As an important part of mechanical equipment, rolling bearing holds significant importance in the normal operation of machinery. However, the parameter and computation of fault diagnosis approaches based on deep learning technique are huge, and most methods are diagnosed in the cloud, which can lead to time delays and non-real-time. To overcome these issues, in edge computing scenarios, a real-time bearing fault diagnosis network MobiKanViT is proposed in this paper. The network is a lightweight and low-latency vision fault diagnosis network with enhanced discriminative feature learning capability. The Efficient Multi-scale Attention (EMA) module is introduced to enhance the ability of feature recognition. The ordinary convolution module is substituted with the Kolmogorov-Arnold Networks (KAN) convolution module to solve the problem of large parameter and computation. The MobiKanViT is compressed and quantized with depth parameter γ to make the model more lightweight and easy to be deployed on edge devices. Verification experiments were conducted on two sets of experimental equipment, and three mobile phones were selected as mobile edge computing platforms. The experimental results show that a depth parameter of γ = 0.5 and INT8 quantization yield the most effective results for MobiKanViT. When juxtaposed with current large model techniques, the suggested approach decreases memory consumption by an average of 94.6%, while simultaneously boosting inference speed by about 15.87 times. In comparison to existing lightweight models, this new method also improves diagnostic accuracy by an average of 2.6%.

Unmanned aerial vehicles (UAVs) play an important role in mobile edge computing (MEC) systems because of their high mobility and flexibility. However, existing task offloading strategies suffer from considerable resource allocation imbalances in multi-agent decision-making, leading to UAVs overload, increased task delays, and higher operational costs. To address these issues, this paper presents a Three-Layer Multi-Agent Strategic Decision-Making Model (3L-MSADM) that integrates Markov Decision Processes (MDP), Stackelberg game theory, and auction mechanisms to optimize task offloading, mitigate resource imbalances, and enhance computational efficiency. Additionally, a task offloading ratio optimization mechanism is proposed to dynamically adjust task distribution according to system load, thereby minimizing task latency and improving overall efficiency. Furthermore, we introduce the Stackelberg-guided multi-agent policy gradient (SG-MAPG) algorithm, utilizing a centralized training and decentralized execution (CTDE) paradigm to improve decision-making efficiency and service fairness. Simulation results demonstrate that our approach improves service fairness by 12.3% and reduces system costs by 22.7% compared to benchmark algorithms, significantly enhancing the task processing capabilities of UAVs-assisted MEC systems. This study provides an innovative solution for multi-agent decision-making and resource management in wireless networks, offering substantial theoretical and practical contributions.

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the highly dynamic nature of edge environments and the limited computational resources of EDs result in significant energy consumption and compromised inference quality. To address these issues, we propose the Demand-Aware Evolutionary Reinforcement Learning (DA-ERL) framework, a novel approach for optimizing Partitioned Collaborative Inference (PCI) across multiple EDs and Mobile Edge Computing (MEC) servers. At the core of DA-ERL is a Demand-Aware Spatio-Temporal Graph Convolutional Network (DA-STGCN). This new architecture creates a predictive state representation by uniquely integrating two channels: a Spatial Graph Channel using Graph Convolutional Networks to model the network topology, and a Temporal Prediction Channel using Temporal Convolutional Networks to capture the evolution of system dynamics. Moreover, we design and formulate a task dynamic demand index to model the dynamic task characteristics, which guides the agent's learning policy. Furthermore, we train DA-ERL within a Cross-Entropy Method (CEM) based evolutionary framework that leverages elite-guided exploration to enhance sample efficiency in complex search spaces. Extensive simulations demonstrate that the proposed DA-ERL framework significantly outperforms conventional methods, achieving a 23.4% reduction in system cost while maintaining a near-perfect task completion rate in high-density scenarios.

