Last mile delivery by drones is a core component of innovative logistics systems, relying heavily on AI models for essential operations such as path planning and object recognition. However, models trained on region specific data often experience significant performance degradation when deployed in unfamiliar environments due to geographic domain shifts. This limitation impedes the rapid deployment of logistics networks and hinders model adaptation. Federated Learning (FL), as a distributed machine learning paradigm, enables multiple clients with diverse data to collaborate in training a global model. Nevertheless, within Mobile Edge Computing (MEC) environments, FL faces critical challenges, including data bias, high drone mobility, and intermittent communication windows between drones and edge servers. This paper proposes FedMO, a mobility aware FL framework for drone based last mile delivery with edge cloud collaboration. FedMO introduces a novel algorithmic insight by treating the drone’s flight path as a unified proxy for both communication reliability and data distribution heterogeneity. The framework implements a synergistic three stage selection policy that jointly optimizes connectivity success, data value, and resource efficiency. This effectively transforms mobility from a disruption risk into a diversity enhancing asset. Experimental results using real world drone video datasets demonstrate that FedMO improves convergence speed by approximately 15% compared to baseline methods with only 30%-40% client selection. With equivalent client participation, FedMO achieves a 25% improvement in convergence speed over the FedAvg algorithm.

Despite domain generalization (DG) has significantly addressed the performance degradation of pre-trained models caused by domain shifts, it often falls short in real-world deployment. Test-time adaptation (TTA), which adjusts a learned model using unlabeled test data, presents a promising solution. However, most existing TTA methods struggle to deliver strong performance in medical image segmentation, primarily because they overlook the crucial prior knowledge inherent to medical images. To address this challenge, we incorporate morphological information and propose a framework based on multi-graph matching. Specifically, we introduce learnable universe embeddings that integrate morphological priors during multi-source training, along with novel unsupervised test-time paradigms for domain adaptation. This approach guarantees cycle-consistency in multi-matching while enabling the model to more effectively capture the invariant priors of unseen data, significantly mitigating the effects of domain shifts. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches on two medical image segmentation benchmarks for both multi-source and single-source domain generalization tasks. The source code is available at https://github.com/Yore0/TTDG-MGM.

FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system

Effective last-mile delivery is pivotal in smart logistics system. While existing delivery network architectures, such as Drone-as-a-Service (DaaS), are capable of enhancing order delivery effectiveness, they often fall short in provisioning diverse delivery services. Furthermore, DaaS-based last-mile delivery systems face challenges from limited payload capacity and range. In this paper, we propose a UAV-UGV integrated network architecture based on Multi-access Edge Computing (MEC), denoted as DaaS+, encompassing the diverse delivery services from both Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) for last-mile delivery. To optimize the effectiveness in the delivery process, the intricate overhead and constraints of heterogeneous delivery services are taken into the full consideration. Specifically, we present an energy-aware service model for the UAV-UGV integrated network that considers the deadline constraints of services. Additionally, we address issues of service unavailability during service provisioning. To identify optimal service provisioning plans, we design a novel Energy-Aware Holistic Service Provisioning Mechanism based on Particle Swarm Optimization (ES-PSO), which minimizes delivery energy consumption while adhering to service deadline constraints. Experimental results substantiate the effectiveness of our proposed solution, demonstrating its ability to generate superior service provisioning plans and significantly reduce total delivery energy consumption.

Uncrewed delivery utilizes autonomous vehicles, such as drones or uncrewed ground vehicles (UGVs), to transport goods, parcels, or other materials without human intervention which can significantly reduce delivery costs and time. Servitised drones and UGVs provide flexible, on-demand delivery services that optimize resource usage, improve logistics efficiency. Drone-as-A-Service (DaaS), as a typical paradigm of uncrewed delivery, can maximize delivery efficiency while minimizing costs. Most existing DaaS systems only support modeling for a single type of delivery service, overlooking the diversity of available delivery services. In fact, the collaboration between different types of heterogeneous delivery services can not only reduce delivery costs but also enhance user satisfaction. However, heterogeneous services require necessitating effective service placement strategies to optimize the allocation and coordination of service resources, ultimately improving delivery efficiency and cost-effectiveness. Therefore, how to generate a suitable service placement plan with the objective of minimizing costs, has become a significant challenge. In this paper, a multi-access edge computing based heterogeneous delivery service framework (HDaaS) is proposed for effectively managing heterogeneous delivery resources. Building upon this framework, we design a cost-aware delivery service placement strategy (CDS) consisting of two phases: recommendation phase using CDSR-GA to identify optimal service and placement phase employing CDSP-RL to generate and refine placement plans. Experimental results show that the CDS strategy can generate delivery service placement plans that effectively reduce service provider costs under deadline constraints by about 29.84% on average.

