Edge Environment Research Articles

Mobility and Dependence-aware QoS Monitoring in Mobile Edge Computing

Mobile edge computing is a new computing paradigm that performs computing on the edge of a network. It provides services to users by deploying edge servers near mobile devices. Services may be unavailable or do not satisfy the needs of users due to changing edge environments. Quality of service (QoS) is commonly employed as a critical means to indicate qualitative status of services. It is particularly important to monitor QoS of services timely and effectively in the mobile edge environment. However, user mobility and dependencies among QoS values often cause the monitoring results to deviate from the real results in the mobile edge environment. Existing QoS monitoring approaches have not taken into account these problems. To address the problems, this paper proposes ghBSRM-MEC (Gaussian hidden BayeSian Runtime Monitoring for Mobile Edge Computing), a novel mobility and dependence-aware QoS monitoring approach for the mobile edge environment. This approach assumes that the QoS attribute values of edge servers obey Gaussian distribution. It constructs a parent property for each property, thus reducing the dependence between properties. During the training stage, a Gaussian Hidden Bayesian classifier is constructed for each edge server. During the monitoring stage, combining with a KNN algorithm, the classifier is changed dynamically based on user mobility to realize QoS monitoring in the mobile edge environment. The experimental results validate the feasibility, effectiveness, and efficiency of ghBSRM-MEC

Recent development of LIF diagnostics in ASIPP

In this article, recent research progress of laser induced fluorescence (LIF) diagnostics in ASIPP is reviewed. The fundamental goal of ASIPP's effort on the LIF diagnostics is to develop a working velocity distribution function (VDF) diagnostic to monitor helium ash removal in burning plasmas. Real-time monitoring of helium ash VDF requires consideration in radiation safety, measurement response time, and compatibility of tokamak operation, as well as ion/neutral VDFs with a temperature at possibly >20 eV. These considerations, almost unique to large-scale instruments or specifically to tokamak plasmas, require renewed research and development (R&D) efforts in optical design, automation, and expansion of laser functionality for the LIF diagnostics that were often not considered in conventional applications of the LIF diagnostics. To this end, ASIPP invested a series of research effort into the development of LIF techniques. Signal evaluation has been performed on a diagnostics-test device (LTS), and the obtained signal strength has been extrapolated to tokamak edge and divertor relevant environments with promising results. The extrapolations compared the available solid angle and plasma density between the test device and the design scenario where the LIF diagnostics will potentially be implemented in a tokamak edge and divertor plasma. The estimation neglects the higher density of energetic electrons in the edge and divertor regions, making it a conservative assessment. Automated post-DAQ processing of LIF signals using both “conventional” methods and an AI-augmented method were also explored. Both approaches yielded usable results, but the AI-trained method showed a faster response, which is advantageous for real-time plasma diagnostics. Basic mechanisms of LIF diagnostics were also explored. Specifically, the limitations of lock-in modulation was demonstrated to determine the limitation of time-resolved measurements using such methods. De-modulation and degradation of the LIF signal occurred as the modulation frequency exceeded 1/10th of the fluorescence frequency. This finding sets a practical limit of the modulation frequency up to which we can benefit from lock-in amplification. These published works along with other unpublished progress will be thoroughly discussed in this article to illustrate ASIPP's effort towards the implementation of LIF diagnostics in future tokamak devices and to promote the application of LIF diagnostics in other fields of research.

A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource optimization. Machine Learning (ML), as the most advanced branch of AI in the past few years, has shown encouraging results and applications in the edge environment. Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements. Despite the fact that a great deal of the attention garnered by Edge ML is gained in both the academic and industrial communities, we noticed the lack of a complete survey on existing Edge ML technologies to provide a common understanding of this concept. To tackle this, this paper aims at providing a comprehensive taxonomy and a systematic review of Edge ML techniques, focusing on the soft computing aspects of existing paradigms and techniques. We start by identifying the Edge ML requirements driven by the joint constraints. We then extensively survey more than twenty paradigms and techniques along with their representative work, covering two main parts: edge inference, and edge learning. In particular, we analyze how each technique fits into Edge ML by meeting a subset of the identified requirements. We also summarize Edge ML frameworks and open issues to shed light on future directions for Edge ML.

Stateless Q-learning algorithm for service caching in resource constrained edge environment

In resource constrained edge environment, multiple service providers can compete to rent the limited resources to cache their service instances on edge servers close to end users, thereby significantly reducing the service delay and improving quality of service (QoS). However, service providers renting the resources of different edge servers to deploy their service instances can incur different resource usage costs and service delay. To make full use of the limited resources of the edge servers to further reduce resource usage costs, multiple service providers on an edge server can form a coalition and share the limited resource of an edge server. In this paper, we investigate the service caching problem of multiple service providers in resource constrained edge environment, and propose an independent learners-based services caching scheme (ILSCS) which adopts a stateless Q-learning to learn an optimal service caching scheme. To verify the effectiveness of ILSCS scheme, we implement COALITION, RANDOM, MDU, and MCS four baseline algorithms, and compare the total collaboration cost and service latency of ILSCS scheme with these of these four baseline algorithms under different experimental parameter settings. The extensive experimental results show that the ILSCS scheme can achieve lower total collaboration cost and service latency.

A Modular Framework for Data Processing at the Edge: Design and Implementation.

There is a rapid increase in the number of edge devices in IoT solutions, generating vast amounts of data that need to be processed and analyzed efficiently. Traditional cloud-based architectures can face latency, bandwidth, and privacy challenges when dealing with this data flood. There is currently no unified approach to the creation of edge computing solutions. This work addresses this problem by exploring containerization for data processing solutions at the network's edge. The current approach involves creating a specialized application compatible with the device used. Another approach involves using containerization for deployment and monitoring. The heterogeneity of edge environments would greatly benefit from a universal modular platform. Our proposed edge computing-based framework implements a streaming extract, transform, and load pipeline for data processing and analysis using ZeroMQ as the communication backbone and containerization for scalable deployment. Results demonstrate the effectiveness of the proposed framework, making it suitable for time-sensitive IoT applications.

SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments

In recent years, deep learning models have become ubiquitous in industry and academia alike. Deep neural networks can solve some of the most complex pattern-recognition problems today, but come with the price of massive compute and memory requirements. This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mobile edge computing platforms. To solve this, a promising solution is to split resource-hungry neural networks into lightweight disjoint smaller components for pipelined distributed processing. At present, there are two main approaches to do this: semantic and layer-wise splitting. The former partitions a neural network into parallel disjoint models that produce a part of the result, whereas the latter partitions into sequential models that produce intermediate results. However, there is no intelligent algorithm that decides which splitting strategy to use and places such modular splits to edge nodes for optimal performance. To combat this, this work proposes a novel AI-driven online policy, SplitPlace, that uses Multi-Armed-Bandits to intelligently decide between layer and semantic splitting strategies based on the input task's service deadline demands. SplitPlace places such neural network split fragments on mobile edge devices using decision-aware reinforcement learning for efficient and scalable computing. Moreover, SplitPlace fine-tunes its placement engine to adapt to volatile environments. Our experiments on physical mobile-edge environments with real-world workloads show that SplitPlace can significantly improve the state-of-the-art in terms of average response time, deadline violation rate, inference accuracy, and total reward by up to 46, 69, 3 and 12 percent respectively.

Early Cretaceous shifting of Zoophycos in the Ouarsenis Mountains (northwestern Algeria)

