This paper proposes an integrated satellite-Unmanned Aerial Vehicle (UAV)-terrestrial collaborative computing framework that incorporates Low Earth Orbit (LEO) satellite, UAVs equipped with Mobile Edge Computing (MEC) servers, ground infrastructures, and terrestrial users. A novel three-tier hybrid task decomposition and computation architecture is designed to support efficient task offloading in dynamic and heterogeneous environments. By exploiting the complementary advantages of LEO satellite and UAVs in communication coverage and deployment flexibility, and by utilizing remote high-performance servers, the system ensures improved Quality of Service (QoS), enhanced coverage, scalability, and robustness. To address the heterogeneity in computation demand, latency sensitivity, and mobility patterns of ground devices, the cross-regional task offloading and decomposition problem is formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), enabling efficient collaborative decision-making among agents with overlapping observations. Furthermore, under the centralized training with decentralized execution (CTDE) paradigm, we propose a hierarchical scheduling algorithm based on heterogeneous multi-agent deep reinforcement learning (DRL), allowing LEO satellite and UAVs to jointly learn optimal strategies during training while autonomously executing decisions in real time. To address the challenges posed by high-dimensional state spaces, we introduce an attention-driven UAV and satellite-assisted task offloading algorithm (ADUS). A multi-head attention mechanism is incorporated into the critic networks, allowing agents to focus on critical features and avoid reliance on full joint state-action representations. Experimental results demonstrate that the proposed algorithm significantly outperforms other baseline methods, including ADUS-NAT, ADUS-NPP, MADDPG, DDPG and MATORA, achieving improvements of 5.45%, 11.25%, 12.04%, 19.61%, 33.25%, and 42.35%, respectively.

In smart grids, fine-grained data aggregation is a key technology for efficient power management and demand-side response. However, large-scale collection of high-precision electricity usage data poses significant privacy risks. Although various privacy-preserving fine-grained data aggregation schemes have been proposed, they still face several challenges, including limited support for statistical functions, poor stability, and lack of adaptability. To address these challenges, we propose a versatile fine-grained data aggregation (FPDA) scheme based on cloud-edge collaboration. First, we adopt the Qin Jiushao algorithm to process multidimensional data, and combine it with an enhanced Paillier encryption algorithm to securely aggregate the data while supporting multiple statistical functions. Second, we optimize the Boneh–Lynn–Shacham (BLS) signature scheme to enable batch verification of data integrity. Third, our scheme supports dynamic user management and fault tolerance, enabling scalability and robustness. The theoretical analysis shows that FPDA meets the security requirements of smart grids, while performance evaluations demonstrate its efficiency and practical applicability.

Equipment manufacturing industry plays a pivotal role in a nation's economy, national development, and technological innovation. Traditional equipment manufacturing enterprises often face the problem of untimely information sharing and inconsistent focus of various departments in the process of operation, which causes difficulties in analysis and conflict in decision-making. Considering the subjectivity of interdepartmental decisions, reinforcement learning is introduced to analyze the decisions of various departments and get the decisions that best meet the needs of the enterprise. In addition, due to the instability of the internal and external environment of the enterprise, the decision obtained only by the machine usually cannot meet the development requirements of the enterprise, so human–machine coordination is used to adapt to the environmental changes. Therefore, a decision conflict resolution for equipment manufacturing enterprises based on human–machine coordination is proposed in this article. The resolution of conflicts in the context of deep human–computer collaborative decision-making is achieved through the incorporation of expert knowledge twice within the framework of reinforcement learning, thereby expressing preferences for both production factors and decision model structures. The experiments indicate that our model achieves superior performance in resolving decision conflicts.

With the popularization of smartphones with GPS, user information attached to the locations is facing the risk of leakage. In the real world, even if the locations of a user are well protected, attackers can also mine user privacy by analyzing the user correlations in common hotspots. To solve the above problems, we propose a local differential privacy extension scheme in hotspot areas. In this scheme, we firstly obtain the user’s hotspots by mining the user trajectories based on the sliding time window, and then, we extract the correlation degrees among users from these hotspots using the Jaccard correlation coefficient, and finally we introduce the personalization correlation sensitivity to extend the local differential privacy so as to protect sensitive locations in hotspot areas. Experiments show that, compared with existing methods, our scheme can improve the usability of trajectories up to $\mathbf{6. 6 4 \%}$ at the same privacy level.