In recent years, drone delivery has drawn significant attention for its promising potential in solving the last-mile delivery problem. As a novel service paradigm, Drone-as-a-Service (DaaS) is emerging as an effective way to address service provisioning problems within complex delivery networks. However, existing DaaS composition frameworks often fail to consider the sharing of delivery services and are not applicable to multi-package drone delivery tasks. Meanwhile, as one of the most critical real-world environmental factors for drones, the impact of dynamic wind conditions is not adequately considered by existing studies. This may lead to Quality of Service (QoS) degradation of delivery services. To address the above issues, in this paper, we propose a wind-aware service provisioning strategy for multi-package drone delivery. Given the advantages of Edge Computing (EC) in handling such dynamic factors due to its low latency and high reliability, we first establish a spatio-temporal DaaS model based on service sharing according to the edge-based drone delivery system. Then, we propose a novel wind-aware drone delivery service provisioning strategy for multi-package delivery to minimize energy consumption of drones. The proposed strategy consists of two phases: service sharing and service composition. In the service sharing phase, the service sharing plan is generated by an improved genetic algorithm. In the service composition phase, the edge server dynamically generates the service composition plan through our proposed policy iteration based DaaS composition method. Experimental results using real delivery network and wind data demonstrate that our strategy is able to reduce delivery energy consumption of drone by about 10.1%.

Artificial Intelligence of Things (AIoT) systems are playing an important role in scenarios such as smart factories, smart healthcare, and smart logistics. Edge Computing reduces the network latency by pushing compute and storage resources near the IoT devices. However, the massive of data and task requests from IoT devices and datacenters raise the optimization requirement of schedule plans. Existing studies consider either task scheduling or data placement problems. They ignore the complex relationship between data and tasks leading to an increase the task completion time and energy consumption. Therefore, this paper first formalizes the joint task scheduling and data placement problem as a constrained multi-objective optimization model. Then, a Learns to Improve (L2I) algorithm is proposed, which is a reinforcement learning-based algorithm for task scheduling and data placement to minimize the task completion time and transmission energy consumption of IoT devices. In the L2I algorithm, we design a set of low-level improvement operators to generate new schedule plans to speed up the selection process of the optimal schedule plan. The simulation experiments show that the proposed algorithm effectively outperforms traditional strategies in solving task scheduling and data placement problems.

The widespread use of cloud-based face recognition technology raises privacy concerns, as unauthorized access to face images can expose personal information or be exploited for fraudulent purposes. In response, privacy-preserving face recognition (PPFR) schemes have emerged to hide visual information and thwart unauthorized access. However, the validation methods employed by these schemes often rely on unrealistic assumptions, leaving doubts about their true effectiveness in safeguarding facial privacy. In this paper, we introduce a new approach to pri-vacy validation called Minimum Assumption Privacy Protection Validation (Map2 V). This is the first exploration of formulating a privacy validation method utilizing deep image priors and zeroth-order gradient estimation, with the potential to serve as a general framework for PPFR eval-uation. Building upon Map2v, we comprehensively vali-date the privacy-preserving capability of PPFRs through a combination of human and machine vision. The exper-iment results and analysis demonstrate the effectiveness and generalizability of the proposed Map2v, showcasing its superiority over native privacy validation methods from PPFR works of literature. Additionally, this work exposes privacy vulnerabilities in evaluated state-of-the-art P P FR schemes, laying the foundation for the subsequent effective proposal of countermeasures. The source code is available at https://github.com/Beauty9882/MAP2V.

Biometric technology has driven the rise of Biometrics as a Service (BaaS) due to its unique security and convenience. However, in the traditional cloud-based BaaS systems, raw biometric data leakage and biometric efficiency issues are still concerns, which may reduce user trust and engagement in biometric services. In this paper, we propose an innovative BaaS system named Bio-CEC in a Cloud-Edge Cooperative environment. Bio-CEC features a novel biometric template transformation scheme, rooted in multivariate polynomial transformation and random virtual feature replacement. This innovative scheme enables the revocation and regeneration of biometric templates, enhancing security in case of system breaches. For the biometric template protect scheme, a template matching algorithm using filtering operations is proposed, aiming to facilitate secure and accurate authentication within the transformation domain. Our comprehensive experiments and in-depth safety analysis verify the superiority of Bio-CEC. The results clearly demonstrate that Bio-CEC outperforms traditional cloud-based biometric system and provides a safer and more efficient solution for practical biometric system applications.

Drone-as-a-Service (DaaS) is emerging as an effective way to address service provision problems within complex delivery networks. However, existing DaaS composition frameworks often fail to consider the sharing of delivery services and are not applicable to multi-package drone delivery tasks. Meanwhile, the impact of wind conditions is not adequately considered by existing studies. To address the above issues, we propose a novel wind-aware drone delivery service provision strategy for multi-package delivery to minimize the energy consumption of drones. Experimental results show that our strategy can significantly reduce the delivery energy consumption of drones.