Early Cretaceous succession of the Oued Fodda Formation in the Ouarsenis Mountains (northwestern Algeria) is mainly composed of marl–limestone alternations, which are subdivided into four informal units (Units 1 to 4), based on distinct lithological, stratonomical, and ichnological features. The ichnological analysis reveals a low diversity of the trace-fossil assemblage, which is exclusively reported from Units 2 and 3. The ichnoassemblage contains six ichnotaxa (Chondrites intricatus, Ophiomorpha isp., Planolites isp., Thalassinoides isp., Zoophycos brianteus, and Zoophycos cauda-galli), among which Zoophycos and Chondrites are the most common elements of the assemblage and occur in distinct mud-rich substrates showing different bioturbation intensities. The development of Zoophycos in the middle part of Unit 2 shows a high degree of bioturbation (bioturbation index (BI) = 4). Zoophycos specimens are of large size, between 45 cm and 75 cm in width, which were interpreted to have formed in a lower offshore environment where the oxygenation amount was optimal, the sedimentation rate was low, and the benthic food was abundant on the seafloor. Toward the upper part of Unit 2, Zoophycos-bearing levels exhibit a less intense degree of bioturbation (BI between 1 and 2) in contrast to Planolites- and Chondrites-bearing levels which have a bioturbation index (BI) between 3 and 4. At these levels, Zoophycos displays relatively small, coiled to U-shaped spreiten, probably in response to stressful and dysoxic conditions prevailing in the water bottom. With improved oxygenation in a quiet lower offshore to shelf margin environment in Unit 3, the benthic organisms recovered, as represented by medium to large size Zoophycos in association with Ophiomorpha and scarce Chondrites burrows, even if the overall bioturbation intensity is very low. The combination of trace-fossil assemblage and lithofacies of the Oued Fodda Formation indicates relatively stable outer shelf environments below the storm wave base, which corresponds classically to the lower offshore to shelf edge environments, and the prevailing palaeoecological conditions are optimal and stressful for the benthic organisms.

PMP: A partition-match parallel mechanism for DNN inference acceleration in cloud–edge collaborative environments

To address the challenges of delay-sensitive deep learning tasks, Deep Neural Network (DNN) models are often partitioned and deployed to the cloud–edge environment for parallel and collaborative inference. However, existing parallel coordination mechanisms are not suitable for the cloud–edge environment, as the high inter-layer dependence of DNNs can increase transmission latency and wait times for inference, which contradicts the advantage of low latency in edge computing. To resolve this contradiction, the PMP mechanism takes into account the inter-layer transfer dependence of partitioning solutions and employs a multi-objective equalization algorithm to derive DNN model partitioning strategies suitable for multi-way parallel computing. Moreover, the mechanism establishes a DNN inference time prediction model based on these partitions and utilizes an iterative matching algorithm to approximate an optimal DNN inference workflow. Extensive evaluations of the proposed mechanism are conducted using various DNN models, and the results demonstrate its superiority over existing schemes, including local, CoEdge, and EdgeFlow. Notably, PMP achieves significant reductions in total inference latency compared to these schemes, with reductions of 80.9%, 37.9%, and 9.1%, respectively.

A Network Fault Prediction-Based Service Migration Approach for Unstable Mobile Edge Environment

How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration.

OCAP: On-device Class-Aware Pruning for personalized edge DNN models

In this paper, we propose a new on-device class-aware pruning method for edge systems, namely OCAP. The motivation behind is that Deep Neural Network (DNN) models are usually trained with a large dataset so that they can learn more diverse features and be generalized to accurately predict numerous classes. Some works reveal that some features (channels) are only related to some classes. And edge systems are usually implemented in a specific environment, where classes the system detects are limited. As a result, implementing a general-trained model for a specific edge environment leads to unnecessary redundancy. Meanwhile, transferring some data and models to the cloud for personalization will cause privacy issues. Thus, we may have an on-device class-aware pruning method to remove the channels which are irrelevant for the classes the edge system observes mostly, thereby reducing the model’s Floating Point Operations (FLOPs), memory footprint, latency, improving energy efficiency and keeping a relatively high accuracy for the observed classes while protecting the in-situ data privacy. OCAP proposes a novel class-aware pruning method based on the intermediate activation of input images to identify the class-irrelevant channels. Moreover, we propose a method based on KL-divergence to select diverse and representative data for effectively fine-tuning the pruned model. The experimental results show the effectiveness and efficiency of OCAP. In comparison with state-of-the-art class-aware pruning methods, OCAP has better accuracy and higher compression ratio. Additionally, we evaluate OCAP on Nvidia Jetson Nano, Nvidia Jetson TX2 and Nvidia Jetson AGX Xavier in terms of efficiency, where the experimental results demonstrate the applicability of OCAP on edge systems. The code is available at https://github.com/mzd2222/OCAP.

