Cross-domain recommendation (CDR) has emerged as a promising solution by effectively alleviating data sparsity by leveraging information from auxiliary domains. However, a major challenge in CDR is its dependence on predefined alignment rules (e.g., structural or distribution matching) to achieve cross-domain knowledge transfer, which impose fixed transfer patterns and lack the flexibly need for diverse cross-domain scenarios. Furthermore, most existing approaches still rely on coarse-grained representations. Knowledge transfer built upon imprecise representations can, even with improved alignment rules, instead lead to negative transfer in the target domain. To address these challenges and optimize recommendation efficacy, a new framework named meta-learning driven few-shot knowledge transfer with dual-stage adaptive data replay for cross-domain recommendation (MFACDR) is proposed. Specifically, a new meta-learning driven few-shot knowledge transfer method is proposed. This method leverages overlapping parts as anchors to guide the non-overlapping parts in autonomously exploring alignment rules through meta-learning, thus enabling few-shot knowledge transfer and flexible handling of different cross-domain scenarios. In addition, a dual-stage adaptive data replay mechanism is proposed, which enables fine-grained cross-domain adaptability and helps to mitigate negative transfer. Extensive experiments on three real-world datasets consistently demonstrate the superior effectiveness and robustness of the proposed MFACDR.

To address the challenges of high uncertainty, inter-subject variability, and inefficiency multi-feature utilization in motor imagery electroencephalogram (MI-EEG) classification, this study proposes amultiviewtransferTakagi-Sugeno-Kang (TSK) fuzzy classifier withsoftvariable embedded anddiscriminativestructural preservation (MVT-TSK-SVDS). First, a transfer learning mechanism incorporating soft variable embedding in the consequent part is developed. This mechanism establishes cross-domain correlations via a shared consequent and representation matrix. Within this framework, soft variable embedding and low-rank constrained discriminative learning work in concert to effectively capture supervision information and cross-domain relationships. Second, a local-global structural preservation term incorporating graph embedding and low-rank constraint is implemented to maintain local discriminative information from source domain while integrating global geometric patterns across all data. Third, a multiview adaptive learning framework is designed to address feature representation diversity and information loss during knowledge transfer. MVT-TSK-SVDS dynamically optimizes view-specific contributions through entropy maximization criterion while ensuring collaborative decision via consistency constraints. Experimental results validate strong generalization between and across datasets. Our model achieves 62.16% and 72.71% accuracy in cross-subject tasks on BCI-IV 2a and OpenBMI, respectively. In cross-dataset evaluations, it attains 62.75% accuracy on BCI-IV 2a to OpenBMI and 65.08% accuracy on OpenBMI to BCI-IV 2a, respectively.

High Frequency (HF) RFID technology enhances operations in logistics, retail, and access control, necessitating efficient tag identification. This paper addresses the challenge of tag collisions in HF RFID systems, where existing protocols such as Time Division Multiple Access (TDMA) are not fully optimized for HF requirements. We introduce a novel hybrid-mode protocol utilizing the ISO 15693 standard, which supports either 2 or 16 branches per node in its tree-based anti-collision mechanism. By dynamically switching configurations based on real-time tag density, our protocol significantly improves identification efficiency. We validate our approach through theoretical analysis and extensive simulations, along with real-world experiments, demonstrating marked improvements in operational speed and efficiency. This study is the first to tailor a time-efficient tag identification protocol for commercial HF RFID systems, offering substantial benefits in operational efficiency and user experience across various industries.

UAV-enabled edge storage systems provide data storage services to users by deploying UAVs in areas lacking infrastructure coverage, overcoming delay limitations and improving Quality of Service (QoS). Most existing studies focus on storing replicas on UAVs to ensure low-latency data access. Nonetheless, replica-based strategies incur high storage cost, posing significant challenges for UAVs with limited storage resources. In this paper, we introduce erasure coding into the UAV-enabled edge storage system, aiming to reduce user data access latency while minimizing storage cost. However, the mobility of users and the non-fully-connected nature of the UAV network pose new challenges for the coupled decisions of data encoding, block placement, and access. In this paper, we propose a Mobility-Enhanced Hierarchical Deep Reinforcement Learning algorithm (ME-HDRL). Specifically, we design a trajectory prediction algorithm combining CNN and ConvLSTM to account for user mobility in decision-making. We further decompose the original problem into two subproblems: data encoding and placement, as well as block access. A hierarchical deep reinforcement learning algorithm involving multiple UAV agents and an edge agent is proposed to collaboratively learn optimal decisions. To improve the convergence of the algorithm, we design an invalid action filter to reduce the action space. Experimental results show that our approach outperforms existing rule-based and reinforcement learning-based algorithms in various scenarios, exhibiting significant convergence improvements and a substantial reduction in both storage cost and user data access latency.

With increasing computational demands and limited network resources in marine environments, efficient service caching and task offloading have become critical. In such environments, Autonomous Underwater Vehicles (AUVs) rely on Unmanned Surface Vehicles (USVs) as relays, forming a cross-domain network comprising underwater acoustic and above-water RF links. However, the heterogeneity in bandwidth, latency, and bit error rates introduces challenges for reachability analysis and delay estimation. This paper addresses the joint optimization of caching, task offloading, and resource allocation in a cross-domain marine network composed of offshore base stations, USVs, and AUVs. To tackle the inherent heterogeneity in network links and decision timescales, we formulate the problem as a two-time-scale Hierarchical Markov Decision Process (H-MDP) and propose a Two Time-Scale Deep Reinforcement Learning (T2S-DRL) approach that integrates a hybrid policy network and a lightweight structure-aware action masking mechanism. The large time-scale agent optimizes caching decisions, while the short time-scale agent focuses on offloading and resource allocation. Extensive simulations show that our approach significantly reduces task execution delay and energy consumption, validating its effectiveness.

We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, includingwhichtasks need to be processed by unmanned aerial vehicles (UAVs),howto allocate these tasks and balance energy across UAVs for delay-sensitive requirements. However, little attention has been devoted to exploring the above coupled decision-making problem in AEC with various resource and energy constraints, which is further complicated by energy dynamics (UAV battery states), task-specific consumption, and allocation-feedback balance. In this paper, we formulate a multi-dimensional joint optimization problem, simultaneously optimizing task allocation and energy rewarding to maximize long-term system rewards while balancing service quality and energy efficiency. To this end, we propose a green aerial edge computing framework where partial UAVs are equipped with energy harvesting modules to collect ambient energy. To circumvent the intractable computational complexity arising from the coupled energy states of massive UAVs, we design a distributed solution method based on the mean field game, which decouples the dense multi-agent interactions into a game between an individual UAV and the aggregate population state, thereby transforming the complex global optimization problem into a set of equivalent scalable subproblems. We develop an optimal energy valuation scheme to guide UAV behavior. Numerical results show that our mechanism can effectively ensure sustainable system operation while maintaining high quality of service for metaverse users, outperforming existing methods in both system sustainability and service responsiveness.

We consider the efficient resource allocation task in the blockchain-based mobile edge computing (MEC) system that requires decentralized transaction management to validate transactions between edge servers (ESs) and mobile devices (MDs). In such task allocation process (where MDs' resources are limited and privacy-sensitive), it is a significant challenge to guarantee individual rationality with satisfactory system stability while enabling flexible task offloading under various locality constraints (e.g., communication distance, bandwidth and delay). In this paper, we formulate the target problem as a blockchain-assisted task-resource matching model, and then propose a near optimal locality-aware resource allocation mechanism over smart contract to enable automatic and efficient transactions in MEC system. More specifically, for the service agents selection, we design the preference-based selection strategy to get highest estimated profit. For the flexible task offloading, we develop the minimum delay task graph partitioning algorithm to determine the optimal task offloading solution for MD under different resource bundles. For the task-resource matching, we propose a task-resource matching game (based on potential game) with the second lowest cost strategy to determine the matching of task-resource and decide the price of resource bundle. For the transaction verification and block allocation, we propose a social welfare-driven consensus mechanism to enable verified transaction and fair block allocation in a reward-free way. Strict theoretical analysis and extensive simulations demonstrate that our mechanism guarantees individual rationality, Nash Equilibrium, and stable near optimal solution.

