Zhangbing Zhou’s research while affiliated with China University of Geosciences (Beijing) and other places

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Publications (263)


Primary Attribute Migration Based Anomalous Event Detection in Digital Twin-Enabled Device-Edge-Cloud Network
  • Article

February 2025

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5 Reads

IEEE Internet of Things Journal

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Deliang Kong

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Xiaotong Ma

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[...]

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Zhangbing Zhou

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-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 digital twin 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.


Web 3.0-Enabled Microservice Re-Scheduling for Heterogenous Resources Co-Optimization in Metaverse-Integrated Edge Networks

January 2025

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4 Reads

ACM Transactions on Autonomous and Adaptive Systems

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User's requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this paper proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the microservice re-scheduling task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4% and 2.2% in terms of network throughput, latency and energy consumption.


Service Migration for Delay-Sensitive IoT Applications in Edge Networks

January 2025

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3 Reads

IEEE Transactions on Services Computing

The proliferation of I nternet o f T hings ( IoT ) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a r E source- E fficient se r vice C onfiguration ( E2E^{2} rC ) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced D eep R einforcement L earning ( DRL ) approach with a two-layer Q -network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2E^{2} rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices.


Adaptive Search and Collaborative Offloading Under Device-to-Device Joint Edge Computing Network

January 2025

IEEE Transactions on Mobile Computing

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.



Federated Learning-Assisted Task Offloading Based on Feature Matching and Caching in Collaborative Device-Edge-Cloud Networks

December 2024

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16 Reads

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3 Citations

IEEE Transactions on Mobile Computing

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%.


Energy-Efficient Online Service Migration in Edge Networks

September 2024

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13 Reads

IEEE Internet of Things Journal

Empowered by edge computing, resources and computation capabilities provided by edge devices can be encapsulated as containerized services, and domain applications can be achieved through service compositions. When burst requests are coming to be satisfied, there may exist edge devices which are overloaded, since requests are mostly spatially and temporally constrained, and edge devices are resource-scarceness and capacity-limited. In this setting, overloaded devices should be relieved through optimally migrating one or more activated services to contiguous edge devices. Besides, sensory data gathered by original edge devices should be periodically transmitted to migrated devices for data analysis purpose. To mitigate this issue, this paper proposes an Energy-efficient Online Service Migration (EOSM) mechanism to conduct the migration of multiple services simultaneously. Specifically, a light service sharing strategy is developed to only transmit the top container layer, and a modified NSGA-II algorithm is adopted to generate one or multiple paths for the container layer and time-series sensory data migration of each migrated service. Extensive experimental results show that our EOSM strategy outperforms the state of arts techniques in mitigating overloading devices in terms of access latency, energy consumption, and request success rate.




CSTL: Compositional Signal Temporal Logic for Adaptive Edge Service Monitoring

March 2024

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16 Reads

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2 Citations

IEEE Transactions on Services Computing

Edge service monitoring is essential to guarantee the healthy of service compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, but they are inadequate for that of inter- and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to the execution context. To address these challenges, this paper formulates the problem of edge service monitoring as the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed C ompositional S ignal T emporal L ogic ( CSTL ) with extended compositional modalities and online parameter settings, an adaptive monitoring mechanism is developed, where constraints are converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results show that CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.


Citations (67)


... The Internet of Things (IoT) often has constrained devices, and LLM-based agents might seek out weak links like unpatched IoT firmware or default credentials to take over devices in the IoT Supply Chain [54,55,169]. Ferrag et al. ...

Reference:

Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks
Adversarial Attacks on IoT Systems Leveraging Large Language Models
  • Citing Conference Paper
  • December 2024

... Early research, such as [9], proposed a multi-feature matching scheme combining federated learning for task offloading in IoT scenarios. Reference [10] introduced a reinforcement learningbased active caching strategy for mobile educational networks that reduces costs and latency. ...

