Collaborative task offloading in vehicular edge computing (VEC) primarily emphasizes the diversity of offloading destinations, such as cloud centers, roadside units (RSUs), and other entities with underutilized resources. However, it often neglects the collaborative potential among vehicles whose tasks are associated with the same service. In this paper, we propose a collaborative task offloading strategy from the perspective of vehicles with offloading requests. Vehicles collectively accomplish task offloading by dividing responsibilities for specific service component offloading. To enhance the performance of the VEC system, we introduce a caching-assisted collaborative task offloading strategy. An optimization problem is formulated to minimize the response latency of tasks in VEC. Due to the complexity of solving this Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into three subproblems: the Task Offloading and Service Caching (TOSA) problem, the Computing Resource Allocation (RA) problem, and the Service Component Assignment (CA) problem. We address the RA problem using a Lagrangian duality-based approach, solve the CA problem with a heuristic algorithm, and tackle the TOSA problem using a Proximal Policy Optimization (PPO)-based deep reinforcement learning (DRL) algorithm. Extensive simulations are conducted to evaluate the performance of the proposed strategy. The simulation results demonstrate that our solution outperforms existing methods in multiple dimensions, including convergence rate, average response latency, and task success rate.

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in handling graph data. Typically, GNNs recursively aggregate node information, including node features and local topological information, through a message-passing scheme. However, most existing GNNs are highly sensitive to neighborhood aggregation, and irrelevant information in the graph topology can lead to inefficient or even invalid node embeddings. To overcome these challenges, we propose a novel Space Gravity-based Graph Neural Network (Gravity-GNN) guided by Deep Reinforcement Learning (DRL). In particular, we introduce a novel similarity measure called ''node gravity'', inspired by the gravitational force between particles in space, to compare nodes within graph data. Furthermore, we employ DRL technology to learn and select the most suitable number of adjacent nodes for each node. Our experimental results on various real-world datasets demonstrate that Gravity-GNN outperforms state-of-the-art methods regarding node classification accuracy, while exhibiting greater robustness against disturbances.

Sequential recommendation(SR) aims to provide personalized recommendations by capturing behavioral intents from existing user interaction sequences. Most previous studies are based on attention mechanisms; however, these approaches suffer from inherent over-smoothing issues that limit their ability to capture transient behavioral signals reflecting the user's immediate intents in interaction sequences. Recently, frequency-domain analysis methods based on the Fourier transform have garnered significant attention in the sequential recommendation domain. By applying the Fourier transform, interaction sequences can be mapped to the frequency domain, enabling direct analysis and targeted manipulation of distinct frequency components. In addition to the inherent limitations of self-attention mechanisms, sequential recommendation faces persistent challenges such as data sparsity and noise. To address these issues, we propose Frequency-Domain Disentanglement-Fusion and Dual Contrastive Learning for Sequential Recommendation (FDCLRec). FDCLRec replaces self-attention mechanisms with a frequency-domain adaptive filtering module, which decouples sequence patterns into distinct high-/low-frequency components and synthesizes comprehensive sequence representations through adaptively weighted fusion. In addition, two auxiliary contrastive learning tasks(augmented-view contrasting and same-target sequence contrastive learning) are strategically integrated to alleviate data sparsity and interaction noise. Extensive experiments on four real-world datasets demonstrate that our model outperforms baseline methods.

Ensuring safety and operational continuity in underground coal mines requires robust mine monitoring. Traditional methods based on fixed sensors and manual inspections suffer from limited coverage, high cost, and poor real-time performance. Mobile Crowd Sensing (MCS), enabled by miner-carried devices, offers flexible coverage but introduces challenges such as data sparsity, noise, and heterogeneity due to device variability and electromagnetic interference. This article proposes a Bidirectional Long Short-Term Memory/Gated Recurrent Unit-based Truth Discovery (BLGTD) method for mine MCS. The model integrates spatiotemporal sequence modeling with Monte Carlo Dropout-based uncertainty quantification, enabling adaptive fusion of multi-source data. Experimental results show that BLGTD achieves a mean absolute error (MAE) of 0.19 ± 0.01 ppm in CH4concentration estimation when 90% of data comes from reliable miners, yielding a 57.8% improvement over traditional weighted averaging. The method demonstrates strong robustness under conditions of data incompleteness, device heterogeneity, and signal interference.