Understanding the complex relationships and behavioral preferences among users in social networks is crucial for elucidating the mechanisms of information diffusion. While recent information diffusion prediction methods, empowered by graph neural networks, have advanced the learning of user representations, they rarely exploit spread interaction feedback. This feedback reflects users' interests in engaging with information and serves as a key driver of information diffusion. Moreover, the underutilization of unlabeled data leads to an overreliance on labeled data, consequently constraining the model's self-learning and generalization capabilities. To address these limitations, we propose a novel microscopic diffusion prediction model based on interaction-enhanced graph neural sequence contrastive learning (IEGSCL). Specifically, we construct a triple graph to explore the diversity of user relationships and preferences through the lenses of trust and interaction. A self-supervised graph contrastive learning module is designed to transfer user intents, maximizing the utility of unlabeled data and tackling the feature extraction challenge. Furthermore, we devise an information-driven gating strategy that adaptively modulates the contributions of social and interactive intents to cascade participation, thereby effectively integrating interaction feedback into the cascade modeling. Finally, we employ maximum mean discrepancy (MMD) to enforce distributional consistency between global relationship representations and local cascade encodings. Extensive experiments on four public datasets validate the superior performance of the proposed model over existing baselines.

Satellite-assisted Mobile Edge Computing (MEC) is a promising paradigm for enabling low-latency and high-efficiency computing in deep-sea and far-offshore marine environments. However, the inherent heterogeneity of three-layer marine networks—comprising satellites, Unmanned Surface Vehicles (USVs), and Autonomous Underwater Vehicles (AUVs)—introduces unique challenges. These include the coupling of underwater acoustic and above-water Radio Frequency (RF) communication links, the highly constrained computing and caching resources of edge devices, and the strong interdependence between service caching, vertical task offloading, and horizontal migration. Existing solutions often overlook these cross-layer dynamics and the spatio-temporal interactions among network nodes, leading to suboptimal task scheduling and degraded system utility. To address these challenges, we formulate a joint optimization problem that maximizes the Quality of Experience (QoE), with decision variables spanning caching placement, task migration, and offloading under resource constraints. Through rigorous theoretical analysis, we prove that the formulated problem is NP-hard, highlighting its inherent computational intractability. To overcome this, we propose an Attention-Enhanced Multi-Agent Reinforcement Learning algorithm (AE-MARL), which adopts a hybrid policy network to learn discrete decisions and continuous resource allocation. Furthermore, a lightweight attention module is integrated to infer the importance of partial observations and guide collaborative decision-making across agents. Extensive experiments and analysis under diverse system configurations demonstrate that AE-MARL consistently outperforms state-of-the-art baselines.

Continuous crowdsensing in Mobile CrowdSensing (MCS) involves ongoing monitoring to gather real-time data over extended periods. A key challenge is determining appropriate intervals between consecutive data samplings to capture temporal variations, especially in unknown dynamic environments. Traditional Age-of-Information (AoI) driven methods maintain data freshness but can be costly and result in data redundancy. To address this, we integrate the AoI metric with information entropy difference to create a novel indicator, Composite Data Value (CDV), balancing data freshness and redundancy. Based on it, we investigate the online adaptive sampling problem for continuous crowdsensing in unknown dynamic environments. This problem is challenging due to the vast space of sensing strategies, difficulty in estimating rewards with unknown distributions and varying rates of change, and the degradation of optimal strategies as the environment evolves. Using a multi-armed bandit framework, we propose AdaScs, an online adaptive sampling optimization approach to maximize long-term CDV performance. First, AdaScs develops compact sensing strategies through limited trials. Then, it adaptively performs online sampling based on evolving reward estimations, identifying optimal strategies, detecting environmental drifts, updating strategies, and adjusting cycle lengths. Our results show that AdaScs outperforms all baselines, with accuracy increasing by 22.6% and reaction time at last improving by 54.6%.

Recommender systems in various applications often encounter the challenge of cold-start, which refers to how to provide recommendations for completely new users. Cross-domain recommendation offers a solution to address this cold-start issue by leveraging user interaction information from other domains and providing recommendations for users in the target domain. However, applying the classic two-tower model in cross-domain scenarios for pure cold-start users proves challenging, and most existing cross-domain cold-start recommendation models adopt an embedding-mapping framework that lacks end-to-end efficiency. The parallel training recommendation method lacks consideration of the domain-level intrinsic characteristics of cross-domain information. In this paper, we propose a generalized framework that Domain-level Disentanglement framework based on information enhancement for Cross-domain Cold-start Recommendation. On one hand, we achieve deep utilization of domain-level information through independent extraction of domain knowledge and fusion using heuristic strategies. On the other hand, our model is incorporated with an information enhancement network based on user attention and a user personalized adaptor. We introduce measures to assess user variability and immutability in cross-domain recommendation, aiming to eliminate inter-domain bias and highlight individual user preferences. Experimental results on widely used cross-domain recommendation datasets demonstrate that our proposed model outperforms state-of-the-art methods, validating its effectiveness.

Social recommendation leverages the social connections between users to mitigate the issue of data sparsity and enhance recommendation quality. Although existing related works show their effectiveness, there remain two critical questions: i) The patterns of preference interactions among users are varied and heterogeneous. Current models struggle to accurately capture preference shifts from user interactions in noisy social environments. ii) Existing methods handle the integration of auxiliary information coarsely, potentially introducing noise and leading to biases in user preferences. To address the limitations above, we introduce a novel framework named Robust Graph Based Social Recommendation through Contrastive Multi-view Learning (RGCML). This framework leverages denoised social relations and global intents as dual auxiliary information sources to provide comprehensive characterization of users. Firstly, RGCML employs the concept of opinion dynamics to simulate how user preferences evolve due to noisy social relations. Then, it utilizes a specifically designed information fusion module to extract critical contextual information from multiple semantic perspectives, thereby achieving efficient personalized information fusion. Finally, it adopts the designed global-local contrastive learning paradigm that untangles and discriminates user preferences from global intents, further addressing the noise problem and enhancing the quality of user representations. Extensive experiments conducted on three real-world datasets demonstrate the superior performance of RGCML compared to several state-of-the-art (SOTA) baselines.

Collaborative filtering (CF) methods are now facing the challenge of data sparsity in recommender systems. In order to reduce the effect of data sparsity, researchers proposed contrastive learning methods to extract self-supervised signals from raw data. Contrastive learning methods address this problem by graph augmentation and maximizing the consistency of node representations between different augmented graphs. However, these methods tends to unintentionally distance the target node from its path nodes on the interaction path, thus limiting its effectiveness. In this regard, we propose a solution that uses paths as samples in the contrastive loss function. In order to obtain the path samples, we design a path sampling method. In addition to the contrast of the relationship between the target node and the nodes within the path (intra-path contrast), we also designed a method of contrasting the relationship between the paths (inter-path contrast) to better pull the target node and its path nodes closer to each other. We use Simplifying and Powering Graph Convolution Network (LightGCN) as the basis and combine with a new path-enhanced graph approach proposed for graph augmentation. It effectively improves the performance of recommendation models. Our proposed Path Enhanced Contrastive Loss (PECL) model replaces the common contrastive loss function with our novel loss function, showing significant performance improvement. Experiments on three real-world datasets demonstrate the effectiveness of our model.

While artificial intelligence (AI) technology has advanced in real-world applications, there is a strong motivation to develop hybrid systems where AI algorithms and humans collaborate, promoting more human-centered approaches in AI system design. This has led to the emergence of a novel human-machine computing (HMC) paradigm, which combines human cognitive abilities with machine computational power to create a collaborative computing framework that meets the demands of large-scale, complex tasks and enables human-machine symbiosis. Human processing units (HPUs) are crucial computing resources in HMC-oriented systems, and efficient HPU resource provisioning is key to boosting system performance. However, existing schemes often fail to assign tasks to the most suitable HPUs and optimize HPU utility, as they either cannot quantitatively measure skills or overlook utility concerns during task assignment and scheduling. To address these challenges, this article proposes a fine-grained HPU virtualization (FingHV) approach, which leverages virtualization techniques to improve flexibility, fairness, and utility in the provisioning process. The core idea is to use a tree-based skill model to precisely measure the levels and correlations of multiple skills within individual HPUs, and to apply a mixed time/event-based scheduling policy to maximize HPU utility. Specifically, we begin by proposing a hierarchical multiskill tree to model HPU skills and their correlations. Next, we formulate the HPU virtualization problem and present a fine-grained virtualization method, which includes a quality-driven HPU assignment process and a mixed time/event-based scheduling policy to improve resource-sharing efficiency. Finally, we evaluate FingHV on a synthetic dataset with varying task sizes and a real-world case. The results demonstrate that FingHV improves global matching quality by up to 39.7% and increases HPU utility by 11.2% compared to the baselines.

