Sixth-generation (6G) edge networks must orchestrate heterogeneous services across massive device populations under strict latency, energy, and privacy constraints. Existing reinforcement-learning (RL) approaches for edge orchestration either ignore semantic task content, lack awareness of network topology, or incur prohibitive communication overhead when deployed in federated settings, limiting applicability to latency-critical 6G scenarios. To address these shortcomings, we propose FedSemGNN, a hierarchical federated RL framework that jointly integrates three complementary capabilities: (i) continual semantic task embeddings regularized by elastic weight consolidation for intent-aware service placement, (ii) graph convolutional network-based topology encoding that captures spatial relationships among edge nodes for structurally coherent decisions, and (iii) a two-level proximal policy optimization architecture with priority-aware scheduling and adaptive semantic thresholds tailored to diverse 6G service classes. Comprehensive evaluation on the EdgeSimPy simulator over 1000 orchestration steps against five representative baselines demonstrates that FedSemGNN achieves 39.08 ms orchestration latency ( 3 . 3 × faster than flat federation), near-perfect semantic fidelity, and 21 × lower communication overhead (0.72 MB). Scalability experiments spanning seven network sizes from 6 to 1000 nodes (a 167 × range) confirm that fidelity is preserved and computation time grows near-linearly. These results position FedSemGNN as a control-plane foundation for privacy-preserving, semantic-aware orchestration in large-scale 6G edge deployments.

Log-based anomaly detection (LAD) is imperative to ensure both the reliability and security of software systems. Although many deep learning approaches have been designed to capture complex and diverse anomaly patterns from log files, they heavily rely on large-scale annotated data. However, collecting sufficient labeled data is impractical when a software system has just been deployed. In this paper, we propose a cross-system few-shot learning log-based anomaly detection approach, namely FSLog, to solve the abnormal label scarcity problem, which is the main challenge of recent LAD research. Specifically, we leverage a pre-trained model from source system to enrich feature representations so that data instances from target system can also be effectively represented. To this end, we introduce a novel adversarial margin loss to enhance our feature distinguishability while preserving their generalizability. Further, we also develop a masked interactive temporal network for robust feature extraction of temporal relationships for log samples. We evaluate the proposed FSLog on three publicly available datasets based on a standard few-shot learning setup protocol. Experimental results demonstrate that our method achieves the best performance in detecting abnormal logs when compared to state-of-the-art methods.

Log anomaly detection (LAD) is of paramount importance to enhance the reliability and stability of software systems. Current state-of-the-art LAD suffers a significant performance degradation when dealing with consistently evolving log events caused by system updates. To build a reliable LAD model under the context of log data evolution, we propose an incremental learning-based method for LAD, namely ilLog, to avoid catastrophic forgetting of previously learned knowledge while continuously updating the model for better detection when processing the evolving log events. In particular, we design a novel entropy-driven sorting algorithm for real log sample replay, which enables the preservation of old knowledge via storing representative samples with discrete sequence features from previous tasks. Additionally, we introduce a Halton-based low discrepancy sequence to better approximate the sliced Cram´ er distance between the probability distributions of two models, thus enhancing the model learning capability. Based on a standard incremental learning protocol setting, we evaluate the newly proposed ilLog method on three publicly available datasets. Experimental results demonstrate that our approach achieves the best performance compared to SOTA LAD methods and models by applying existing IL-based methods in evolving software systems.

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This article presents multiobjective adaptive learning framework for uncrewed aerial vehicle (UAV)-assisted mobile edge computing (MOALF-UAV-MEC), a novel MOALF-UAV-MEC tailored for dynamic IoT environments. The framework integrates multiobjective reinforcement learning (MORL), model predictive control (MPC), adaptive particle swarm optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted mobile edge computing (MEC), including multiobjective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework’s efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations.

Log anomaly detection (LAD) is essential to ensure the safe and stable operation of Cyeber-physical systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or offline fast inference. Furthermore, we design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences in the feature space, thereby enhancing the generalization ability of the model to evolving data. Extensive experiments conducted on two publicly available log datasets, HDFS and BGL, validate the effectiveness of the OMLog approach. When trained using only normal log sequences, the proposed approach achieves the F1-Score of 93.7% and 64.9%, respectively, surpassing the performance of the state-of-the-art (SOTA) LAD methods and demonstrating superior detection efficiency.

Log-based anomaly detection (LAD) is one of the dominant approaches to improving the reliability and security of software systems. Presently, despite the efficacy demonstrated by state-of-the-art LAD approaches in processing static log events, their performance significantly degrades when confronting changes of log event types from system updates. To construct a reliable LAD model that could adapt well to the evolution of log data, we propose a method grounded in semi-supervised domain adaptation on the rationale of incremental log anomaly detection dubbed as SSDALog, which dynamically updates the model utilizing limited labeled samples to reconcile distributional shifts between evolving and historical data. Specifically, the proposed approach addresses the issue through two primary mechanisms: (i) creation of a cross-domain mixup algorithm, which computes the feature salience of log discrete sequences through occlusion strategy, thus enhancing the adaptability of the model to unknown patterns by mixing evolving features; and (ii) design of an incremental semi-supervised domain adaptation training framework based on noisy label learning to obtain a robust feature extractor, thus improving the generalization ability of the detection model. We empirically assess the efficacy of the SSDALog approach across two publicly available datasets. The experimental results show that our method outperforms the SOTA LAD approach, particularly for evolving systems.