The Vehicular Edge Computing (VEC) paradigm significantly reduces task processing latency in Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS) by deploying computational resources at Roadside Units (RSUs). However, the high mobility of vehicles and dynamic task arrivals lead to uneven load distribution among RSUs, severely impacting system performance. Actually, load balancing as an important evaluation metric for VEC system greatly affects the performance of individual edge servers in terms of latency, energy consumption, and task completion rates. In view of this, we propose a Proximal Policy Optimization (PPO) based deep reinforcement learning (DRL) approach to determine the task offloading and migration decisions and incorporate the fairness into the constraint, aiming to achieve efficient load-balanced task offloading in VEC. Particularly, we introduce a metric named Load Balancing Metric (LBM) to optimize RSU resource allocation and employ dynamical task migration strategies to optimize the metric. Simulation results demonstrate that this approach significantly enhances load balancing performance, reduces average latency and energy consumption, and provides an efficient resource scheduling solution for VEC systems.

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.

In Vehicular Edge Computing (VEC) environments, the increasingly complicated functional and non-functional requirements from vehicular applications such as MetaVehicles usually incur larger sizes of task-input data, which not only increase the transmission delay of task-input data via the front-haul links but also degrade the quality of experience for users, even if computation tasks can be offloaded and executed at the network edge. In this article, we put forward a caching-enabled task offloading strategy, by caching and reusing the universal context data at the edge server, to avoid duplicated data transmission in VEC systems. The goal is to minimize the overall response latency for all the tasks, by jointly optimizing task offloading, content caching, and resource allocation decisions in VEC. The optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. To efficiently solve this problem, we decompose this problem into two subproblems, namely, the computing Resource Allocation (RA) problem and the Joint Offloading and Caching (JOC) problem. The corresponding algorithms are put forward to solve the content caching and task offloading problems, respectively. Numeric evaluation reveals that our strategies and algorithms can achieve better performance in minimizing the overall response latency, in comparison with other approaches.

In Vehicular Edge Computing (VEC), apart from the Road Side Units (RSUs) that can undertake the computation, smart vehicles that incorporate high-end multi-core processors into On-Board Units (OBU) can also contribute their computing resources for vehicular tasks in a pay-as-you-go fashion. Designing an appropriate pricing strategy for vehicles with abundant computing resources is essential yet challenging, as it requires balancing profit-seeking objectives with the needs of service requestors. On the other hand, considering the perspective of vehicles with offloading requests, task offloading should strike a balance between achieving ultra-low task latency and minimizing the associated offloading costs. To tackle these issues, we propose a Collaborative Computation Offloading Scheme (CCOS) for the VEC system. In particular, we take into account the fluctuation of service pricing, to cater to the monetary constraints of service requesters. A Mixed-Integer Nonlinear Programming (MINLP) problem is formulated to minimize the weighted sum of task completion latency and the offloading costs. The optimization problem is decomposed into two subproblems, i.e., the task offloading problem and the computing resource allocation problem, respectively. The task offloading problem is essentially a combinatorial optimization problem that necessitates exponential time complexity for determining the optimal solution. Hence, a Deep Reinforcement Learning (DRL)-based algorithm is put forward to solve this subproblem. The resource allocation problem, however, has been proven to be a convex optimization problem, and the scheduling and allocation of computing resources can be performed in parallel, since each edge node is aware of its own task offloading requests. Simulation results demonstrate that our strategy outperforms other approaches in terms of the convergence rate, task completion rate, and optimal values.

Mobile Edge Computing (MEC) revolutionizes the traditional cloud-based computing paradigm by moving resources in proximity to the network edge, aiming to cater to the rigorous requirements of emerging latency-sensitive applications. However, the escalating resource demands intensify the competition among user devices (UDs). Thus, it is essential to coordinate task offloading and resource scheduling while ensuring fairness among users in MEC. Despite the crucial role of user fairness in motivating task offloading in MEC, it is often overlooked in existing literature. Therefore, we in this paper propose a caching-enhanced MEC framework and formulate a collaborative service caching, task offloading, and multi-resource allocation problem to maximize average user satisfaction. Multiple factors contribute to the difficulty in solving the optimization problem, including constrained resource capabilities, user mobility, service heterogeneity, and spatial demand coupling. Consequently, we transform the origin problem into two distinct subproblems – the service caching and task offloading problem, and the multi-resource allocation problem, respectively. Then, the Advantage Actor-Critic (A2C) based approach is proposed to address the former problem, while a Lagrangian duality-based approach is adopted to tackle the latter problem. The simulation results demonstrate the superior performance of the proposed solution in comparison to several baseline methods.