Workflows are pervasive in software systems where business processes and scientific methods are implemented as workflow models to achieve automated process execution. However, despite the benefit of no/low-code workflow automation, creating workflow models requires in-depth domain knowledge and nontrivial workflow modeling skills, which becomes a hurdle for the proliferation of workflow applications. Recently, Large language models (LLMs) have been widely applied in software code generation given their outstanding ability to understand complex instructions and generate accurate, context-aware code. Inspired by the success of LLMs in code generation, this paper aims to investigate how to use LLMs to automate workflow model generation. We present LLM4Workflow, an LLM-based automated workflow model generation tool. Using workflow descriptions as the input, LLM4Workflow can automatically embed relevant API knowledge and leverage LLM's powerful contextual learning abilities to generate correct and executable workflow models. Its effectiveness was validated through functional verification and simulation tests on a real-world workflow system. LLM4Workflow is open sourced at https://github.com/ISEC-AHU/LLM4Workflow, and the demo video is provided at https://youtu.be/XRQ0saKkuxY.

With the rise of smart city applications, the accessibility of users’ location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis.

Clustering ensemble often provides robust and stable results without accessing original features of data, and thus has been widely studied. The conventional clustering ensemble methods often take the full multiple base partitions as inputs and provide a consensus clustering result. However, in many real-world applications, full base partitions are hard to obtain because some data may be missing in some base partitions. To tackle this problem, in this paper, we propose a novel partial clustering ensemble method, which takes the partial multiple base partitions as inputs. In this method, we simultaneously fill the missing values in the base partitions and ensemble them by fully considering the consensus and diversity. Moreover, to address the unreliability issue in the partial data scenario, we seamlessly plug it into a self-paced learning framework. The extensive experiments on benchmark data sets demonstrate the effectiveness and efficiency of the proposed method when handling incomplete data.

A clustering ensemble provides an elegant framework to learn a consensus result from multiple prespecified clustering partitions. Though conventional clustering ensemble methods achieve promising performance in various applications, we observe that they may usually be misled by some unreliable instances due to the absence of labels. To tackle this issue, we propose a novel active clustering ensemble method, which selects the uncertain or unreliable data for querying the annotations in the process of the ensemble. To fulfill this idea, we seamlessly integrate the active clustering ensemble method into a self-paced learning framework, leading to a novel self-paced active clustering ensemble (SPACE) method. The proposed SPACE can jointly select unreliable data to label via automatically evaluating their difficulty and applying easy data to ensemble the clusterings. In this way, these two tasks can be boosted by each other, with the aim to achieve better clustering performance. The experimental results on benchmark datasets demonstrate the significant effectiveness of our method. The codes of this article are released in https://Doctor-Nobody.github.io/codes/space.zip.

With the widespread use of Internet of Things (IoT) technology, an enormous number of end devices that request various kinds of cloud services have been connected to the Internet. Multi-access edge computing (MEC) can reduce the service response time by selecting the required edge computing resources closer to the end device. However, MEC-based smart systems require heterogeneous and diverse services support. Taking unmanned aerial vehicle (UAV) last-mile delivery system as an example, there are two types of services required: delivery and computational services. The edge services in MEC environments are distributed and limited. Inefficient service selection plans will affect the quality of services of such smart systems. Therefore, how to design a suitable service selection strategy is a crucial issue for MEC-based smart systems. To address this issue, we propose a service selection framework and a holistic and hybrid service selection ($H^{2}S^{2}$) strategy for MEC-based UAV last-mile delivery systems in real-world UAV last-mile delivery scenarios. This framework considers three important characteristics of UAV delivery systems: diverse service requirements, service availability, and service mobility. The$H^{2}S^{2}$strategy focuses on selecting the optimal delivery and computational services and provides an integrated approach with a static service selection algorithm and a dynamic service re-selection algorithm. The$H^{2}S^{2}$strategy determines the optimal delivery and computational service selection plans with the lowest UAV energy consumption and shortest service response time. We assess the effectiveness and efficiency of the$H^{2}S^{2}$strategy through ablation studies and comparative analyses with diverse representative strategies. The experimental results show that the$H^{2}S^{2}$strategy improves the effectiveness and efficiency of the UAV delivery system by significantly reducing UAV's energy consumption and service response time.

Currently, smart systems are widely used in various scenarios such as smart cities, smart healthcare and smart logistics. Meanwhile, centralized cloud computing has become the mainstream platform for smart systems due to the strong demand for huge data storage and processing capacities. Edge computing can effectively reduce network latency and improve service response time for smart systems. However, edge servers are usually limited with data storage and processing capacities. Therefore, how to utilize both cloud and edge servers effectively is an open issue. In this paper, we focus on the problem of data placement in an edge-cloud collaborative smart system. Specifically, we propose a Differential Evolution Particle Swarm Optimization based data placement strategy (called DE-PSO) to optimize the data storage cost under deadline constraints. DE-PSO considers different features, requirements and environments of edge and cloud data storage services to generate an optimized data placement scheme. The comprehensive experimental results show that the proposed strategy proves its superior performance compared with several representative data placement strategies.