Time series anomaly detection is crucial for improving the security and reliability of Cyber-Physical systems (CPS). While significant progress has been made, existing methods struggle to learn discriminative representations from multivariate time series with complex interactions and noise. To address this challenge, we propose a semi-supervised anomaly detection method based on denoising-aware contrastive learning, namely SSDCL, which can achieve robust performance for CPS anomaly detection using limited supervision. Specifically, we first design a similarity combination data augmentation algorithm to handle complex interactions among continuous sensor measurements and discrete actuator states. Furthermore, we develop a denoising hierarchical contrastive loss function that mitigates data noise interference while ensuring discriminative spatio-temporal representation. To validate the effectiveness of SSDCL, we conducted empirical evaluations on three publicly available CPS time series datasets including PUMP, SWaT and WADI. The experimental results show that the proposed method achieves F1 Score of 97.5%, 93.0%, and 74.4%, respectively, outperforming the state-of-the-art (SOTA) CPS anomaly detection methods.

With the increasing focus on the applications of artificial intelligence (AI), the number of layers and neurons in deep neural networks (DNN) is rapidly increasing, requiring a large amount of computational resources to execute DNN inference tasks. In order to address the resource bottleneck of the terminal device(TD) and achieve better user satisfaction at the terminal, DNN partitioning is explored in the client-edge-cloud (CEC) joint computing environment. The combination offloading decision of DNN sub-layers and the strong coupling between task execution is a mixed-integer programming (MIP) problem, making it particularly difficult to solve. To address this issue, we propose the graph reinforcement learning based DNN tasks offloading (GRLDO) scheme. Firstly, the CEC is modeled as a graph, and offloading strategies are performed through graph state transitions. Then, GRLDO combines GNN with the actor-critic network to train unlabeled offloading decision makers. In addition, to effectively train GRLDO, we propose a method for rapidly exploring action spaces and approaching the optimal solution. Numerical results show that, for various types of DNN tasks, this approach achieves up to 99.7% optimal performance, demonstrating better user satisfaction than existing algorithms.

Due to the high flexibility and wide coverage, unmanned aerial vehicle (UAV) has always been a popular issue in the area of mobile edge computing. UAV swarms can be deployed on-site to serve user equipment (UE) and handle offloading tasks. However, it’s inefficient that the uneven distribution of tasks cause some UAVs to be assigned heavy tasks while others are idle within the swarm. To address above issue, this paper investigates a cooperative offloading problem of minimizing the total system latency and the energy consumption, subject to constraints on battery capacity and execution latency. The problem is confirmed to be a challenging mixed-integer nonconvex programming problem with Non-deterministic Polynomial feature. Therefore we propose a joint UAV dynamic role switch scheme and cooperative offloading (MARSCO) algorithm to solve it efficiently, where two sub-problems are optimized iteratively. Specifically, both of them are optimized utilizing multi-agent deep reinforcement learning (MADRL) algorithm to interact with the environment for optimization. Finally, numerical results illustrate that the proposed algorithm utilizes the system resources to significantly reduce the total system latency and energy consumption compared with the benchmark algorithms.

With the increasing number of IoT devices, human activities tend to create dynamic multi-hotspot regions, which cannot be adapted to such highly dynamic scenarios due to the immobility of base station edge servers. UAVs serve as an effective solution but have limited resources to carry, thus, solving the dynamic scheduling and collaboration of UAVs in multi-hotspot regions is an important problem. In this paper, we propose a scheme called Dynamic Scheduling based on Graph Reinforcement Learning (DSGR), which aims to maximize the long-term energy efficiency of UAVs by optimizing their flight trajectories and charging timing. First, we abstract the dynamic scheduling problem of UAVs among multiple hotspots as a dynamic topological graph and use graph convolution to learn the temporal and spatial relationships among hotspots. Then, the UAV flight strategies are learned using the neighbor vectors of the graph nodes as action masks as well as the temporal and spatial relationships of the hotspots modeled as inputs to the PPO network. Compared with the benchmark algorithm, our proposed DSGR algorithm achieves the best performance in terms of UAV energy efficiency.

Wireless Industrial IoT plays a crucial role in smart factories, where many sensors are rapidly generating task requests scheduled for timely responses. Maintaining information freshness is necessary but challenging. Edge networks that combine emerging serverless feathers can enable significant improvements in development efficiency and more flexible adaptation to workloads. However, the cost of scheduling cannot be ignored. Most of the present work in serverless edge computing does not consider the impact of the age of information (AoI) and cost in task scheduling. In this paper, we consider the relationship between AoI in users and cost in service providers in practical scenarios. We model the task scheduling problem in a serverless edge computing scenario as a Markov Decision Process (MDP) and consider multi-hop forwarding task scheduling with guaranteed AoI and costs under different pressures of workloads. To solve the highly dynamic problem, we design a multi-agent deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO), validate it on real datasets, and experiments show that our algorithm reduces 10% cost in low workload and up to 16% AoI in the high workload situation.