In recent years, the effective utilization of edge servers to assist vehicles in handling compute-intensive and latency-sensitive tasks has emerged as a pivotal concern in Vehicular Edge Computing (VEC). In this paper, we adopt a cooperative approach that leverages the collective capabilities of multiple edge servers. This strategy is designed to effectively manage tasks and alleviate the computational burden imposed on these servers. Specifically, Graph Neural Network (GNN) is applied to extract and classify features such as the geographical locations and communication statuses of multiple edge servers, enabling the selection of the most suitable servers for collaborative task execution. We have utilized solar energy for local computing, effectively achieving environmental protection and reducing the local energy burden on vehicles. Moreover, a novel edge attraction formula is defined to refine the rationality of clustering. In addition, Deep Reinforcement Learning (DRL) is employed to make real-time offloading decisions. To ensure experimental accuracy while mitigating costs, we establish a corresponding digital twin environment to acquire experimental data. By conducting a comparative analysis against three other baseline methods, we effectively reduce task completion time and thus meet the stringent demands of time-sensitive tasks.

In centralized mobile crowd sensing (MCS), the cloud platform assigns all the tasks to participants every time. Since the cloud platform consumes a lot of computing and communication resources to provide services for participants, it will bring about high communication delay and request congestion. The cloud-edge-end architecture for service provisioning has aroused extensive attention recently, owing to its advantages in resource provisioning in close proximity to the resource requestors. Despite the advantages of this architecture, we also observe that it cannot dynamically adjust the allocation scheme when the corresponding computing services are not available to the participants after the initial task allocation. To address this issue, we put forward a re-schedulable task allocation approach in the cloud-edge-end architecture. We aim to improve the efficiency of task execution such as the maximization of task completion rate, while considering service types provided by edge servers and multiple constraints such as resource balancing on the edge servers and deadlines for the task responses. An improved Grey Wolf Optimization (GWO) algorithm is adopted for task rescheduling in this paper. Simulation results indicate that the proposed algorithm performs well in terms of task completion rates and task average response time.

The cloud-edge cooperative network presents both opportunities and challenges for latency-sensitive and computation-intensive tasks. Effectively harnessing the strengths of edge computing and cloud computing enables real-time task handling, thus reaching a win-win situation where not only the stated quality of service (QoS) is delivered from the angle of service providers, but also the quality of experience (QoE) is improved from the angle of service requestors. However, due to the unpredictable task generation and time-varying environments, it is challenging to achieve optimal task scheduling and effective resource management and allocation. To address this issue, we propose an innovative cloud-edge framework that incorporates task offloading, service caching, and resource allocation in this paper. In this framework, we can determine where to offload the task, e.g., locally, at the edge, or in the cloud center. In view of the importance of the superior user experience, we aim to maximize the user satisfaction regarding task offloading in this framework. The problem is actually a mixed-integer nonlinear programming (MINLP) problem that entails simultaneously addressing cache decisions, offloading decisions, and resources allocation in a dynamic cloud-edge computing system. Owing to the NP-hardness, our original problem is decomposed into two layers of alternating problems. Specifically, we adopt a genetic algorithm (GA) based approach to jointly make cache and offloading decisions, and then iteratively optimize the communication and computing resources allocation. Extensive experimentation has demonstrated the feasibility and effectiveness of the proposed approach.

Physiological signals have been widely used for emotion recognition, but current works seldom apply the feature fusion and attention technologies to ECG emotion recognition. In this paper, we propose a novel ECG emotion recognition method, which adopts a spatial and temporal ECG emotion recognition model based on dynamic feature fusion (DFF-STM) to learn spatial-temporal representations of different ECG areas. Considering the difference in roles played by the different ECG areas in ECG emotion recognition, a dynamic weight distribution layer is introduced into DFF-STM to extract ECG temporal features and learn weights to adjust (e.g., enhance or weaken) the contribution of the ECG areas at the same time. Finally, we conduct experiments using real ECG data on the AMIGOS dataset to evaluate the performance of the DFF-STM on valence and arousal labels. Experiments show that dynamic feature fusion for ECG emotion recognition is much better than those using only handcraft features and deep features.