Low Earth Orbit (LEO) satellite networks hold great promise in the field of wireless communication due to their global coverage. However, the long communication distances and massive data computations present significant challenges for current satellite networks. To overcome these barriers, we propose SkyLink, a universal Integrated Ground-Air-Space Collaborative Edge Computing system that leverages horizontal collaboration among aerial platforms (AirXs) as well as vertical collaboration among Ground-Air-Space. We propose a bi-level optimization framework based on a Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) with Hybrid Action Space and constructe a latent representation space for each agent to allow the agent to learn the latent policy. This enabling each AirX to act as an agent and autonomously optimize its hybrid action decisions to improve system efficiency in real-time based on the dynamic network environment, a capability not achievable by conventional DRL methods. This includes continuous optimization variables such as AirX deployment (location changes) and resource allocation, as well as discrete optimization variables for collaborative task offloading decisions. Extensive experiments against state-of-the-art algorithms (e.g., MADDPG, QMIX) demonstrate that the proposed system improves energy efficiency by 27.2% and task completion rate by 6.8% compared to traditional Integrated Ground-Air-Space (IG) Network.

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the limited computational resources of EDs often result in significant energy consumption and compromised inference quality. To address these challenges, we propose a Partitioned Collaborative Inference (PCI) system that reduces on-device model inference costs by distributing the inference process across multiple EDs and MEC servers. To dynamically model the relationships between computing nodes, inference tasks, and resources, we employ Graph Neural Networks to construct the current state representation of the system. Furthermore, we develop a Cross-Entropy Method (CEM) based Evolutionary Reinforcement Learning algorithm, which leverages negative temporal difference (TD) error as a population fitness metric to generate elite individuals. The elite produces high-quality samples to improve learning efficiency, thereby obtaining optimal partitioned collaborative inference decisions and resource allocation in highly dynamic and complex search spaces. Extensive simulations demonstrate that the proposed approach significantly outperforms existing methods and benchmark schemes, achieving a 57. 5% increase in the inference task completion rate and a 65.7% reduction in system costs.

Vehicle Edge Computing (VEC) has become a new computing paradigm in the field of intelligent transportation systems. In the VEC environment, system performance is seriously challenged by delay-sensitive and dependent tasks. To address this issue, this paper investigates the joint problem of task offloading and resource allocation in VEC, taking into account and highlighting the delay sensitivity and dependencies of tasks. The corresponding mixed-integer nonlinear programming problem is formulated. Then this joint optimization problem is modeled as a Markov decision process. In order to solve the problem, an algorithm for joint optimization Delay-Sensitive and Dependent Tasks offloading decision and resource allocation in VEC Based on Double Deep Q-Network (DDT-DDQN) is proposed. Given the resource allocation and bandwidth allocation, the task queues are arranged by a prioritization policy, which takes into account the dependencies of tasks and delay-sensitive of tasks, and the Double Deep Q-Network (DDQN) is used to solve the offloading decision and channel allocation.Then, given the offloading decision and channel allocation schemes, the DDQN is utilized to solve the problem the computational resources and bandwidth allocation. Simulation results show that the DDT-DDQN algorithm proposed in this paper outperforms other comparative algorithms in terms of task completion rate and significantly improves the network performance, given the same task arrival rate, computational resources and taking into account the inter-task dependencies.

Multi-Unmanned Aerial Vehicle (UAV)-supported Mobile Edge Computing (MEC) can meet the computational requirements of tasks with high complexity and latency sensitivity to compensate for the lack of computational resources and coverage. In this paper, a multi-user and multi-UAV MEC networks is built as a two-tier UAV system in a task-intensive region where base stations are insufficient, with a centralized top-center UAV and a set of distributed bottom-UAVs providing computing services. The total energy consumption of the system is minimized by jointly optimizing the task offloading decision, 3D deployment of two-tier UAVs, the elevation angle of the bottom UAV, the number of UAVs, and computational resource allocation. To this end, an algorithm based on Differential Evolution and greedy algorithm with the objective of minimizing Energy Consumption (DEEC) is proposed in this paper. The algorithm uses a two-tier optimization framework where the upper tier uses a population optimization algorithm to solve for the location and elevation angle of the bottom UAV and the number of UAVs based on the actual ground equipment and the lower tier uses clustering and greedy algorithms to solve for the position of the top UAV, the offloading decision of the task, and the allocation of computational resources based on the results of the upper layer. The simulation results show that the algorithm effectively reduces the total energy consumption of the system while satisfying the task computation success rate and time delay.

Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist the Internet of Things (IoT) network and solve various problems, such as expanding network coverage, improving network performance, transmitting energy to IoT devices, and performing IoT compute-intensive tasks. However, due to the communication between UAVs and the migration of computing tasks, privacy and security during the computing offloading process are challenging issues. To this end, we design an air-to-air multi-UAVs MEC network system based on multi-coalition game, and introduce blockchain technology to ensure privacy and security between UAVs, effectively ensuring the security and confidentiality of computing offloading between UAVs. In this paper, the joint optimization problem of UAV channel selection, UAV location deployment, block processor decision, block processor transmission power, and block processor generation frequency is studied. The goal is to minimize the weighted average sum of energy consumption and delay for MEC task computing and blockchain task processing. To handle this intractable issue, the original problem is decomposed into two subproblems and solved alternately with each other. In addition, the Joint Convex Optimization and Stackelberg Game Hierarchical (JCSH) algorithm is proposed, which solves the problem of blockchain-enabled computing offloading and resource allocation. The simulation results show that the JCSH algorithm has better performance and stronger robustness compared to other algorithms under different parameter settings.

Intelligent Reflecting Surface (IRS) enabled Unmanned Aerial Vehicle (UAV) edge computing, a new communication technology, can provide sufficient capacity for edge computing system. However, due to the Line-of-Sight (LoS) or the Non Line of Sight (NLoS) of communicating environments will impact transmitting rate or delay, the Intelligent Reflective Surface (IRS) can be utilized to compensate the channel fading in the IRS-enabled UAV edge computing. In this paper, the joint problem of IRS phase shift, UAV trajectory and power allocation in the system is investigated, aiming to maximize the energy efficient. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To solve the problem, the original problem is decomposed into two subproblems, and an iterative method framework based on ConVex optimization and Deep Reinforcement Learning (CV-DRL) is proposed. Given the UAV trajectory and IRS phase shift, the Convex optimization algorithm is used to solve the power allocation schemes. Then, given the power allocation schemes, the Double Deep Q Network (Double DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are utilized to solve the problem of optimal UAV trajectory and IRS phase shift. The simulation results demonstrate that our proposed method outperforms other schemes in terms of energy efficiency, providing significant enhancements

Unmanned aerial vehicle (UAV)-enabled mobile-edge computing (MEC) is expected to provide low-latency, ultrareliable, and highly robust network services to improve user service experience. In this article, the UAV deployment, task offloading, and resource allocation problem is investigated in a multi-UAV-enabled MEC system with task-intensive region. UAVs as edge servers to provide computing services for ground terminal devices (TDs). The time-sensitive tasks of TDs can be computed locally or offloaded to UAVs. The goal is to improve the utility of tasks, i.e., maximize the number of tasks offloaded to UAVs under conditions of ensuring a desired task computed success rate and satisfying the energy and latency constraints. The jointly optimizing problem of the 3-D deployment, elevation angle, computational resource allocation of the UAV, and task offloading decision is formulated. To this end, a two-layer optimization approach is proposed to solve the formulated problem. Specifically, the upper layer decides the UAV position, elevation angle, and transmission power of TDs based on the actual ground situation. The lower layer determines the computational resource allocation of UAVs and the task offloading decision based on the optimized results derived from the upper layer. Through the two-layer joint optimization, our goal is finally achieved. Simulation results demonstrate that our proposed algorithm effectively improves the number of tasks offloaded to UAVs and the task completion rate simultaneously with the flexible UAV deployment and well-designed task offloading strategy.

