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Communication-Aware Secret Share Placement in hierarchical edge computing

IEEE INTERNET OF THINGS JOURNAL 2022年第5期9卷 3717-3728页

作者： Zhang, Hengrun Zeng, Kai George Mason Univ Dept Comp Sci Fairfax VA 22030 USA George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

Secret sharing (SS) and secure multiparty computation (MPC) are now widely considered for privacy-preserving data processing. However, related applications can suffer from large transmission overhead. In this article, we propose a communication-aware secret share placement strategy to optimize communication overhead by minimizing transmission hop counts in a hierarchical edge computing architecture. Meanwhile, relevant privacy constraints in SS can still be guaranteed. We show that the constructed optimization problem is NP-hard, and efficient heuristic algorithms can be applied to find suboptimal solutions. With this consideration, we first evaluate two traditional heuristics, i.e., the genetic algorithm (GA) and particle swarm optimization (PSO). Besides, we introduce two basic heuristics, i.e., top-down and bottom-up heuristic, which can outperform GA and PSO in certain cases. Finally, we propose an algorithm, called bottom-up top-down (BUTD) heuristic, which can outperform all of the above four heuristics when communication among different shares of the same secret is comparable to that among different secrets. Comprehensive experimental results demonstrate the advantage of the proposed algorithms.

关键词： Servers Cryptography edge computing Cloud computing Optimization Genetic algorithms Privacy Communication aware heuristic algorithms hierarchical edge computing secret sharing (SS) secure multiparty computation (MPC)

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Smart Privacy Protection for Big Video Data Storage Based on hierarchical edge computing

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SENSORS 2020年第5期20卷 1517-1517页

作者： Xiao, Di Li, Min Zheng, Hongying Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.

关键词： smart privacy protection hierarchical edge computing color video low computation complexity cloud storage

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AdaMM: Adaptive Object Movement and Motion Tracking in hierarchical edge computing System

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SENSORS 2021年第12期21卷 4089-4089页

作者： Kim, Jingyeom Lee, Joohyung Kim, Taeyeon Gachon Univ Sch Comp Seongnam 13120 South Korea Elect & Telecommun Res Inst ETRI Daejeon 34129 South Korea

This paper presents a novel adaptive object movement and motion tracking (AdaMM) framework in a hierarchical edge computing system for achieving GPU memory footprint reduction of deep learning (DL)-based video surveillance services. DL-based object movement and motion tracking requires a significant amount of resources, such as (1) GPU processing power for the inference phase and (2) GPU memory for model loading. Despite the absence of an object in the video, if the DL model is loaded, the GPU memory must be kept allocated for the loaded model. Moreover, in several cases, video surveillance tries to capture events that rarely occur (e.g., abnormal object behaviors);therefore, such standby GPU memory might be easily wasted. To alleviate this problem, the proposed AdaMM framework categorizes the tasks used for the object movement and motion tracking procedure in an increasing order of the required processing and memory resources as task (1) frame difference calculation, task (2) object detection, and task (3) object motion and movement tracking. The proposed framework aims to adaptively release the unnecessary standby object motion and movement tracking model to save GPU memory by utilizing light tasks, such as frame difference calculation and object detection in a hierarchical manner. Consequently, object movement and motion tracking are adaptively triggered if the object is detected within the specified threshold time;otherwise, the GPU memory for the model of task (3) can be released. Moreover, object detection is also adaptively performed if the frame difference over time is greater than the specified threshold. We implemented the proposed AdaMM framework using commercial edge devices by considering a three-tier system, such as the 1st edge node for both tasks (1) and (2), the 2nd edge node for task (3), and the cloud for sending a push alarm. A measurement-based experiment reveals that the proposed framework achieves a maximum GPU memory reduction of 76.8% compar

关键词： edgeAI hierarchical edge computing deep learning object detection and tracking software implementation

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Cognitive Video Surveillance Management in hierarchical edge computing System with Long Short-Term Memory Model

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SENSORS 2023年第5期23卷 2869-2869页

作者： Ugli, Dilshod Bazarov Ravshan Kim, Jingyeom Mohammed, Alaelddin F. Y. Lee, Joohyung Gachon Univ Dept Comp Seongnam Si 13120 South Korea NHN Cloud Corp Adv Res Team Seongnam Si 13487 South Korea

Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.

关键词： LSTM cognitivevideo surveillance management hierarchical edge computing deep learning object detection and tracking

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Adaptive Anomaly Detection for Internet of Things in hierarchical edge computing: A Contextual-Bandit Approach

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ACM TRANSACTIONS ON INTERNET OF THINGS 2022年第1期3卷 1–23页

作者： Ngo, Mao V. Luo, Tie Quek, Tony Q. S. Singapore Univ Technol & Design 8 Somapah Rd Singapore 487372 Singapore Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65401 USA

The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma persists: Complex DNN models offer higher accuracy, but typical IoT devices can barely afford the computation load, and the remedy of offloading the load to the cloud incurs long delay. In this article, we address this challenge by proposing an adaptive anomaly detection scheme with hierarchical edge computing (HEC). Specifically, we first construct multiple anomaly detection DNN models with increasing complexity and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network. We also incorporate a parallelism policy training method to accelerate the training process by taking advantage of distributed models. We build an HEC testbed using real IoT devices and implement and evaluate our contextual-bandit approach with both univariate and multivariate IoT datasets. In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.

关键词： Internet of things anomaly detection hierarchical edge computing reinforcement learning autoencoder LSTM

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hierarchical Energy-Efficient Mobile-edge computing in IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2020年第12期7卷 11626-11639页

作者： Wang, Qun Tan, Le Thanh Hu, Rose Qingyang Qian, Yi Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA Univ Nebraska Lincoln Dept Elect & Comp Engn Omaha NE 68182 USA

The ever-growing demand of the Internet of Things (IoT) imposes great challenges in the existing cellular systems and calls for novel approaches for the wireless network design. In this article, we develop a joint energy and computation optimization paradigm in an IoT network. The tasks collected at local IoT devices can be computed at hierarchical mobile-edge computing facilities. Both nonorthogonal multiple access (NOMA) and frequency-division multiple access (FDMA) are used for computation offloading. The system model considers both long-term and short-term system behaviors and makes the best decision for energy consumption and computation efficiency. The long short-term memory (LSTM) network is applied to predict the long-term workload, based on which the number of active process units in the edge layer is optimized. In the short-term model, a resource optimization problem is formulated. Due to the dynamic arrival workload and nonconvex features of the problem, the Lyapunov optimization approach and successive convex approximation for the low-complexity method are applied to solve this problem. The simulation results show that the proposed scheme can significantly improve the delay and energy consumption performance.

关键词： NOMA Servers Task analysis Cloud computing Energy consumption Resource management Internet of Things Energy efficiency hierarchical edge computing Internet of Things (IoT) long short-term memory (LSTM) machine learning mobile-edge computing (MEC) nonorthogonal multiple access (NOMA) offloading optimization

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EPFLU: Efficient Peer-to-Peer Federated Learning for Personalized User Models in edge-Cloud Environments 4th

EPFLU: Efficient Peer-to-Peer Federated Learning for Persona...

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24th International Conference Web Engineering (ICWE)

作者： Song, Xiangchi Wang, Zhaoyan Baek, KyeongDeok Ko, In-Young Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9783031751097;9783031751103

As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for large-scale Internet of Things (IoT) environments. Federated Learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: Efficient Peer-to-peer Federated Learning for Users (EPFLU). EPFLU transitions the paradigms from vertical FL to horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework to make it more suitable for real-world large-scale IoT. Within this framework, we design extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under extreme data distribution scenarios. Compared with the selected vertical aggregation and horizontal transmission cumulative aggregation methods, EPFLU achieves communication improve