AIoT (Artificial Intelligence of Things) which integrates AI and IoT has received rapidly growing interest from the software engineering community in recent years. It is crucial to design scalable, efficient, and reliable software solutions for large-scale AIoT systems in edge computing environments. However, the lack of effective service management including the support for service collaboration, AI application, and data security in the edge, has seriously limited the development of AIoT systems. To seal this gap, we propose EXPRESS 2.0 which is an intelligent service management framework for AI oT in the edge. Specifically, on top of the existing EXPRESS platform, EXPRESS 2.0 includes the intelligent service collaboration management module, AI application management module, and data security management module. To demonstrate the effectiveness of the framework, we design and implement a last-mile delivery system using both UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles). The EXPRESS 2.0 is open-sourced at https://github.com/ISEC-AHU/EXPRESS2.0. A video demonstration of EXPRESS 2.0 is at https://youtu.be/GHKD_VvJD88.

To cope with the rapid development of the Internet of Things (IoT) and the increasing demand for real-time services, mobile edge computing (MEC) has become a promising solution which extends centralised cloud computing, to provision computing resources, storage and network services closer to the mobile device from the network edge. While computation offloading is a key feature in MEC to enable real-time services, offloading workflow tasks in MEC is an NP-hard problem. Typically, the problem of multi-workflow offloading with multi-objective optimization is still an open and challenging issue. Therefore, this article proposes a multi-objective clustering evolutionary algorithm called MCEA to minimize the cost and energy consumption of multi-workflow execution under the deadline constraint. First, the sub-deadline constraint is added during initialization to generate more initial solutions that satisfy the deadline constraint. Then an adaptive clustering method is adopted to guide individuals to find a suitable mate during crossover operation. Finally, the probabilities of crossover and mutation are dynamically adjusted based on the historical information to control the evolution direction and convergence speed of algorithm. Comprehensive experiments are carried out for complex workflow applications on FogWorkflowSim, which demonstrate that MCEA can achieve better performance than four representative algorithms in three evaluation metrics.

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.

In this article, we cryptanalyzed a Verifiable Threshold Predicate Encryption (VTPE) enabled Privacy-Preserving Biometric Authentication (PPBA) protocol reported in IEEE-TDSC and revealed discrepancies between its security claims and our security analysis. To be precise, the underlying authentication and key agreement scheme which is based on a challenge-response mechanism and watermark signal unsatisfactorily meets the following security scenario: (a) resistance to man-in-the-middle attacks, (b) biometric template protection, and (c) user anonymity and untraceability. To address these issues, we utilize Physical Unclonable Functions (PUF) to design a PUF driven Verifiable Threshold Predicate Encryption (PUF-VTPE) scheme and a secure PPBA protocol. The PUF-VTPE-based PPBA protocol equips with dual authentication using biometric and mobile device, which offers strong authenticity before establishing the session key. Simultaneously, the non-invertible property of PUF protects the biometric templates in the physical layer. The proposed storage-free mechanism that hides the challenge of device PUF in biometric template alleviates data leakage caused by storage challenges in PUF-based authentication protocols. Moreover, the experimental analysis suggests that the proposed PPBA protocol possesses ISO/IEC 24745 criteria of non-invertibility, unlinkability, and revocability. Additionally, the proposed PPBA protocol reduces the computational cost by about 50% compared to that of the cryptanalyzed scheme.

Brain 18F-FDG PET images are commonly-known materials for effectively predicting Alzheimer's disease (AD). However, the data volume of PET is usually insufficient, which is unfavorable to train an accurate AD prediction networks. Furthermore, the PET image is noisy with low signal-to-noise ratio, and simultaneously the feature (metabolic abnormality) used for predicting AD in PET image is not always obvious. Therefore, a contrastive-based learning method is proposed to address the challenges of PET image inherently possessed. Firstly, the slices of 3D PET image are amplified by cropping the image of anchors (i.e., an augmented version of the same image) to generate extended training data. Meanwhile, contrastive loss is adopted to enlarge inter-class feature distances and reduce intra-class feature differences using subject fuzzy labels as supervised information. Secondly, we construct a double convolutional hybrid attention module to enhance the network to learn different perceptual domains where two convolutional layers with different convolutional kernels ($7\times 7$ and $5\times 5$) are constructed. Moreover, we recommend a diagnosis mechanism by analyzing the consistency of predicted result for PET slices alone with clinical neuropsychological assessment to achieve a better AD diagnosis. The experimental results show that the proposed method outperforms the state-of-the-arts for brain 18F-FDG PET images, and hence demonstrate the advantage of the method in effectively predicting AD.

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.

Consensus clustering, which learns a consensus clustering result from multiple weak base results, has been widely studied. However, conventional consensus clustering methods only focus on the ensemble process while ignoring the quality improvement of the base results, and thus they just use the fixed base results for consensus learning. In this paper, we provide an alternative idea to improve the final consensus clustering performance by considering the base results refining. In our framework, we adaptively refine the base results in the process of the ensemble. In more detail, on one hand, we ensemble multiple K-means results to learn the consensus one by considering the consensus and diversity; on the other hand, we apply the consensus result to design a graph filter to learn a more cluster-friendly embedding for refining the base K-means results. In our framework, the consensus learning and base results refining are integrated into one unified objective function so that these two tasks can be boosted by each other. Then we design an effective iterative algorithm to optimize the carefully designed objective function. The extensive experiments on benchmark data sets demonstrate that the proposed method can outperform both the single clustering and the state-of-the-art consensus clustering methods. The codes of this paper are released inhttp://Doctor-Nobody.github.io/codes/ACMK.zip.

