ABSTRACT Accurate and robust medical image analysis plays a critical role in disease screening, diagnosis, and prognosis. However, its development is often constrained by data scarcity, privacy concerns, and domain discrepancies. To address these challenges, we propose TransMedVision—a transitional training framework tailored for cross‐domain few‐shot medical image analysis tasks. The framework consists of three stages: (1) initializing the model with a vision backbone pretrained on large‐scale natural image datasets; (2) performing short‐term transitional training on intermediate medical image datasets to reduce the representation gap between natural and medical domains, while stabilizing feature learning; and (3) fine‐tuning on the target few‐shot CT dataset to obtain the final classifier. By preserving general visual features and gradually adapting them to medical domains, TransMedVision enhances both cross‐domain transfer accuracy and training stability. In cross‐domain few‐shot COVID‐19 pneumonia CT classification tasks, TransMedVision achieves state‐of‐the‐art performance (Accuracy = 0.9113, F1 = 0.9032, AUC = 0.9514). All datasets, code, and models are publicly released via https://github.com/01Matrix/TransMedVision to facilitate reproducibility and future research.

MobileEdgeComputing (MEC) has incentivized App vendors to outsource various services and applications to distributed edge nodes for low access latency. However, the data cached on these nodes is vulnerable to both intentional and accidental corruption, necessitating periodic audits ofEdgeDataIntegrity (EDI). Existing solutions either rely on a “fully trustworthy”ThirdPartyAuditor (TPA) or leverage blockchain to enhance trust. However, they overlook the security risks brought by the use of blockchain, particularly collusion attacks. Furthermore, while they employ achallenge-responsemechanism to enhance efficiency by batch verification, they fail to account for the heterogeneity of edge nodes. To address these challenges, we propose$\mathtt {CTCV}$, aCollusion-resistant andTime-awareCollaborativeVerification framework.$\mathtt {CTCV}$aims to accommodate edge node heterogeneity while enabling public audits and batch verification without introducing additional security risks. Specifically, it incorporates blockchain to allow edge nodes to collaboratively verify EDI without trust dependencies, while mitigating collusion attacks through a carefully designed proof generation and verification approach. Considering the resource and state heterogeneity of edge nodes,$\mathtt {CTCV}$employs atime-constrained challenge-responsemechanism that sets a time threshold$\mathcal {T}$between the verification request issuance and the integrity proof inspection to avoid excessive delays. The selection guideline of$\mathcal {T}$, along with the correctness, efficiency, and collusion resistance of$\mathtt {CTCV}$, are rigorously analyzed. Extensive experiments validate that$\mathtt {CTCV}$is computationally and communicationally efficient compared to three baselines: EdgeWatch, EDI-S, and EDI-V. On average, given 10 edge nodes,$\mathtt {CTCV}$outperforms EdgeWatch, EDI-S, and EDI-V with computation efficiency improvements of 7.9, 9.0, and 5.0 times, and communication efficiency improvement of 2063.0, 4.8, and 2.6 times, respectively.

Multiaccess edge computing (MEC) enables low-latency service delivery by deploying application instances on edge servers. However, edge servers are prone to failures, making it challenging to meet diverse user reliability requirements. A common approach is to deploy redundant instances across multiple edge servers, which improves reliability but increases costs and limits the number of users that can be served within budget. Therefore, efficient deployment strategies are needed to balance cost-effectiveness and reliability guarantees, thereby maximizing the app vendor’s revenue. In this article, we investigate the problem ofRevenue maximization forReliability-awareEdgeApplicationDeployment ($\text{R}^{2}\text{EAD}$). Our objective is to maximize the app vendor’s revenue by deploying its applications on heterogeneous edge servers, subject to budget and resource constraints and users’ diverse reliability requirements. We prove that the$\text{R}^{2}\text{EAD}$problem is$\mathcal {\text{NP}}$-hard and propose an efficient approximation algorithm named$\text{R}^{2}\text{EAD}$-A. By reducing the problem to a nonmonotone submodular maximization problem with curvature$\alpha$under multiple knapsack constraints, we prove that$\text{R}^{2}\text{EAD}$-A achieves a constant approximation ratio of$\frac{1}{\alpha }(1 - e^{-\alpha })$. Extensive evaluations demonstrate that$\text{R}^{2}\text{EAD}$-A outperforms the representative approaches across all tested cases.

