The need to improve smart health systems to monitor the health situation of patients has grown as a result of the spread of epidemic diseases, the ageing of the population, the increase in the number of patients and the lack of facilities to treat them. This led to an increased demand for remote healthcare systems using biosensors. These biosensors produce a large volume of sensed data that will be received by the edge of the Internet of Medical Things (IoMT) to be forwarded to the data centers of the Cloud for further treatment. An Edge-Fog Computing Enabled Lossless EEG data compression with Epileptic Seizure Detection in IoMT networks is proposed in this paper. The proposed approach achieves three functionalities. First, it reduces the amount of sent data from the Edge to the Fog gateway using lossless EEG data compression based on a hybrid approach of k-means Clustering and Human Encoding (KCHE) at the Edge Gateway. Second, it decides the epileptic seizure situation of the patient at the Fog gateway based on the Epileptic Seizure Detector based Naive Bayes (ESDNB) algorithm. Third, it reduces the size of IoMT EEG data delivered to the Cloud using the same lossless compression algorithm in the rst step. Various measures implemented to show the eectiveness of the suggested approach and the comparison results conrm that the KCHE reduces the amount of EEG

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In the Tactile Internet-based fog computing architecture, the sensor devices represent the basic elements for sensing the surrounding environment. They gather a large amount of data due to their use in various real-world Tactile Internet applications. The huge amount of transmitted data from sensor devices to the fog gateway then to the cloud would lead to high data traffic over the network, increased consumed energy, and increased delay to provide the decision at the Fog gateway. These challenges represent a hurdle in the Tactile Internetbased fog system. This paper suggests a Data Transmission Optimization Scheme (DaTOS) in Tactile Internet-based Fog Computing Applications. The protocol works on two-level devices in the Tactile Internet-based fog computing architecture: sensor devices and fog gateway. The DaTOS implements a Lightweight Redundant Data Removing (LiReDaR) Algorithm at the sensor devices level to lower the gathered data before sending them to the fog gateway. In fog gateway, it executes a Data Set Redundancy Elimination (DaSeRE) approach to discard the repetitive data set resulting from the spatial correlation among the data readings sets of sensor nodes. To evaluate the performance of the DaTOS, it was compared to its counterpart methods in the literature like ATP, PFF and Harb. Simulation results indicate that DaTOS outperforms these methods in terms of transmitted data, energy consumption, and data accuracy.

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