Edge computing enables real-time Internet of Things data processing by bringing computation closer to data sources, but its distributed architecture creates cybersecurity vulnerabilities requiring privacy-preserving attack detection mechanisms capable of handling heterogeneous data distributions. This article proposes federated generative adversarial divergence (FedGAD), a plug-and-play modular framework that enhances existing federated learning methods through Jacobian-based regularization and dynamic complexity-aware weighting to address cyber-attack detection in non-independent and identically distributed (IID) and unlabeled edge data environments. Unlike existing approaches suffering from mode collapse and training instability, FedGAD maintains statistical consistency across distributed nodes through gradient-based stability mechanisms, supported by rigorous theoretical analysis establishing convergence guarantees and mode coverage properties. We conduct comprehensive experiments comparing FedGAD against four federated generative learning baselines federated trustworthy (FedTrust), anomaly detection generative adversarial network (ADGAN), federated generative adversarial network for intrusion detection system (FedGAN-IDS), and federated temporal sequential recurrent generative network (FedTSRGNet) and four regularization-based methods federated averaging (FedAvg), federated proximal (FedProx), learning with collaborative aggregation method (LeCam), and Jensen Shannon (JS) Divergence on telemetry data of networks - internet of things (ToN_IoT) and Communications Security Establishment in Canadian Institute for Cybersecurity - Intrusion Detection System (CSE_CIC_IDS) datasets, demonstrating FedGAD's superiority with accuracy improvements up to 3.5%, achieving 100% mode coverage compared to 25% for baseline methods while maintaining computational efficiency for resource-constrained edge deployments.

Nowadays IoT devices in Mobile Edge Computing (MEC) networks have been deployed in large-scale quantities to guarantee sensing data collection for anomalous event detection as full as possible even if some devices are in fault. Some techniques, such as clustering and dimensionality reduction, are adopted to eliminate redundant sensing data collection in this large-scale deployment. However, they not only have high computational complexity and easily cause the loss of information on the primary sensing attributes for detection, but also bring certain errors to the detection because of their low sensitivity to data processed. In addition, insufficient collection of primary attribute data samples often results from physical or human factors, and blind imputation of large-scale data gaps without basis may lead to greater irreparable losses. To address the above challenges, we first complete the selection of optimal primary attribute device collection and aggregation (PADCA) path based on minimum spanning tree, reducing data communication cost for redundant primary attributes collection. Then, we propose an anomalous impact correlation search strategy to quickly locate all MEC servers whose management regions have cascading anomalous event and help determine the transferable source MEC servers. Leveraging this, we use transfer learning to help detect anomalous events in the management regions of the MEC servers with insufficient primary attribute data samples, where a particle swarm optimization based back-propagation (PSO-BP) neural network model is used to infer the fusion weight of each primary attribute. Experimental results show that our method achieves higher detection performance in terms of detection time, energy consumption, accuracy, and receiver operating characteristic (ROC) curve compared to the benchmarks by at least 24%, 34%, 0.5 and 0.05.

With the rapid development of 6 G communication technology and the Internet of Things (IoT), mobile edge computing (MEC) is regarded as an effective paradigm of providing low-delay, high-quality services to mobile users. In the IoT device-edge-cloud network, the optimal deployment of MEC servers is a prerequisite for a better task offloading, while the improved performance of mobile users task offloading also indicates the deployment scheme is optimal. Most of current MEC servers deployment studies focus on reducing delay and deployment costs, but ignore the offloading requirements of mobile users with similar task type and cooperative relationship arriving at the same community. In this paper, we study the MEC servers deployment driven by the task offloading requirements of community mobile users in current period by utilizing the stability of their social cooperative relationships to maximize the service satisfaction of all community mobile users in the future task offloading. First, the cooperative relationship strength between mobile users is measured to form a group of resource requesters based on interaction probability, movement trajectory and credit strength. Then, we implement the optimal search of base stations (BSs) using spatial index, followed by the one-to-many matching theory between BSs and community group resource requesters, to balance the load of BSs and reduce the communication delay between them. Finally, we use TD($\lambda$) algorithm and task similarity between cooperative users to deploy MEC servers with suitable resources around BSs so that the deployment scheme can significantly improve the future task offloading performance of all community mobile users. Based on the real data set provided by Shanghai Telecom, it is confirmed that the proposed scheme has significant advantages in improving all community mobile users service satisfaction, with an average improvement of 18.49% compared with the baselines.

