The Internet of Things (IoT) revolution has led to unprecedented data generation, necessitating a shift from traditional centralized computing to more decentralized approaches. To address the challenges of data processing closer to the source, the paradigm of Mobile Edge Computing (MEC) has emerged. It facilitates task offloading to nearby edge servers, thereby reducing delay and enhancing privacy. However, limited computation resources at the edge necessitate intelligent resource allocation through effective scheduling to maintain Quality of Service (QoS). Typically, a task comprises multiple subtasks with inherent dependencies, some of which are locally dependent and unsuitable for offloading. In subtask scheduling, one must account for both inter-subtask and local dependencies, deploying different subtask types to near-optimal computing devices, whether edge servers or User Equipment (UEs). This requirement presents significant challenges to scheduling strategies. Furthermore, since task offloading requests are inherently online, without prior task information before their arrival, improper scheduling can result in resource wastage and increased delays. To tackle these challenges, we formulate the Online Multi-DAG task Scheduling with Dependency awareness (OMSD) problem within a DAG-MEC framework. This problem is modeled as an Integer Linear Programming (ILP) problem and proven to be NP-hard. We propose a Dynamic Priority List Scheduling (DPLS) algorithm to address this problem effectively. Our algorithm strategically determines subtask execution order by evaluating upward and downward ranks, task volume, and contention levels. Simulation results demonstrate that DPLS significantly outperforms existing benchmark algorithms regarding mean task completion time, server load balance, and maximum task completion time, offering a robust solution to the OMSD challenge in MEC environments.

In recent years, Mobile Edge Computing (MEC) has been widely used for latency-sensitive tasks, but task scheduling in dynamic edge environments still faces two key challenges. First, edge devices are prone to failures, and existing fault-tolerance mechanisms lack task-aware modeling, making it hard to ensure timeliness and reliability under failures. Second, due to limited perception, high communication costs, and complex task structures, current scheduling strategies still struggle with adaptability and stability in dynamic systems. In this paper, we propose a Fault-Tolerant Discrete Soft Actor-Critic scheduling algorithm (FT-DSAC). Initially, we design a Primary-Backup-based Fault-Tolerant (PBFT) scheduling mechanism, which constrains task offloading locations and start times to effectively mitigate the impact of failures on task execution. Furthermore, we incorporate the Centralized Training and Distributed Execution (CTDE) architecture, which enables implicit collaborative scheduling decisions among edge servers to optimize system performance and reduce communication overhead. Finally, We conduct extensive experiments using both simulated data generated by DAGGEN and real-world workflow data. Experimental results show that the proposed algorithm significantly improves task execution success rates by 6%-19% and reduces latency by 9%-27% compared to mainstream benchmarks.

In sparse mobile crowdsensing, the platform con-structs services based on low-cost data collection through data inference schemes, where the quality of the inferred data directly affects the service quality. Existing data inference schemes assume that workers report trustworthy data, which is not practical in SMCS. It is urgent to establish a high-quality data collection scheme for data inference that can tolerate false data to enhance inferred data quality. To address this challenge, we propose a Truthful Multi-Task Allocation (TMTA) scheme. First, we estimate the spatiotemporal correlation between areas for iden-tifying areas with high importance to the data inference process. Second, a trust-based multi-task allocation algorithm is proposed to ensure that the sensing data from high-importance areas have high trust levels. Third, a multi-armed bandit based trustworthy worker identification strategy is proposed to prioritize Multi-Task allocation for workers who can be effectively identified as trustworthy. Finally, a truthful discrete heuristic algorithm is proposed to optimize the multi-task allocation using the proposed hybrid neighbor-mode strategy, which reduces the difficulty of searching for high-utility multi-task allocations. Extensive exper-iments on two real-world air-quality datasets demonstrate that TMTA consistently outperforms six baseline methods, achieving average reductions of 24.70% in RMSE and 17.79% in sensing cost across five experimental scenarios.