For intelligent unmanned aerial vehicles working in complex environments, it is necessary to have a certain autonomous flight control decision-making ability to adapt to complex and changeable environments. In order to realize the rapid path planning of intelligent unmanned aerial vehicle in complex flight environment and ensure its accurate positioning, we consider the constraints of error correction and turning radius and so on, and establish a multiobjective optimization model with the shortest path and the least correction times. In addition, a novel projection algorithm is proposed to solve this model. The evaluation of our proposed method is done from a dataset. We clearly show its effectiveness and its superiority compared to several state-of-the art approaches.

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues – especially when multiple users issue concurrentFL requests. Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

Mobile edge computing (MEC) has become a new computing paradigm, which has caused new challenges, including how to dynamically place services to maintain user-perceived delays and determine the number of simultaneous executions of partitionable applications to optimize the quality of experience (QoE). What's more, the battery energy level of mobile devices and the operating cost of the service provider will also increase the difficulty of improving service performance. In order to solve the contradiction between the above factors and service performance, we study the performance optimization of mobile edge service placement for partitionable applications under the constraints of long-term cost budget and battery energy level. A centralized online service placement algorithm (COSPA) based on Lyapunov optimization is proposed, and the performance boundary of COSPA is theoretically analyzed. By stabilizing the average migration cost and the battery energy of the mobile device near a constant, the COSPA algorithm can obtain an asymptotically optimal solution. The experimental results based on the real dataset imply that the COSPA algorithm can obtain higher performance gains compared with the benchmarks and the Distributed Algorithm (DA).

The age of data (AoD) is identified as one of the most novel and important metrics to measure the quality of big data analytics for Internet-of-Things (IoT) applications. Meanwhile, mobile edge computing (MEC) is envisioned as an enabling technology to minimize the AoD of IoT applications by processing the data in edge servers close to IoT devices. In this paper, we study the AoD minimization problem for IoT big data processing in MEC networks. We first propose an exact solution for the problem by formulating it as an Integer Linear Program (ILP). We then propose an efficient heuristic for the offline AoD minimization problem. We also devise an approximation algorithm with a provable approximation ratio for a special case of the problem, by leveraging the parametric rounding technique. We thirdly develop an online learning algorithm with a bounded regret for the online AoD minimization problem under dynamic arrivals of IoT requests and uncertain network delay assumptions, by adopting the Multi-Armed Bandit (MAB) technique. We finally evaluate the performance of the proposed algorithms by extensive simulations and implementations in a real test-bed. Results show that the proposed algorithms outperform existing approaches by reducing the AoD around 10%.

The implementation of a variety of complex and energy-intensive mobile applications by resource-limited mobile devices (MDs) is a huge challenge. Fortunately, mobile edge computing (MEC) as a new computing paragon can offer rich resources to perform all or part of the MD’s task, which greatly reduces the energy consumption of the MD and improves the quality of service (QoS) for applications. However, offloading tasks to the edge server is vulnerable to attacks such as tampering and snooping, resulting in a deep learning (DL) security feature developed by major cloud service providers. An effective security strategy method to minimize ongoing attacks in the MEC setting is proposed. The algorithm is based on the synthetic principle of a special set of strategies, and it can quickly construct suboptimal solutions even if the number of targets achieves hundreds of millions. In addition, for a given structure and a given number of patrollers, the upper bound of the protection level can be obtained, and the lower bound required for a given protection level can also be inferred. These bounds apply to universal strategies. By comparing with the previous three basic experiments, it can be proved that our algorithm is better than the previous ones in terms of security and running time.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

Crowdsourcing is a promising solution of collaborative computing aiming at solving settling problems within distributed environment. A collaborative crowdsourcing system(CCS) usually consists of a platform, users performing tasks and thousands or even millions of tasks, each of which is usually simple such as choice making or item rating. However, existing works always match users and tasks without addressing users' interest, which could lead to a reduction of users' enthusiasm involved in the subsequent tasks. In addition, researchers often ignore the truth inference while completing task matching, which should be both significant in CCS. To this end, we jointly investigate the task matching and truth inference in CCS. We propose an integrated framework that can motivate users' participates while guaranteeing the quality of tasks. Finally, extensive simulations illustrate that our algorithm has an outstanding performance on both task matching and truth inferring.

Academic social networks (ASNs) have attracted significant attention in recent years as researchers try to understand and improve how research is conducted. Several approaches have been investigated to identify, predict, and recommend scientific collaborators but few have considered to explore a strategy using the game theory. This article investigates the social phenomenon of upstream reciprocity (UR) using a game-theoretical framework. UR occurs when a person who has just received help, in turn, offers help to another. A suitable multiplayer game, the public goods game (PGG), is adopted to model scholarly interactions of coauthorship networks. Experiments are performed on real datasets in which cascades of UR scholars are identified. More importantly, the proposed UR strategy achieves better performance than non-UR scholars as they are found to have a higher publication and citation count. Furthermore, UR behavior is found to replicate throughout the network, which in turn increases the likelihood of others adopting it. Finally, theoretical proof and simulations suggest that UR has the potential to become an evolutionary stable strategy (ESS).