The Unmanned Aerial Vehicles (UAVs) delivery service is being increasingly used in logistics. However, it is challenging for a UAV to precisely identify the position for parcel delivering if it is only aided by the GPS, especially in some complex environments with weak signals and high interference. For this issue, we present a knowledge distillation empowered edge intelligence architecture, KeepEdge, to achieve visual information-assisted positioning for the UAV delivery services. Specifically, we integrate deep neural networks (DNN) into an edge computing framework to enable edge intelligence which empowers the UAVs to autonomously identify the expected delivery position. Deploying the DNN model and conducting model inference on UAVs however, requires high computing performance. To manage the trade-off between the limited resources onboard the UAVs and high-performance requirements, we employ knowledge distillation to produce a lightweight model with high accuracy based on the full model trained in the cloud. The lightweight model with significantly lower complexity and less inference latency is used onboard of the UAVs for accurate positioning. Comprehensive experiments show that the proposed architecture achieves satisfactory performance for assisted positioning. A real-world case study is presented to demonstrate the effectiveness of the proposed edge intelligence solution for UAV delivery services.

With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy on FogWorkflowSim platform for complex workflow applications. Extensive numerical results demonstrate that the end device's energy consumption can be effectively reduced by 7.81% and 9.51% compared with PSO and GA by the proposed strategy. Meanwhile, the strategy running time is 1% and 0.2% of PSO and GA, respectively.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3180067]

Abstract Single image superresolution (SISR) has achieved substantial progress based on deep learning. Many SISR methods acquire pairs of low‐resolution (LR) images from their corresponding high‐resolution (HR) counterparts. Being unsupervised, this kind of method also demands large‐scale training data. However, these paired images and a large amount of training data are difficult to obtain. Recently, several internal, learning‐based methods have been introduced to address this issue. Although requiring a large quantity of training data pairs is solved, the ability to improve the image resolution is limited if only the information of the LR image itself is applied. Therefore, we further expand this kind of approach by using similar HR reference images as prior knowledge to assist the single input image. In this paper, we proposed zero‐shot single image superresolution with a reference image (Ref‐ZSSR). First, we use an unconditional generative model to learn the internal distribution of the HR reference image. Second, a dual‐path architecture that contains a downsampler and an upsampler is introduced to learn the mapping between the input image and its downscaled image. Finally, we combine the reference image learning module and dual‐path architecture module to train a new generative model that can generate a superresolution (SR) image with the details of the HR reference image. Such a design encourages a simple and accurate way to transfer relevant textures from the reference high‐definition (HD) image to LR image. Compared with using only the image itself, the HD feature of the reference image improves the SR performance. In the experiment, we show that the proposed method outperforms previous image‐specific network and internal learning‐based methods.

Summary The “last mile” problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real‐time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain‐based UAV delivery system to resolve the “last mile” problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC‐based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the “last mile” problem and address the security issues in UAV delivery systems.

Brain magnetic resonance images (MRI) are widely used for the classification of Alzheimer's disease (AD). The size of 3D images is, however, too large. Some of the sliced image features are lost, which results in conflicting network size and classification performance. This article uses key components in the transformer model to propose a new lightweight method, ensuring the lightness of the network and achieving highly accurate classification. First, the transformer model is imitated by using image patch input to enhance feature perception. Second, the Gaussian error linear unit (GELU), commonly used in transformer models, is used to enhance the generalization ability of the network. Finally, the network uses MRI slices as learning data. The depthwise separable convolution makes the network more lightweight. Experiments are carried out on the ADNI public database. The accuracy rate of AD vs. normal control (NC) experiments reaches 98.54%. The amount of network parameters is 1.3% of existing similar networks.

As the most important part of a smart city and long-standing challenging issue, a highly efficient smart logistic system has attracted a great deal of attention in recent years. In particular, unmanned aerial vehicles (UAVs) are ideal solutions for last-mile delivery scenarios in recent years due to their fast speed and easy deployment. However, because of the highly automatic delivery process, UAVs are still constrained by the limited payload, battery, and computing capacity for complex computational tasks. With the aid of the mobile edge computing (MEC) technology, UAVs can offload computational tasks to the MEC computational resources in various types of IoT environments. In spite of the task offloading which can enhance their task process capability, it also brings extra overhead, such as data transfer time and energy consumption. These extra overheads may significantly impact the efficiency and payload of UAV-based delivery systems. Therefore, taking the UAV last-mile delivery system with MEC as an example, this article investigates the energy-aware multi-UAV task computation management problem according to a realistic autonomous delivery network (ADNET). Specifically, we propose a computation management strategy, namely, the MEC-based task offloading and scheduling strategy (TOSS), to provide an integral approach covering both the static task offloading and scheduling algorithm, as well as the dynamic resource conflict resolution algorithm. Grounded on real-world scenarios, our experimental results show that TOSS can achieve a higher payload for UAVs by using minimum energy consumption and task makespan within the given constraints of the deadline compared to the state-of-the-art methods.