WebAssembly (Wasm), a binary instruction format designed for efficient cross-platform execution, has rapidly become a foundational web standard, widely adopted in browsers, client-side, and server-side applications. However, its growing popularity has led to an increase in Wasm-targeted malware, including cryptojackers and obfuscated malicious scripts, which pose significant threats to web security. In spite of progress in deep learning based detection methods for Wasm malware, such as MINOS, these approaches face substantial performance degradation in adversarial environments. In our experiments, MINOS's detection accuracy dropped to 49.90% under adversarial attacks, revealing critical vulnerabilities. To address this, we introduce WasmGuard, a robust malware detection framework tailored for Wasm. WasmGuard employs FGSM-based adversarial training with prior-based initialization for perturbation bytes in customized sections, coupled with a novel adversarial contrastive learning objective. Using our large-scale dataset, WasmMal-15K (publicly available at https://github.com/Yuxia-Sun/WasmMal GitHub), WasmGuard outperforms six competing methods, achieving up to 99.20% Robust Accuracy and 99.93% Standard Accuracy under PGD-50 adversarial attacks, while maintaining low training overhead. Additionally, we have released WebChecker, a WasmGuard-powered browser plugin, providing real-time protection against malicious Wasm files, at https://github.com/Yuxia-Sun/WasmGuard.

Decentralized Applications (DApps) have garnered significant attention due to their decentralization, anonymity, and data autonomy. However, these systems face potential privacy challenge. The privacy challenge arises from the necessity for external service providers to collect and process user interaction data. The untrustworthiness of these providers may lead to privacy breaches, compromising the overall security of such DApp environments. To address this challenge, we model the interaction data in the DApp environments as dynamic graphs and propose a dynamic graph publication method named HMG (Hidden Markov Model for Dynamic Graphs). HMG estimates the interaction probabilities between users by extracting the temporal information from historically collected data and constructs an optimized model to generate synthetic graphs. The synthetic graphs can preserve the dynamic topological characteristics of the interaction processes within DApp environments while effectively protecting user privacy, thus assisting external service providers in performing effective analyses. Finally, we evaluate the performance of HMG using real-world datasets and benchmark it against commonly used graph metrics. The results demonstrate that the synthetic graphs preserve essential features, making them suitable for analysis by service providers.

Federated learning enables training across multiple entities while ensuring data security and the effectiveness of knowledge dissemination. Despite its benefits, it remains susceptible to privacy breaches by both external and internal adversaries, who may exploit data or model parameters to glean sensitive participant information or disrupt the training process, thus compromising participant privacy and security. This paper proposes a novel methodology, Adversarial Samples for Defense Inference Attack (ASDIA), aimed at dual protection of data privacy and model robustness within federated learning through adversarial samples and gradient reconstruction. ASDIA includes gradient processing approach before uploading: initially identifying privacy-sensitive gradient, followed by the injection of well-calibrated noise to these gradients. This method not only obfuscates the adversary's classification demarcations but also aids in model performance recovery, all the while maintaining computational efficiency. ASDIA reduces the efficacy of attacks to near-random guessing levels and shows better balance between the model utility and privacy protection compared to the most advanced defense strategies. Additionally, regarding model performance, ASDIA proves its merit across diverse datasets under overfitting and non-overfitting scenarios.

Edge computing (EC) reduces task processing and content download delay by providing computation and caching resources directly to task offloading (TO) users and content request (CR) users. However, existing studies often focus exclusively on either TO users or CR users within EC networks, neglecting the interaction between these two groups. To address this gap, we investigate the offloading and caching decision-making in scenarios where TO and CR users coexist. Furthermore, we employ full-duplex (FD) technology to enhance spectral utilization for edge-end transmissions. Specifically, we jointly optimize offloading and caching in FD-enabled EC networks. To accomplish this, we decompose the formulated optimization problem into three sub-problems using the alternating optimization (AO) method. We then propose a three-subproblem alternating iterative delay minimization algorithm to effectively tackle the challenges of offloading and caching. Additionally, we analyze the convergence and complexity of our proposed algorithm. Finally, we conduct extensive simulations to evaluate the effectiveness of our approach. The simulation results demonstrate that the delay reduction achieved by our algorithm is between 24.78% and 89.23% greater than that of comparative algorithms.

