IoT

The dataset originally was taken from DAIAD, which has the mechanism to monitor the water consumption in real time using a validated platform for different cities. These datasets had the record of different water consumption values taken from the smart water meters that indicates, total water consumption by different users in Litres with the time interval of one hour for a year.

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It is now widely known fact that the Cloud computing and Software defined network paradigms have received a wide acceptance from researchers, academia and the industry. But the wider acceptance of cloud computing and SDN paradigms are hampered by increasing security threats. One of the several facts is that the advancements in processing facilities currently available are implicitly helping the attackers to attack in various directions. For example, it is visible that the conventional DoS attacks are now extended to cloud environments as DDoS attacks.

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With the modern day technological advancements and the evolution of Industry 4.0, it is very important to make sure that the problem of Intrusion detection in Cloud , IoT and other modern networking environments is addressed as an immediate concern. It is a fact that Cloud and Cyber Physical Systems are the basis for Industry 4.0. Thus, intrusion detection in cyber physical systems plays a crucial role in Industry 4.0. Here, we provide the an intrusion detection dataset for performance evaluation of machine learning and deep learning based intrusion detection systems.

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data of fishing operations from beidou, AIS and policy.

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Opportunity++ is a precisely annotated dataset designed to support AI and machine learning research focused on the multimodal perception and learning of human activities (e.g. short actions, gestures, modes of locomotion, higher-level behavior).

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1640 Views

README.txt for simulation files IEEE_Collaboration_N_entities_2021.mdl and IEEE_2_Platforms_federation_SF_2021.mdl

 

This is a README.txt for the model published on the paper titled:

 

Improving IoT Federation Resiliency with Distributed Ledger Technology, 2021, Elo T, et al.

 

This readme describes how to replicate the main simulation results from the paper using a Vensim

model file. The model file has been generated using the Vensim DSS Macintosh Version 

8.0.7 Double Precision x64.

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Abstract—In the 2021 and later we know that the technology

will have key participation of to help us in all kind of tasks

mainly using internet connection, due the new normality.

Industry 4.0 has been one of the most relevant field. IoT as part

of it. This Systematic Literature Review (SLR) we will cover

the South America countries and their development status,

addressing the development categories and the Hardware that

has been cited on papers on the last 5 years.

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We elaborate on the dataset collected from our testbed developed at Washington University in St. Louis, to perform real-world IIoT operations, carrying out attacks that are more prelevant against IIoT systems. This dataset is to be utilized in the research of AI/ML based security solutions to tackle the intrusion problem.

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2054 Views

Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems

Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems is called ERT-CORE. It defines specific input parameters, i.e., system's workload, average request processing time and availability. Defined parameters reflect system's state and react on its changes. Based on these parameters system reliability evaluation is performed.

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