The disadvantages of personnel tracking methods are such that traditional manual methods are inefficient, while internet-based tracking methods overload users with information. Manual viewing of surveillance videos is time-consuming and labor-intensive, and real-time search methods based on machine vision require excessive computation. Therefore, this paper proposes a specific pedestrian tracking method based on location prediction and edge computing. This method involves retrieving videos from relevant areas based on the given personnel image information, generating an image set containing the target and the target’s approximate trajectory, and performing personnel tracking. The proposed pedestrian tracking framework treats surveillance videos as three-dimensional spatiotemporal maps, transforming the pedestrian tracking task into a map search problem. The framework consists of two algorithms: a location prediction algorithm based on road network information and a multistep trajectory tracking algorithm for tracking specific pedestrians in an edge computing environment. The location prediction algorithm determines the target’s location and predicts the target’s next position, while the multistep trajectory tracking algorithm enables trajectory tracking and distributed training of the model in the context of an edge network. Experimental results demonstrate that the proposed solutions are highly practical and perform well in personnel tracking compared to previous approaches.

The use of unmanned aerial vehicles (UAVs) is a promising solution for collecting data from wireless Internet of Things (IoT) devices and offloading to mobile edge computing (MEC) server embedded access points (APs) equipped with powerful servers. This article presents a solution to optimize the energy efficiency of UAV relaying in the IoT system, which assists in programming the multiple UAV flight trajectories and bandwidth allocation schemes to scientifically and energy-efficiently transmit the data from IoT devices to MEC servers. Furthermore, in order to ensure the continuous and effective data relaying of the UAVs, we deploy a number of decentralized wireless charging stations (CSs) in the system to replenish the UAVs’ energy and enable them to provide long-term services. Specifically, we propose a deep reinforcement learning-based efficient IoT data relaying method, where we mainly apply deep deterministic policy gradient (DDPG) to solve this dynamic programming problem with large action spaces. Experimental results demonstrate that the DDPG-based method for UAV efficient data collection and offloading (DDPG-UCO) algorithm outperforms other five baseline methods in terms of the UAV energy efficiency, amount of data relayed and data interaction energy consumption rate while maintaining a high level of geographical fairness of the relaying service.

With the popularity of mobile terminals with GPS functions, location-based services are widely used, and all kinds of user information attached to the location are facing the risk of disclosure, and privacy protection is being challenged. In current researches, it is usually assumed that the locations of different users are independent of each other. However, in real world, the locations of different users have some certain internal correlation. Even if the locations of a single user are well protected, attackers can still mine user privacy through the correlation analysis of locations. To solve the above problems, this article proposes a multiuser location-correlated differential privacy extension scheme under strict privacy budget. In this scheme, we first extract the user spatiotemporal characteristics by mining the stay points and stay areas from trajectories based on locations with timestamps, and then, we calculate the correlation degree among users using the Jaccard correlation coefficient according to the spatiotemporal characteristics, and further, we realize the adaptive differential privacy protection of different users by introducing the concept of individual correlation sensitivity, and finally, we design the differential privacy extension method to protect sensitive locations in the stay areas. Experimental results show that, compared with the existing methods, our proposed scheme not only can improve the usability of the trajectories after privacy protection, but also can enhance the privacy protection of the sensitive locations in the stay areas.

Federated Learning (FL) offers a distributed machine learning framework that enables collaborative model training across multiple data sources without the need to share raw data, thereby preserving data privacy. This framework is particularly well-suited for cross-departmental and cross-enterprise intelligent decision-making in smart manufacturing. However, challenges remain in selecting reliable participants and ensuring the secure transmission of parameters to defend against potential attacks. Malicious participants may upload low-quality data or compromise data privacy during model aggregation. To address these issues, we propose the Federated Reputation Evaluation Blockchain (FREB), which integrates a reputation evaluation mechanism with blockchain technology. By leveraging blockchain, FL tasks are executed through trusted transactions, with smart contracts ensuring transparency and accountability. In contrast to traditional contribution evaluation methods, FREB employs a multi-weight subjective logic model combined with Shapley values to assess participant reliability. Reputation scores are calculated based on factors such as activity, model contribution, stability, and data quality, guiding the selection of participants. Additionally, a PoR-based model aggregation method is implemented, and noise is added to the model parameters to protect sensitive data from potential attacks. Experimental results on real-world datasets demonstrate that FREB effectively mitigates malicious node attacks and encourages high-quality participants, while maintaining model accuracy and data privacy.