Employing the caching resources of end users via Device-to-Device (D2D) communication to assist the edge server in microservice caching is promising to further alleviate the network congestion of the Internet of Things (IoT). However, significant extra energy consumption prevents the caching system from maximizing cache utility if all end users cache simultaneously. In this paper, we propose two novel end-edge collaborative microservice caching algorithms in D2D-assisted networks. First, we construct a D2D caching sharing link graph from the aspects of physical and social attributes of end users and introduce the Entropy-based Partitioning Around Medoid (EPAM) algorithm to identify critical users. Second, to address the challenges posed by unknown time-varying user preferences, we model the end-edge collaborative caching problem as a Multi-Agent Multi-Armed Bandit (MAMAB) problem, thus developing two caching decision schemes, i.e, Edge-Centric Scheme (ECS) and User-Centric Scheme (UCS), to accommodate different decision sequences. The simulation results show that the EPAM-ECS and EPAM-UCS have at least 29.2% and 39.3% improvement compared with other baseline algorithms.

Crowdsensing Data Trading (CDT) has emerged as a novel data trading paradigm, where market stability is crucial during the transaction matching process. However, most existing CDT systems usually assume that the preferences of both parties are known and the third-party trading platform is trustworthy, which is impractical in real-world scenarios and leads to significant challenges in reliability and privacy preservation. To address these challenges, we propose a Privacy-Preserving and Stable Data Trading for Unknown Market based on Blockchain and Bilateral Reputation (PPSDT-UMBBR) scheme in the decentralized CDT system. First, a privacy-preserving bilateral preference initialization method is designed to achieve the initial matching of buyers and sellers without exposing their location and attribute privacy. Then, a stable matching method based on dynamic bilateral preference updating is proposed, integrating Differential Privacy, Stable matching theory, and a strategy based on Asymmetric Bilateral Preferences with Multi-Armed Bandits (DPS-ABPMAB). Finally, we theoretically analyze the security and prove that the market outcome is$\delta$-stable. Furthermore, compared to other benchmark methods based on real datasets, our proposed DPS-ABPMAB algorithm improves the average accumulative reward by at least 4.22%, and reduces the average accumulative regret and the mean evaluation error rate by at least 66.86% and 7.35%, respectively.

Time-series data is crucial for the development of mobile crowdsensing (MCS). Participant’s privacy is one of the major concerns because MCS data often contain sensitive individual information. Existing privacy-preserving mechanisms for time-series data do not preserve salient patterns of the time series and take into account that the perturbed data may fall outside the valid data interval, leading to data distortion. To overcome these deficiencies, we first perform dynamic feature extraction and incorporate an adaptive sampling scheme that is sensitive to the distinction of short-term patterns and stable patterns. Then a Bounded Laplace (BLP) mechanism is adopted with a theoretical guarantee on the data perturbation range so as to address the issue of data going beyond the valid range. We establish theoretically that the proposed Adaptive Sampling and Randomized perturbation mechanism based on dynamic Temporal patterns (ASRT) satisfies the metric-based$w$-event$\epsilon$-LDP for privacy protection. Empirical results of extensive experiments on realworld datasets demonstrate that our proposed method is superior to existing protection mechanisms and the efficacy of our ASRT in enhancing data utility without introducing outliers.

Driven by the increasing demands of vehicular tasks, edge offloading has emerged as a promising paradigm to enhance quality of experience (QoE) in Internet of Vehicles (IoV) networks. This approach enables vehicles to offload computation-intensive tasks to edge servers, resulting in reduced computation delays and lower energy consumption. However, traditional binary offloading limits the efficiency of edge offloading. To address this gap, we propose a partial offloading strategy that jointly optimizes the offloading ratio, computation, and communication resources in IoV. Recognizing the varying priorities of vehicular tasks regarding task delay and energy consumption, we formulate two distinct scenarios: one focused on minimizing delay and the other on minimizing energy consumption. Furthermore, we employ a reinforcement learning approach to establish a multi-dimensional joint optimization function by setting different objectives for each scenario. Based on this framework, we introduce a multi-state iteration deep deterministic policy gradient algorithm (SIDDPG), which effectively determines task partitioning and resource allocation. Simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of task delay and energy consumption.