There exist two severe challenges in cloud file-sharing networks: cooperation dilemma and trust dilemma. The mechanism designed to promote cooperation could suffer from malicious users, while the trust management that only considers the trust dilemma is subjected to denial-of-service attacks. To address these two dilemmas simultaneously, we present a dependable cloud file-sharing scheme-EigenCloud. The main contributions include the following. First, we propose a modified EigenTrust algorithm to calculate the global cooperation value and global trust value of each cloud user based on her/his past behaviors. Second, we propose cooperation and trust-aware worker recommendation mechanism by determining a Pareto front from all cloud users. Thus, a cloud user who adopts the recommendation mechanism by paying an additional fee could have a higher probability of receiving a valid file in one transaction. Last but not least, we use the evolutionary game theory (EGT) to study the acceptance and effectiveness of the proposed EigenCloud by strategically modeling cloud users. The Lyapunov stability theory is employed to mathematically investigate the stability of evolutionary equilibriums of our EigenCloud. Finally, both numerical simulations and simulator-driving experiments illustrate that our EigenCloud has an outstanding performance in promoting cooperation and inhibiting malicious activity.

Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.

Activity selection is critical for the smart environment and Cyber‐Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high‐level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real‐world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature.

In the era of big data, the same question can get many answers from multiple sources. These answers may conflict with each other. Therefore, how to get the true information (i.e., the truths) from many answers has been a hot research topic. At present, there are many truth discovery methods which employ source reliability to improve the quality of truths. Most existing methods can only handle the categorical data or numerical data, while performs bad on text data. Meanwhile, we observed that the text data contains not only the question answer, but also some implicit information, for example, possible, may, make sure, similar, the same as, etc. These words have nothing to do with the answer but can reflect the self-confidence degree of source. In this paper, we propose a truth discovery framework which takes the implicit information into account. We first analyze text data and extract the answers of questions, then we create two dictionaries composed of self-confidence increasing words and self-confidence decreasing words respectively. Using the dictionaries we extract the self-confidence information from answer descriptions. Finally, we take full advantage of the self-confidence information to improve the performance of truth discovery. We perform experiments using a categorical data and a real-world Chinese text data. Comparing with other methods, our framework performs better, which demonstrates the superiority of our proposed framework.

The significant volume, variety, and velocity of data received from the many Industrial Internet of Things (IIoT) devices and other systems in a cloud-based or fog-based environment can complicate an organization's effort in ensuring high quality of experience for data users (DUs). For example, how do we efficiently and fairly allocate resources among cloud centers (CCs), fog service providers (FSPs), and DUs? This is particularly crucial for the IIoT environment, such as those in critical infrastructure sectors, such as energy and dams. Therefore, in this article, we propose an optimal resource allocation scheme for a fog-based IIoT environment. Specifically, we introduce fog nodes (or FSPs) that compete with each other to provide services for the DUs using resources from the CC. To maximize resource utilization, we model the resource allocation problem as a double-stage Stackelberg game and propose three algorithms to achieve Nash equilibrium and Stackelberg equilibrium. Then, we evaluate the performance of our proposed scheme with and without having FSPs, as well as with another competing scheme. The findings demonstrate the importance of fog computing in resource allocation, and the performance of our scheme outperforms that of the other scheme.

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.

With the fast growing of artificial intelligence (AI), more and more applications require querying uncertain data, especially from social media and crowd sourcing platform. In situations where it is impossible to increase data quality by controlling the sources, we may resort to algorithms to make the best use of the collected data. Since crowdsourcing provides a useful way to distributing tasks to mass people, and collects labels from as many workers as possible, many researchers have been study crowd-sourcing inference algorithms. In our work, we propose a novel crowd-sourcing inference algorithm to infer ground truth and obtain worker reliability and task difficulty at the same time.

We propose an algorithm for constructing efficient security strategies in the mobile edge computing (MEC), where the protected targets are nodes connected to the MEC and the mobile users (MUs) are agents capable of preventing undesirable activities on the nodes. The algorithm is designed based on the synthetic principles of a specific set of strategies, and it can quickly construct suboptimal solutions even if the number of targets reaches hundreds of millions.

Mobile Crowdsourcing (MC) provides a great platform for people to collect sensing data. Requesters and workers can interact and gain revenue through the platform. For tasks, the requester often has a budget, and the worker also has a price requirement. However, it is a difficult problem to determine the payments which should be accepted by both requesters and workers. In this paper, We propose a complete task assignment mechanism. The first step, we group tasks by Tasks Grouping with Cohesion (TGC), Tasks Grouping with Relevance (TGR) and Tasks Grouping with Similarity (TGS) respectively to form task groups. In the second step, we assign workers to task groups through worker selection mechanisms Workers Selection with Fixed Price (WSFP) and Workers Selection with Changing Price (WSCP). We prove that our mechanism is individual-rational, budget-balance, truthful and computationally efficient. Finally, we evaluate the mechanism through a lot of experiments.

Reputation plays a key role in online marketplace communities improving trust among community members. Reputation works as a decision-making tool for understanding the behavior of the business partners. Success of any online business depends on the trust the business agents share with each other. However, untrustworthy agents have anno place in online marketplaces and are forced to leave the market even if they will potentially cooperate. In this study, we propose an exploration strategy based on a forgiveness mechanism for untrustworthy agents to recover their reputation. Furthermore, a number of experiments based on the NetLogo simulation are performed to validate the applicability of the proposed mechanism. The results show that the online marketplaces incorporating a forgiveness mechanism can be used with the existing reputation systems and improve the efficiency of online marketplaces.

