Additionally, we used Matthews correlation coefficient (MCC) to study the impact of our proposed model on the imbalanced dataset as the Bot-IoT. The RF classifier is implemented to enhance the IDS performances. For evaluation, we used the Bot-IoT and the wustl_iiot_2021 datasets to evaluate ...
New technology is needed to meet the latency and bandwidth issues present in cloud computing architecture specially to support the currency of 5G networks. Accordingly, mobile edge computing (MEC) came into picture as novel emerging solutions to overcome many cloud computing issues. In this contempora...
Globally, the traffic data sources of small and medium-sized cities are relatively single, and most of them are composed of a single traffic violation monitoring dataset at intersections. These monitoring systems often do not form a network to operate independently. To enrich the dataset types and...
These can then be labeled and added to the training dataset in your ML pipeline. The primary goal of MLOps for IoT and edge is to relieve these challenges. Continuous loops are more difficult to set up in the realm of edge inference. In a usual set up, your model is trained on the ...
An edge computing intrusion detection system is also beneficial for resource contained network devices, such as IoT, which have limited computational resources and cannot dynamically analyze the network traffic and run a strong intrusion detection system. We worked on CICAndMal2017 dataset and proposed...
The integrated dataset combines AI and machine learning-based road weather forecasting models with billions of data points gathered from connected vehicles by NIRA Dynamics. "Partnering with NIRA Dynamics aligns seamlessly with our mission to connect road users, automotive manufacturers, and maintenance te...
2. Data Enrichment - The data enrichment process is the operation in which the sensor-collected raw data is combined with the other dataset to get the results. 3. Storing Data - This task includes storing the data at the required storage location. Most Common Protocols Used in IoT Analytics...
The synthetic trace data is collected by us from the dataset of MMSyS2015 [38]. It has Twitch.tv and YouTube live-streaming sessions. Each video content has log data on the viewers’ amount that watched it over time. However, most information on individual viewers is absent (Except for ...
Data availability The dataset used is publicly available.References Ge M, Fu X, Syed N, Baig Z, Teo G, Robles-Kelly A (2019) Deep learning-based intrusion detection for IoT networks. In: 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), Kyoto, Japan pp 256...
Recently, we observe that the edge nodes that acquire the dataset of heterogeneous IoT devices are becoming overwhelmed with the issue of tuple non-classification at the level of data encapsulation. This issue raises a few concerns such as (a) ineffective tuple wrapup, (b) bundle compression ...