion probability and system failure risk, we examine two decision problems: when to abort missions and how to select the performance level prior to mission abort. Our objective is to maximize the expected revenue through dynamic performance control and mission abort (PCMA) decisions. We consider condition-based PCMA decisions and formulate the joint optimization problem into a Markov decision process. We establish the monotonicity and concavity of the value function. Based on this insight, we show that optimizing the mission abort policy requires a series of control limits. In addition, we provide conditions under which the performance control policies are monotone. For comparative purposes, we analytically evaluate the performances of some heuristic policies. Finally, we present a case study involving unmanned aerial vehicles executing power line inspections. The results indicate the superiority of our proposed risk control policies in enhancing operational performance for safety-critical systems. Dynamic performance adjustment and mission abort decisions provide opportunities to reduce the failure risk and increase operational rewards of safety-criticalsystems.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

Joint optimization of offloading strategy and resource allocation for multi-user in dynamic vehicular edge computing systems

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

Access strategies in mmWave cell-free network: A matching and auction theory based approach

The mmWave cell-free network is viewed as an energy-efficient and emerging architecture for beyond 5G systems. However, many practical issues should be solved for this system before the benefits are enjoyed, whereof feasible implementation of access strategy is one of the most vital. To this end, the access strategy including user scheduling and bandwidth allocation is investigated in this paper. Firstly, to maximize the defined utility function, the joint optimization problem of user scheduling and bandwidth allocation is formulated as a mixed integer nonlinear programming problem, which is intractable to search for an optimal solution in polynomial time. Secondly, a two-stage matching and sealed-auction-based access strategy is proposed to obtain a sub-optimal solution, where UEs with high-quality channel conditions are scheduled in the first-stage matching as much as possible, and the rest unmatched UEs are scheduled in the second-stage matching with the sealed-auction based bandwidth allocation. Thirdly, a simplified full-matching based access strategy with lower complexity is proposed for the high-dynamic scenario caused by the high-speed movement of UEs, and the complexities and convergence of the two proposed strategies are quantitatively analyzed. Finally, the performance of proposed strategies is evaluated through abundant simulations in different scenarios, and the superiorities of the proposed strategies are demonstrated through the comparison with reference strategies.

Distributed reinforcement learning-based optimization of resource scheduling for telematics

As a reliable computational model in telematics, Mobile Edge Computing (MEC) is utilized to lighten the load on individual autonomous vehicles. It enables computationally expensive and latency-sensitive jobs to be offloaded from cars with limited processing power to servers at edge nodes. This paper presents the joint optimization problem as a mixed nonlinear integer programming problem. The scheduling of vehicle tasks and the distribution of communication resources are then rationalized using a resource allocation algorithm based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning to arrive at a suboptimal solution based on the resource allocation policies of single-intelligent DDPG and multi-intelligent DDPG. The numerical simulation demonstrates the superior service experience of the proposed joint optimization strategy of distributed task offloading and resource allocation based on multi-intelligent DDPG over other association and channel selection strategies and joint resource allocation based on single-intelligent DDPG.