Efficient Hardware Acceleration Techniques for Deep Learning on Edge Devices: A Comprehensive Performance Analysis

Implementing deep learning models on edge devices presents significant challenges due to the limited computing power, memory limitations, and processing power of these devices As deep learning models become more complex, cf ensuring proper execution on an edge platform is critical for real-time implementation Hardware -Addresses these challenges by exploring ways to accelerate. The problem lies in the resource-hungry nature of today’s deep learning models, which are typically designed for cloud environments with high computing capacity, making them unsuitable for edge environments with restricted resources. The main objective of this review is to analyze and compare various hardware acceleration strategies, such as graphics processing units (GPUs), tensor processing units (TPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs); . . . . (pruning, quantization, and knowledge storage), memory management, and data flow optimization to improve the performance and energy efficiency of deep learning models on edge devices The results of this comprehensive performance analysis suggests that hardware accelerators can significantly improve throughput and reduce latency while maintaining acceptable levels of power consumption and accuracy. In addition to techniques such as quantization and pruning that are seen to reduce computational load and memory footprint, enabling more efficient deep learning inference on edge platforms, the study highlights trade-offs between speed, consumption power efficiency and model accuracy between for each hardware accelerator are emphasized. The findings suggest that by choosing the right hardware and applying the right optimization techniques, edge devices may be able to optimize deep learning models, meeting the requirements of real-time AI applications in resource-constrained environments handle the role there.

Simulation-based joint user assignment and edge resource allocation optimization for hybrid tasks in vehicular edge computing

With the swift advancement of 5G communication technology and the widespread prevalence of intelligent connected vehicles, a multitude of applications for the Internet of Vehicles (IoV) have been steadily burgeoning. Nevertheless, owing to vehicular mobility and the variety of tasks generated by IoV applications competing for restricted edge resources, the task of assigning users to edge nodes, in addition to appropriating edge node resources to hybrid tasks within edge environments, persists as a formidable challenge. In addressing this predicament, we establish a model delineating the execution duration of hybrid tasks engendered by IoV applications, and introduce a joint mechanism for user assignment and edge resource allocation. Within this mechanism, we architect an optimal strategy for edge resource allocation, predicated upon given user assignment outcomes. In order to ascertain the user assignment strategy, we employ an enhanced genetic algorithm in scenarios of a large user base, and utilize the branch-and-bound algorithm when dealing with a small user base. Finally, we design and implement an experimental setup for joint user assignment and edge resource allocation. The comprehensive simulation results provide robust verification of the effectiveness of our proposed mechanism.

Multi-Feature Video Recommendation Based on Hypergraph Convolution for Mobile Edge Environment

With the massive growth of edge devices, how to provide users with video recommendation services in a mobile edge environment has become a research hotspot. Most traditional video recommendation methods regard the relationship between user and neighbor to be linear and ignore higher-order connectivity among users, which results in poor recommendation performance. Besides, these methods use a single feature to represent user preferences, which cannot effectively alleviate the data sparsity problem. To improve the performance of video recommendation, this article proposes a multi-feature video recommendation method based on hypergraph convolution (MVRHC). Hypergraph convolution is adopted to compute user neighborhood-level features for modeling high-order correlations among users. Final features are obtained by fusing multi-party features through attention mechanism. And video recommendation is then implemented based on the obtained features. Experimental results on two real-world datasets demonstrate that MVRHC has better performance compared with baseline methods.

MUS3E: A Mobility Ubiquitous Sensor Edge Environment for the Elderly

With the ageing of society, the number of households with older individuals or couples living alone is increasing. An “ageing-in-place” approach allows older adults to continue to live at home and receive help only when needed. However, this approach is insufficient for emergencies, such as falls, as well as for individuals with gradually deteriorating health conditions, such as frailty. Unexpected accidents significantly reduce the quality of life (QoL) of older adults. This paper proposes a new framework, the mobility ubiquitous sensor edge environment (MUS3E), to digitally transform ordinary houses to detect the movement of older individuals throughout their home environment and to notify family members and care providers of residents’ health status and safety information. The framework can be easily and inexpensively installed in any home, transforming an ordinary house into a smart home with an automated function for monitoring older residents. It uses ambient sensors such as passive infrared ray sensors to automatically measure health conditions by measuring factors such as walking speed. Residents need not interact with or control the system and can go about their daily lives. Since the sensors used in this system are mass-produced consumer products, they are inexpensive and easily replaceable, as there are many alternatives. In this study, we were able to demonstrate the practically and feasibility of this framework using a prototype that uses open architecture Internet of Things (IoT) software (Debian GNU/Linux 11, Arduino 1.8.19, ESP8266 2.7.4, ESP32 1.0.6, PubSubClient 2.8.0, ESPPerfectTime 0.3.0, mosquitto 2.0.11) components to digitally transform the living environment of older individuals.