Cloud service centers (CSCs) can purchase edge computation resources to improve service quality in mobile cloud-edge computing networks. However, edge servers (ESs) are owned by different entities, and dishonest entities may launch computational forgery attacks, i.e., the ES falsely reports its idle computation resources to win more tasks for increased revenue. Most existing approaches ignore the threat of dishonest ESs. To address the challenges, we design aTrust-basedComputationOffloading (TCO) framework. First, we construct the problem for minimizing thedifference between the CSC'scost and theexpectedrevenue (DCER), which is a mixed-integer nonlinear programming problem. Second, we develop a trust-based computation offloading method that quickly finds a good solution by decomposing the problem. Finally, a two-tier trust evaluation method was proposed to obtain accurate trust values. Experimental results indicate that TCO's comprehensive performance surpasses the benchmarks and significantly enhances computation offloading reliability with a lower performance loss. Notably, tasks are preferentially offloaded to honest ESs to ensure their revenue and promote ESs’ honesty under the TCO framework. Additionally, compared with no trust mechanisms, TCO reduces the service timeout count in an interval by 34.37% - 73.80% with a performance loss of only 1.42% - 4.10%.

Graph data analysis has been used in various real-world applications to improve services or scientific research, which, however, may expose sensitive personal information. Differential privacy (DP) has become the gold standard for publishing graph data while still protecting personal privacy. However, most existing studies over differentially private graph data publication mainly focus on static unweighted graphs. As interactions between entities in real systems are often dynamically changing and associated with weights, it is desirable to consider the more general scenario of continuous weighted graph publication under DP in the temporal dimension. Therefore, we investigate the problem of publishing weighted graphs satisfying DP under continuous monitoring. Specifically, we consider a server that continuously monitors user data and publishes a sequence of weighted graph snapshots. We propose SwgDP, a novel framework that leverages historical graph data to guide current snapshot generation. SwgDP consists of four key components: node adaptive sampling, dynamic weight optimization, prediction-based community detection and weighted graph generation. We demonstrate that SwgDP satisfies DP, and comprehensive experiments on four real-world datasets and four commonly used graph metrics show that SwgDP can effectively synthesize weighted graph at any time step.

Mobile crowdsourcing (MCS) has attracted widespread attention by recruiting users with mobile devices to collect crowdsourcing data. Existing research on MCS assumes that the platform has sufficient users. However, platforms in their early stages of development face the cold start problem, which can lead to their inability to grow or even result in bankruptcy. While some studies try to solve it by recruiting users through social networks to participate in crowdsourcing tasks, they only focus on how to recruit more users without addressing the issue of user retention. This can lead to an increasing proportion of users losing interest in the platform and dropping out and thus it fails to solve the cold start problem truly. In light of this, we present a task recommendation-based method to recruit new users via the social network and keep registered users active on the platform. Specifically, we first use an extended independent cascade model to describe the recruitment of users through social networks. Secondly, we use a task acceptance model to describe user decisions. Finally, we utilize a fuzzy control system that incorporates spatiotemporal crowdsourcing information to predict user behaviour and recommend tasks to users most likely to complete them. Extensive experiments on large-scale real datasets were conducted to evaluate the proposed solution. The results indicate that compared to existing methods such as SocialRecruiter, our solution reduces the 30-day average user churn rate by 23.90% while significantly boosting user retention and task completion rates by up to 23.73% and 48.7%, respectively.

Recently, the lightweight single image super-resolution (SISR) model based on information distillation has attracted the attention of many researchers due to its ability to recover high-resolution images quickly. We reassess and delve into the advantages and disadvantages of information distillation structures, and propose an information scaling distillation network (ISDN) for lightweight single image super-resolution, which can accurately and efficiently restore high-resolution images. By optimizing the distillation branch and feature branch of the information distillation, we meticulously designed the stacked block deep scaling distillation block (DSDB) to enlarge the receptive field and increase the network depth. We mainly optimize and design from two aspects. Firstly, we extract redundant information in the distillation branch and integrate it into multiple layers to form deep information transmission. Secondly, we design a blueprint deep scaling residual (BDSR) in the feature branch, which can extract advanced semantic image information, compress and expand feature channels. The qualitative and quantitative results on various benchmark datasets demonstrate the advantages of our model in terms of model parameters, multiply-accumulate operations, test efficiency, and image reconstruction quality. Code is available at https://github.com/ycLi-CV/ISDN-main.