Unmanned aerial vehicle (UAV) edge computing systems provide easy-to-deploy and low-cost services at those areas with inadequate infrastructure by deploying UAVs as moving edge servers for large-scale users. However, user devices are generally distributed unevenly in a large area, which makes it difficult for existing efforts to cope with this realistic scenario for optimal deployment of UAVs. Therefore, this paper considers a multiple UAV (Multi-UAV) Collaborative edge Computing (UCC) system by utilizing collaboration among them to split computation tasks at UAVs to balance the load and improve resource utilization. In order to maximize the energy-efficiency of the UCC system under the satisfaction of the delay constraint, we study the joint problem of UAV deployment, task collaborative offloading, computation and communication resource allocation in UCC system. We propose a bi-level optimization framework to solve the formulated non-convex mixed-integer optimization problem. In the upper level, the UAV deployment is optimized based on an improved differential evolution (DE) algorithm, and in the lower level the offloading decision and resource allocation are optimized based on a Reinforcement Learning (RL) algorithm with Twin Delayed Deep Deterministic policy gradient. Experimental results demonstrate the effectiveness and superiority of multi-UAV collaborative computing, with the proposed framework achieving a 32.4% reduction in energy consumption and an average 30% increase in task completion rate compared to DDPG, ToDeTaS, and other benchmark schemes.

With the advantages of high mobility and flexible deployment, Unmanned Aerial Vehicle (UAV) combines with Mobile Edge Computing (MEC) is a promising technology. When dynamic Terminal Users (TUs) offload tasks to UAVs, eavesdroppers may eavesdrop on the channel information. The offloading decisions, trajectory plannings of UAVs and resource allocation with the objective of high-capacity secure communication is a challenging problem. In this paper, we design a multi-UAVs MEC system, where the original region is divided into several sub-regions and TUs offload tasks to UAVs which provide computing services for these TUs. Meanwhile, A joint optimization problem of offloading decision, resource allocation and trajectory planning is formulated, where TUs move with the Gauss-Markov random model. In addition, the Base Station (BS) emits jamming signals to evade the eavesdropping of offloading information from eavesdroppers. The goal of the optimization problem is to maximize the TUs’ minimum secure calculation capacity, and a Joint Dynamic Programming and Bidding (JDPB) algorithm is proposed to solve it. The Successive Convex Approximation (SCA) and Block Coordinate Descent (BCD) algorithms are used to handle the resource allocation and trajectory planning problems, and the bidding method is used to address the task offloading decision problem. Simulation results show that JDPB has better performance and better robustness under different parameter settings than other schemes.

A growing number of studies have confirmed the important role of microRNAs (miRNAs) in human diseases and the aberrant expression of miRNAs affects the onset and progression of human diseases. The discovery of disease-associated miRNAs as new biomarkers promote the progress of disease pathology and clinical medicine. However, only a small proportion of miRNA-disease correlations have been validated by biological experiments. And identifying miRNA-disease associations through biological experiments is both expensive and inefficient. Therefore, it is important to develop efficient and highly accurate computational methods to predict miRNA-disease associations. A miRNA-disease associations prediction algorithm based on Graph Convolutional neural Networks and Principal Component Analysis (GCNPCA) is proposed in this paper. Specifically, the deep topological structure information is extracted from the heterogeneous network composed of miRNA and disease nodes by a Graph Convolutional neural Network (GCN) with an additional attention mechanism. The internal attribute information of the nodes is obtained by the Principal Component Analysis (PCA). Then, the topological structure information and the node attribute information are combined to construct comprehensive feature descriptors. Finally, the Random Forest (RF) is used to train and classify these feature descriptors. In the five-fold cross-validation experiment, the AUC and AUPR for the GCNPCA algorithm are 0.983 and 0.988 respectively.

The circRNAs and miRNAs play an important role in the development of human diseases, and they can be widely used as biomarkers of diseases for disease diagnosis. In particular, circRNAs can act as sponge adsorbers for miRNAs and act together in certain diseases. However, the associations between the vast majority of circRNAs and diseases and between miRNAs and diseases remain unclear. Computational-based approaches are urgently needed to discover the unknown interactions between circRNAs and miRNAs. In this paper, we propose a novel deep learning algorithm based on Node2vec and Graph ATtention network (GAT), Conditional Random Field (CRF) layer and Inductive Matrix Completion (IMC) to predict circRNAs and miRNAs interactions (NGCICM). We construct a GAT-based encoder for deep feature learning by fusing the talking-heads attention mechanism and the CRF layer. The IMC-based decoder is also constructed to obtain interaction scores. The Area Under the receiver operating characteristic Curve (AUC) of the NGCICM method is 0.9697, 0.9932 and 0.9980, and the Area Under the Precision-Recall curve (AUPR) is 0.9671, 0.9935 and 0.9981, respectively, using 2-fold, 5-fold and 10-fold Cross-Validation (CV) as the benchmark. The experimental results confirm the effectiveness of the NGCICM algorithm in predicting the interactions between circRNAs and miRNAs.