As advanced computing and communication technologies penetrate every aspect of our life, we have witnessed the persistent growth of open systems where entities interact with one another without prior knowledge or experiences. Trust becomes an important metric in such open systems. This paper presents a dependable trust management scheme-GroupTrust, and a working system to support GroupTrust. It makes three original contributions. First, we identify a set of vulnerabilities that are common in existing reputation based trust models. We show that reputation trust built solely on direct experiences or by combining direct experiences with uniform trust propagation can be vulnerable. Second, we develop GroupTrust, a dependable trust management scheme to provide reliable trust management in the presence of dishonest ratings, malicious camouflage, and malicious collusive behaviors. The GroupTrust scheme is novel in two aspects: (i) we develop a pairwise similarity based feedback credibility to enhance the resilience of trust computation in the presence of dishonest ratings; (ii) we propose to propagate trust based on a Susceptible-Infected-Recovered (SIR) model, which defines trust propagation threshold to control how trust should be propagated. Finally, we evaluate the effectiveness of GroupTrust against fourthreat models using both simulated and real world datasets. Our experimental results show that feedback credibility based local trust computation can effectively constrain strategically malicious participants from taking advantages of their dishonest ratings. SIR-based trust propagation control enables safe trust propagation and blocks irrational trust propagation. We show that GroupTrust scheme significantly outperforms other trust models in terms of both performance and attack resilience in the presence of dishonest feedbacks, sparse feedbacks, and strategically malicious participants against four representative threat models.

Peer-to-peer (P2P) streaming systems rely on that peers voluntarily share their bandwidth to maintain high performance. Credit-based incentive mechanisms are widely used to encourage peers to share more bandwidth. However, in credit-based mechanisms, how to set a suitable bandwidth price is a critical issue. In this paper, we propose a new dynamic bandwidth pricing mechanism based on Stackelberg game and trust management. This mechanism can dynamically adjust the bandwidth price based on peers' trust values: the lower trust a peer has, the higher price the peer pays. A Stackelberg game is formulated to obtain peers' optimal bandwidth pricing and purchasing strategies. Through mathematical analysis, we derive the equilibrium of our proposed game and an algorithm to obtain equilibrium is also proposed. Extensive simulations show that our proposed mechanism can effectively induce peers to share more bandwidth, maintain system fairness and defend typical data pollution attack to guarantee the robustness of the system.

Abstract Asymmetric group key agreement is a cryptographic primitive allowing a group of users to negotiate a common public encryption key while each of them holds a different secret private decryption key. Anyone (including outsiders) with the public encryption key can send encrypted messages to the group members, and then the group members can decrypt the messages. Authenticated key agreement protocols authenticate the identities of users to ensure that only the intended group members can establish a session in which the group members can communicate with each other. Dynamic asymmetric group key agreement concerns about the scenarios such as ad hoc networks in which the group members may join or leave at any given time. In this paper, we propose a one‐round authenticated dynamic protocol for symmetric group key agreement. For efficiency reasons, we employ the identity‐based public‐key cryptography (IB‐PKC) to authenticate users rather than the public key infrastructure and the certificate‐less public‐key cryptography. Our analysis shows that the proposals in the paper can resist active attacks and meet many desirable security attributes. Besides, our protocol allows users to join or leave the group at the same time. Furthermore, our protocol is round‐optimal and has a quite good performance as compared with previous works. Copyright © 2016 John Wiley & Sons, Ltd.

In autonomous environment (such as P2P, ad hoc, social networks and so on), all the rational individuals make independent decisions to maximize their profits. However, many interactions among individuals can be modeled as Prisoner's Dilemma game, which suppresses the emergence of cooperation. In order to provide scalable and robust services in such systems, incentive mechanisms need to be introduced. In this paper, we propose a novel incentive mechanism called recommendation incentive mechanism based on evolutionary dynamics(RIMBED). In our RIMBED system, players who pay an additional cost for recommendation service not only can get the information of the opponents, but also can have a higher probability to interact with cooperative individuals. Using the replicator dynamics equations in evolutionary game theory, we mathematically analyze the robustness and effectiveness of our RIMBED system. Meanwhile, simulation experiments can also validate our mathematical analysis. In our RIMBED system, players have three alternative strategies: always cooperative(ALLC), always defective(ALLD) and rational cooperative(RC). No one strategy can dominate the others forever and all the three strategies can survive in our system. When we bring in population invasion and a small mutation, our system can still work at an excellent level.

Summary In peer‐to‐peer (P2P) networks, contributions are made by peers voluntarily for the autonomous character of peers. However, selfish peers may refuse to be cooperative when considering their limited transmission resources. Incentive mechanisms are always used to guarantee successful cooperations among peers. Although the inventive mechanisms have been widely investigated on the basis of game theory, most researches assume that peers are well mixed in the network, regardless of the influence of peers' transaction relationships. In this paper, a novel analysis framework based on spatial evolutionary game theory is proposed to verify the effectiveness of incentive mechanisms. In the framework, a transaction overlay network is used to model the transaction relationships of peers. The transactions between clients and servers are modeled as the donor‐recipient game to satisfy their asymmetric characters. Influences of the learning noise and some common behaviors of peers on incentive mechanisms are also considered. Moreover, in order to demonstrate the utility of the framework, a reciprocation‐based incentive mechanism, which considers the requestors' behaviors of providing and consuming services, is thoroughly investigated under the framework in scenarios with homogeneous and heterogeneous benefits of services. By using the framework, besides the effectiveness of incentive mechanisms, the detailed spatiotemporal evolutions of peers' strategies driven by incentive mechanisms can also be obtained. Copyright © 2014 John Wiley & Sons, Ltd.