Addressing expanded surveillance scopes, cross-device person re-identification emerges as a crucial issue. Traditional approaches to re-identification and tracking confront challenges in feature matching challenges, and fail to harness the potential of edge computing environments, which offer viable solutions to bandwidth and latency constraints. The paper introduces a novel, edge computing and shared cache-based method for person re-identification and tracking. In this framework, edge nodes exploit deep learning method to execute image recognition for person re-identification and trajectory tracking. Edge nodes are deployed within a tri-layered architecture, encompassing Hierarchical Edge Cloud (HEC), Mobile Edge Computing (MEC), and Central Cloud (CC). Emphasizing part feature significance for person re-identification, the approach employs a refined Part-based Convolutional Baseline model guided by the triplet loss function. Experimental outcomes validate the high re-identification precision and efficacious load balancing capability of this method in person re-identification and tracking operations. Achieving a harmonized trade-off between task accuracy and response time, the proposed framework offers a robust solution for person re-identification and tracking within edge computing contexts.

We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time. Different from the settings in traditional PU learning where the negative class consists of a single class, the negative samples which cannot be identified by a client in the federated setting may come from multiple classes which are unknown to the client. Therefore, existing PU learning methods can be hardly applied in this situation. To address this problem, we propose a novel framework, namely Federated learning with Positive and Unlabeled data (FedPU), to minimize the expected risk of multiple negative classes by leveraging the labeled data in other clients. We theoretically analyze the generalization bound of the proposed FedPU. Empirical experiments show that the FedPU can achieve much better performance than conventional supervised and semi-supervised federated learning methods.

Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-hungry and latency-sensitive services by enabling the placement of services and execution of computing tasks at the edge of radio access networks much closer to resource-constrained devices. However, how to serve more requests while reducing service latency by exploiting limited resources (storage capacities, CPU cycles, communication bandwidth) is still a critical issue in the multidevice MEC-assisted IoT networks, since the time-varying computing demands of devices and unavailability of future information make it difficult to determine where to handle computation tasks and which services to cache. In this article, we propose a twin-timescale framework to jointly optimize adaptive request scheduling (RS) and cooperative service caching (SC) in the multidevices and MEC-assisted networks, in order to explore request dynamic, MECs heterogeneity, service difference. To accommodate the unavailability of future information and unknown system dynamics, we, respectively, formulate RS and SC as partially observable Markov decision process (POMDP) problems. Then, we propose a deep reinforcement learning (DRL)-based online algorithm to improve the service latency reduction ratio and hit rate, which do not requirea prioriknowledge such as service popularity. Moreover, we give the optimal CPU cycles and communication bandwidth allocations in order to further minimize the average service latency. Extensive and trace-driven simulation results demonstrate the efficacy of the proposed approach.

In this article, a fine-grained query authorization scheme with integrity verification is proposed over encrypted spatial data for location-based services (LBS). The fine-grained query authorization is enabled based on a distribution of the spatial data by employing a non-uniform partition in the spatial domain to generate a density-based space filling curve (DSC), which can be used to generate index values for querying and transformation keys. The transformation keys can be used to generate query tokens for a secure spatial query as well as construct a transformation key tree whose subtree can be distributed by the LBS provider to an authorized user as transformation key for query tokens generation. Furthermore, the proposed scheme constructs a Merkle quad tree (MQ-tree) to support integrity verification by aggregating a digest of the spatial data based on the DSC and employing the MQ-tree as a verification structure. The LBS provider can share a subtree of the MQ-tree to authorized user as his verification structure, which corresponds to the transformation key of the authorized user. In this way, the authorized user can only generate the valid query tokens and verify the query results in his authorized region. The security properties of the proposed scheme is discussed, and extensive experimental results demonstrate the high efficiency of verification structure generation and verification operations.

With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS’ execution, the participants’ various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant’s privacy.

Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framework called Multi-turn Multiple-choice Machine reading comprehension (M3) to solve the short text EL from a new perspective: a query is generated for each ambiguous mention exploiting its surrounding context, and an option selection module is employed to identify the golden entity from candidates using the query. In this way, M3 framework sufficiently interacts limited context with candidate entities during the encoding process, as well as implicitly considers the dissimilarities inside the candidate bunch in the selection stage. In addition, we design a two-stage verifier incorporated into M3 to address the commonly existed unlinkable problem in short text. To further consider the topical coherence and interdependence among referred entities, M3 leverages a multi-turn fashion to deal with mentions in a sequence manner by retrospecting historical cues. Evaluation shows that our M3 framework achieves the state-of-the-art performance on five Chinese and English datasets for the real-world short text EL.

Recently with the popularity of embedded smart devices, mobile crowdsensing has become a new data collection paradigm. However, these traditional schemes rely on a central authority, leading to potential privacy disclosure, and there is no the guarantee that a center is completely credible in reality. In addition, the issues of security and privacy caused by the center platform should not be ignored, and the computation related to privacy data cannot be verified. In the paper, we propose a decentralized privacy-preserving model(PPNS) based on blockchain, which avoids data from being tampered with, and denied. In the prototype of PPNS, a privacy preserving node selection scheme is constructed and the result can be verified (i.e. security against malicious node) without any third-party arbiter. The experimental results show that compared with prior methods, our solution has a better performance and will facilitate node selection without violating participants' privacy.