Vehicular edge computing (VEC) as a promising computing paradigm has accelerated the reformation of existing dominating computing infrastructures, enabling resource provisioning in close proximity to resource requestors. However, several challenges still exist, including efficient resource scheduling and management, dynamic wireless channel state, and limited bandwidth usage. To address these issues, we introduce the digital twin (DT) technology into VEC, enabling DTs of physical entities in VEC to achieve real-time offloading decision-making in the DT simulation cycle. In particular, we propose a DT-empowered VEC (DT-VEC) architecture, aiming to achieve efficient task offloading while considering extra latency incurred by task migration. We further put forward an efficient algorithm to minimize the response latency for all the tasks in the optimization period. The simulation results have proven that our approach outperforms the other two greedy approaches.

Abstract Cross-domain recommendation (CDR) has become an important research direction in the field of recommender systems due to the increasing demand for personalized recommendations across different domains. However, CDR faces multiple challenges, including data sparsity, popularity bias, and long-tail problems. To address these challenges, we propose a novel framework that combines graph contrastive embedding and multi-head cross-attention transfer for cross-domain recommendation, called GCE-MCAT. Specifically, in the pre-training process, we generate more uniform user and item embeddings through contrastive learning, effectively solving the problem of inconsistent data embedding space distribution and recommendation popularity bias. Moreover, we propose a multi-head cross-attention transfer mechanism that allows the model to extract user common and specific domain features from multiple perspectives and perform cross-domain bidirectional knowledge transfer. Finally, we propose a cross-domain feature fusion mechanism that dynamically assigns weights to common user features and specific domain features. This enables the model to more effectively learn common user interests. We evaluate the proposed framework on three real-world CDR datasets and show that GCE-MCAT consistently and significantly improves recommendation performance compared to state-of-the-art methods. In particular, the proposed framework has demonstrated remarkable effectiveness in addressing long-tail distribution and enhancing recommendation novelty, providing users with more diversified recommendations and reducing popularity bias.

With the rapid development of Internet of Things (IoT) and next-generation communication technologies, resource-constrained mobile devices (MDs) fail to meet the demand of resource-hungry and compute-intensive applications. To cope with this challenge, with the assistance of mobile-edge computing (MEC), offloading complex tasks from MDs to edge cloud servers (CSs) or central CSs can reduce the computational burden of devices and improve the efficiency of task processing. However, it is difficult to obtain optimal offloading decisions by conventional heuristic optimization methods, because the decision-making problem is usually NP-hard. In addition, there are shortcomings in using intelligent decision-making methods, e.g., lack of training samples and poor ability of migration under different MEC environments. To this end, we propose a novel offloading algorithm named meta reinforcement-deep reinforcement learning-based offloading, consisting of a meta-reinforcement learning (meta-RL) model, which improves the migration ability of the whole model, and a deep reinforcement learning (DRL) model, which combines multiple parallel deep neural networks (DNNs) to learn from historical task offloading scenarios. Simulation results demonstrate that our approach can effectively and efficiently generate near-optimal offloading decisions in IoT environments with edge and cloud collaboration, which further improves the computational performance and has strong portability when making offloading decisions.

The rapid development of Vehicular Edge Computing (VEC) provides great support for Collaborative Vehicle Infrastructure System (CVIS) and promotes the safety of autonomous driving. In CVIS, crowd-sensing data will be uploaded to the VEC server to fuse the data and generate tasks. However, when there are too many vehicles, it brings huge challenges for VEC to make proper decisions according to the information from vehicles and roadside infrastructure. In this paper, a reverse offloading framework is constructed, which comprehensively considers the relationship balance between task completion delay and the energy consumption of User Vehicle (UV). Furthermore, in order to minimize the overall system consumption, we establish an adaptive optimal reverse offloading strategy based on Deep Q-Network (DQN). Simulation results demonstrate that the proposed algorithm can effectively reduce the energy consumption and task delay, when compared with the full local and fixed offloading schemes.

Vehicular edge computing (VEC) has gained worldwide attention in both academia and industry. Current works on VEC mainly focus on task offloading and resource allocation to improve the performance of VEC systems, but seldom consider the satisfaction level of vehicles. Whereas, the satisfaction level of vehicles has been playing an important role in stimulating vehicles to pursue better quality of experience by task offloading and service outsourcing operations. In the meanwhile, there is an inescapable fact, i.e., the task execution in VEC may fail due to various reasons, and thus it is important to incorporate the failure-resisted task offloading into the failure-prone VEC system. In this paper, we aim to maximize the satisfaction of all the vehicles, while considering the potential failures in VEC. Specifically, we model satisfaction optimization as a multiple knapsack problem and further put forward a greedy heuristic approach to solve this problem in polynomial time. Extensive simulation is carried out to validate the efficiency of our approach in terms of the optimal values and the running time. The simulation results have shown that our approach can achieve a better result compared to other benchmarks.