Edge Intelligence aims to push deep learning (DL) services to network edge to reduce response time and protect privacy. In implementations, proximity deployment of DL models and timely updates can improve the quality of experience (QoE) for users, but increase the operation cost as well as pose a challenge for task assignment. To address the challenge, a joint online optimization problem for DL model deployment (including placement and update) and freshness-sensitive task assignment is formulated to improve QoE and application service provider (ASP) profit. In the problem, we introduce the age of information (AOI) to quantify the freshness of the DL model and represent user QoE as an AOI based utility function. To solve the problem, an online model placement, update, and task assignment (MPUTA) algorithm is proposed. It first converts the time-slot coupled problem into a single time-slot problem using the regularization technique, and decomposes the single time-slot problem into model deployment and task assignment subproblems. Then, using the randomized round technique to deal with the model deployment subproblem and the graph matching technique to solve the task assignment subproblem. In simulation experiments, MPUTA is shown to outperform other benchmark algorithms in terms of both user QoE and ASP profit.

Mobile Crowd Sensing (MCS) has become a prevalent large-scale and low-cost data collection paradigm by employing workers, and the location and bid privacy of both task and workers should not be leaked to the third party to prevent the adversary from attacking. Existing privacy preserving worker recruitment schemes have taken the location and quality into consideration, but ignore the bid privacy. To tackle this issue, a two-stage Location and Bid Privacy Preserving based Quality-aware Worker Recruitment (LBPP-QWR) scheme is proposed in this paper. In the first stage, to select those workers who satisfy the specified location and bid range of the task in the encrypted state, we propose a hybrid encryption scheme of matrix encryption and asymmetric encryption technique in the MCS platform. For the second stage, after obtaining the preliminary worker set via the platform, we propose a Knapsack Worker Selection (KWS) algorithm to recruit those high-quality and low bid workers under the budget constraint in the Data Requester (DR). Considering that there are quality-unknown workers, we further propose an improved.-KWS algorithm based on.-greedy algorithm by combining the exploration and exploitation mechanism to learn the quality of worker. Extensive experiments conducted on real-world datasets demonstrate that our proposed scheme can improve the average total quality by 17.96%-83.34%, and the cost efficiency by 27.99%-67.90% for the DR compared with other benchmark methods.

Mobile crowdsourcing (MCS) endeavors to attain reliable truth by recruiting large numbers of users with handheld mobile devices to collect the data. However, during the early stages of platform development, MCS encounters the cold start problem, failing to complete the task. Existing research addresses this issue by leveraging social networks for user recruitment. Nevertheless, there is a predominant focus on the user quantity, and the quality of task completion is ignored. Additionally, fairness considerations among users are lacking. Therefore, this article proposes recruitment based on social users’ trust (RSUT) to solve the cold start problem while maintaining high task completion quality. Specifically, we propose the activation model based on the user awareness to simulate the influence of social users and task attributes on activation from the perspective of unregistered users, which is more realistic. Additionally, we measure the user’s contribution and then design a reward system based on the user’s contribution to ensure fairness. Finally, social network-based trust evaluation is proposed to identify malicious users and update rewards in real time according to task requirements to ensure high-quality completion of tasks within budget constraints. Extensive experimental results demonstrate the superior performance of RSUT compared to the state-of-the-art methods in task completion quality, user recruitment, and task completion rate.

In mobile crowd sensing (MCS), how to obtain accurate truth estimation under privacy preservation has gained much attention. It is important to prevent the leakage of the sensing data, weight, estimated truth, and intermediate truth to third parties to avoid attacks from adversaries when aggregating a large amount of data collected by workers. In addition, dishonest or malicious workers may report false or malicious data. Therefore, we propose a weight-based trust identification for privacy-preserving truth discovery (WTIPPTD) scheme to enhance the accuracy of truth discovery by identifying the trust and data qualities of workers, and then recruiting trusted high-quality workers. First, the garbled circuit (GC) is used for weight update in the encrypted state, and a trust evaluation scheme is proposed based on weight credibility. Second, a data quality evaluation scheme for workers is designed, and the trusted high-quality workers are recruited to improve the accuracy of truth discovery while reducing the recruitment cost. Finally, we conduct experiments with a large number of real and synthetic data sets, and the results show that our proposed scheme significantly improves the accuracy of truth discovery by 17.80%–98.61% and substantially reduces worker recruitment cost by 16.43%–26.50%.