In traditional secret image sharing schemes, the participants have the same status, and shadow images are of approximately equal importance, which cannot satisfy some special requirements in real situations. In this paper, we considered the problem of secret image sharing with the weighted threshold access structure, which means different participants can have different status and significance. With this approach, each shadow image has one weight, and the secret image can be reconstructed losslessly if, and only if, the sum of all of the shadow images' weights is no less than the given weight threshold. In our scheme, we constructed the weighted threshold access structure of shadow images using the weighted threshold secret sharing scheme. Then, we used the quantization operation to embed the secret image's information into a host image to generate shadow images with different weights. In the retrieving procedure, a set of shadow images that satisfied the weighted threshold was utilized to restore the distortion-free secret image. Our experimental results confirmed that the proposed scheme was feasible, and both the visual quality of the shadow images and the embedding capacity of the host images were satisfactory. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Reversible data hiding enables the cover image to be restored from the stego image without any loss after the secret message is extracted. In this paper, we proposed a novel reversible data‐hiding scheme for high image quality based on the histogram‐shifting method. In our scheme, we proposed two mechanisms for choosing reference pixels according to the high correlation of adjacent pixels. The differences between reference pixels and their corresponding, neighboring, non‐reference pixels are exploited to hide secret messages according to the difference shifting mechanism. To achieve larger embedding capacity, the reference pixels are designed to further carry secret messages based on the interpolation prediction method. Experimental results showed that our proposed scheme achieved higher embedding payload while maintaining better visual quality of the images than did other schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Distributed service networks are popular platforms for service providers to offer services to consumers and for service consumers to acquire services from unknown parties. eBay and Amazon are two well-known examples of enabling and hosting such service networks to connect service providers to service consumers. Trust management is a critical component for scaling such distributed service networks to a large and growing number of participants. In this article, we present ServiceTrust ++ , a feedback quality--sensitive and attack resilient trust management scheme for empowering distributed service networks with effective trust management capability. Compared with existing trust models, ServiceTrust ++ has several novel features. First, we present six attack models to capture both independent and colluding attacks with malicious cliques, malicious spies, and malicious camouflages. Second, we aggregate the feedback ratings based on the variances of participants’ feedback behaviors and incorporate feedback similarity as weight into the local trust algorithm. Third, we compute the global trust of a participant by employing conditional trust propagation based on the feedback similarity threshold. This allows ServiceTrust ++ to control and prevent malicious spies and malicious camouflage peers from boosting their global trust scores by manipulating the feedback ratings of good peers and by taking advantage of the uniform trust propagation. Finally, we systematically combine a trust-decaying strategy with a threshold value--based conditional trust propagation to further strengthen the robustness of our global trust computation against sophisticated malicious feedback. Experimental evaluation with both simulation-based networks and real network dataset Epinion show that ServiceTrust ++ is highly resilient against all six attack models and highly effective compared to EigenTrust, the most popular and representative trust propagation model to date.

Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as the unique annotations (like Fingerprints) of recorded locations. In this study, we investigate the characteristics of RSS (e.g., how the RSS values change as time goes on and between consecutive positions?). On this basis, we design LuPI (Locating using Prior Information) that exploits the characteristics of RSS: with user motion, LuPI uses novel sensors integrated in smartphones to construct the RSS variation space (like radio map) of a floor plan as prior information. The deployment of LuPI is easy and rapid since little human intervention is needed. In LuPI, the calibration of ``radio map'' is crowd-sourced, automatic and scheduled. Experimental results show that LuPI achieves comparable location accuracy to previous approaches, even without the statistical information of site survey.

In a group‐based trust management scheme, peers are partitioned into groups based on chosen characteristics, such as location and interest. The super peer (SP), who is responsible for the storage and distribution of reputation value, has an important role in group‐based trust management. Thus, if the SP is a disguised or malicious peer, serious security problems could occur. To solve these security problems, the authors propose a traceable, group‐oriented, signature scheme with multiple signing policies for trust management. The SP's signature is generated by a designated group called the signature group. In the authors scheme, peers in the signature group will decide whether to generate the signature for the SP based on the SP's reputation, meaning that attackers cannot forge a valid signature. In addition, an outsider also can trace the signers who were involved in generating the signature for reputation valuation.

ABSTRACT Trust mechanism has become a research focus in recent years as a novel and valid way to ensure the transaction security in peer‐to‐peer file sharing networks. Nevertheless, some fundamental challenges still exist, for example: How can malicious peers be effectively isolated? How can various threats of manipulation by strategic peers be resisted? What strategy should be used to ensure that the service providers are authentic peers? Considering these challenges in our minds, in this paper, we propose a new trust mechanism based on the maximum flow theory. We firstly add a few prestigious peers into a cluster as the original members according to their transaction behaviors in a period; then, we perform maximum flow algorithm and identify those peers that still link from (to) the peers in the cluster as new members, which is carried out repeatedly, and almost every normal peer would finally become the member of the cluster. Each request peer has the priority to select downloading sources from this cluster according to our trust mechanism. In this way, the malicious peers are isolated, and their transaction behaviors are also confined largely even though they have high reputation. Extensive experimental results confirm the efficiency of our trust mechanism against the threats of exaggeration, cheat, collusion, and disguise. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT In this paper, we present attribute‐based ring signcryption scheme, which realizes the concept of ring signcryption in the attribute‐based encryption frame firstly. In our system, it allows a user to signcrypt a message by a set of attributes that are chosen without revealing its identity. In additional, we propose the security models and prove the confidentiality and unforgeability of our schemes. We also present the efficiency of our scheme by comparisons. Copyright © 2012 John Wiley & Sons, Ltd.