Spatial Crowdsourcing (SC), which outsources location-dependent tasks to workers for physical completion, is gaining popularity. Recently, more complex tasks have emerged that require a group of workers collaborating in a coalition. Several pioneering studies have examined this issue using the server assigned tasks mode from an overall perspective, such as maximizing the total benefits of all workers. Unfortunately, maximizing the overall benefit does not necessarily align with maximizing individual benefits. In practice, crowd workers are often self-interested and autonomous, making decisions based on their personal perspectives. In this article, under the worker selected tasks mode, we investigate an important problem: Selfish Workers Coalition Formation (SWCF) problem in SC. Here, selfish workers autonomously form coalitions to accomplish tasks to maximize their individual benefits. Achieving a stable coalition formation for SWCF problem requires balancing cooperation and competition. First, we transform the SWCF problem into a hedonic coalition formation game using a devised exploited skills-based reward distribution model. Subsequently, we propose a distributed algorithm HCFTA and prove its Nash stability and performance bounds. Additionally, to enhance coalition formation efficiency, we propose a Markov blanket coloring parallel optimization algorithm MCPHCF . Extensive experiments demonstrate the superiority of the proposed methods on both synthetic and real-world datasets.

Integrating Unmanned Aerial Vehicles (UAVs) and autonomous vehicles within the crowdsensing paradigm offers a promising approach to collecting environment-relevant data over large spatial areas, particularly in disaster-stricken or high-risk regions. However, deploying crowdsensing systems in emergency response scenarios presents substantial challenges. The lack of prior environmental knowledge complicates the selection of optimal sensing locations and strategy optimization, often relying on costly trial-and-error methods. Additionally, realtime decision-making is critical in such scenarios, requiring the rapid identification of optimal deployment strategies. Yet, the absence of prior knowledge further complicates the assessment of the optimality of these strategies. This gap remains inadequately addressed in existing research. To address this, we present the first framework that frames these challenges as a rapid online strategy optimization problem for mobile agent-based crowdsensing systems operating in unknown environments during emergency response scenarios. We propose DGap-UCB, a novel approach within the multi-armed bandit (MAB) framework, which efficiently identifies the optimal sensing strategy with highconfidence guarantees. Leveraging the Upper-Confidence Bound (UCB) technique, DGap-UCB iteratively refines strategy selection based on reward feedback. To accelerate learning, we introduce a gap-confidence pair (Δt, δt)-based Quick Stopping Criterion, enabling rapid and high-confidence identification of the optimal strategy. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of DGap-UCB over stateof-the-art techniques

Unlike traditional edge caching paradigms, similarity edge caching enables the retrieval of similar content from local caches to fulfill user requests, reducing reliance on remote data centers and improving system performance. Although several pioneering works have contributed to similarity edge caching, most focus on single-edge nodes and/or static environment settings, which are impractical for real-world applications. To address this gap, we investigate the similarity caching problem in dynamic cooperative edge networks, where a set of edge nodes cooperatively serve requests generated from arbitrary distributions with similar content over fluctuating transmission links. This presents a significant challenge, as it requires balancing content similarity with delivery latency over the transmission network and learning the environment in real-time to optimize caching policies. We frame this problem within an adversarial Multi-Armed Bandit framework to accommodate the continuously changing operational environment. To solve this, we propose an online learning-based approach named MABSCP, which dynamically updates caching policies based on real-time feedback to minimize the service cost of edge caching networks. To enhance implementation efficiency, we devise both an offline compact strategy construction method and an online Gibbs sampling method. Finally, trace-driven simulation results demonstrate that our proposed approach outperforms several existing methods in terms of system performance.

Visual Simultaneous Localization and Mapping (vSLAM) is a prevailing technology for many emerging robotic applications. Achieving real-time SLAM on mobile robotic systems with limited computational resources is challenging because the complexity of SLAM algorithms increases over time. This restriction can be lifted by offloading computations to edge servers, forming the emerging paradigm ofedge-assisted SLAM. Nevertheless, the exogenous and stochastic input processes affect the dynamics of the edge-assisted SLAM system. Moreover, the requirements of clients on SLAM metrics change over time, exerting implicit and time-varying effects on the system. In this paper, we aim to push the limit beyond existing edge-assist SLAM by proposing a new architecture that can handle the input-driven processes and also satisfy clients’ implicit and time-varying requirements. The key innovations of our work involve a regional feature prediction method for importance-aware local data processing, a configuration adaptation policy that integrates data compression/decompression and task offloading, and an input-dependent learning framework for task scheduling with constraint satisfaction. Extensive experiments prove that our architecture improves pose estimation accuracy and saves up to 47% of communication costs compared with a popular edge-assisted SLAM system, as well as effectively satisfies the clients’ requirements.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has received extensive attention because it provides resilient computation services for multiple Mobile Users (MUs). However, due to the increasing scale of offloaded tasks, the uncertain mobility of MUs, and the limited energy budget of UAV and MUs, it is extremely challenging to achieve satisfactory Quality-of-Service (QoS). Non-Orthogonal Multiple Access (NOMA), a promising technology to serve multiple MUs with limited communication resources, has great potential to be integrated with MEC. To this end, this paper proposes a QoS-oriented NOMA-enhanced UAV-MEC system, which aims to capture the potential gains of uplink NOMA and enable more MUs to benefit from edge computing servers in resource-constrained UAV-assisted MEC environments. This synergy reduces MUs' uplink energy consumption but poses new challenges in resource allocation and UAV trajectory design. To address these challenges, we define a new metric called System Overhead Ratio (SOR) to reflect the system's QoS, and then consider a joint optimization problem of resource allocation, transmission power control, and UAV trajectory design, with the goal of minimizing the SOR. Given the NP-hard nature of the optimization problem, we propose a Lyapunov and convex optimization-based Low-complexity Online Resource allocation and Trajectory optimization method (LORT) to solve it, and further analyze the convergence and complexity of LORT. Finally, extensive simulations show that the proposed method surpasses other benchmarks, reducing the SOR by approximately$10\%$-$25\%$under various scenarios.

With advances in the edge computing (EC) and federated learning (FL) technologies in jointcloud, the edge FL service market has emerged recently and it requires trading edge resources between model requesters and data owners to complete FL tasks, which needs to incentivize sufficient data owners to participate in model training tasks. However, the limitations of resource trading and incentive design for edge FL service market have not been well addressed. In this paper, we propose a two-level blockchain-aided resource trading mechanism for encouraging appropriate edge servers to compete for dynamic FL tasks from the market while incentivizing data owners to participate in the FL tasks. At the upper level, we apply the deep learning-based reverse auction to model the dynamics of the task server selection process, with the aim of maximizing the total social welfare of the edge FL service market, where the edge server, as a seller, considers not only the data contribution of edge devices but also the cost of using blockchain when bidding. At the lower level, the edge servers offer rewards in exchange for the data owners’ participation, while the parameter aggregation is completed through the blockchain in a decentralized manner, which improves the FL’s robustness. Then, we utilize the Stackelberg game to model the dynamic process that the data owners compete for the servers’ revenue. We conduct extensive simulation experiments and the experimental results show that the proposed mechanism is able to get maximized social welfare and provide effective insights and strategies for the resource trading in the edge FL market to complete the federated training.

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O($\ln m$), where$m$represents the number of users’ tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios.

UAV-enabled multi-access edge computing (MEC) is expanding possibilities for integrated space-air-ground networks, especially in the 5G era and beyond. In this scenario, tasks from mobile users (MUs) are offloaded to nearby UAVs for execution, with results returned upon completion. However, the unpredictable mobility of MUs, coupled with dynamic network conditions and fluctuating resource availability, can degrade the reliability of communication links, leading to increased delivery latency, particularly for tasks involving large computational results. To meet stringent QoS requirements, adaptive task migration across UAVs is essential to minimize latency. To address this issue, in this paper, we first investigateComputationTaskMiGration (CTMiG) problem in UAV-enabled dynamic MEC networks, focusing on joint optimization of task-serving (offloading and migration) decisions to reduce latency for all MUs. We propose the ILCTS algorithm, an imitation learning-based joint optimization method that adaptively adjusts scheduling strategies in response to environmental changes. An improved PPO algorithm is first proposed to train a policy and generate expert data, followed by generative adversarial imitation learning to imitate the data and continuously explore new ones through online learning to enhance the policy. Experimental results demonstrate that our algorithm achieves superior performance in training accuracy and average latency compared to other representative methods.

