Modeling process-aware Internet of Things services over an ARDUINO community computing environment

2017 ◽  
Author(s):  
Meesun Kim ◽  
Kyoungsook Kim ◽  
Kyongduck Seo ◽  
Joosang Lee ◽  
Kyoungsik Park ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Computing Environment ◽  
Modeling Process ◽  
Community Computing

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

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.

scholarly journals A Secure Authentication and Key Agreement Scheme for IoT-Based Cloud Computing Environment

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 150 ◽  
Author(s):  
Yicheng Yu ◽  
Liang Hu ◽  
Jianfeng Chu
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Secure Communication ◽  
Key Agreement ◽  
Security Analysis ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Authentication And Key Agreement ◽  
Insider Attack ◽  
Cloud Servers

The integration of Internet of things (IoT) and cloud computing technology has made our life more convenient in recent years. Cooperating with cloud computing, Internet of things can provide more efficient and practical services. People can accept IoT services via cloud servers anytime and anywhere in the IoT-based cloud computing environment. However, plenty of possible network attacks threaten the security of users and cloud servers. To implement effective access control and secure communication in the IoT-based cloud computing environment, identity authentication is essential. In 2016, He et al. put forward an anonymous authentication scheme, which is based on asymmetric cryptography. It is claimed that their scheme is capable of withstanding all kinds of known attacks and has good performance. However, their scheme has serious security weaknesses according to our cryptanalysis. The scheme is vulnerable to insider attack and DoS attack. For overcoming these weaknesses, we present an improved authentication and key agreement scheme for IoT-based cloud computing environment. The automated security verification (ProVerif), BAN-logic verification, and informal security analysis were performed. The results show that our proposed scheme is secure and can effectively resist all kinds of known attacks. Furthermore, compared with the original scheme in terms of security features and performance, our proposed scheme is feasible.

scholarly journals Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

2021 ◽  
Author(s):  
E. Laxmi Lydia ◽  
C. S. S. Anupama ◽  
A. Beno ◽  
Mohamed Elhoseny ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
Cognitive Computing ◽  
Computing Environment

scholarly journals Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
DongHo Kang ◽  
ByoungKoo Kim ◽  
JungChan Na ◽  
KyoungSon Jhang
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Real Time ◽  
Supervisory Control ◽  
Cyber Attacks ◽  
Computing Environment ◽  
Time Data ◽  
Real Time Data ◽  
Filtering Technique

Internet of Things (IoT) consists of several tiny devices connected together to form a collaborative computing environment. Recently IoT technologies begin to merge with supervisory control and data acquisition (SCADA) sensor networks to more efficiently gather and analyze real-time data from sensors in industrial environments. But SCADA sensor networks are becoming more and more vulnerable to cyber-attacks due to increased connectivity. To safely adopt IoT technologies in the SCADA environments, it is important to improve the security of SCADA sensor networks. In this paper we propose a multiple filtering technique based on whitelists to detect illegitimate packets. Our proposed system detects the traffic of network and application protocol attacks with a set of whitelists collected from normal traffic.

scholarly journals An Optimal Task Assignment Strategy in Cloud-Fog Computing Environment

2021 ◽  
Vol 11 (4) ◽  
pp. 1909
Author(s):  
Jung-Fa Tsai ◽  
Chun-Hua Huang ◽  
Ming-Hua Lin
Keyword(s):  
Internet Of Things ◽  
Assignment Problem ◽  
Fog Computing ◽  
Task Assignment ◽  
The Internet ◽  
Computing Environment ◽  
Computing Systems ◽  
Assignment Strategy ◽  
The Internet Of Things ◽  
Task Assignment Problem

With the advent of the Internet of Things era, more and more emerging applications need to provide real-time interactive services. Although cloud computing has many advantages, the massive expansion of the Internet of Things devices and the explosive growth of data may induce network congestion and add network latency. Cloud-fog computing processes some data locally on edge devices to reduce the network delay. This paper investigates the optimal task assignment strategy by considering the execution time and operating costs in a cloud-fog computing environment. Linear transformation techniques are used to solve the nonlinear mathematical programming model of the task assignment problem in cloud-fog computing systems. The proposed method can determine the globally optimal solution for the task assignment problem based on the requirements of the tasks, the processing speed of nodes, and the resource usage cost of nodes in cloud-fog computing systems.

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