Unmanned aerial vehicles (UAVs) have been considered as a promising approach for providing additional computation capability and extensive coverage to ground mobile devices (MDs), especially in the scenario where the communication infrastructure is unavailable. This article investigates a UAV-assisted multiaccess edge computing system, where a number of ground MDs are served by a flying UAV and a ground base station, both of them are equipped with computation resources. The system aims to minimize the weighted cost of time latency and energy consumption, subject to the constraints on offloading decisions and resource competition. Since this problem is NP-hard, and the MDs are autonomous, we propose a game theory-based scheme to find the optimal solution and prove the existence of the Nash equilibrium. Meanwhile, we propose another two schemes as the benchmark to evaluate the efficiency and effectiveness of the game-theoretic solution. The simulation results show that the game-theoretic scheme is able to achieve a near-optimal performance, and the convergence time is also stable when the number of MDs increases.

In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.

In recent years, with the popularity of smartphones, the indoor positioning systems based on mobile crowdsensing (MCS) have gained considerable interest and exploit. However, it is still challenging to construct a largescale indoor positioning system. 1) In indoor positioning model, storage and computing resources are very important. 2) The calibration operation of data label and selection of model parameters require the operation of professionals. 3) User location privacy may be compromise, which greatly affects participant safety and enthusiasm. To solve these problems, our model firstly provides an edge-crowdsourcing indoor localization architecture to improve storage, computing power and response speed. Then, based on manifold regularization, a semi-supervised indoor localization model is determined by an adaptive manner in terms of both similarity and manifold structure, which reduces the workload of the positioning model and improve localization accuracy. In addition, we propose a new privacy-aware indoor localization algorithm based on secure multi-party computation to protect location privacy. Experimental results on real-world datasets show that, compared with the previous methods, our method improves accuracy by 0.87m, and in terms of time overhead of privacy protection, our method reduces the running time of the thousand seconds level.

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive‐weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low‐dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

By allocating tasks to participants, crowdsensing has shown large potential in addressing large-scale data sensing problems. Considering the problem of unfair payment, negative work of participants, and cooperative cheating, how to assess data quality of tasks reliably is an important problem in crowdsensing. Therefore, a lightweight blockchain-based model for data quality assessment is proposed in this article. First, there are two data quality assessment processes in the model. One is implemented in the selection of participants and the other is implemented in data quality assessment. Second, consensus mechanism and smart contracts are redesigned to be suitable for crowdsensing. The lightweight consensus mechanism delegated proof of reputation (DPoR) is proposed in the blockchain-based model instead of proof of work (PoW). Furthermore, three smart contracts, verifiers selection contract (VSC), participants employment contract (PEC), and data verify contract (DVC), are generated to constrain the behaviors of the involved parties. Finally, expectation-maximization (EM) algorithm with multiverifiers is proposed to evaluate the performance of task participants. Experiments on the open data sets Wine Quality show that our new method outperforms the existing methods in improving the quality of sensing task.

With the prosperity of mobile application markets, mobile advertising is becoming an increasingly important economic force. In order to maximize revenue, ads are recommended to be delivered to potentially interested users, which requires user targeting, i.e., analyzing users' profiles and exploring users' interests. However, collecting user personal information for targeted mobile advertising services raises critical privacy concerns. Although some solutions like anonymization and obfuscation have been proposed for privacy-aware targeted advertising, they undesirably face the issues of security, efficiency, and/or ad relevance. In this paper, we propose a practical system enabling secure and efficient targeted mobile advertising services. It allows the ad network to perform accurate user targeting, while ensuring strong privacy protection for mobile users. Specifically, we show how to properly leverage a cryptographic primitive called private stream searching to support secure, accurate, and practical targeted mobile ad delivery. Moreover, we propose secure billing schemes to enable the ad network to charge advertisers in a privacy-preserving manner. The security strength of our system is thoroughly analyzed. Through extensive experiments, we show that our system achieves practical efficiency on mobile devices.

Applying Multipath TCP (MPTCP) towards transport-layer parallel multipath transmission to increase the throughput performance of mobile devices has attracted considerable attention. Significant results in this area have resulted in many highly promising solutions; however most of the existing solutions follow the conventional sender-centric design and the strict layering principle, without considering the fact that the receiver's intelligence and cross-layer activities can result in distinct performance advantages. In this paper, we propose MPTCP-RC, a receiver-centric cross-layer solution to aid MPTCP towards efficient parallel multipath data transmission in a wireless environment. In MPTCP-RC, the receiver performs path usage decision and cross-layer activity, by making use of its acquired first-hand knowledge of both transport layer and MAC layer, rather than only giving feedback to the sender then waiting for the decision. We evaluate and demonstrate the benefits of our proposal by simulations.