In recent years, edge computing has become the ideal computing paradigm for various smart systems, such as smart logistics, smart health and smart transportation. This is due to its advantages including fast response times, energy efficiency and cost effectiveness over conventional cloud computing platforms. However, running complex computational scientific workflow tasks is still a very challenging issue at the edge, due to its typical three-layered computing environment consisting of an end device layer, an edge server layer, and a cloud server layer. A large number of recent studies have proposed different solutions for optimizing such computing resource management problems in an edge computing environment. However, since evaluation of most such studies is conducted through simulation, the effectiveness cannot be guaranteed in a real world environment. Therefore, to advance research on efficient execution and deployment problems for real world workflow applications using edge computing, an open-source edge workflow management system with comprehensive empirical evaluation capabilities is urgently required. This paper presents the first edge workflow system (named EdgeWorkflow) that is able to deploy user-created workflow applications to a real-world edge computing environment with “one-click” after optimizing the configuration with the simulation tool. With the aid of EdgeWorkflow, the user can automate the generation of specific edge computing environments, easily model and generate executable workflow applications with a visual modelling tool, effectively select various resource management methods included in the systems or apply their own resource management and task scheduling algorithms, efficiently monitor the statuses of computational tasks and obtain comprehensive reports on the execution results (such as those regarding time, cost and energy). We use an edge computing-based unmanned aerial vehicle (UAV) last-mile delivery system as a real-world case study, and a number of representative scientific workflows are employed for our experiments. Our experimental results show that EdgeWorkflow can effectively evaluate the performance of different resource management and workflow task scheduling algorithms and efficiently deploy and execute user-defined scientific workflow applications to user-specified edge computing environments.

Clustering ensemble generates a consensus clustering result by integrating multiple weak base clustering results. Although it often provides more robust results compared with single clustering methods, it still suffers from the robustness problem if it does not treat the unreliability of base results carefully. Conventional clustering ensemble methods often use all data for ensemble, while ignoring the noises or outliers on the data. Although some robust clustering ensemble methods are proposed, which extract the noises on the data, they still characterize the robustness in a single level, and thus they cannot comprehensively handle the complicated robustness problem. In this paper, to address this problem, we propose a novel Tri-level Robust Clustering Ensemble (TRCE) method by transforming the clustering ensemble problem to a multiple graph learning problem. Just as its name implies, the proposed method tackles robustness problem in three levels: base clustering level, graph level and instance level. By considering the robustness problem in a more comprehensive way, the proposed TRCE can achieve a more robust consensus clustering result. Experimental results on benchmark datasets also demonstrate it. Our method often outperforms other state-of-the-art clustering ensemble methods. Even compared with the robust ensemble methods, ours also performs better.

Edge computing is becoming more and more popular in both academics and industries. With the booming of edge computing technology, the Unmanned Aerial Vehicle (UAV) based delivery system is expected to achieve higher efficiency and low latency. However, the UAV often collects user-specific data during the delivery process, the data-leakage or security/privacy breaching could occur during the data-sharing process between the edge nodes and UAV devices. Privacy-preserving issues are further refraining from the popularity of the UAV-based logistic systems. It is believed that the UAV tracking and identity verification system can provide imminent access-level security and privacy protection, which is urgently required to eliminate the practical concerns under the edge computing-based environment. To the best knowledge of authors, for the first time, this paper proposes a Self-Sovereign Identity (SSI) integrated framework with the latest Blockchain technology for UAV-based delivery system. It is expected to protect the edge computing-based UAV delivery system against security flaws and privacy concerns. In this work, the benefits of using SSI with Blockchain technology are analyzed, the efficiency of identifying and authenticating UAVs and their respective users is further experimented and discussed. The experimental results show that the integrated SSI framework with Blockchain can effectively improve the efficiency of the user identity management system as well as the identity verification process in the delivery process.

With the rapid growth of Internet of Things (IoT), Mobile Edge Computing (MEC) is becoming the major platform for many smart systems such as smart logistics, smart healthcare, and smart transportation, given its lower latency and higher reliability compared with centralized cloud computing. There is a growing interest in Drone-as-a-Service in recent years which enables the MEC-based smart UAV delivery system. However, most existing works on Drone-as-a-Service focus on the static service composition or the dynamic service provisioning, rather than a holistic service provisioning strategy for the entire UAV delivery process. In this paper, we propose a holistic service provisioning strategy for Drone-as-a-Service in MEC-based UAV delivery to address such an issue. Specifically, a MEC-based UAV relay delivery system framework (RDS) is designed, which considers both the static stage for provisioning delivery services and the dynamic stage for provisioning computing services. Based on the service models for both static and dynamic stages, an energy-efficient service provision strategy (ESP-GA) for MEC-based UAV last-mile delivery is proposed, which aims to minimize the overall energy consumption under deadline constraints. Through the simulation experiments based on a prototype UAV delivery system, the experimental results have successfully demonstrated the superior performance of the proposed holistic strategy in comparison with several representative service provisioning strategies.