This paper argues that trust and reputation models should take into account not only direct experiences (local trust)and experiences from the circle of ”friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrus

In peer-to-peer (P2P) network, incentive mechanism is crucial to encourage cooperation among peers. Hence, how to construct a framework to analyze and evaluate the effectiveness of incentive mechanism is a very significant problem. Considering the peers' interactions are influenced by the network structure in real network, we propose a novel framework based on spatial evolutionary game theory. Different from most of other researches based on classical and evolutionary game theory, square lattice network is adopted as the network structure in this paper, without the assumption that peers are well-mixed in P2P network. The square lattice network structure can be easily extended to other realistic complex networks, such as small-world network and scale-free network. The reciprocative incentive mechanism is analyzed and evaluated under the framework with different service benefit. Through the simulation, the range of the parameter Q (cost/benefit) that makes the incentive mechanism work effectively under the framework is got, and the reason is analyzed. In addition, the influences of zero-cost identity and strategy mutation of peers on the incentive mechanism are evaluated. The framework is general to analyze and evaluate the effectiveness of other incentive mechanisms.

With the increasing complexity of computing tasks, the resource capability of a single cloud is generally limited, some applications often require various cloud source over internet to deliver services together. Resource collaborative scheduling becomes a critical problem in cloud computing. This paper propose a resources collaboration scheduling model to improve the efficiency of the virtual resources collaboration scheduling, the model bases on virtual organization and makes use of the trust mechanism to estimate the credibility of the virtual organization and improves it. The trust mechanism represents and calculates the credibility of the resources from three dimensions of system trust, user trust and collaboration trust by taking advantage of 2-Tuple fuzzy linguistic representation. The simulation results show that the model can analyze the trust and reputation of resources and improve the credibility of virtual organization. At the same time, it can slash the impact of the malicious evaluation and improve the efficiency of resource scheduling.

Cooperation within selfish individuals can be promoted by natural selection only in the presence of an additional mechanism. In this paper, we focus on an indirect reciprocity mechanism in dynamical structured populations. In social networks rational individuals update their strategies and adjust their social relationships. We propose a three-strategy prisoner's dilemma game model to investigate the evolution of cooperation on dynamical networks. In the coevolution of state and structure process, reciprocators adapt their behaviors and switch their partners based on reputation. Simulation results show that the dynamics of strategies and links can promote cooperation provided the partners switch proceeds much faster than the strategy updating.

SUMMARY The accuracy of feedback presentation, the sensitivity, and the robustness of reputation evaluation are critical issues to be addressed in a reputation system under the grid environment. This paper proposes a QoS‐based fine‐grained grid reputation system, where economic elements are considered to make the reputation system more sensitive in the commercial grid environments. A novel fine‐grained feedback presentation model based on aggregation of objective QoS attributes and subjective opinions of evaluators is proposed to enable a semi‐automatic, personalized and accurate feedback presentation. Through the introduction of a punishment factor and the adaptive reference of a previous trust value, the proposed reputation system effectively improves the sensitivity of the reputation evaluation. Moreover, the weighted combination of interorganizational trust, direct trust and recommended trust makes the reputation system more robust against collusion attacks. Simulation results show that the proposed reputation system can practically predict feedback and effectively resist malicious attacks such as fake transaction attacks and badmouthing attacks. This provides a clear advantage in the applications of grid service selection. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Many traditional steganography methods do not disperse and hide secret data smoothly over all the capacity of cover images. Rather, they severely modify part of the cover image(s) to embed secret data, which results in stego images that have poor visual quality. In addition, there are still some secret image‐sharing approaches that cannot reveal the secret image losslessly without pixel expansion or extra storage or restore distortion‐free cover image(s) if they use steganography. In this paper, a novel scheme, which is based on Shamir's (t,n)‐threshold scheme (1979) and Galois Field GF(28) and uses dynamic embedding and least significant bit construction, is proposed to solve the issues mentioned above. Our experimental results showed that the dynamic embedding performance in our scheme was satisfactory and that both the secret image and the cover image can be restored losslessly without pixel expansion or extra storage. Copyright © 2012 John Wiley & Sons, Ltd.

Location information of nodes is the basis for many applications in wireless sensor networks (WSNs). However, most previous localization methods make the unrealistic assumptions: (i) all nodes in WSN are always awake and (ii) the radio range of nodes is an ideal circle. This overlooks the common scenario that sensor nodes are duty-cycled in order to save energy and the radio range of nodes is irregular. In this paper we revisit the Distance-Vector-based (DV-based) positioning algorithms, particularly, Hop-Count-Ratio based Localization (HCRL) algorithm and investigate the following problems: (i) how is the relationship between the number of sleeping neighbor sensor nodes and the localization accuracy and (ii) how is the relationship between the degree of irregularity (DOI, which is a parameter of radio range irregularity) and the localization accuracy. We conduct a large number of experiments in WSNs' simulator NetTopo, and find that the parameters: the number of waking nodes, DOI, anchor node density and localization error, are interactional, i.e., for a given deployed static WSN, there is an optimal number of waking nodes and an optimal anchor node density, which can minimize network energy consumption without losing much of the localization accuracy. Furthermore, waking up more sensor nodes cannot always help to increase the localization accuracy, which actually is different from our intuitive thinking: more waking nodes can help to increase the localization accuracy of DV-based localization algorithms at all time.