Detection of anomalous event at the edge of network has attracted wide attention from both academic and industrial fields recently. During the detection process, several primary sensing attributes are jointly utilized to determine whether an anomalous event occurs or not. However, as the primary attributes of some Internet of Things (IoT) devices are easy missing due to the natural wear and they cannot be timely and accurately accessed, the event detection efficiency is very low. In view of this, our work introduces a digital twin (DT)-assisted detection technology for anomaly identification in a device-edge–cloud architecture. Specifically, for an edge server with missing primary attributes, the probability of anomalous event occurring on it can be calculated by analyzing the primary attribute fusion values of its adjacent edge servers. As a result, it is unnecessary to carry on detection in advance on the edge servers with a low anomaly occurring probability, efficiently reducing the detection cost. For the remaining edge servers with a high probability, the primary attributes with high accuracy are migrated by considering the difference on the historical value variant trend and the fusion effect. Based on this, a decision tree will be built in the integrated DT model for anomalous event detection in advance. Further, the cloud collects other relevant attributes to build a random forest for the final identification and judgment of anomalous events. Experimental results show that our method achieves a higher detection performance in terms of energy consumption, detection time, and accuracy by at least 37.1%, 39.5%, and 1.82% compared to the baselines.

Federated Learning (FL) allows collaborative training of a Machine Learning (ML) model while preserving data privacy across participating clients. Most existing studies consider FL clients to be proactive and completely honest in their participation. However, in reality, clients might lack the motivation to participate, and malicious behavior among some clients could negatively impact the interests of others. For these reasons, ensuring trust and fairness among FL clients is paramount but remains challenging due to limitations in FL consensus mechanisms and incentive strategies. To address these challenges, we introduce a Trustworthy and Fair FL (TFFL) framework that develops a reputation-based consensus mechanism called Dynamic Reputation Consensus (DRC), where clients’ reputations are dynamically assessed based on subjective opinions by evaluating real-time client behavior. We also incorporate time decay and temporal discounting of TFFL interactions along with the weighted measures of clients’ data quality, performance, and reliability to accurately reflect the evolving nature of client behavior over time. By adaptively adjusting clients’ incentives based on reputations and a cooperative game theory, DRC incentivizes honest participation and discourages malicious intent. In addition, we utilize blockchain and smart contracts to provide decentralized, regularized, and secure reputation management that is resistant to tampering and non-repudiation. Theoretical analysis and empirical results on widely used datasets (MNIST, CIFAR-10, and CIFAR-100) demonstrate the effectiveness of DRC in enhancing trust and fairness, improving performance, and providing robust security in FL settings. Results further exhibit that DRC offers superior performance in local model validation, consensus decision, and convergence time compared to related research approaches across various experimental settings.

Mobile Edge Computing (MEC) and Device-toDevice (D2D) peer offloading are two promising paradigms in the mobile Internet of Things (IoT). In this paper, we study the collaborative task offloading with redundant data and codes in large-scale IoT networks, where computing resource-starved IoT devices can offload their tasks to MEC servers via cellular links or to nearby peer devices (PDs) with idle resources through D2D links for execution. IoT tasks usually consist of a series of dependent and parallel subtasks, and the difficulties in current research are (i) how to eliminate redundancy in data or codes between subtasks, and (ii) how to leverage previous experience to adaptively search a set of collaborative MEC servers and PDs for matching offloading of dependent and parallel subtasks. From this, we propose a redundancy-aware adaptive search offloading (RASO) method based on the deep Q-network (DQN). Specifically, we first design a fine-grained task recombination scheme by judging the consistency of subtask data and codes. After that, we organize the global devices into a spatial index MP-tree to reduce the search solution space, and propose a fast adaptive search method based on the DQN combined with MP-tree, where optimal path-guiding parameters training of inner and outer layers is involved to efficiently help achieve collaborative devices to complete specific tasks with the same type. After finding the collaborative MEC servers and PDs along MP-tree for a certain task, a centralized stable matching algorithm is further developed to give a decision of offloading each of its divided dependent and parallel subtasks to the matched one, thereby optimizing offloading delay and energy consumption. Extensive simulation results show that compared to other counterpart solutions, our proposed method has improved task offloading performance in terms of delay and energy consumption.

Mobile edge computing provides relatively rich computation resources for Internet-of-Things (IoT) task offloading at the edge of networks. As time goes on, user tasks present diverse requirements in function, type, dependency, urgency, etc., which makes edge servers take on dynamically diversified service features to adapt to the requirements of user tasks. Moreover, cache has been studied a lot in recent years for reducing the execution cost of related or dependent tasks. However, jointly considering which result data required to be cached and where to cache is still an intractable problem in task offloading due to dynamically diversified and sensitive features of task and edge servers for prediction. To provide more comprehensive consideration, we propose a multiple features matching scheme, coupled with federated learning-assisted collaborative caching, to enhance the efficiency of task offloading. Specifically, we first build a common features of historical tasks based FI-tree to help search for an edge server that best matches the requested task features. This helps to obtain optimal task allocation and improve offloading performance. Further, the results of tasks related to or dependent on cached results can be obtained directly through the collaborative edge cache prediction model trained by two-stage federated learning. In this way, the amount of data executed for offloaded tasks is reduced, thereby speeding up the return of final results as well as reducing the delay and energy of task execution. Meanwhile, it avoids massive transmission of task results correlated data and also protects the privacy of these data when training the prediction model. Experimental results show that our proposed method outperforms the benchmark approaches through reducing the time delay and energy consumption by at least 15.6% and 18.2%.

Anomaly detection is playing an increasingly important role in Internet of Things applications since anomalous events may cause some damage to the physical–social environment monitored by different kinds of smart object devices. In some cases, the occurrence of an anomalous event is caused by the fusion impact derived from several primary monitoring factors. Considering this, we propose a novel anomaly detection mechanism for the events with multiple decisive primary attributes in a collaborative device–edge–cloud architecture, in which a propagation and influence-based correlation is further explored in the edge layer for improving the detection efficiency. During the detection process, multiple primary attributes first cooperate to detect an anomaly in the edge layer in advance. If an anomaly occurs in the subregion managed by an edge device, social-aware interaction relationships between edge devices are further integrated to give a guidance on the detection of correlative anomaly in neighbor subregions. The cloud further analyzes the primary attributes information and the interaction relationship to determine the secondary attributes that are helpful in identifying the final anomaly. A large number of experiments show that our method is superior to the alternative methods in terms of energy consumption, detection time, and accuracy.

Anomaly event coverage is usually related to several attributes, among which the primary attribute dominates at the time of improving detection efficiency. In the case of Internet of Things (IoT) devices with complex social-aware relationships, IoT nodes with primary attributes should cooperate with each other through their social-aware interactions, to detect potential event anomalies and further determine the coverage of such anomalies. Existing research has put a lot of effort into designing IoT detection frameworks to discover anomalous sensor data, rarely caring about the social-aware interactions. This paper targets this important efficiency problem, and develops a novel anomaly detection mechanism in collaborative social-edge-cloud architecture. The focus of it is to first construct a vector space based Aggregation Behavior Comparison Detection Model, and quantify the change of monitoring behavior by defining the clustering threshold of vector space. This can quickly judge whether a local social network is abnormal and speed up the abnormal detection rate. If it is, a Social Behavior Correlation Detection Model is further designed based on the correlation of primary attributes derived from the dominating social-aware interaction behavior captured by (primary) edge nodes. This strategy can help detect specific “abnormal” areas managed by one or more edge devices with higher accuracy. In the process of anomaly detection, we also propose a spatial index tree to store the information of IoT nodes, so as to effectively collect and route the perceived data of IoT nodes for anomaly analysis. Experimental results demonstrate that our anomaly detection method promotes the detection efficiency and accuracy in comparison with the state of art’s techniques.

Edge computing is a popular paradigm in solving the problems of long time delay and high energy consumption in Internet of Things (IoT) network, which can effectively realize the IoT task offloading by collaboration of multiple edge servers. Nevertheless, how to choose the appropriate edge servers for offloading the dependent subtasks is still a big challenge, considering the limited resources and computing power of the edge servers as well as the start and end execution time of each subtask. These factors have a great impact on the execution efficiency of the whole task. At present, most of the research works focus on single-hop or multi-hop task offloading, where the edge servers farther away are not considered in the offloading decision. Such task offloading strategy is not optimal, and difficult to achieve high parallel execution of tasks, resulting in some delay-sensitive tasks not being completed within the specified time. In this paper, a two-stage optimization method is proposed to solve the resource allocation problem between edge servers and tasks. In the first stage, we group tasks according to their priorities, and the group with a higher priority is given the preference to resource allocation, thereby ensuring the timeliness of delay-sensitive tasks. Within the same group, resources are competed according to the game theory, and the total delay of all tasks is optimized. In the second stage, we aim to optimize the energy consumption of each task without increasing its completion time by allocating the computing resources to its subtasks based on their maximum completion time. For group resource allocation, we propose a spatial index tree to store the information of all edge servers for optimal server selection. During the selection process, an online learning based double prediction model is utilized to reduce the energy consumption caused by information transmission. We have evaluated the performance of the experiment on iFogSim simulator, and the experimental results show that our proposed method can achieve better performance in terms of time delay and energy consumption.