Sparse Mobile CrowdSensing (SMCS) effectively lowers sensing costs while maintaining data quality, offering an alternative approach to data collection. Unfortunately, the fact that data contain sensitive information raises serious privacy concerns. Local Differential Privacy (LDP) has emerged as the de facto standard for ensuring data privacy. However, the LDP based on the perturbation concept causes a substantial reduction in the data utility of the SMCS system. To address this problem, we propose a novel scheme named enhancing Sparse mobile crowdsensing With manifold Optimization and differential Privacy (SWOP). Specifically, we first revisit the Gaussian mechanism based on the fact that data utility intervals are ubiquitous in sensing tasks, and introduce a novel perturbation mechanism, namely Truncated Gaussian Mechanism (TGM). Subsequently, we perturb user-collected data by locally injecting noise sampled from TGM and deduce a sufficient condition for the scale parameter to ensure ϵ-LDP. Furthermore, we model the data inference with privacy-preserving properties as an unconstrained optimization problem on a Riemannian manifold and solve it using the nonlinear conjugate gradient method. Extensive experiments on large-scale real-world and synthetic datasets are conducted to evaluate the proposed scheme. The results demonstrate that SWOP can greatly enhance the utility of data inference while ensuring workers’ data privacy compared to baseline models.

Mobile crowdsourcing aims to recruit enough workers holding mobile devices to collect data. Nevertheless, the platform will have cold start problems when the number of workers is limited. Existing studies have proposed solving this problem by propagating tasks to social networks for social recruitment. However, they neglect to verify workers’ propagation, leading to malicious workers reducing the platform's utility. Furthermore, during propagation verification, it is imperative to protect the privacy of social relationships among workers, as it can significantly influence the propagation. Therefore, this paper proposes Zero-knowledge Propagation Verification based on Social Relationship Encryption (ZPV-SRE) to improve the platform's utility. Specifically, we transform the propagation verification problem into a problem of computing the solution of the function. Then, the Zero-knowledge proof is used to prove the propagation, in which the worker's social relationship is protected through homomorphic encryption. Considering that ZPV-SRE will incur a significant time cost, we propose Trust-guided Zero-knowledge Propagation Verification based on Social Relationship Encryption (TZPV-SRE), which updates the worker's trust based on the verification results and selects suspicious workers for verification. The experimental results show ZPV-SRE improves the platform's utility as high as 104.05% over the state-of-the-art methods, while TZPV-SRE reduces time costs and ensures improvement.

Mobile Crowdsourcing (MCS) has become a novel paradigm for enabling data collection by worker recruitment, and the reputation plays a crucial role in achieving high-quality data. Although identity, data, and bid privacy preserving have been thoroughly investigated with the advance of blockchain technology, existing literature barely focuses on reputation privacy, which prevents malicious workers from submitting false data that could affect truth discovery for data requester. Therefore, we propose a Blockchain-Based Reputation Privacy Preserving for Quality-Aware Worker Recruitment Scheme (BRPP-QWR). First, we design a lightweight privacy preserving scheme for the whole life cycle of the worker’s reputation, which adopts sub-address retrieval technique combined with Pedersen Commitment and Compact Linkable Spontaneous Anonymous Group (CLSAG) signature to enable fast and anonymous verification of the reputation update process. Subsequently, to tackle the unknown worker recruitment problem, we propose a Reputation, Selfishness, and Quality-based Multi-Armed Bandit (RSQ-MAB) learning algorithm to select reliable and high-quality workers. Lastly, we implement a prototype system on Hyperledger Fabric to evaluate the performance of the reputation management scheme. The results indicate that the execution latency for the reputation score verification and retrieval latency can be reduced by an average of 6.30%–56.90% compared with ARMS-MCS. In addition, experimental results on both real and synthetic datasets show that the proposed RSQ-MAB algorithm achieves an increase of at least 20.05% in regard to the data requester’s total revenue and a decrease of at least 48.55% and 3.18% in regret and Multi-round Average Error (MAE), respectively, compared with other benchmark methods.

Multi-tier computing (MC) is a promising architecture that integrates cloud computing, fog computing, and edge computing to provide users with a consistent experience of computing services by fusing computing devices within the network through virtualization technology. Although MC combines powerful computation and communication resources, the massive demand from Service Function Chain (SFC) deployments continues to make it challenging regarding resource constraints, latency satisfaction, and revenue-cost tradeoffs. To this end, in this article, we study an SFC deployment problem in MC and formulate a problem for maximizing the revenue of online SFC deployment under latency, computation resources, and communication resources constraints. To solve this online problem better, we construct a computation and communication resource cost model and transform the original online problem into a deployment cost minimization problem and a request admission problem by an alternating optimization approach. To solve the two subproblems, we propose an online approximation algorithm with a provable competitive ratio for the particular scenario with no latency requirements. Then, based on the cost model, we propose an online heuristic algorithm that adopts a binary search method for the original problem with latency requirements. Simulation experiments show that our two proposed online algorithms have advantages in total revenue, running time, and load balancing compared with other comparison algorithms.

With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets’ computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.

Abstract Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of intensity inhomogeneous images. Firstly, global and local features are utilised to collaboratively estimate the image, which devotes to compensating for intensity inhomogeneity. The local estimation retains detailed spatial information, and the global estimation mainly contains the regional information of the partitioned object. Then, during the construction of the energy functional, joint estimation is introduced to create the external energy. To acquire the precise location of the boundary, a weighting factor indicated by the gradient is introduced into the internal energy. Finally, after the numerical calculation of the energy functional by additive operator splitting algorithm, this method achieves the desired performance in terms of accuracy and robustness. Experimental results verify this method outperforms the comparative methods and can be applied to many real‐world scenarios.

The large number of mobile-sensing vehicles traveling in cities offer a novel solution to the collection of vast amounts of multimedia data packets. When a vehicle passes through the data center (DC), the collected multimedia data packets will be transmitted to the DC. Due to the mobile characteristic of vehicular sensor networks, the main challenge lies in how to improve the multimedia data delivery ratio and balance the data packet collections. In this paper, in consideration of delay and delivery factors, a novel routing method is proposed to optimize multimedia data collections in mobile sensing vehicles (DDSVs). This method targets at balancing multimedia data collections, improving the delivery ratio of the multimedia data, and reducing the delay ratio in Internet of Things (IoT) networks. In the DDSV scheme, two rules are designed for improving the collection of multimedia data in the IoT. These rules pertain to: 1) data and 2) vehicular priorities. First, different regions hold different priorities of data packet transmission, which can improve the delivery ratio in the suburban areas and reduce the delay ratio. Meanwhile, this scheme is capable of guaranteeing the balance of multimedia data collection. Second, the vehicular priority is proportional to the probability of a vehicle reaching a DC. Therefore, the data should be forwarded to vehicles with higher priorities, that is, the vehicles which are more likely to pass by the DC. By using these two rules, the DDSV scheme can improve the performances of the multimedia data delivery ratio, compared with the conventional optimal vehicular data forwarding scheme. In the simulation experiments, the DDSV scheme utilizes multidatasets of Beijing city, where the average delay for data collection can be decreased by 17.3% in general, and by 41.8% in the suburban areas; the average data delivery ratio can be improved by 16.9% in comparison to the previous studies.

Rapid, reliable and energy-efficient programing code dissemination is a challenging issue and offers a programmable and flexible network architecture for software-defined wireless networks (SDWNs). However, many schemes for programing codes in large-scale network incur a longer dissemination convergence time (DCT) and lower energy efficient in loss nature of wireless channels. In this paper, an adaptive broadcast dissemination (ABD) scheme is proposed to achieve low DCT and high network lifetime for SDWNs. An ABD scheme makes full use of energy left of nodes in areas far from the sink. In an ABD scheme, codes are broadcast constantly few times by nodes near the sink to save energy, and many times by nodes in areas far from the sink to rapidly spread software codes. Thus, an ABD scheme can reduce transmission delay for spreading software code while retaining network lifetime. Theoretical analysis and experimental results show that DCT in an ABD scheme is reduced by 28.12–29.86% compared with the hybrid scheme, while retaining network lifetime.