The fast-growing Internet of Things (IoT) have generated a vast number of IoT tasks, and these tasks are usually featured by strict response latency requirements. To cater for the time-sensitive IoT application scenarios, vehicular fog computing (VFC) can be adopted to serve the offloading requests from the IoT devices. However, current works in VFC seldom consider the task execution failures that are actually inevitable owing to limited computing resources in VFC compared to cloud computing. Hence, we strive to enhance the VFC system by incorporating the failures for task execution into our system model, which makes task offloading more general and practical. We formulate our energy consumption optimization as a mixed integer nonlinear programming problem and further put forward an iterative algorithm to solve it. We validate our approach by extensive simulation and the experimental results have proven its advantages in terms of the optimal values.

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 article. 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 the 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.

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

The fast-growing Internet of Thing (IoT) has generated a vast number of tasks which need to be performed efficiently. Owing to the drawback of the sensor-to-cloud computing paradigm in IoT, mobile edge computing (MEC) has become a hot topic recently. Against this backdrop, we focus on the offloading of tasks characterized by intrinsic correlations in this paper, which have not been considered in most of existing works. For the sequential arrival of such correlated tasks, the future workload can be efficiently reduced by caching the current computational result. Specifically, we resort to the Lyapunov optimization to handle the long-term constraint on energy consumption. Simulation results reveal that our approach is superior to other approaches in the optimization of response latency and energy consumption.

Mobile applications in the present have created tremendous pressure on the computational capabilities of user equipments. Against this background, mobile edge computing (MEC) has been proposed to tackle this issue, e.g., by shifting the computational workload to the edge server. We in this paper consider a caching enabled task offloading in MEC, for the sake of joint optimization of task offloading and caching. We consider both energy consumption and response latency in the optimization problem and solve the problem by an alternate optimization algorithm. Extensive experiments have been conducted to evaluate the algorithm and the simulation results have shown its advantages such as rapid response latency and powerful convergence capability.

Coping with diverse channel access attacks (CAAs) has been a major obstacle to realize the full potential of wireless networks as a basic building block of smart applications. Identifying and classifying different types of CAAs in a timely manner is a great challenge because of the inherently shared nature and randomness of the wireless medium. To overcome the difficulties encountered in existing methods, such as long latency, high data collection overhead, and limited applicable range, a deep learning-based CAA detection framework is proposed in this paper. First, we show the challenges of CAA classification by analyzing the impacts of CAAs on wireless network performance using an event-driven network simulator. Second, a state-transition model is built for the channel access process at a node, whose output sequences characterize the changing patterns of the node's transmission status in different CAA scenarios. Third, a deep learning-based CAA classification framework is presented, which takes state transition sequences of a node as input and outputs predicted CAA types. The performance of three deep neural networks, i.e., fully-connected, convolutional, and Long Short-Term Memory (LSTM) network, for classifying CAAs are evaluated under our CAA classification framework in five CAA scenarios and the normal scenario without CAA. Experimental results show that LSTM outperforms the other two neural network architectures, and its CAA classification accuracy is higher than 95%. We successfully transferred the learned LSTM model to classify CAAs on other nodes in the same network and the nodes in other networks, which verifies the generality of our proposed framework.

We in this paper consider a scenario where multiple vehicles jointly provision computing resources to obtain their benefits in the contexts of vehicular fog computing. A community that vehicles can freely join and leave is sponsored by a road side unit (RSU) and thus a resource pool is established such that tasks can be performed by sufficient computing resources. RSU as a coordinator takes in charge of decision making for task scheduling. A permutation of community members is established in advance and updated periodically so as to make the most suitable decision. A task scheduling strategy is proposed from the perspective of service oriented architecture. We have carried out the experiments to investigate our approach and the experimental results have revealed our approach has a great advantage over other approaches in terms of pursuing the values of the community.