We consider a general multi-user mobile edge computing (MEC) system with multiple MEC servers. For each user, a MEC serves both as the network access point and a computation service provider, where users can offload part of their tasks. We formulate the sum cost of time delay and energy consumption for all mobile users as our optimization objective. This problem is NP-hard in general. In this paper, we jointly optimize the offloading decisions of all users tasks as well as the allocation of computation and communication resources, pursing the minimal sum cost for all users. We proposed an efficient two-stage algorithm comprising of one-dimensional search (ODS) and alternating optimization (AO). The first stage is responsible for obtaining the optimal offloading decision, and the second stage is in charge of computing a locally optimal solution for resource allocation. It is shown to give nearly optimal performance under a wide range of parameter settings. Through evaluating the performance of different combinations of the two stages of ODS-AO algorithm, we provide insights into their roles and contributions in the overall solution. Our simulation results show that the proposed scheme achieves significant reduction on the average delay and sum cost compared to other baselines.

With the popularization of intelligent terminals, crowdsensing has become increasingly prominent because of its advantages, such as low cost, high convenience, and fast speed in conducting tasks. However, the quality of the data collected through crowdsensing is varied and is difficult to evaluate. Furthermore, the existing crowdsensing quality control methods are mostly based on a central platform, which is not completely trusted in reality and results in the existence of fraud and other problems. To solve these two questions, a crowdsensing quality control model based on a two-consensus blockchain is proposed in this paper. First, the idea of a blockchain is introduced into this model. The credit-based verifier selection mechanism and the two-consensus approach are proposed to realize the nonrepudiation and nontampering of information in crowdsensing. Then, to help task publishers obtain higher-quality sensing data, the methods of node matching and QGE are proposed. The former method uses the idea of the calculation of matching degree to select workers, and the latter uses the idea of clustering and fuzzy theories to evaluate the quality of the sensing data. Finally, the experiments show that the running time of the block generation in our model is acceptable, and comparing with the other methods, our model can acquire data of higher ioj.

With the rapid growth of smart terminals in recent years, crowdsensing which utilizes the human intelligence to solve complicated problems have gained considerable interest and exploit. The majority of the existing crowdsensing systems rely on a trusted third-party platform to complete sensing tasks and collect large-scale data. However, the platform cannot completely ensure trust in the real world. The issues of security and privacy caused by the center platform should not be ignored. In this paper, we propose a decentralized privacy-preserving model based on twice verifications and consensuses of blockchain (TCNS). In the prototype of TCNS, an anonymity strategy which can be verified based on the elliptic curve algorithm is proposed to protect the user identity privacy. Then, we propose a twice consensus mechanism, which ensures that the data can be traced and avoids data from being impersonated, tampered with, and denied. Moreover, we propose a user attribute protection scheme based on the lightweight homomorphic encryption algorithm. Finally, considering various influencing factors comprehensively, TCNS uses fuzzy theories to select the candidate mobile nodes. Further, we implement the prototype with real-world datasets, the experimental analysis of privacy protection and safety shows that TCNS can effectively prevent association analysis attacks and background knowledge attacks. More gratifying, the time overhead for generating a new block is acceptable.

The mobile smart devices are becoming more and more powerful, and they have been pervasively applied in crowdsensing as effective tools to solve large-scale sensing tasks in urban areas. However, some crowdsensing tasks may bring high network traffic costs to participants using 3G/4G network. In this paper, a novel data diffusing and transmission method is proposed in Crowdsensing based on city Public Traffic System (PTS). This method makes full use of the advantages that the bus has predictable trajectory, wide coverage area, fast moving speed and long contact duration among passengers, so as to realize the rapid transmission of large-scale sensed data. Firstly, we design a data diffusing and transmission model in PTS, and emphatically discuss the Multi-data diffusion and transmission in budget constraints. Secondly, we propose a new algorithm called BUI-BA (Backhaul Utility Increment Based Backhaul Algorithm), which is able to transmit multi- data at the same time. And the algorithm is explained in detail with an example. Finally, the performance of BUI-BA is evaluated with comparisons to Greedy and effSense from aspects like overall transmission utility, fairness, success rate of transmission and transmission redundancy. The result has proved that BUI-BA has a better overall performance and can achieve a tradeoff between overall transmission utility and transmission redundancy, and save more network traffic costs and some other resources for mobile nodes.

Mobile edge computing (MEC) has emerged as a promising technique to address the challenge arising from the exponentially increasing data traffic. It not only supports mobile users to offload computations but also caches and delivers popular contents to mobile users. In this paper, we aim at designing novel content caching strategies in MEC networks to reduce access latency and improve energy efficiency. First, the distributed content delivery network based on MECs is developed to support users' requests locally. Moreover, based on users' distribution characteristics and MECs' location, a grouping-based and hierarchical collaborative caching strategy is proposed. Simulation results prove that our caching strategy is more efficient than alternative benchmark strategies in terms of average access latency, total energy consumption and content diversity.

Multi-cloud collaborative environment consists of multiple data centers, which is a typical processing platform for big data. This paper focuses on the trust computing requirement of multi-cloud collaborative services and develops a Data-driven and Feedback-Enhanced Trust (DFET) computing pattern across multiple data centers with several innovative mechanisms. First, a trust-aware service monitoring architecture is proposed based on distributed soft agents to serve as middleware for multi-cloud trust computing and task scheduling. A data-driven trust computation scheme based on multi-indicator monitoring data is then proposed. The integration of several key service indicators into trust computing makes this scheme suitable for service-oriented cloud applications. More importantly, according to the intrinsic relationship among users, monitors, and service providers, we propose an enhanced and hierarchical feedback mechanism that can effectively reduce networking risk while improving system dependability. Theoretical analysis shows that DFET pattern is highly dependable against garnished and bad-mouthing attacks. We also build a prototype system to verify the feasibility of DFET pattern and the experiments yield meaningful observations that can facilitate the effective utilization of DFET in the large-scale multi-cloud collaborative environment.

Mobile edge computing (MEC) is an emerging paradigm to support the proliferation of smart phones and recent outstanding mobile data traffic growth. The network edge devices with computation, communication and storage capabilities can be applied to cache popular resource in close proximity to end uses and directly distribute resource to them, which is a promising solution to provide enhanced service. Firstly, in order to respond to the users' access requests at the edge of network, the distributed resource distribution network (DRDN) is constructed to realize the quick query, positioning and distribution of resource. It mainly consists of network elements: NEF (Network Exposure Function) node for global management and MEC nodes for distribution service. Secondly, on the basis of DRDN, two kinds of MEC's service models of distributing resource are defined and classified, the total energy consumption and average service delay for distributing resource to end users are analyzed. Finally, for the heavy access load and high latency of root caching MEC node, a hierarchical caching scheme (HCS) is designed and a caching node selection algorithm based on fuzzy C-means clustering is proposed according to the location characteristics of end users. The simulation results show that the HCS can save a lot of energy and reduce the average service delay. Additionally, for the different total arrival rate of users' access requests, the change of average service delay is stable with good adaptability in the HCS.

Security is one of the biggest concerns for the further adoption of Clouds. However, Cloud providers usually assign VMs leased by different customers upon the same physical server. Albeit maximizing resource efficiency, this cross-domain sharing poses a serious threat to customers' privacy concerns. A malicious VM could break or bypass the isolation mechanism and execute certain cross-VM attacks, such as side channel attacks or memory Dos attacks, etc. However, most of previous solutions are either attack-specific or unsuitable for immediate deployment, making the mitigation techniques for co-resident attacks still an important and worth-studying problem in cloud security. In this paper, we propose a novel grouping-based VM placement strategy to provide a secure optimization for existing VM placement policies. The theoretical analysis and simulation results show that our strategy decreases enormously the probability of co-residence while incurring only a slight loss on resource efficiency. The results also demonstrate that our strategy is significantly more effective in terms of both co-location resistance and resources efficiency, compared with the CLR policy.

Homomorphic encryption can protect user’s privacy when operating on user’s data in cloud computing. But it is not practical for wide using as the data and services types in cloud computing are diverse. Among these data types, digital image is an important personal data for users. There are also many image processing services in cloud computing. To protect user’s privacy in these services, this paper proposed a scheme using homomorphic encryption in image processing. Firstly, a secret key homomorphic encryption (IGHE) was constructed for encrypting image. IGHE can operate on encrypted floating numbers efficiently to adapt to the image processing service. Then, by translating the traditional image processing methods into the operations on encrypted pixels, the encrypted image can be processed homomorphically. That is, service can process the encrypted image directly, and the result after decryption is the same as processing the plain image. To illustrate our scheme, three common image processing instances were given in this paper. The experiments show that our scheme is secure, correct, and efficient enough to be used in practical image processing applications.

The explosive growth of multimedia contents, especially videos, is pushing forward the paradigm of cloud-based media hosting today. However, the wide attacking surface of the public cloud and the growing security awareness from the society are both calling for data encryption before outsourcing to cloud. Under the circumstance of encrypted videos, how to still preserve all the service benefits of cloud media center remains to be fully explored. In this paper, we present a secure system architecture design as our initial effort toward this direction, which bridges together the advancements of video coding techniques and secure deduplication. Our design enables the cloud with the crucial deduplication functionality to completely eliminate the extra storage and bandwidth cost, which would have been incurred by hosting encrypted videos from different entities. The design is also carefully tailored to the scalable video coding (SVC) techniques to support heterogeneous networks and devices for high-quality adaptive video dissemination. We show fully functional system implementations with structure-aware encryption design and structure-aware deduplication strategies that are both completely compliant with the video format in SVC. Extensive security analysis and experiments via our prototype deployed on Azure cloud platform show the practicality of the design. Our work can also be easily extended to support other media applications that employ media files with scalable structures.

Crowd sensing (CS) is a new paradigm of collecting large amounts of sensing information from a crowd. Unfortunately, not everyone is willing to participate in sensing tasks or provide high-quality information. Offering incentives is a common method of increasing the success of CS. Existing incentive mechanisms based on auction models lack deep consideration regarding the effects of malicious competition behavior and the “free-riding” phenomenon in crowdsourcing services. The design proposed in this paper focuses on an incentive mechanism based on a reverse auction. First, the crowdsourcing system incentive model is built. Second, an incentive mechanism is proposed based on an auction which combines the concepts of reverse auctions and Vickrey auctions. Next, proof that the mechanism is computationally efficient, individually rational, budget-balanced, truthful, and honest is provided. Simulation results indicate that the proposed incentive mechanism can effectively improve fairness of the bids and the quality of the sensed data.

Multimedia contents, especially videos, are being exponentially generated today. Due to the limited local storage, people are willing to store the videos at the remote cloud media center for its low cost and scalable storage. However, videos may have to be encrypted before outsourcing for privacy concerns. For practical purposes, the cloud media center should also provide the deduplication functionality to eliminate the storage and bandwidth redundancy, and adaptively disseminate videos to heterogeneous networks and different devices to ensure the quality of service. In light of the observations, we present a secure architecture enabling the encrypted cloud media center. It builds on top of latest advancements on secure deduplication and video coding techniques, with fully functional system implementations on encrypted video deduplication and adaptive video dissemination services. Specifically, to support efficient adaptive dissemination, we utilize the scalable video coding (SVC) techniques and propose a tailored layer-level secure deduplication strategy to be compatible with the internal structure of SVC. Accordingly, we adopt a structure-compatible encryption mechanism and optimize the way how encrypted SVC videos are stored for fast retrieval and efficient dissemination. We thoroughly analyze the security strength of our system design with strong video protection. Furthermore, we give a prototype implementation with encrypted end-to-end deployment on Amazon cloud platform. Extensive experiments demonstrate the practicality of our system.

Mobile advertising has become a key economic force with the growing mobile app markets. Targeted advertising has been attracting the attention of business and research communities because of its high return on investment. However, the fact that it requires continuous collection of user profiles raises severe privacy concerns. In this paper, we propose a secure and practical mobile advertising architecture, which aims to enable targeted advertising over encrypted mobile user profiles. The proposed idea is based upon a cryptographic primitive, i.e., private stream searching (PSS). We first propose a basic construction of secure targeted mobile advertising. Thorough analysis shows that our basic design achieves semantic security. After that, we introduce a number of techniques to make our design more efficient for mobile devices. Experimental results demonstrate the practicality of our proposed architecture.

With the powerful sensing capability of mobile smart devices, users can easily obtained the crowd sensing services with smart devices in the Internet of Things (IoT). However, credible interaction issues between mobile users are still the hard problems in the past. In this paper, we focus on how to assign the crowdsourcing sensing tasks based on the credible interaction between users. First, a novel credible crowdsourcing assignment model is proposed based on social relationship cognition and community detection. Second, the service quality factor (SQF), link reliability factor (LRF), and region heat factor (RHF) are introduced to scientifically evaluate the user crowdsourcing preferences. Then, a crowdsourcing algorithm based on analytic hierarchy process (AHP) theory is proposed. Finally, the simulation experiments prove the correctness, effectiveness, and robustness of our method.