Federated Learning-Assisted Task Offloading Based on Feature Matching and Caching in Collaborative Device-Edge-Cloud Networks
  • Citing Article
  • December 2024

IEEE Transactions on Mobile Computing

... For instance, in healthcare, service composition facilitates the integration of electronic health records, lab results, and remote consultations, enabling personalized patient care plans. Similarly, in smart cities, it orchestrates traffic management, energy distribution, and public safety services, optimizing resource utilization and meeting citizen requirements [5,6]. ...

CSTL: Compositional Signal Temporal Logic for Adaptive Edge Service Monitoring
  • Citing Article
  • March 2024

IEEE Transactions on Services Computing

... Integrating biometrics into the healthcare metaverse comes with significant security challenges, which must be addressed to ensure the privacy and integrity of sensitive medical information. Biometric data includes fingerprints, iris scans and facial recognition software, so it can be highly personal and unique to the individual (Yang et al, 2023). Therefore, unauthorised access and other breaches in privacy related to these data should be considered severe security threats, and protecting against these risks is crucial to the confidentiality and trust associated with healthcare services. ...

Metaverse for Healthcare: Technologies, Challenges, and Vision
  • Citing Article
  • December 2023

International Journal of Crowd Science

... Exploring the evidence supporting the improved efficiency of service migration in edge computing, we find an efficient and robust system. Edge computing features such as reducing latency, efficient bandwidth, improved service efficiency, and dynamic service migration contribute to this confidence in its capabilities [85][86][87]. These studies unequivocally demonstrate that edge computing's proximity, reduced latency, and efficient resource utilization are not just features but powerful tools for optimizing service migration and enhancing overall system performance, providing a reassuring outlook for the future. ...

Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
  • Citing Article
  • Full-text available
  • November 2023

... v. IoT devices have restricted processing power, which may prevent large-scale deployment of ensemble methods. Edge or fog computing can mitigate this by offloading [107] heavy computations. ...

Dynamic Computation Offloading Leveraging Horizontal Task Offloading and Service Migration in Edge Networks
  • Citing Chapter
  • November 2023

Communications in Computer and Information Science

... Manual petrographic methods are essential for initial investigation and gaining a solid understanding of rocks composition and structure. However, quantitative approaches become necessary with larger datasets, where the time-consuming, subjective, and inconsistent nature of manual analysis hinders progress (Gao et al., 2024). While computer vision and machine learning have shown promise in tasks like grain boundary detection and mineral identification (Ahari, 2024;Long et al., 2022), many approaches require large, manually labelled training datasets. ...

Mineral identification based on natural feature-oriented image processing and multi-label image classification

Expert Systems with Applications

... This method has demonstrated good performance in experiments and can e®ectively identify various types of cyber attacks. Guo et al. 9 research focuses on the application of attention mechanisms in network intrusion detection. They propose an attention mechanism based on collaborative¯ltering, which can adaptively focus on important nodes and connections in the network. ...

EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network
  • Citing Article
  • September 2023

Future Generation Computer Systems

... However, most existing works evaluate the performance of the CP detectors based on the typical trade-off between detection delay in data instances and false alarm rate. Therefore, important aspects related to the computational demands, e.g., resource utilization, actual detection delay and scalability properties, remain under-discussed, with only a few exceptions delving into the theoretical computational complexity [8] or energy consumption [9]. Nevertheless, when dealing with edge devices, computational cost plays a crucial role and may influence the detection efficiency. ...

Accurate Anomaly Detection With Energy Efficiency in IoT–Edge–Cloud Collaborative Networks
  • Citing Article
  • October 2023

IEEE Internet of Things Journal

... Mishra et al. [28] presented a Deep Q-learning-based method using federated learning techniques. In [29], a deep Q-learning network algorithm was proposed to solve dynamic task offloading problems, where tasks are processed using deep reinforcement learning. Khadir et al. [30] presented a RoadSide Parked Vehicles offloading scheme using the idle computing resources of RSPVs. ...

Dynamic Task Offloading and Service Migration Optimization in Edge Networks

International Journal of Crowd Science