Abstract Trust research is a key issue in peer‐to‐peer (P2P) networks. Reputation‐based trust models as one of the good solutions to resolve the trust problems in P2P network are received more and more attention in recent years. One of the fundamental challenges is to capture the evolving nature of a trust relationship between peers and reflect the varied bias or preference of peers in a distributed and open environment. In this paper, we present a fine‐grained trust computation model for P2P networks. Our model defines the service as a fined‐grained quality‐of‐service (QoS) (N‐dimensional vector), and in order to accurate the recommendation trust computing, several concepts are introduced to reflect the recommenders' current status, history behavior, and the gap between these two behaviors. Also, we firstly introduce the Gauss‐bar function to measure the preference similarity between peers. All these will result in a flexible model which represents trust in a manner more close to human intuitions and satisfies the diverse QoS requirements of peers in P2P networks. The extensive simulations have confirmed the efficiency of our model. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The purpose of this paper is to introduce a technique to eliminate parallel attacks to protocol in virtual organization (VO) through enforcing dynamic authorization policies. Grid realizes coordinated resource sharing across multiple management domains. VO is defined as a key concept for operation and management of grid services. Due to the fact that VO focuses on dynamic, cross‐organizational sharing relationships, one of the central challenges in the construction of scalable VO is that protocol specified by VO may have process of parallel running. To solve this problem, we present a formal definition of non‐honest participants' malicious coordination operations which are necessary for parallel attack counterexample in VO. Based on that, we present the two‐level dynamic authorization policy deploying scheme in VO for eliminating parallel attacks. Copyright © 2011 John Wiley & Sons, Ltd.

This paper aims at improving the throughput of the wireless sensor networks (WSNs), particularly to overcome the so-called funneling effect for WSNs with converge-cast patterns. Due to the disproportionate larger number of packets accumulated in the sensors that are closer to the sink, there is a need to decrease the collisions and increase the throughput around the sink area as well as the nodes that experience a heavy pass-through traffic. In this paper, we proposed a new scheme, namely PFB (Proportional Fairness Backoff), which provides additional scheduling opportunities to nodes closer to the sink. The new scheme employs Kelly's shadow price theory to achieve the proportional fairness, which takes advantage of the tree topology that is the de facto standard in today's WSNs. In PFB, the size of backoff window is dynamically adjusted with respect to the height of nodes belong in the tree. With close-form analysis and extensive simulations, we show that PFB can achieve up to 100% throughput increase over the widely used CSMA when the network is highly loaded.

A series of distributed processes usually need to be created in order to complete a user's task in grid environment, and these processes which have been created at different grid site form a process organization (called process-tree). Due to the dynamic of grid resource and some uncertain factors, some process nodes in this tree may be not accessible by the other related ones. It leads to a process-tree broken problem. As a result, it would block further execution of the processes we have created. Up to now, there is no good solution to solve the problem. In this paper we explore this problem and discuss how to handle it if some inaccessible nodes in process-tree happened, and how to ensure the integrity of organization structure of distributed processes. After making a comparison and analysis with the traditional distributed systems, we provide a new mechanism to increase reliability of grid-based computing environments and restore the inaccessible process-node over process tree in grid environment and endeavoring to keep the integrity of the original process organization structure. This new mechanism would adopts two types of additional information and two extra process actions to help restore the original process but not disturb the existing dynamic characters of grid resources, and grid tasks can be performed efficiently as well.

Proxy credentials are commonly used in security system when one entity wishes to grant some set of its privileges to another entity. Proxy credential chain is produced when new entities with proxy credentials use their proxy credentials to authenticate and establish secured connections with other entities in the same manner and are asked to wait for the completion of a task online. Due to network unstable, some middle node of the credential chain is not accessed by certain reasons, and, as a result, proxy credential chain problem occurs. The problem is an important research issue in grid security. In this paper, we explore the problem by using double signatures and applying X.509 proxy credential. We provides a method to create double signatures using data redundancy and to establish proxy credential chain with double signatures, and provide a recovery mechanism of proxy credential chain in grid when certificate chain broken problem occurs. We analyze the disadvantages of existing mechanism when the middle-node of the credentials chain was broken, and present a new scheme to extend the existing mechanism (including the description of new proxy credential format, the creation mechanism of proxy credentials and the strategy of validating). We also analyze the security of our new scheme.

The classic nonlinear filter performs well in impulse noise suppression and edge preserving. However, the classic nonlinear filtering is not good at reducing the mixture of Gaussian noise and impulse noise. In this paper, we investigate the nonlinear filtering techniques to eliminate the mixture of impulse noise and Gaussian noise. Based on fuzzy theory, we present a weighted average filter by making use of the fuzzy membership functions to optimize the weights of the filter. Computational results, which have been obtained from experiments for noise attenuation, indicate that the new algorithm is promising.