Privacy-Preserving Multi-Source Image Retrieval in Edge Computing

Users outsource images to edge servers physically closer to their location for real time applications because of the low latency and low transmission overhead. Outsourcing to these edge servers however, increases the risks to data privacy. Almost all existing privacy preserving image retrieval schemes utilize a single cloud server to execute retrieval tasks and provide centralized image retrieval but at high computational costs, thus are not suitable for the distributed edge environments with limited computing resources. We propose a lightweight privacy-preserving multi-source image retrieval scheme adapted specifically for the distributed edge environment. We apply high efficiency orthogonal decomposition and learning with errors (LWE) strategy to encrypt image features and construct cipher indexes and trapdoors, guaranteeing the security of the data, while reducing computational costs. The orthogonality of data ensures that the accuracy of retrieval results is not compromised by the random numbers used in the scheme. In addition, the proxy re-encryption technology is adopted to support the retrieval of multi-source images encrypted by unique data owners with different keys. A detailed performance analysis and comprehensive experiments demonstrate that our scheme guarantees data security with very high retrieval accuracy and a low computational burden, consistent with the demands of edge environments.

Delay-Aware Container Scheduling in Kubernetes

Kubernetes is a powerful tool to manage containerized applications, which is also regarded as one promising platform to support microservices in edge computing. The scheduler is a key component of Kubernetes. It allocates each pod (i.e., a set of running containers) to one worker node (i.e., a machine). The default scheduler in Kubernetes is designed for the cloud environment containing homogeneous nodes. However, IoT edge nodes usually have various computing power and network bandwidth. The paper proposes a delay-aware container scheduling (DACS) algorithm to address the issue of node heterogeneity in edge computing. To efficiently assign pods to worker nodes, DACS takes account of not only residual resources of worker nodes but also potential delays caused by the pod assignment. We build a Kubernetes cluster by VMware to evaluate system performance. Experimental results reveal that DACS can significantly reduce both processing and network delays, thereby helping Kubernetes perform more efficiently in an edge environment.

A Generic Framework for Deploying Video Analytic Services on the Edge

This paper introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Spark ©) and an edge-deployement framework (Kubernetes©), have been deployed. A cloud service has also been considered. Experiments have shown that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a caching and a QoS monitoring service based on Long-Term-Short-Term models are introduced and evaluated.

Budget-Constrained Optimal Deployment of Redundant Services in Edge Computing Environment

With the development of multi-access edge computing (also called mobile edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired quality of service (QoS), especially in the scenario of Internet of things (IoT). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to the limited resources, heterogeneous servers and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies about the deployment problem in edge environment consider such property of services. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work firstly builds a redundant service deployment model under heterogeneous edge environment, and defines it as a multi-objective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. We used the Grid Workloads Archive dataset to evaluate our proposed TOPSIS-based Resource Allocation (TOPREAL) approach. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215 kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

Research on Data Poisoning Attack against Smart Grid Cyber-Physical System Based on Edge Computing.

Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack can be fatal to the operation of a smart grid, addressing data poisoning is of utmost importance. However, this attack requires solving an expensive two-level optimization problem, which can be challenging to implement in resource-constrained edge environments of the smart grid. To mitigate this issue, it is crucial to enhance efficiency and reduce the costs of the attack. This paper proposes an online data poisoning attack framework based on the online regression task model. The framework achieves the goal of manipulating the model by polluting the sample data stream that arrives at the cache incrementally. Furthermore, a point selection strategy based on sample loss is proposed in this framework. Compared to the traditional random point selection strategy, this strategy makes the attack more targeted, thereby enhancing the attack's efficiency. Additionally, a batch-polluting strategy is proposed in this paper, which synchronously updates the poisoning points based on the direction of gradient ascent. This strategy reduces the number of iterations required for inner optimization and thus reduces the time overhead. Finally, multiple experiments are conducted to compare the proposed method with the baseline method, and the evaluation index of loss over time is proposed to demonstrate the effectiveness of the method. The results show that the proposed method outperforms the existing baseline method in both attack effectiveness and overhead.