Recommender systems continuously strive to recommend items that the users potentially like accurately. Most recommender systems assume that latent user preferences and item features are linearly combined. However, the existing linear interaction patterns do not realistically reflect users’ decision-making processes. The formation of users’ opinions on items and the evolutionary preference interaction process among users needs to be explored. In our work, we bridge social psychology and recommender systems to develop a social recommendation model, nonlinearly utilizing latent user preferences and item features to simulate the intrinsic formation of users’ decision-making. We extend the cognitive opinion formation mechanism by improving the two-stage process and seamlessly combine it and matrix factorization, simulating the nonlinear interactions between users and items. We incorporate the implicit user influence and explicit social dynamics with bounded confidence effect into the nonlinear cognitive recommendation framework to characterize the evolutionary preference interactions among users. We conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that our method makes notable improvements in rating prediction for all users and cold-start users. In addition, the nonlinear cognitive opinion formation has a significant effect on improving performance, conferring higher interpretability to the recommendation.

In autonomous robotic systems, the parallel processing of multiple tasks often competes for limited resources, affecting system performance and the robot’s responsiveness to environmental changes. Traditional computational task scheduling methods often overlook the dynamic nature of task priorities in autonomous robotic systems, where task importance can shift based on interactions with the external environment. Therefore, there’s a crucial need for a mechanism capable of adaptively adjusting task scheduling in response to environmental changes, ensuring timely access to resources for critical tasks. To address this challenge, this study presents Priorest, a neural network model that incorporates multimodal data processing and multitask learning. Priorest integrates sensor data with logs monitoring computational device performance to predict events influencing task priority, enabling task adjustments while preserving essential resource allocations. When deployed in autonomous robotic systems, Priorest’s event-prediction-based adjustment strategy reduced critical task completion times by 18.7%, which demonstrates the effectiveness of Priorest in enhancing parallel task scheduling.

Contrastive learning (CL) has recently catalyzed a productive avenue of research for recommendation. The efficacy of most CL methods for recommendation may hinge on their capacity to learn representation uniformity by mapping the data onto a hypersphere. Nonetheless, applying contrastive learning to downstream recommendation tasks remains challenging, as existing CL methods encounter difficulties in capturing the nonlinear dependence of representations in high-dimensional space and struggle to learn hierarchical social dependency among users-essential points for modeling user preferences. Moreover, the subtle distinctions between the augmented representations render CL methods sensitive to noise perturbations. Inspired by the Hilbert-Schmidt independence criterion (HSIC), we propose a graph Contrastive Learning model with Kernel Dependence Maximization CL-KDM for social recommendation to address these challenges. Specifically, to explicitly learn the kernel dependence of representations and improve the robustness and generalization of recommendation, we maximize the kernel dependence of augmented representations in kernel Hilbert space by introducing HSIC into the graph contrastive learning. Additionally, to simultaneously extract the hierarchical social dependency across users while preserving underlying structures, we design a hierarchical mutual information maximization module for generating augmented user representations, which are injected into the message passing of a graph neural network to enhance recommendation. Extensive experiments are conducted on three social recommendation datasets, and the results indicate that CL-KDM outperforms various baseline recommendation methods.

With rapid advancements in artificial intelligence (AI) technologies, AI-powered machines are increasingly capable of collaborating with humans to enhance decision-making in various human–machine collaboration scenarios, e.g., medical diagnosis, criminal justice, and autonomous driving. As a result, human–machine computing (HMC) has emerged as a promising computing paradigm that integrates the expertise of humans with the reliable data processing capabilities of machines. Using HMC to facilitate the processing of domain-specific tasks has a lot of potential, but is limited in system-level scalability, i.e., there is no one common easy-to-use interface. In this article, we present human-machine operating system(hmOS), an open extensible platform for researchers to experiment with HMC for investigating system-centric human–machine collaboration problems.hmOSsupports flexible human–machine collaboration on the strength of the quality-aware task decomposition and allocation. To achieve that, the underlying system architecture and runtime environment are first developed to build a foundational abstraction for the kernel ofhmOS. Second,hmOSfacilitates flexible human–machine collaboration through a suitability-based task allocation mechanism, quality estimation guided by fuzzy rules, and iterative feedback on result tuning. We implement the newly proposedhmOSin a prototype featuring interactive interfaces. Finally, we conduct extensive and realistic experiments to validate the effectiveness of our platform across diverse tasks, showcasing the broad feasibility ofhmOS.

Cell selection and data offloading are the keys to obtaining MCS services with low sensing cost and low data processing delay. Due to the spatiotemporal correlation between data and the local-area coverage of edge servers, cell selection and data offloading will affect each other and require co-optimization. To achieve the co-optimization, we design the method OptInter based on the hierarchical reinforcement learning. OptInter can realize the interactive training between cell selection model and data offloading model. Finally, we evaluate our proposed method based on four datasets, each of which composited by real-world (e.g., NO$_{2}$concentration, AQI value, Didi order, and Didi trajectory) data and simulated data. Compared with the four baseline methods (e.g., OptMOEA/D, OptStageCD, OptStageDC, and OptWeight), the comprehensive performance of our proposed method can be improved by 11.83%, 20.48%, 10.14%, and 42.27% on average, respectively.

This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining controller adaptivity and scalability stands out as a primary challenge. Capturing the spatial-temporal correlation among traffic lights under the framework of Multi-Agent Reinforcement Learning (MARL) is a promising solution. Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents. In this paper, we design a new decentralized control architecture with improved environmental observability to capture the spatial-temporal correlation. Specifically, we first develop atopology-aware information aggregationstrategy to extract correlation-related information from unstructured data gathered in the road network. Particularly, we transfer the road network topology into a graph shift operator by forming a diffusion process on the topology, which subsequently facilitates the construction of graph signals. A diffusion convolution module is developed, forming a new MARL algorithm, which endows agents with the capabilities of graph learning. Extensive experiments based on both synthetic and real-world datasets verify that our proposal outperforms existing decentralized algorithms.

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.

Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.

Code translation, i.e., translating one kind of code language to another, plays an important role in scenarios such as application modernization and multi-language versions of applications on different platforms. Even the most advanced machine-based code translation methods can not guarantee an error-free result. Therefore, the participance of software engineer is necessary. Considering both accuracy and efficiency, it is suggested to work in a human-machine collaborative way. However, in many realistic scenarios, human and machine collaborate ineffectively - model translates first and then human makes further editing, without any interaction. To solve this problem, we propose hmCodeTrans, a novel method that achieves code translation in aninteractive human-machine collaborative way. It can (1) save the human effort by introducing two novel human-machine collaboration patterns: prefix-based and segment-based ones, which feed the software engineer's sequential or scattered editing back to model and thus enabling the model to make a better retranslation; (2) reduce the response time based on two proposed modules: attention cache module that avoids duplicate prefix inference with cached attention information, and suffix splicing module that reduces invalid suffix inference by splicing a predefined suffix. The experiments are conducted on two real datasets. Results show that compared with the baselines, our approach can effectively save the human effort and reduce the response time. Last but not least, a user study involving five real software engineers is given, which validates that the proposed approach owns the lowest human effort and shows the users’ satisfaction towards the approach.

Graph neural networks have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by the cold-start problem and data sparsity. Using social relationships to assist recommendation is an effective practice, but it can only moderately alleviate these problems. In addition, rich attributes are often unavailable, which prevents graph neural networks from being fully effective. Hence, we propose to enrich the model by mining multiple implicit feedback and constructing a triple GCN component. We have noticed that users may be influenced not only by their trusted friends but also by the ratings that already exist. The implicit influence spreads among the item’s previous and potential raters, and makes a difference on future ratings. The implicit influence is analyzed on the mechanism of information propagation, and fused with the user’s binary implicit attitude, since negative influence propagates as well as the positive one. Furthermore, we leverage explicit feedback, social relationships, and multiple implicit feedback in the triple GCN component. Abundant experiments on real-world datasets reveal that our model has improved significantly in the rating prediction task compared with other state-of-the-art methods.