关键词： Federated Learning Peer-to-peer Learning hierarchical edge computing edge-Cloud Environment

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Personalized User Models in a Real-world edge computing Environment: A Peer-to-peer Federated Learning Framework

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JOURNAL OF WEB ENGINEERING 2024年第8期23卷 1057-1084页

作者： Song, Xiangchi Wang, Zhaoyan Baek, Kyeongdeok Ko, In-Young Korea Adv Inst Sci & Technol Daejeon South Korea

As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for largescale Internet of Things (IoT) environments. Federated learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: efficient peer-to-peer federated learning for users (EPFLU). EPFLU transitions the paradigms from vertical FL to a horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus Xiangchi Song information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework, and uses the EUA dataset containing real-world edge server distribution, making it more suitable for real-world large-scale IoT. Within this framework, we design two extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under challenging data distribution scenarios. Compared with the traditional FL and selected P2

关键词： personalized federated learning hierarchical edge computing edge-cloud environment

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Shepherd: Seamless Stream Processing on the edge 7

Shepherd: Seamless Stream Processing on the Edge

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IEEE/ACM 7th Symposium on edge computing (SEC)

作者： Ramprasad, Brian Mishra, Pritish Thiessen, Myles Chen, Hongkai Veith, Alexandre da Silva Gabel, Moshe Balmau, Oana Chow, Abelard de Lara, Eyal Huawei Markham ON Canada McGill Univ Montreal PQ Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665486118

Next generation applications such as augmented/virtual reality, autonomous driving, and Industry 4.0, have tight latency constraints and produce large amounts of data. To address the real-time nature and high bandwidth usage of new applications, edge computing provides an extension to the cloud infrastructure through a hierarchy of datacenters located between the edge devices and the cloud. Outside of the cloud and closer to the edge, the network becomes more dynamic requiring stream processing frameworks to adapt more frequently. Cloud based frameworks adapt very slowly because they employ a stop-the-world approach and it can take several minutes to reconfigure jobs resulting in downtime. In this paper, we propose Shepherd, a new stream processing framework for edge computing. Shepherd minimizes downtime during application reconfiguration, with almost no impact on data processing latency. Our experiments show that, compared to Apache Storm, Shepherd reduces application downtime from several minutes to a few tens of milliseconds.

关键词： stream processing reconfiguration late binding hierarchical edge computing seamless

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Falcon: Live Reconfiguration for Stateful Stream Processing on the edge 9

Falcon: Live Reconfiguration for Stateful Stream Processing ...

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9th Symposium on edge computing

作者： Mishra, Pritish Bore, Nelson Ramprasad, Brian Thiessen, Myles Gabel, Moshe Veith, Alexandre da Silva Balmau, Oana de Lara, Eyal Nokia Bell Labs Murray Hill NJ 07974 USA McGill Univ Montreal PQ Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350378290;9798350378283

Stream processing is an attractive paradigm for deploying applications in geo-distributed edge-cloud environments. However, the reverse economics of scale in edge networks and the movement of data sources between edges require the ability to dynamically reconfigure the deployment of stateful applications to adapt to workload variations and user mobility. Unfortunately, existing stream processing engines either do not support the reconfiguration of stateful operators or are ill-suited to edge-cloud environments since they stop application processing during reconfiguration or require costly duplication of application state. We propose Falcon, a new stream processing engine. At its core lies a live key migration approach to allow reconfiguration to occur with minimal disruption to processing, even across distant datacenters. Falcon supports the reconfiguration of stateful operators including different windowing approaches and source mobility across different edge regions. It scales gracefully with network latency, the number of datacenters, and the size and number of keys. Our evaluation in geo-distributed edge-cloud deployments shows that Falcon reduces the length of processing interruptions and their impact on latency by 2 to 4 orders of magnitude compared to the existing state-of-the-art frameworks such as Apache Flink, Trisk, and Meces.

关键词： stateful stream processing reconfiguration hierarchical edge computing seamless

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