AI (Artificial Intelligence) technology has been widely used in smart systems which usually require computing services with high availability and fast response. However, the rapid growth of data and service requests generated by end devices brings critical challenges to the centralised cloud computing paradigm in terms of network bandwidth, reliability and response time. In addition, the problem of data privacy is arising due to a large amount of data being transferred to the cloud server. Recently, edge computing is becoming a popular platform for smart systems as its provisions computing services close to the end devices, and Federated Learning (FL) is emerging as a promising solution for AI applications to address the data privacy issue. Inspired by their success, in this paper, we propose an edge based FL framework named Fed-UAV to solve the person reidentification problem in the UAV delivery service which is a typical AI application in smart logistics. This framework enables the UAV to efficiently locate the target receivers, and effectively reduce the data transmission between the UAV and the cloud server to improve the response time and protect the data privacy. Comprehensive experiments are conducted on three real-world datasets, and the experimental result successfully demonstrates that Fed-UAV can achieve both high accuracy and efficiency in person re-identification while protecting data privacy.

As the latest computing paradigm, edge computing has attracted increasing attention from both academia and industry in recent years. It significantly affects the design and development of many smart systems and challenges conventional security solutions. Therefore, a new security framework targeted for edge computing-based smart systems is urgently required. In this paper, we investigate various security issues in the edge computing-based Unmanned Aerial Vehicle (UAV) delivery system which is a typical system in smart logistics. Specifically, we focus on security issues related to abnormal intrusion detection, various security attacks, and authentication/access control. To address these security issues, we propose A2DSEC which is a novel security framework characterized by the capabilities such as detection, defense, and authentication. With A2DSEC, we can guarantee the security of user identity authentication, and provide effective early warnings so that corresponding security measures can be taken in a timely fashion. To verify the effectiveness of A2DSEC, we test and implement the core part of the framework on a real-world edge computing-based UAV delivery system. The experiment results show that the A2DSEC can effectively ensure the security of the UAV delivery system via the timely detection of a variety of attacks as well as unknown potential attacks.

Abstract Nowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and restricted battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV‐based smart systems and the quality of experience (QoE) of end users. Recently, Mobile Edge Computing (MEC) which provisions computing resources close to the mobile end devices has become a promising solution. However, since high‐speed UAV often flies through the signal range of the different edge nodes, the interruption of services in the MEC‐based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In this paper, we investigate the task migration issue for multiple UAVs in the MEC‐based UAV delivery system. Specifically, we propose an energy‐aware decision‐making strategy for the dynamic task migration named GAD to optimize the UAV energy consumption. Given the real‐time system status and QoS constraints, and through a dynamic two‐tier decision‐making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, viz. No Migration, Data Migration Only, Cold Migration, and Live Migration. Experimental results based on a real‐world scenario show that our strategy can well outperform other baseline strategies in various metrics including the flying distances and the energy consumption of UAVs.

The clustering ensemble has emerged as an important extension of the classical clustering problem. It provides an elegant framework to integrate multiple weak base clusterings to generate a strong consensus result. Most existing clustering ensemble methods usually exploit all data to learn a consensus clustering result, which does not sufficiently consider the adverse effects caused by some difficult instances. To handle this problem, we propose a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning. In our method, we integrate the evaluation of the difficulty of instances and ensemble learning into a unified framework, which can automatically estimate the difficulty of instances and ensemble the base clusterings. To optimize the corresponding objective function, we propose a joint learning algorithm to obtain the final consensus clustering result. Experimental results on benchmark data sets demonstrate the effectiveness of our method.

Consensus clustering provides a framework to ensemble multiple clustering results to obtain a consensus and robust result. Most existing consensus clustering methods usually apply all data to ensemble learning, whereas ignoring the side effects caused by some difficult or unreliable instances. To tackle this problem, we propose a novel self-paced consensus clustering method to gradually involve instances from more reliable to less reliable ones into the ensemble learning. We first construct an initial bipartite graph from the multiple base clustering results, where the nodes represent the instances and clusters and the edges indicate that an instance belongs to a cluster. Then, we learn a structured bipartite graph from the initial one by self-paced learning, i.e., we automatically decide the reliability of each edge and involves the edges into graph learning in order of their reliability. At last, we obtain the final consensus clustering result from the learned bipartite graph. The extensive experimental results demonstrate the effectiveness and superiority of the proposed method.

At present, most of the smart systems are based on cloud computing, and massive data generated at the smart end device will need to be transferred to the cloud where AI models are deployed. Therefore, a big challenge for smart system engineers is that cloud based smart systems often face issues such as network congestion and high latency. In recent years, mobile edge computing (MEC) is becoming a promising solution which supports computation-intensive tasks such as deep learning through computation offloading to the servers located at the local network edge. To take full advantage of MEC, an effective collaboration between the end device and the edge server is essential. In this paper, as an initial investigation, we propose Edge4Sys, a Device-Edge Collaborative Framework for MEC based Smart System. Specifically, we employ the deep learning based user identification process in a MEC-based UAV (Unmanned Aerial Vehicle) delivery system as a case study to demonstrate the effectiveness of the proposed framework which can significantly reduce the network traffic and the response time.

As most smart systems such as smart logistic and smart manufacturing are delay sensitive, the current mainstream cloud computing based system architecture is facing the critical issue of high latency over the Internet. Meanwhile, as huge amount of data is generated by smart devices with limited battery and computing power, the increasing demand for energy-efficient machine learning and secure data communication at the network edge has become a hurdle to the success of smart systems. To address these challenges with using smart UAV (Unmanned Aerial Vehicle) delivery system as an example, we propose EXPRESS, a novel energy-efficient and secure framework based on mobile edge computing and blockchain technologies. We focus on computation and data (resource) management which are two of the most prominent components in this framework. The effectiveness of the EXPRESS framework is demonstrated through the implementation of a real-world UAV delivery system. As an open-source framework, EXPRESS can help researchers implement their own prototypes and test their computation and data management strategies in different smart systems. The demo video can be found at https://youtu.be/r3U1iU8tSmk.

Workflow underlies most process automation software, such as those for product lines, business processes, and scientific computing. However, current Cloud Computing based workflow systems cannot support real-time applications due to network latency, which limits their application in many IoT systems such as smart healthcare and smart traffic. Fog Computing extends the Cloud by providing virtualized computing resources close to the End Devices so that the response time of accessing computing resources can be reduced significantly. However, how to most effectively manage heterogeneous resources and different computing tasks in the Fog is a big challenge. In this paper, we introduce "FogWorkflowSim" an efficient and extensible toolkit for automatically evaluating resource and task management strategies in Fog Computing with simulated user-defined workflow applications. Specifically, FogWorkflowSim is able to: 1) automatically set up a simulated Fog Computing environment for workflow applications; 2) automatically execute user submitted workflow applications; 3) automatically evaluate and compare the performance of different computation offloading and task scheduling strategies with three basic performance metrics, including time, energy and cost. FogWorkflowSim can serve as an effective experimental platform for researchers in Fog based workflow systems as well as practitioners interested in adopting Fog Computing and workflow systems for their new software projects. (Demo video: https://youtu.be/AsMovcuSkx8).

Cloud computing can provide a more cost-effective way to deploy scientific workflows than traditional distributed computing environments such as cluster and grid. Due to the large size of scientific datasets, data placement plays an important role in scientific cloud workflow systems for improving system performance and reducing data transfer cost. Traditional task-level data placement strategy only considers shared datasets within individual workflows to reduce data transfer cost. However, it is obvious that task-level strategy is not necessarily good enough for the situation of multiple workflows at the workflow level. In this paper, a novel workflow-level data placement model is constructed, which regards multiple workflows as a whole. Then, a two-stage data placement strategy is proposed which first pre-allocates initial datasets to proper datacenters during workflow build-time stage, and then dynamically distributes newly generated datasets to appropriate datacenters during runtime stage. Both stages use an efficient discrete particle swarm optimization algorithm to place flexible-location datasets. Comprehensive experiments demonstrate that our workflow-level data placement strategy can be more cost-effective than its task-level counterpart for data-sharing scientific cloud workflows.

Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.

As an important tool, clustering validity index (CVI) is usually used to evaluate the validity of the clustering results and determine the optimal clustering number (Kopt). However, the existing CVIs have some shortcomings, such as too complex calculation, low computational efficiency and narrow range of applications. Aiming at these problems, an improved K-means algorithm that uses density parameters for initial centers selection is firstly proposed to make the results of traditional clustering algorithms more stable. Then, a new variance based clustering validity index (VCVI) from the perspective of spatial distribution of data sets is introduced to extend the application of the partitional clustering algorithms and better evaluate the effect of clustering results. Finally, a new algorithm integrated with the improved K-means algorithm and the new proposed VCVI is designed to effectively determine the Koptand the optimal clustering partition. The experimental results have shown that the new proposed algorithm with VCVI is effective in forming the Kopt and the optimal clustering partition for the tested data sets.

Nowadays, the demand for wireless spectrum resources is growing rapidly with the continuous development of wireless communication technology. However, the vast majority of available spectrums have been allocated, making the new demanders have difficulty to acquire their appropriate spectrums. In order to reallocate the scanty spectrum resources for new demanders, many spectrum auction schemes that are based on truthfulness are proposed. However, the insufficient protective measures been deployed in these schemes made the users private information be easily exposed during the process of the spectrum auction. By combining homomorphic encryption and garbled circuits technology, this paper proposes a secure and efficient collaborative spectrum auction scheme for redistributing the scarcity spectrum resources. Furthermore, the security of the protocol used in the scheme has been proved. The emulation experimental results have demonstrated that the proposed scheme can effective enhance the privacy information of the spectrum users while consume relatively lower computing and communicating overhead.