It is pivotal and challenging for crowdsensing systems to guarantee the reliable uploading of sensory data from source devices (workers) to a centralized platform, in order to process sensing tasks accurately and fast. On one hand, with limited communication resources, uploading a massive amount of sensory data is not cost-effective. On the other hand, the disruption of uploading is inevitable because of stochastic network environments and worker dropout, resulting in extra wasting of resources. To address that, we focus on a collaborative uploading scenario and propose to reduce the uploading latency of sensory data by adaptive data allocation while retaining data integrity at the destination. A key technical challenge is to identify proper collaborative paths such that corresponding data allocation and uploading are reliable enough. As such, we formulate a joint optimization problem with the minimization goal of uploading latency by considering both path selection and data allocation. To mine helpful information from unstructured topology-aware data, we propose a new diffusion graph convolution module by forming information aggregation based on the diffusion process that characterizes the stochastic correlation of devices. After transforming the original problem into a primal-dual problem, an algorithm is then developed by adapting Advantage Actor-Critic (A2C) framework embedded with the diffusion graph convolution module. With extensive experiments, it is validated that the newly developed algorithm improves collaborative uploading by reducing uploading latency and also stabilizing the queue state of intermediate devices, compared to existing heuristic and learning-based methods.

The task of network alignment is to obtain the node pairs which belong to the same entity from different networks. Existing embedding-based network alignment methods either use node structural or attribute information as inputs for node embeddings. These pieces of information are not always available in real-world datasets, and current methods that consider single information embedding may fail when there is excessive network noise. To address the aforementioned issue, we utilize a multi-layer Graph Attention Networks(GATs) to design an unsupervised node embedding model, which trains two GATs for structural and attribute information in a single graph and embeds the source nodes and target nodes into the same embedding space. By applying graph augmentation techniques, the model learns structural embeddings and attribute embeddings for every node in the networks based on structural and attribute consistency. Moreover, we apply a topological alignment refinement process to get aligned node pairs, which further enhances the accuracy of network alignment by leveraging the similarity of the structure between networks. Through extensive experiments, we have demonstrated that our model outperforms the state-of-the-art models in terms of alignment accuracy and its ability to handle attribute and structural noise. Additionally, our model exhibits relatively low complexity.

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.

Recently, a novel Mobile Agent-based Mobile Crowdsensing (MA-MCS) paradigm has emerged, which utilizes unmanned aerial vehicles, pilotless automobiles, etc., to implement data sensing from surroundings. Practically, in time-sensitive applications the freshness of collected data is of critical importance. However, restricted by the intermittent bandwidth of wireless channels, the generated data might not be successfully transferred to the application server for further processing. Fortunately, benefit from the hosted edge computing capability, it is feasible to locally process the sensing data and then transfer the concise result. In this paper, we study a novel and practical problem in the Age-of-Information (AoI) driven MA-MCS systems in wireless edge computing. Specifically, restricted by the issues of battery recharging, etc., we strive to schedule the mobile agents and make transmission/computing decisions in a collaborative manner, aiming at minimizing the total AoI threshold violation. To this end, we propose a two-phase deep reinforcement learning-based solution, namely KCDDQN, including spatiotemporal clustering and decision-making learning. Finally, extensive simulations are carried out to demonstrate the effectiveness of our proposed KCDDQN approach.

Abstract Program translation aims to translate one kind of programming language to another, e.g., from Python to Java. Due to the inefficiency of translation rules construction with pure human effort (software engineer) and the low quality of machine translation results with pure machine effort, it is suggested to implement program translation in a human–machine cooperative way. However, existing human–machine program translation methods fail to utilize the human’s ability effectively, which require human to post-edit the results (i.e., statically modified directly on the model generated code). To solve this problem, we propose HMPT (Human-Machine Program Translation), a novel method that achieves program translation based on human–machine cooperation. It can (1) reduce the human effort by introducing a prefix-based interactive protocol that feeds the human’s edit into the model as the prefix and regenerates better output code, and (2) reduce the interactive response time resulted by excessive program length in the regeneration process from two aspects: avoiding duplicate prefix generation with cache attention information, as well as reducing invalid suffix generation by splicing the suffix of the results. The experiments are conducted on two real datasets. Results show compared to the baselines, our method reduces the human effort up to 73.5% at the token level and reduces the response time up to 76.1%.

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based approaches have shown promising results over shallow methods. However, they fail to address two core challenges of anomaly detection in dynamic graphs: the lack of informative encoding for unattributed nodes and the difficulty of learning discriminate knowledge from coupled spatial-temporal dynamic graphs. To overcome these challenges, in this paper, we present a novelTransformer-basedAnomalyDetection framework forDYnamic graphs (TADDY). Our framework constructs a comprehensive node encoding strategy to better represent each node’s structural and temporal roles in an evolving graphs stream. Meanwhile, TADDY captures informative representation from dynamic graphs with coupled spatial-temporal patterns via a dynamic graph transformer model. The extensive experimental results demonstrate that our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on six real-world datasets.

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks. Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph structures evolve over time. Such a streaming graph raises great challenges for graph embedding techniques not only in capturing the structural dynamics of the graph, but also in efficiently accommodating high-speed edge streams. Against this background, we propose SGSketch, a highly-efficient streaming graph embedding technique via incremental neighborhood sketching. SGSketch cannot only generate high-quality node embeddings from a streaming graph by gradually forgetting outdated streaming edges, but also efficiently update the generated node embeddings via an incremental embedding updating mechanism. Our extensive evaluation compares SGSketch against a sizable collection of state-of-the-art techniques using both synthetic and real-world streaming graphs. The results show that SGSketch achieves superior performance on different graph analysis tasks, showing 31.9% and 21.9% improvement on average over the best-performing static and dynamic graph embedding baselines, respectively. Moreover, SGSketch is significantly more efficient in both embedding learning and incremental embedding updating processes, showing 54x-1813x and 118x-1955x speedup over the baseline techniques, respectively.

Online gathering large-scale heterogeneous tasks and multi-skilled participant can make the tasks and participants to be shared in real time. However, their online gathering will bring many intractable objective requirements, which makes task-participant matching become extremely complex. To cope well with the gathering, we design a hierarchy tree and time-series queue to organize tasks and participants. The data structures we designed can effectively meet all requirements that are brought due to tasks and participants gathering online. In addition, based on the designed data structures, we study online large-scale heterogeneous task allocation problem from three aspects: the computing pattern, the tree creation method, and the extension of matching strategy. Our best method (TsPY) is based on parallel computing in the computing pattern, adopts time first and then space in the tree creation method, and increases the short-distance first strategy in the matching strategy. Finally, we conducted detailed experiments under the conditions of different participant geographical distributions (i.e., uniform distribution, Gaussian distribution, and check-in empirical distribution), different sensing methods (i.e., participatory sensing and opportunistic sensing), and different recommendation methods (i.e., point recommendation and trajectory recommendation). The experimental results show that TsPY has a good performance in multiple indicators such as algorithm running time, task-participant matching rate, participant travel distance, and redundant tasks removed. Compared with serial computing, parallel computing can reduce the algorithm running time by more than 66% on average in our experimental environment. Compared with space first and then time, creating a tree based on time first and then space can increase task-participant matching rate by more than 13% on average. Increasing the short-distance first strategy can reduce the participant travel distance by more than 4% on average.

Characterizing human driver's driving behaviors from GPS trajectories is an important yet challenging trajectory mining task. Previous works heavily rely on high-quality GPS data to learn such driving style representations through deep neural networks. However, they have overlooked the driving contexts that greatly govern drivers' driving activities and the data sparsity issue of practical GPS trajectories collected at a low-sampling rate. Besides, existing works omit the cold start problem, where the newly joined drivers usually have insufficient data to learn accurate driving style representations. To address these limitations, we present an adversarial driving style representation learning approach, named$\mathtt {Radar}$. In addition to summarizing statistic features from raw GPS data,$\mathtt {Radar}$also extracts contextual features from three aspects of road condition, geographic semantic, and traffic condition. We exploit the advanced semi-supervised generative adversarial networks to construct our learning model. By jointly considering statistic features and contextual features, the trained model is able to efficiently learn driving style representations from practical GPS trajectory data. Furthermore, we enhance$\mathtt {Radar}$'s representation learning for drivers owning limited training data with some basic data augmentation strategies and a novel auxiliary driver based data augmentation method. Experiments on two benchmark applications,i.e., driver identification and driver number estimation, with a large real-world GPS trajectory dataset demonstrate that$\mathtt {Radar}$can outperform the state-of-the-art approaches by learning more effective and accurate driving style representations.

Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the benefit of MCS systems. Practically, via reactively shifting the pre-determined assignment scheme in real time, it is usually impossible to develop reassignment schemes without a sacrifice of the system performance. Against this background, we turn to an alternative solution, i.e., proactively creating a robust task assignment scheme offline. In this work, we provide the first attempt to investigate an important and realisticRoBustTaskAssignment (RBTA) problem in MCS systems, and try to strengthen the assignment scheme's robustness while minimizing the workers’ traveling detour cost simultaneously. By leveraging the workers’ spatiotemporal mobility, we propose an assignment-graph-based approach. First, an assignment graph is constructed to locally model the assignment relationship between the released MCS tasks and available workers. And then, under the framework of evolutionary multi-tasking, we devise a population-based optimization algorithm, namelyEMTRA, to effectively achieve adequate Pareto-optimal schemes. Comprehensive experiments on two real-world datasets clearly validate the effectiveness and applicability of our proposed approach.

With the increasing prominence of smart mobile devices, an innovative distributed computing paradigm, namely Mobile Crowdsourcing (MCS), has emerged. By directly recruiting skilled workers, MCS exploits the power of the crowd to complete location-dependent tasks. Currently, based on online social networks, a new and complementary worker recruitment mode, i.e., socially aware MCS, has been proposed to effectively enlarge worker pool and enhance task execution quality, by harnessing underlying social relationships. In this paper, we propose and develop a novel worker recruitment game in socially aware MCS, i.e.,Acceptance-awareWorkerRecruitment (AWR). To accommodate MCS task invitation diffusion over social networks, we design a Random Diffusion model, where workers randomly propagate task invitations to social neighbors, and receivers independently make a decision whether to accept or not. Based on the diffusion model, we formulate the AWR game as a combinatorial optimization problem, which strives to search a subset of seed workers to maximize overall task acceptance under a pre-given incentive budget. We prove its NP hardness, and devise a meta-heuristic-based evolutionary approach namedMA-RAWRto balance exploration and exploitation during the search process. Comprehensive experiments using two real-world data sets clearly validate the effectiveness and efficiency of our proposed approach.

Until recently, a novel spatial crowdsourcing paradigm, namely Three-Dimensional (3D) spatial crowdsourcing, has emerged, in which the task requestors and the workers need travel to their designated third-party workplaces, e.g., shared offices, to deliver certain services, such as DiDi station ride-sharing service, Quyundong sport training service in the Online-To-Offline (O2O) applications. In 3D spatial crowdsourcing applications, a core issue is to develop an efficient global tasklist plan, based on the tripartite matching among the three parties, i.e., task requestors, workers and workplaces, which is different from the conventional spatial crowdsourcing. In this context, one key challenge is how to suitably schedule the available workers with the consideration of the interests of all the parties, under the constraint of worker resource. To answer the questions, in this paper, we propose and study a new problem, namely Social-Welfare-driven Task Scheduling (SWTS) problem, which strives to schedule the workers' continuous routines, i.e., successively implementing tasks for different requestors at different workplaces, to promote the social welfare for all the involved parties. We prove our studied problem is NP-hard, and devise two heuristic optimization algorithms to solve it. Finally, we conduct extensive experiments which verify the efficiency and effectiveness of the proposed algorithms on both real and synthetic data sets.

With the rapid development of wireless sensor networks (WSNs) and the Internet of Things (IoT), increasing computing tasks are sinking to mobile edge networks, such as distributed learning systems. These systems benefit from the massive amounts of data and computing power on mobile devices and can learn qualified models on the premise of protecting user privacy. In fact, coordinating mobile devices to participate in computing is challenging. On the one hand, the heterogeneous performance of devices makes it difficult to guarantee computing efficiency. On the other hand, there are unreliable factors in the mobile network, which will destroy the stability of the distributed learning. Therefore, we design a three-layer framework called an edge-intelligence-based distributed learning system (EIDLS). Specifically, a novel multilayer perceptron-based device availability evaluation model is proposed to select devices with good performance. The evaluation model performs online learning and optimization according to the resources (CPU, battery, etc.) of devices. Meanwhile, we propose a dynamic trust evaluation algorithm to reduce the side effects of unreliable devices. The experimental results of some commonly used datasets validate that the proposed EIDLS dramatically minimizes the energy consumption and communication cost and improves the calculation accuracy and the stability of the system.

The past flourishing years of sequential point-of-interest (POI) recommendation began with the introduction of Self-Attention Network (SAN), which quickly superseded CNN or RNN as the state-of-the-art backbone. To realize the fine-grained users' behavior patterns modeling, recent works utilize modified attention mechanisms or neural network layers to process spatial-temporal factors. However, due to the significant increase on either model's parameter scale or computational burden, we argue that these methods can be further improved. In this paper, we exploit two lightweight approaches, Time Aware Position Encoder (TAPE) and Interval Aware Attention Block (IAAB), to impel SAN by considering the spatial-temporal intervals among POIs separately, where requiring neither extra parameters nor high computational cost. On the one hand, TAPE, adjusting the positions in sequences based on the timestamps dynamically and generating positional representations with sinusoidal transformation, can enhance sequence representations to reflect both the absolute order and relative temporal proximity among all POIs. On the other hand, IAAB, point-wise adding the scaled spatial-temporal intervals to the attention map, can promote the attention mechanism attaching importance to the spatial relation among all POIs under the constraints of time conditions and providing more explainable recommendation. We integrate these two modules into SAN and propose a Spatial-Temporal Interval-Aware sequential POI recommender, namely STiSAN, as an end-to-end deployment. Experimental results based on three public LBSN datasets and one real-world city transportation dataset demonstrate STiSAN's superior performance (average 13.01% improvement against the strongest baseline). Moreover, we validate the extensibility and interpretability of TAPE and IAAB through metric evaluation and visualization separately.

By placing popular contents on the network edges, edge caching becomes a promising technique to improve the quality of experience (QoE) of the end users and reduce backhaul link congestion. In this paper, we examine an open market of crowdsourced video edge caching, where within each time slot, the newcome private edge devices strategically declare their own bids to the Video Content Provider (VCP) operator for contributions; and the operator optimally recruits caching devices among the newcome and existing served devices to maximize the expected QoE, under a budget constraint. From the perspective of newcome edge devices, we propose and study a novel pricing problem, namely Pri-CVEC, to determine the bid prices for profit maximization. The problem is challenging due to the importing of strategic interactions between the newcome devices and the VCP operator, and competition between the newcome and the existing served devices.We formulate it as a stackelberg knapsack problem. By leveraging the dynamic programming and linear programming-relaxation method, we propose Pri-DP and Pri-LPR algorithm, respectively. We extensively conduct simulation experiments to verify the advantages of our approaches.

Transfer learning uses auxiliary domains to help complete learning tasks of the target domain. However, the combination of recommendation and transfer learning often has two problems. One is that it's difficult to find an auxiliary domain which is highly related to the target domain. The other is that useful information in auxiliary domains cannot be fully utilized. To make use of the knowledge in auxiliary domains as much as possible, this paper proposes a cyclic transfer learning method which can transfer the shared knowledge in the auxiliary domain and target domain multiple times. Combining this method with recommendation, this paper presents a recommendation framework based on heterogeneous feedbacks and cyclic transfer learning (HCTL-Rec). By studying the relationship between different behaviors of users, this paper proposes two specific recommendation algorithms which combine the novel framework with two auxiliary domains. One is to use users' binary attitude information as an auxiliary domain to better represent users' ratings. The other is to use users' trust relationship as an auxiliary domain and make social recommendation. Experiments are carried out on two real-world datasets with trust relationship. The results show that recommendation quality of the two specific algorithms can achieve significant improvement compared with other state-of-the-art algorithms and can effectively relieve the cold-start problem.