The edge computing paradigm provides fine-grained and distributed resources to users with low service latency. To further utilize the advantage of edge computing to improve users' satisfaction, it is essential to jointly optimize service deployment, task offloading, and resource allocation. However, this is challenging because of limited edge resources, diverse task demands, and coupled decisions. In this paper, we propose EdgePro, a novel adaptive edge service provision approach based on safe deep reinforcement learning, aiming to maximize user satisfaction while fulfilling multiple constraints including deployment budget and edge server resources. Specifically, we formulate the optimization problem as a constrained Markov decision process. By designing a constraint-penalty function, we transform the original multi-constraint problem into an equivalent single-constraint problem, addressing the training oscillations caused by conflicts in satisfying multiple constraints. To handle the discrete-continuous coupled decisions, we employ multiple deep neural networks for coordinated control. Then, we propose a safe deep reinforcement learning algorithm based on augmented proximal policy optimization, which adaptively solves the formulated problem while satisfying safety constraints. Experimental results show that EdgePro significantly outperforms benchmark approaches in user satisfaction, convergence speed, and satisfying constraints.

Human activity recognition (HAR) is an evolving technique that offers innovative solutions across various domains, such as healthcare, sports training, and human-computer interactions. This paper addresses the novel challenge of video-based activity recognition, focusing on detecting and classifying athletes' actions to enable precision sports training. Conventional HAR methods based on direct video analysis incur excessive computational overhead and constrained applicability. In contrast, our novel transformer-based framework, namely RSFomer, converts videos into multivariate time series, and then detects and classifies the athletes' actions. However, sports videos often suffer from severe occlusion, which introduces significant noise to the converted time series and thus deteriorates recognition performance. To address this challenge, we implement several innovative strategies to improve the robustness of our framework. First, we propose a dual-scale filtering mechanism that leverages the unscented Kalman filter and kinematic constraints to reduce noise and outliers in the converted time series. Second, we incorporate the masking mechanism and temporal slicing mechanism to enhance the transformer's ability to handle anomalies and extract multi-scale features for accurate action recognition. We perform extensive evaluations on our Boxing dataset as well as the UEA and FineGym datasets. The results demonstrate that our RSFomer is effective, outperforming existing state-of-the-art methods with significant advantages.

The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from these textual diabetes data is crucial but challenging. Named entity recognition (NER) - an important corner-stone technology of natural language processing - has been studied well in the general medical field. However, there is still a lack of effective NER methods to handle diabetes data. Briefly, there are three challenges in the real world, including 1) the large volume of diabetes-related data to be processed, 2) the lack of labeled data, and 3) the high costs of manual labeling. To mitigate those challenges, this paper proposes a novel NER method based on semi-supervised learning, namely SNER, for diabetes data processing. It utilizes large amounts of unlabeled data to solve the problem of lack of labeled data. Specifically, it filters the predicted labels based on their confidence and uncertainty scores to reduce the noise entering the model and divide them into positive pseudo-labels and negative pseudo-labels. Also, it utilizes negative pseudo-labels reasonably to improve the training effect of pseudo-labels. Experiments on two public diabetes datasets show that SNER achieves the best performance compared with existing state-of-the-art models.

Edge computing enables low-latency services by deploying application instances near users. Application vendors tend to serve more users with higher service satisfaction and a limited budget. This raises a critical yet open problem - optimally deploying application instances and allocating users to edge servers to maximize service utility while fulfilling multiple constraints, including user latency requirements, budget limitations, and edge resource constraints. To address this problem, we formulate it as a constrained optimization problem that jointly solves two tightly coupled sub-problems:ApplicationDeployment andUserAllocation (named as ADUA problem). The objective is to maximize overall service utility by optimizing resource utilization and service satisfaction under latency, budget, and resource constraints. We reformulate the ADUA problem as a multi-armed bandit (MAB) problem and then propose a novel approach namedUMESPbased on penalized MAB framework. Specifically, we design a marginal utility–aware reward function to align bandit learning with the optimization objective. We introduce a penalty mechanism to effectively handle constraint violations. By further integrating the upper confidence bound policy,UMESPachieves adaptive and constraint-aware balance between exploration and exploitation. Theoretical analysis demonstrates thatUMESPachieves bounded regret. Extensive experiments verify thatUMESPexhibits stable convergence in all tests and, on average, outperforms six representative approaches in overall service utility.

Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novelAvailability-awareRevenue-effectiveApplicationDeployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is$\mathcal {NP}$-hard. Then, we propose an approximation algorithm namedARAD-Ato find the ARAD solution efficiently with a constant approximation ratio of$\frac{1}{2}$. We extensively evaluate the performance ofARAD-Aagainst five representative approaches. Experimental results demonstrate that ourARAD-Acan achieve the best performance in securing the app vendor's overall revenue.

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis's validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster; 2) reduces dissemination costs by 48.21% to 91.87%; and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs.

The prosperity of software applications brings fierce market competition to developers. Employing third-party libraries (TPLs) to add new features to projects under development and to reduce the time to market has become a popular way in the community. However, given the tremendous TPLs ready for use, it is challenging for developers to effectively and efficiently identify the most suitable TPLs. To tackle this obstacle, we propose an innovative approach named PyRec to recommend potentially useful TPLs to developers for their projects. Taking Python project development as a use case, PyRec embeds Python projects, TPLs, contextual information, and relations between those entities into a knowledge graph. Then, it employs a graph neural network to capture useful information from the graph to make TPL recommendations. Different from existing approaches, PyRec can make full use of not only project-library interaction information but also contextual information to make more accurate TPL recommendations. Comprehensive evaluations are conducted based on 12,421 Python projects involving 963 TPLs, 9,675 extra entities, 121,474 library usage records, and 73,277 contextual records. Compared with five representative approaches, PyRec improves the recommendation performance significantly in all cases.

Multi-access Edge Computing (MEC) has emerged as a new distributed computing paradigm for its ability to offer low-latency services to users. Suffering from constrained computational resources because of their limited physical sizes, edge servers usually cannot handle all the incoming compute tasks on time when they operate independently. Thus, they need to cooperate by peer-offloading. Incentive and trust are the two major challenges towards to cooperative computing among edge servers operating in a distrusted environment. Another specific challenge in the MEC environment is to facilitate incentive and trust in a decentralized manner. This article proposes CoopEdge+, a novel blockchain-based decentralized platform, to drive and support cooperative multi-access edge computing to tackle these challenges in a systematic manner. On CoopEdge+, an edge server can publish a compute task for other edge servers to contend for. A winner is selected from candidate edge servers as the task executor based on their reputation to perform the compute task. After that, CoopEdge+ employs a random leader election scheme to elect a task recorder without revealing its leadership until its consensus epoch. The task recorder will coordinate a consensus among edge servers to record the task executor's performance on blockchain. We implement CoopEdge+ based on Hyperledger fabric and evaluate it experimentally against a baseline implementation and three state-of-the-art implementations in a simulated MEC environment. The results validate the usefulness of CoopEdge+ and demonstrate its performance.