IoT search engines have attracted increasing attention from both academia and industry, since they are capable of crawling heterogeneous data sources in highly dynamic environment. To process tens of thousands of spatial-temporal-keyword queries per second, query efficiency and communication cost in IoT search engines become critical issues. To address these challenges, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n-hop neighboring activity regions, is proposed in this paper. Thereafter, frequent query results can be achieved quickly leveraging the spatial-temporal-keyword filtering index of n-hop neighbor regions through modeling keywords relevance and uncertain traveling time. Besides, we adopt STK-tree proposed previously to directly answer non-frequent queries. Extensive experiments on real-life dataset demonstrate that our method outperforms the state-of-the-art's techniques in terms of the reduction of the query time and the number of transmitted messages.

With the Internet of Things (IoT) becoming the infrastructure to support domain applications, IoT search engines have attracted increasing attention from users, industry, and research community, since they are capable of crawling heterogeneous data sources in a highly dynamic environment. IoT search engines have to be able to process tens of thousands of spatial-time-keyword queries per second, making query throughput a critical issue. To achieve this heavy workload, caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n -hop neighbor activity regions, is first proposed in this article. With our design, the frequent query result can be gained quickly from the spatial-time-keyword filtering index of n -hop neighbor regions by modeling keywords relevance and uncertain traveling time. In addition, we use STK-tree proposed previously to directly answer nonfrequent queries. Extensive experiments on real-life and synthetic data sets demonstrate that our proposed method outperforms the state-of-the-art approaches with respect to query time and message number.

The large-scale and distributed characteristic of multiattribute sensors requires the fog computing paradigm to support location-awareness and latency-sensitive monitoring and query in industrial applications. In these settings, supporting the preference top-k query processing in skewness distribution is a challenge. In this paper, we propose to mitigate the problem of processing a large number of continuous multiattribute (i.e., multidimensional) top-k queries, each with its specific preference, in fog-supported wireless sensor networks. Specifically, a priority-aware index tree is constructed to support the efficient filtering through querying branch nodes according to their top-k result generation probabilities. We have also considered three situations to generate the filter thresholds for the preference user queries. To further eliminate the transmission of invalid thresholds and query results, an enhanced top-k query processing mechanism based on dual transform and K-sky band is developed. Experiments using synthetic dataset and Intel Berkeley Lab dataset show that our proposed approach can have significant improvements in energy efficiency over other reactive methods.

In the Internet of Things, smart things communicate with each other, and sensed data are aggregated and queried for satisfying certain requests of end-users. When a region of interest requires to be monitored continuously, the strategy that each query is to be executed independently through gathering sensed data of target sub-regions may not be energy efficient, since the values of sensors may have no significant difference in proximate sensing time-slot in some applications. To mitigate the energy consumption in this context, in this paper we propose an energy-efficiency hierarchical clustering index tree for organizing these grid cells. Then, we develop a time-correlated region query technique for answering continuous queries. Theoretical analysis show that our technique is energy efficient compared with traditional techniques.

The application of spatial database is increasingly widespread. How to effectively store and organize multidimensional space data and improve the processing efficiency of multidimensional data has become a central issue. R-tree is one of the most widely used spatial indexes. Due to the existing large overlap and coverage among the nodes of R-tree, the search path of a data object is not unique, and the search efficiency declines sharply when the amount of data increases. Based on the analysis and research of previous index trees, a novel DR-tree index based on the diagonal lines of MBR is proposed in this paper. DR-tree uses the diagonal line of MBR to indicate spatial data objects and construct R-tree, still adopt the endpoint coordinates of MBR diagonal to signify the location of leaf nodes or non-leaf nodes. Since MBR is simplified, the coverage and overlap among regions are also reduced. Experimental results show that the new index tree is superior to R-tree in performance. The query paths of data objects are single, the insertion, deletion, and query efficiency of data objects are significantly improved, and the performance of the new index tree becomes more apparent when the amount of data objects increases.