Internet of Medical Things (IoMT), intended for real-time health monitoring, are generating quantity of health data such as electrocardiogram, oxygen saturation, and blood pressure every second. The captured data should be processed and analyzed in a delay sensitive way which is vital to the survival rate for cardiovascular and cerebrovascular diseases. In this regard, Unmanned Aerial Vehicles (UAVs) have already demonstrated the enormous potentials. To begin with, due to better line-of-sight, wider communication and more flexible on-demand deployment, UAVs can realize seamless wireless connection to IoMT. Furthermore, UAVs can act as fog nodes to provision services for IoMTs such as task performing and data analysis. We in this paper focus on a sub-problem, i.e., the placement of UAVs over the serving area when they function as fog nodes. In the airborne fog computing, the placement of UAVs has an important influence on energy consumption and exploration area, let alone the communication coverage of the personal health devices on the ground. Therefore, we in this paper propose a particle swarm optimization (PSO) based algorithm to optimize the UAV placement over the serving area for the IoMT devices. We have conducted extensive simulations to evaluate it. The results show that our approach can significantly reduce the number of UAVs needed to deploy while considering the communication coverage and other factors.

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.

In addition to military applications, Unmanned Aerial Vehicles (UAVs) have attracted more and more attention in civilian applications such as the post-disaster relief assistance. Indeed, advantages including better line-of-sight (LOS), wider communication range and more flexible on-demand deployment make UAVs play a unique role in rescue and disaster scenarios. Emergency tasks assigned to UAVs such as people search and rescue usually require real-time responses, since it is a life-and-death matter regarding the post-disaster relief. Considering the limited computing resources and harsh energy supply replenishment for UAVs in the post-disaster relief operations, we in this paper propose a hybrid fog computing paradigm called H-FVFC that integrates UAVs and vehicular fog computing (VFC) to run the highly demanding tasks with strict latency requirements. An architecture of H-FVFC consisting of three layers is proposed and investigated in this paper, with hope to explore the possibilities of applying this computing paradigm to post-disaster relief operations. Experiments are carried out to evaluate the task offloading in H-FVFC compared to UAV-to-Cloud scheduling strategy. The results show that task offloading in the UAV-to-Vehicle way can significantly reduce the response latency. Issues not addressed in this paper are also discussed with purpose of providing some insights to the application of integration of UAV and fog-enabled vehicle in the post-disaster relief.

Unmanned aerial vehicles (UAVs) are considered a promising solution for carrying communications and computational facilities to increase the flexibility of cloud-to-thing continuum, where short-range and long-range wireless links are adopted to connect mobile devices to the fog node and the fog node to the remote data center, respectively. Most existing UAV-involved resource allocation algorithms focus mainly on the radio resource allocation problem, and much less attention has been paid to the allocation of computational resources. Moreover, the dynamic arrival of tasks and the queueing delay at each computation entity is usually neglected. In this paper, a joint optimization problem is formulated that takes the weighted sum of energy consumption and delay experienced by tasks as the objective function. Processing frequencies and transmission powers of mobile devices and the fog node are the decision variables in the problem. To solve this problem, three decision-making algorithms are presented. The first one is used to decide the UAV's position. The processing frequency, transmission power, and task assignment results at mobile devices are determined by the second algorithm. The last one is adopted by the fog node to optimize its processing frequency and transmission power. A series of simulation experiments are conducted to evaluate the effectiveness of the proposed algorithms. Compared with the random task assignment scheme with fixed parameters, the combination of our three algorithms always perform much better for a wide range of parameter settings.

In this paper, we propose a cooperative model combined the multi-task reverse sparse representation model (MTRSR) and the AdaBoost classifier, which were used to cope with the disturbing of target gradient information caused by motion blur or target serious occlusion, and a descriptive dictionary were used to estimate the weights of each candidates. First, we use the MTRSR model to get the blur kernel which were used to get the blur target template set, meanwhile the confidence of the candidates is also obtained by the reconstruction error. Then we use the HOG features of the target templates to get the descriptive dictionary to calculate the weights of the candidates, and a AdaBoost classifier is used to calculate the confidences of all candidates. Finally, the best target is retrieved by the sum of production of weight value and the two confidences. The experimental data show that the proposed algorithm can fully cope with the target's information change which were caused by motion blur and target occlusion in the complex scene, and our algorithm can further improve the accuracy and robustness in visual tracking.

Service recommendation in a Web of services with uncertain QoS is a challenging problem. In this paper, we propose a reputation-based service recommendation framework. We formally define a service reputation model that analyzes the relations between uncertain QoS and reputation. We also devise a two-phase planning approach to constructing a composite service as the recommendation when none of existing services can fulfill the user's requirements alone. Furthermore, we design a utility difference based approach that can fairly distribute the overall rating of a composite service to its component services and theoretically prove its fairness. We evaluate the efficiency and fairness of our framework on a publicly available dataset: ICEBE05.