Abstract With the rapid growth of various data collected by companies and organizations, there is an increasing need for the data owners to share their data with the third party for the purpose of data mining. Therefore, protecting the privacy of the shared data has received considerable attention from academia and industry. However, existing methods do not work well for distance‐based mining algorithms. This paper proposes a novel privacy‐preserving approach to support distance‐based mining algorithms while reducing the shared data size. This approach transforms the shared data to discrete cosine transformation (DCT) coefficients, and adaptively selects DCT coefficients so as to form the compressed coefficient matrix. To enhance data privacy, this approach employs rotation‐based transformation with modified constraints to process the compressed coefficient matrix. Extensive experiments show that the proposed approach achieves the best tradeoff between data privacy and mining quality comparing with other existing ones. Copyright © 2015 John Wiley & Sons, Ltd.

This paper proposes a service operator-aware trust scheme (SOTS) for resource matchmaking across multiple clouds. Through analyzing the built-in relationship between the users, the broker, and the service resources, this paper proposes a middleware framework of trust management that can effectively reduces user burden and improve system dependability. Based on multidimensional resource service operators, we model the problem of trust evaluation as a process of multi-attribute decision-making, and develop an adaptive trust evaluation approach based on information entropy theory. This adaptive approach can overcome the limitations of traditional trust schemes, whereby the trusted operators are weighted manually or subjectively. As a result, using SOTS, the broker can efficiently and accurately prepare the most trusted resources in advance, and thus provide more dependable resources to users. Our experiments yield interesting and meaningful observations that can facilitate the effective utilization of SOTS in a large-scale multi-cloud environment.

We propose a new K-nearest neighbor (KNN) algorithm based on a nearest-neighbor self-contained criterion (NNscKNN) by utilizing the unlabeled data information. Our algorithm incorporates other discriminant information to train KNN classifier. This new KNN scheme is also applied in a community detection algorithm for mobile-aware service: First, as the edges of networks, the social relation between mobile nodes is quantified with social network theory; second, we would construct the mobile nodes optimal path tree and calculate the similarity index of adjacent nodes; finally, the community dispersion is defined to evaluate the clustering results and measure the quality of community structure. Promising experiments on benchmarks demonstrate the effectiveness of our approach for recognition and detection tasks.

The conventional security mechanism based on cryptography and authentication is not sufficient for countering all types of security problems resulting from internal malicious nodes and wrong act resulting from hardware breakdown and resources loss. The construction of a reputation based trustworthy sensor networks has recently become a hot point of making up for the shortcoming of conventional security mechanism. Firstly, a familiar reputation-based framework for sensor networks (RFSN) is presented, and problems caused by trust computing in the unreliable wireless channel are analyzed. Secondly, A Heider-theory based reputation framework for sensor networks (HRFSN) is presented for improving on RFSN using Heider theory. A node communicating with another node is observed by the mutual neighbor nodes of them, optimizing the calculation of direct reputation value when the channel is unreliable and enhancing the reliability of trust value. Finally, according to simulation analysis, the HRFSN framework can get more exact direct reputation value and dependable trust value than the RFSN framework. At the same time, this framework can maintain its resiliency against bad mouthing attacks and collusion attacks toward the recommended results.

Multicast and MPLS are two complementary technologies. Merging MPLS and multicast technologies can acquire two advantages: multicast saving bandwidth and MPLS supporting high-speed, QoS and traffic engineering, VPN. Implementation of MPLS multicast has two key expectations: how to construct multicast tree over MPLS domain and how to enhance multicast scalability. In this paper, we design a scalable MPLS multicast algorithm. The algorithm uses tunneling technology and branching nodes technology, which can merge source and shared trees multicast in MPLS networks. Using tunneling technology to implement multipoint-to-multipoint (MP2MP) shared-tree multicast can solve one of the difficulties of MPLS multicast: MP2MP label distribution. Using branching nodes technology can reduce multicast forwarding state in routers and enhance multicast scalability. We also evaluate our proposal in terms of scalability and efficiency and present some simulation results based on NS-2

Abstract To dynamically improve system selections of the waiting jobs under de‐centralized scheduling frameworks, each computation system together with its neighbors is assumed to compose a subgrid, and distributed backfilling is proposed as the scheduling strategy in each subgrid. Whenever a job terminates, distributed backfilling is triggered to re‐backfill all waiting jobs on the corresponding subgrid in order of their submittal. Each subgrid is overlapped with another, so the waiting jobs may be migrated around the Grid. To evaluate distributed backfilling, Grid resources and scheduling framework are configured, the model of Grid workload is constructed by extending the workload models of parallel systems and the Job Scheduling Simulation System is designed to simulate the process of de‐centralized job scheduling. In addition, job speedup is presented as performance metrics of scheduling strategies. Results show the dynamic optimization of system selections brought by distributed backfilling is Grid‐wide, and can improve scheduling performance remarkably as long as Grid load is not too light and the job migration costs are not too high. Copyright © 2006 John Wiley & Sons, Ltd.