Mobile edge computing (MEC) offers the infrastructure for improving data caching performance structurally by deploying edge servers at the network edge within users' close geographic proximity. Popular data like viral videos can be cached on edge servers to serve users with low latency. Investigating the integrity of these edge data is critical and challenging as edge servers often suffer from unreliability and constrained resources. Meanwhile, EDI (edge data integrity) investigation must be performed by edge servers collaboratively at the edge to avoid excessive backhaul network traffic. There are two main challenges in practice: 1) there is a lack of Byzantine-tolerant collaborative investigation method; and 2) edge servers may be reluctant to collaborate without proper incentives. To tackle these challenges systematically, this paper proposes a novel scheme named EdgeWatch to enable robust and collaborative EDI investigation in a decentralized manner based on blockchain. Under EdgeWatch, edge servers collaborate on EDI investigation following a novel integrity consensus. A blockchain system comprises of three main components is built as the infrastructure to facilitate integrity consensus: 1) an incentive mechanism that motivates edge servers to participate in EDI investigation; 2) a reputation system that elects reliable leaders for block consensus; and 3) a leader randomization technique that protects leaders from targeted attacks. We evaluate it against three representative schemes experimentally. The results demonstrate the high precision, efficiency, and robustness of EdgeWatch.

Mobile-edge computing (MEC), as an emerging computing paradigm, allows app vendors to deploy their mobile and/or IoT applications on edge servers to deliver low-latency services to their app users. However, when an edge server needs to serve excessive app users concurrently, severe interference is incurred, which immediately reduces app users’ achievable data rates and, consequently, impacts their perceived service quality. This is a major challenge to the app vendor’s attempt to minimize the edge resources required for serving its app users with a satisfactory service quality. To tackle this challenge, in this article, we present and formulate this multiple edge application deployment (MEAD) problem in the MEC environment, aiming to maximize app users’ overall service quality at minimum deployment cost, considering application shareability and communication interference. We prove that the MEAD problem is$\mathcal {NP}$-hard. Then, we propose a heuristic approach, namely, the deployment-priority greedy via the divide-and-conquer strategy (DPG-D&C), to solve the MEAD problem effectively and efficiently. We evaluate our approach extensively by using a widely used real-world data set. The experimental results show that DPG-D&C significantly outperforms state-of-the-art approaches.

In the edge computing environment, app vendors can cache their data on a large number of geographically distributed edge servers to serve their users. However, those cached data are particularly vulnerable to both intentional and accidental corruption, which makes data security a major concern in the EC environment. Given limited computing resources of edge servers, how to effectively and efficiently inspect those data over tremendous edge servers is a critical and open problem. To tackle this edge data integrity (EDI) problem, we first study the entities, threats, system objectives, and the inspection mechanism, then propose a novel approach named EDI-S for inspecting the integrity of edge data and localizing the corrupted ones. Based on the elliptic curve cryptography, EDI-S generates one digital signature as the integrity proof for each replica. Then, multiple integrity proofs can be inspected altogether via an aggregate verification. This allows the integrity of tremendous cache data on multiple edge servers can be inspected more efficiently. EDI-S also provides two methods for localizing the corrupted data on edge servers, one for small-scale scenarios and the other for large-scale scenarios. Both theoretical analysis and experimentally evaluation demonstrate that EDI-S can solve the EDI problem effectively and efficiently.

Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing computing resources onto edge servers that are attached to base stations or access points at the edge of the cloud in close proximity with end-users. Due to edge servers’ geographic distribution, the EC paradigm is challenged by many new security threats, including the notorious distributed Denial-of-Service (DDoS) attack. In the EC environment, edge servers usually have constrained processing capacities due to their limited sizes. Thus, they are particularly vulnerable to DDoS attacks. DDoS attacks in the EC environment render existing DDoS mitigation approaches obsolete with its new characteristics. In this article, we make the first attempt to tackle the edge DDoS mitigation (EDM) problem. We model it as a constraint optimization problem and prove its$\mathcal {NP}$-hardness. To solve this problem, we propose an optimal approach named EDMOpti and a novel game-theoretical approach named EDMGame for mitigating edge DDoS attacks. EDMGame formulates the EDM problem as a potential EDM Game that admits a Nash equilibrium and employs a decentralized algorithm to find the Nash equilibrium as the solution to the EDM problem. Through theoretical analysis and experimental evaluation, we demonstrate that our approaches can solve the EDM problem effectively and efficiently.

Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget$\mathcal {K}$. We formally define this jointCoverage-Reliability for$\mathcal {K}$-BudgetedEdgeApplicationDeployment (CR-BEAD) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (namedBEAD-O) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach namedBEAD-Gwith a constant approximation ratio of$1 - 1/e$to solve large-scale CR-BEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

The new edge computing paradigm extends cloud computing by allowing service vendors to deploy their service instances and data on distributed edge servers to serve their service users in close geographic proximity to those edge servers. Caching edge data on edge servers profoundly reduces the retrieval latency perceived by users. However, these edge data are subject to corruption due to intentional and/or accidental exceptions. This is a major challenge for service vendors but has been overlooked. Thus, verifying the integrity of edge data accurately and efficiently is a critical security problem in the edge computing environment. A unique characteristic of the edge computing environment is that edge servers suffer from constrained computing capacities. Thus, verifying data integrity on massive edge servers individually is computationally expensive and impractical. In this paper, we tackle this Edge Data Integrity (EDI) problem with an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows service vendors to inspect data integrity and localize corrupted edge data cached on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.

In recent years, edge computing has emerged as a prospective distributed computing paradigm that overcomes several limitations of cloud computing. In the edge computing environment, a service provider can deploy its application instances on edge servers at the edge of the network to serve its own users with low latency. Given a limited budget$\mathcal {K}$for deploying applications on the edge servers in a particular geographical area, a number of approaches have been proposed very recently to determine the optimal deployment strategy that achieves various optimization objectives, e.g., to maximize the servers’ coverage, to minimize the average network latency, etc. However, the robustness of the services collectively delivered by the service provider’s applications deployed on the edge servers has not been considered at all. This is a critical issue, especially in the highly distributed, dynamic and volatile edge computing environment. In this article, we make the first attempt to tackle this challenge. Specifically, we formulate thisRobustness-oriented Edge Application Deployment(READ) problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness. Then, we provide an integer programming based approach named READ-$\mathcal {O}$for solving this problem precisely. We also provide an approximation algorithm, namely READ-$\mathcal {A}$, for finding near-optimal solutions to large-scale READ problems efficiently. We prove its approximation ratio is not worse than$\mathcal {K}/2$, which is a constant regardless of the total number of edge servers. We evaluate our approaches experimentally on a widely-used real-world dataset against five representative approaches. The experiment results demonstrate that our approaches can solve the READ problem effectively and efficiently.

Mobile crowdsensing, a new paradigm, has drawn much attention from the online community, in which mobile users are connected by using smartphones with sharing of information via mobile social networks. Multiple cooperative task allocation (MCTA) is a crucial problem in mobile crowdsensing, where each task requires more than one user to cooperatively complete. As more and more users join sensing tasks in groups, it is indispensable to develop a group-oriented crowdsensing mechanism supporting MCTA. However, existing studies generally focus on a group that can provide sufficient users to accomplish a task. Once these groups no longer exist, the corresponding task will be discarded or be performed with compromised quality. In this article, we propose a novel three-phase approach named Group-oriented Cooperative Crowdsensing (GoCC) to tackle the MCTA problem in social mobile crowdsensing. This approach exploits real-life relationships in the social network to form compatible groups, which improves the task coverage via group-oriented cooperation while achieving good task cooperation quality. Specifically, phase 1 selects a subset of users on the social network as initial leaders and directly pushes sensing tasks to them. Phase 2 utilizes the leaders to search for their socially connected users to model groups. Phase 3 presents the process of group-oriented task allocation for solving the MCTA problem. Experiments on the real-world dataset validate that our approach significantly outperforms the representative approaches.