Online medical diagnosis refers to diagnosing diseases and providing treatment suggestions on the websites. It develops rapidly and has become a new choice for patients to seek medical treatment. Although manual online medical diagnosis is reliable, it has problems such as low efficiency, heavy burden on doctors, and long waiting time for patients. Relying on machines for automatic disease diagnosis is highly efficient, which, however, has low accuracy and reliability. In general, online medical diagnosis usually has two stages: inquiry and diagnosis. Inquiry stage refers to asking about the patient’s physiological, where the questions are usually streamlined, and thus can be handled by the machine. Diagnosis stage is to diagnose the disease and provide medical recommendations, which has strict requirements for accuracy and safety, and thus should be handled by the human. Inspired by this, in the paper we propose a human-machine collaboration based online medical diagnosis system, i.e., HM-MDS. In inquiry stage, the system employs the machine. It uses the BERT+CRF to identify symptoms in the patient’s dialogue and uses a DQN-based method to ask about symptoms. In diagnosis stage, the system employs both the machine and the human. The machine generates a pre-diagnosis result by calculating disease probability. Then the human doctor gives the final diagnosis result by checking the pre-diagnosis result and revising it if necessary. Obviously, HM-MDS can effectively save human doctor’s time as well as patient’s time, while ensure the accuracy of the diagnosis result. We conduct experiments on a real-world dataset. The results show our approach improves the online medical diagnosis’s reliability as well as patient satisfaction, and ensures diagnosis accuracy. The time cost for a reliable medical diagnosis is reduced to 36% compared with pure manual work.

With the growth of Internet of Things (IoT) devices, smart travel methods, such as sharing-bike and ride-hailing become popular commuting methods. With people’s growing needs and the rapid dynamics in a city environment, simply using a single travel demand for prediction may be insufficient. Alternatively, modeling multiple travel demands simultaneously can deepen our understanding toward the status of these potentially correlated demands and deploy the transportation in the city better. An important observation in this work is that multiple travel demands in a city often show common patterns, referred to as the shared mobility-aware knowledge. In addition, there are also unique patterns that characterize individual travel demand resulting in unique knowledge. To better leverage the shared and unique knowledge, we propose a novel framework (MultiST) to predict multiple spatial–temporal sequences (multiple travel demands) via two components that extract the shared and unique spatial–temporal dependencies, respectively. For the unique component, we use convolutional neural networks and gated recurrent units to embed unique knowledge. For the shared component, we design a recurrent Gaussian cell to extract temporal dependencies. Empirical results show that MultiST outperforms six state-of-the-art baseline methods and three variants of MultiST. We further visualize the temporal dependencies of the shared knowledge and discuss the practical implications.

Task allocation is a key issue in mobile crowdsensing (MCS), which affects the sensing efficiency and quality. Previous studies focus on the allocation of tasks that have already been published to the platform, but there are some very urgent tasks that need to be executed once they were detected. Existing studies for either delay-tolerant or time-sensitive tasks have a certain time delay from task publishing to execution, so it is impossible to achieve task detection then execution seamlessly. Thus, we first define the instant sensing and then instant actuation (ISIA) problem in MCS and propose a new model to solve it. We aim to allocate POIs where ISIA tasks are most likely to be detected to workers with similar sensing types so that these tasks can be executed once they are detected. This article presents a two-phase task allocation framework called ISIATasker. In the sensing locations clustering and sensor selection phase, we cluster independent sensing locations into several POIs and then select the optimal cooperative sensor set for each POI to assist workers in completing sensing. In the POIs allocation phase, we propose a method called PA-DDQN based on deep reinforcement learning to plan an optimal path for each worker, thus maximizing the overall sensing type matching degree and POI coverage to enable ISIA. Finally, extensive experiments are conducted based on real-world data sets to demonstrate that the matching degree and POI coverage of ISIATasker outperform other baselines.

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.

Mobile edge computing (MEC) emerges as an appealing paradigm to provide time-sensitive computing services for industrial Internet of Things (IIoT) applications. How to guarantee truthfulness and budget-balance under locality constraints is an important issue to the allocation and pricing design of the MEC system. In this paper, we propose a truthful combinatorial double auction mechanism, which integrates the padding concept and the efficient pricing strategy to guarantee desirable properties in constrained MEC environments. This mechanism takes into account the locality characteristics of the MEC systems, where mobile devices (MDs) only offload tasks to edge servers (ESs) in the proximity with various requirements, and ESs only serve their neighboring MDs with limited resources. To be specific, for allocation, a linear programming (LP)-based padding method is used to obtain the near-optimal solution in the polynomial time. For pricing, a critical-value-based pricing strategy and a VCG-based pricing strategy are designed for MDs and ESs to achieve truthfulness and budget-balance. Our theoretical analysis confirms that TCDA is able to hold a set of desirable economic properties, including truthfulness, individual rationality, and budget-balance. Furthermore, simulation results validate the theoretical analysis, and verify the effectiveness and efficiency of TCDA.

With the proliferation of increasingly powerful mobile devices and wireless networks, mobile crowdsourcing has emerged as a novel service paradigm. It enables crowd workers to take over outsourced location-dependent tasks, and has attracted much attention from both research communities and industries. In this paper, we consider a mobile crowdsourcing scenario, where a mobile crowdsourcing task is too complex (e.g., post-earthquake recovery, citywide package delivery) but can be divided into a number of easier subtasks, which have interdependency between them. Under this scenario, we investigate an important problem, namelytask graph scheduling in mobile crowdsourcing(TGS-MC), which seeks to optimize a compact scheduling, such that the task completion time (i.e., makespan) and overall idle time are simultaneously minimized with the consideration of worker reliability. We analyze the complexity and NP-complete of the TGS-MC problem, and propose two heuristic approaches, including BFS-based dynamic priority schedulingBFSPriDalgorithm, and an evolutionary multitasking-basedEMTTSchalgorithm, to solve our problem from local and global optimization perspective, respectively. We conduct extensive evaluation using two real-world data sets, and demonstrate superiority of our proposed approaches.

In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.

Mobile Edge Computing (MEC) has been envisioned as a promising distributed computing paradigm, where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation. However, most existing works only consider the congestion of wireless channels as the crucial factor influencing the strategy-making process, and ignore the impact of the offloading among edge nodes. In addition, centralized task offloading strategies result in heavy computation complexity in center nodes. Along this line, we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users’ offloading strategies. To this end, we first formulate the offloading problem as a multi-user potential game, and then propose a distributed task offloading algorithm to reach an equilibrium state which can also protect individual privacy. Specifically, in the above task offloading algorithm, we propose two subalgorithms to select users for updating strategies: Parallel User Selection Algorithm (PUS) and Single User Selection Algorithm (SUS) in order to substantially accelerate the convergence. Extensive experiments on three real-world data sets validate that the proposed algorithm achieves a Nash equilibrium and effectively decreases the total user cost which is acceptable compared to the optimal solution.

Thanks to the growing popularity and functionality, smartphone has become rapidly valuable potential tool for human behavior research, e.g., friendship relationship recognition, friend ship prediction, etc. Until recently, there have been many research efforts to study this issue using the sensed data collected from smartphones. However, almost previous works in finding friendship strength are based on several physical features or a few dimensions, such as using Bluetooth scanning and demographic data to explain friendship. Actually, friendship is complicated and coupled with many factors, such as physical propinquity, social, physical and psychological homophily. So, it is necessary and beneficial to examine it comprehensively, by taking into account all the involved factors. Aiming at closing part of this research gap, in this paper, from cross-space perspectives, we launch a friendship relationship study with smartphone-based sensing paradigm from cyber space, physical mobility, and personality trait homophily. By integrating the involved heterogeneous interactions, we propose a Deep AutoEncoder-based unified framework to predict the strength of friendship connections between users, where the friendship strength is categorized and asymmetrical. We conduct extensive experiments on a practically collected sensing data set, and show the efficiency and effectiveness of our proposed approaches.

Sparse mobile crowdsensing (MCS) requires participants to collect data from partial cells and then intelligently infer the data of the rest cells. Since collecting data from different cells will probably result in different data inference quality, cell selection (i.e., which cells need to be selected to collect data) is a critical issue in Sparse MCS. Currently, state-of-the-art cell selection algorithms are implemented based on reinforcement learning. These algorithms ignore the problem that the urban environment is usually time dependent, and the cell selection model needs to be kept up-to-date to adapt to the time-dependent environment. However, Sparse MCS applications require participants to collect data only in a few cells, which makes it hard to obtain suitable training data for continuous cell selection model learning. To solve this problem, we model the spatiotemporal correlations in the collected sparse data, and then design various methods to update training data based on it. Particularly, these methods make full use of the gradual changes of data in time and space, and reasonably transform and splice sparse data at different moments. Finally, updated training data is fed to the cell selection model to keep it up-to-date. We conduct experimental evaluations by performing several sensing tasks in air quality monitoring. The results show that our proposed methods can effectively update training data as well as the cell selection model. Compared with several baselines, our best method can reduce inference error by more than 10% on average.