The joint problem of task offloading, collaborative computing, and resource allocation for multi-access edge computing (MEC) is a challenging issue. In this article, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the problem, we propose a deep reinforcement learning (DRL)-based bilevel optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper level, whereas the computing resource allocation subproblem is solved at the lower level. We combine dueling-DQN and double-DQN and add adaptive parameter space noise to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in energy efficiency and task completion rate compared with other DRL-based approaches and other benchmark schemes under various network parameter settings.

In this paper, we investigate the joint problem of task offloading, Unmanned Aerial Vehicle (UAV) trajectory design, and resource allocation for UAV-enabled edge computing, considering and highlighting the dependency among different tasks. The corresponding optimization problem, which is a mixed-integer problem, is formulated. To solve this problem, we propose an iterative method based on Block Coordinate Descent (BCD) to decompose the original problem into two subproblems. Given the offloading decision and resource allocation, the subproblem of UAV trajectory optimization is solved by convex optimization methods. Then, given the UAV trajectory, the subproblem of task offloading decision and the corresponding resource allocation is solved by dynamic programming and convex optimization methods. Simulation results show that our proposed method can significantly reduce energy consumption compared to the benchmark schemes.

Cloud–edge heterogeneous network is an emerging technique built on edge infrastructure, which is based on the core of cloud computing technology and edge computing capabilities. The joint problem of computation offloading, cache decision, and resource allocation for cloud–edge heterogeneous network system is a challenging issue. In this article, we investigate the joint problem of computation offloading, cache decision, transmission power allocation, and CPU frequency allocation for cloud–edge heterogeneous network system with multiple independent tasks. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the transmission power and CPU frequency constraint of the tasks. The constraint of computing resource and cache capacity of each access point (AP) are considered as well. The formulated problem is a mixed-integer nonlinear optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on reinforcement learning (RL) and sequential quadratic programming (SQP). In the upper level, given the allocated transmission power and CPU frequency, the task offloading decision and cache decision problem is solved using the deep$Q$-network method. In the lower level, the optimal transmission power and CPU frequency allocation with the offloading decision and cache decision is obtained by using the SQP technique. Simulation results demonstrate that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

The important role of microRNA (miRNA) in human diseases has been confirmed by some studies. However, only using biological experiments has greater blindness, leading to higher experimental costs. In this paper a high-efficiency algorithm based on a variety of biological source information and applying a combination of a convolutional neural network (CNN) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile kernel similarity of the four biological information of miRNA, disease, long non-coding RNA (lncRNA) and environmental factors (EFs), and the similarities of miRNAs are fused together. Among them, miRNAs similarity is composed of miRNA target information, sequence information, family information, and function information. Then, the dimensionality of the data set is reduced by the autoencoder (AE). Finally, deep features are extracted through CNN, and then the association between miRNA and disease is predicted by ELM. The experimental results show that the average AUC value based on the multi-biological source information (MSCNE) model is 0.9630, which can reach higher performance than the other classic classifier, feature extractor mentioned and the other existing algorithms. The results show the MSCNE algorithm is effective to predict the correlation of miRNA-disease.

Cyber-physical systems (CPSs) realize the automatic control of entities through computing systems and networks. Stability is an important factor in CPS for system upgrading and troubleshooting. Traditional analysis methods focus on simulation and formal analysis, which have two major limitations: first, the current state information of CPS is difficult to obtain; second, most CPS face the state space explosion problem. These problems can be avoided and a good analysis can be provided based on empirical data. The main work of this article is summarized as follows: first, a phase space reconstruction method is designed to divide the dataset into several subsequences with the same shape; second, we propose a stability analysis method based on correlation dimensions. Results indicate that the proposed approach can obtain a stable correlation dimension. CPS perform better if the correlation dimension is maintained within a certain range; otherwise, a destabilizing factor exists. The proposed stability analysis has less complexity and running time.

Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications near the end users. The joint problem of task offloading and resource allocation in the multi-user collaborative mobile edge computing network (C-MEC) based on Orthogonal Frequency-Division Multiple Access (OFDMA) is a challenging issue. In this paper, we investigate the offloading decision, collaboration decision, computing resource allocation and communication resource allocation problem in C-MEC. The delay-sensitive tasks of users can be computed locally, offloaded to collaborative devices or MEC servers. The goal is to minimize the total energy consumption of all mobile users under the delay constraint. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which involves the joint optimization of task offloading decision, collaboration decision, subcarrier and power allocation, and computing resource allocation. A two-level alternation method framework is proposed to solve the formulated MINLP problem. In the upper level, a heuristic algorithm is used to handle the collaboration decision and offloading decisions under the initial setting; and in the lower level, the allocation of power, subcarrier, and computing resources is updated through deep reinforcement learning based on the current offloading decision. Simulation results show that the proposed algorithm achieves excellent performance in energy efficient and task completion rate (CR) for different network parameter settings.

BACKGROUND: The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. RESULTS: In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. CONCLUSIONS: After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

Geographic routing is a research hotspot of the Internet of Vehicles (IoV) and intelligent traffic system (ITS). In practice, the vehicle movement is not only affected by its characteristics and the relationship between the vehicle and position but also affected by some implicit factors. Pointing to this problem, we combine the vehicle moving position probability matrix, the vehicle position association matrix, and the implicit factors to study the influence of vehicle position potential features and vehicle association potential features and propose a routing algorithm based on vehicle position (RAVP) analysis, which can obtain the more accurate vehicle prediction trajectory. Then, the vehicle distance is obtained based on the vehicle prediction trajectory. By the normalization of vehicle distance and cache, the vehicle data forwarding capability is obtained and the transmission decision is made. Simulation results show that the proposed algorithm outperforms the other three routing algorithms in terms of packet delivery ratio, average end-to-end delay, and routing overhead ratio.

Mobile edge computing (MEC) is a promising technique to enhance the computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling and resource allocation for MEC systems is a challenge issue. In this paper, we investigate the problem of partial offloading scheduling and resource allocation for mobile edge computing systems with multiple independent tasks. Our goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraints of the tasks. The execution delay of tasks running in MEC and mobile edge devices are both considered. The energy consumption of both the tasks computing and task data transmission are considered as well. In order to tackle these issues, we formulate an energy-efficient and low-delay partial offloading scheduling and power allocation problem in single-user MEC systems, which is a non-convex mixed-integer optimization problem. A two-level alternation method framework based on decomposition optimization strategy is proposed. Furthermore, we propose Joint Partial Offloading scheduling and power Allocation (JPOA) iterative algorithm based on Lagrangian constrained optimization and Johnson method. Numerical results demonstrate JPOA algorithm achieves the most noticeable delay performance with a large energy consumption reduction.

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate the device-to-device (D2D) user equipments (DUEs) multiplexing cellular user equipments (CUEs) downlink spectrum resources problem for EH-based D2D communication heterogeneous networks (EH-DHNs). Our goal is to maximize the average energy efficiency of all D2D links, in the case of guaranteeing the quality of service of CUEs and the EH constraints of the D2D links. The resource allocation problems contain the EH time slot allocation of DUEs, power and spectrum resource block (RB) allocation. In order to tackle these issues, we formulate an average energy efficiency problem in EH-DHNs, taking into consideration EH time slot allocation, power and spectrum RB allocation for the D2D links, which is a nonconvex problem. Furthermore, we transform the original problem into a tractable convex optimization problem. We propose joint the EH time slot allocation, power and spectrum RB allocation iterative algorithm based on the Dinkelbach and Lagrangian constrained optimization. Numerical results demonstrate that the proposed iterative algorithm achieves higher energy efficiency for different network parameters settings.