With the rapid development of mobile devices, mobile social networks have drawn increasing attention from spatial crowdsourcing in which users sharing information via social networking applications can easily identify and participate in multiple cooperative tasks. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in real-world practices. In this article, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which improves the reliability by recruiting users who are better matched to the tasks. Our R-SAC problem is to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is$\mathcal {NP}$-hard. Then, we propose an approximation algorithm with a factor of$\ln {m} + 1$to solve the R-SAC problem, where$m$is the number of tasks. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar behaviors with the user in the social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. We evaluate the performance of our proposed approach experimentally on two widely-used real-world datasets and the results demonstrate that our approach significantly outperforms five representative approaches.

The rapid growth of mobile apps has significantly promoted the use of third-party libraries in mobile app development. However, mobile app developers are now facing the challenge of finding useful third-party libraries for improving their apps, e.g., to enhance user interfaces, to add social features, etc. An effective approach is to leverage collaborative filtering (CF) to predict useful third-party libraries for developers. We employed Matrix Factorization (MF) approaches - the classic CF-based prediction approaches - to make the predictions based on a total of 31,432 Android apps from Google Play. However, our investigation shows that there is a significant lack of diversity in the prediction results - a small fraction of popular third-party libraries dominate the prediction results while most other libraries are ill-served. The low diversity in the prediction results limits the usefulness of the prediction because it lacks novelty and serendipity which are much appreciated by mobile app developers. In order to increase the diversity in the prediction results, we designed an innovative MF-based approach, namely LibSeek, specifically for predicting useful third-party libraries for mobile apps. It employs an adaptive weighting mechanism to neutralize the bias caused by the popularity of third-party libraries. In addition, it introduces neighborhood information, i.e., information about similar apps and similar third-party libraries, to personalize the predictions for individual apps. The experimental results show that LibSeek can significantly diversify the prediction results, and in the meantime, increase the prediction accuracy.

The mobile app marketplace has fierce competition for mobile app developers, who need to develop and update their apps as soon as possible to gain first mover advantage. Third-party libraries (TPLs) offer developers an easier way to enhance their apps with new features. However, how to find suitable candidates among the high number and fast-changing TPLs is a challenging problem. TPL recommendation is a promising solution, but unfortunately existing approaches suffer from low accuracy in recommendation results. To tackle this challenge, we propose GRec, a graph neural network (GNN) based approach, for recommending potentially useful TPLs for app development. GRec models mobile apps, TPLs, and their interactions into an app-library graph. It then distills app-library interaction information from the app-library graph to make more accurate TPL recommendations. To evaluate GRec’s performance, we conduct comprehensive experiments based on a large-scale real-world Android app dataset containing 31,432 Android apps, 752 distinct TPLs, and 537,011 app-library usage records. Our experimental results illustrate that GRec can significantly increase the prediction accuracy and diversify the prediction results compared with state-of-the-art methods. A user study performed with app developers also confirms GRec's usefulness for real-world mobile app development.

Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.

The new mobile edge computing (MEC) paradigm fundamentally changes the data caching technique by allowing data to be cached on edge servers attached to base stations within hundreds of meters from users. It provides a bounded latency guarantee for latency-sensitive applications, e.g., interactive AR/VR applications, online gaming, etc. However, in the highly distributed MEC environment, cache data is subject to corruption and their integrity must be ensured. Existing centralized data integrity assurance schemes are rendered obsolete by the unique characteristics of MEC, i.e., unlike cloud servers, edge servers have only limited computing and storage resources and they are deployed massively and distributed geographically. Thus, it is a new and significant challenge to ensure cache data integrity over tremendous geographically-distributed resource-constrained edge servers. This paper proposes the CooperEDI scheme to guarantee the edge data integrity in a distributed manner. CooperEDI employs a distributed consensus mechanism to form a self-management edge caching system. In the system, edge servers cooperatively ensure the integrity of cached replicas and repair corrupted ones. We experimentally evaluate its performance against three representative schemes. The results demonstrate that CooperEDI can effectively and efficiently ensure cache data integrity in the MEC environment.

Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users' data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors' cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.