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.

As an emerging social dynamic system, geo-social network can be used to facilitate viral marketing through the wide spread of targeted advertising. However, unlike traditional influence spread problem, the heterogeneous spatial distribution has to incorporated into geo-social network environment. Moreover, from the perspective of business managers, it is indispensable to balance the tradeoff between the objective of influence spread maximization and objective of promotion cost minimization. Therefore, these two goals need to be seamlessly combined and optimized jointly. In this paper, considering the requirements of real-world applications, we develop a multiobjective optimization-based influence spread framework for geo-social networks, revealing the full view of Pareto-optimal solutions for decision makers. Based on the reverse influence sampling (RIS) model, we propose a similarity matching-based RIS sampling method to accommodate diverse users, and then transform our original problem into a weighted coverage problem. Subsequently, to solve this problem, we propose a greedy-based incrementally approximation approach and heuristic-based particle swarm optimization approach. Extensive experiments on two real-world geo-social networks clearly validate the effectiveness and efficiency of our proposed approaches.

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.

Mobile Crowdsensing (MCS) is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing, and industrial manufacturing. Considering the sensing costs, sparse MCS, as a variant, only senses the data in a few subareas, and then infers the data of unsensed subareas by the spatio-temporal relationship of the sensed data. Existing works usually assume that the sensed data are linearly spatiotemporal dependent, which cannot work well in real-world nonlinear systems, and thus, result in low data inference accuracy. Moreover, in many cases, users not only require inferring the current data, but also have an interest in predicting the near future, which can provide more information for users' decision making. Facing these problems, we propose a deep learning-enabled industrial sensing, and prediction scheme based on sparse MCS, which consists of two parts: matrix completion and future prediction. Our goal is to achieve high-precision prediction of future moments under the hypothesis of sparse historical data. To make full use of the sparse data for prediction, we first propose a deep matrix factorization method, which can retain the nonlinear temporal-spatial relationship, and perform high-precision matrix completion. In order to predict the subareas' data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal-spatial correlation between the data from different cycles or subareas. According to the results gained by experiments on four real-world industrial sensing datasets consisting of six typical tasks, it can be seen that the method in this article improves the accuracy of prediction using sparse data.

Different from online promotion, the outdoor billboard advertising industry suffers from a lack of audience-targeted delivery and quantitative dissemination evaluation, which undermine its impact in practice and hinder it from fast development. To bridge this gap, in this paper, we leverage crowdsensing vehicle trajectory data to empower audience-targeted billboard advertising. More specifically, by integrating the information of mobility transition, traffic conditions (traffic volume and average speed), and advertisement semantic topics, we propose a quantitative model to quantify advertisement influence spread, with a special consideration on influence overlapping among mobile users. Based on it, an influence maximization-targeted billboard advertising problem is formulated to find k advertising units over spatiotemporal dimensions, with the goal of maximizing the total expected advertisement influence spread. To tackle the efficiency issue for solving large combinatorial optimization problem, we employ a divide-and-conquer mechanism, and propose a utility evaluation-based optimal searching approach. Extensive experiments on real-world taxicab trajectories clearly validate the effectiveness and efficiency of our proposed approach.

In recent years, takeout ordering and delivery (TOD) has become an emerging service due to its convenience and efficiency. However, current online ordering platforms still suffer from some issues, such as limited delivery coverage and delayed delivery. To address these issues, we propose a spatial crowdsourcing (SC)-based system called crowd delivery network (CrowDNet) to have packages take hitchhiking rides with existing taxis. We first tackle passenger riding queries based on an evolutionary algorithm and then insert appropriate food delivery requests into a partial schedule with an improved insertion approach. Finally, we propose a ranking module based on the modern portfolio theory to recommend the delivery path, which can achieve a balance between the delivery cost and timely services. Evaluations based on three real-world datasets demonstrate that our proposed algorithms outperform baseline methods.

Vehicularad hocnetworks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.

Mobile crowdsensing (MCS) is a new paradigm to collect sensing data and infer useful knowledge over a vast area for numerous monitoring applications. In urban environments, as more and more applications need to utilize multi-source sensing information, it is almost indispensable to develop a generic mechanism supporting multiple concurrent MCS task assignment. However, most existing multi-task assignment methods focus on homogeneous tasks. Due to the diverse spatiotemporal task requirements and sensing contexts, MCS tasks often differ from each other in many aspects (e.g., spatial coverage, temporal interval). To this end, in the paper, we present and formalize an important Heterogeneous Multi-Task Assignment (HMTA) problem in mobile crowdsensing systems, and try to maximize data quality and minimize total incentive budget. By leveraging the implicit spatiotemporal correlations among heterogeneous tasks, we propose a two-stage HMTA problem-solving approach to effectively handle multiple concurrent tasks in a shared resource pool. Finally, in order to improve the assignment search efficiency, a decomposition-and-combination framework is devised to accommodate large-scale problem scenario. We evaluate our approach extensively using two large-scale real-world data sets. The experimental results validate the effectiveness and efficiency of our proposed approach.

With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic device-to-device (D2D) networks. However, coupling between mobile crowdsensing and D2D networks, it raises new technical challenges caused by intermittent routing and indeterminate settings. Considering the operations of data sensing, relaying, aggregating, and uploading simultaneously, in this article, we study collaborative mobile crowdsensing in opportunistic D2D networks. Toward the concerns of sensing data quality, network performance and incentive budget, Minimum-Delay-Maximum-Coverage (MDMC) problem and Minimum-Overhead-Maximum-Coverage (MOMC) problem are formalized to optimally search a complete set of crowdsensing task execution schemes over user, temporal, and spatial three dimensions. By exploiting mobility traces of users, we propose an unified graph-based problem representation framework and transform MDMC and MOMC problems to a connection routing searching problem on weighted directed graphs. Greedy-based recursive optimization approaches are proposed to address the two problems with a divide-and-conquer mode. Empirical evaluation on both real-world and synthetic datasets validates the effectiveness and efficiency of our proposed approaches.

With the rapid development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, which refers to recruiting mobile workers to perform location-based tasks, has gained emerging interest from both research communities and industries. In this paper, we consider a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task has a valid duration, operation complexity, budget limitation, and the number of required workers. Each volunteer worker completes assigned tasks while conducting his/her routine tasks. The system has a desired task probability coverage and budget constraint. Under this scenario, we investigate an important problem, namely heterogeneous spatial crowdsourcing task allocation (HSC-TA), which strives to search a set of representative Pareto-optimal allocation solutions for the multi-objective optimization problem, such that the assigned task coverage is maximized and incentive cost is minimized simultaneously. To accommodate the multi-constraints in heterogeneous spatial crowdsourcing, we build a worker mobility behavior prediction model to align with allocation process. We prove that the HSC-TA problem is NP-hard. We propose effective heuristic methods, including multi-round linear weight optimization and enhanced multi-objective particle swarm optimization algorithms to achieve adequate Pareto-optimal allocation. Comprehensive experiments on both real-world and synthetic data sets clearly validate the effectiveness and efficiency of our proposed approaches.

Moving route prediction offers important benefits for many emerging location-aware applications such as target advertising and urban traffic management. A common approach to route prediction is to match similar trace recordings from a larger volume of historical trajectories, and return the targeted recorded path as desired answer. However, due to privacy concerns, incentive mechanism and other reasons, especially in small business environment, a limited dataset with sparse trajectories is only available. Actually, the existing sparse dataset cannot cover sufficient query routes, and then the match-based approach may return no results at all. Moreover, the existing sparse dataset may fail many trajectory mining approaches that work well on general environment. In this paper, we investigate moving route prediction from sparse trajectory dataset, and propose a novel hybrid model, namely HMRP, to address the above problem. To avoid sparse distribution over spatial semantic layer, a road network map reconstruction methods are proposed to accommodate the sparse trajectories in semantic transformation. And then, by training historical trajectories, the implicit mobility patterns and Markov transition model are constructed to support route prediction. When a query trajectory arrives, towards its derived potential destination, our proposed HMRP model integrates pattern matching strategy and Markov probability distribution to predict its future route gradually in a complementary way. Experiments on real-life taxicab GPS recorded dataset demonstrate that HMRP method can improve movement prediction precision significantly, comparing with the baseline prediction algorithms. And the response time for each query trajectory is acceptable for most application cases.

Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods.