IoT-RECSM—Resource-Constrained Smart Service Migration Framework for IoT Edge Computing Environment

The edge-based computing paradigm (ECP) becomes one of the most innovative modes of processing distributed Interneit of Things (IoT) sensor data. However, the edge nodes in ECP are usually resource-constrained. When more services are executed on an edge node, the resources required by these services may exceed the edge node’s, so as to fail to maintain the normal running of the edge node. In order to solve this problem, this paper proposes a resource-constrained smart service migration framework for edge computing environment in IoT (IoT-RECSM) and a dynamic edge service migration algorithm. Based on this algorithm, the framework can dynamically migrate services of resource-critical edge nodes to resource-rich nodes. In the framework, four abstract models are presented to quantificationally evaluate the resource usage of edge nodes and the resource consumption of edge service in real-time. Finally, an edge smart services migration prototype system is implemented to simulate the edge service migration in IoT environment. Based on the system, an IoT case including 10 edge nodes is simulated to evaluate the proposed approach. According to the experiment results, service migration among edge nodes not only maintains the stability of service execution on edge nodes, but also reduces the sensor data traffic between edge nodes and cloud center.

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AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

Security and Communication Networks ◽

10.1155/2021/9954951 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xiang Yu ◽

Chun Shan ◽

Jilong Bian ◽

Xianfei Yang ◽

Ying Chen ◽

...

Keyword(s):

Internet Of Things ◽

Anomaly Detection ◽

Real Time ◽

Detection Method ◽

Edge Computing ◽

Sensor Data ◽

Computing Environment ◽

The Real ◽

Edge Node ◽

Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

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Particle Swarm Based Service Migration Scheme in the Edge Computing Environment

IEEE Access ◽

10.1109/access.2020.2978093 ◽

2020 ◽

Vol 8 ◽

pp. 45596-45606

Author(s):

Liang Liang ◽

Jintao Xiao ◽

Zhi Ren ◽

Zhengchuan Chen ◽

Yunjian Jia

Keyword(s):

Particle Swarm ◽

Edge Computing ◽

Computing Environment ◽

Service Migration

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An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

Complex & Intelligent Systems ◽

10.1007/s40747-021-00442-6 ◽

2021 ◽

Author(s):

Cong Gao ◽

Ping Yang ◽

Yanping Chen ◽

Zhongmin Wang ◽

Yue Wang

Keyword(s):

Time Series ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Estimation Method ◽

Feature Representation ◽

Sensor Data ◽

Wireless Sensor ◽

Data Sets ◽

Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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