Underwater wireless sensor networks (UWSNs) have recently been proposed as a way to observe and explore aquatic environments. Sensors in such networks are used to perform pollution monitoring, disaster prevention, or assisted navigation and to send monitored data to the sink. Compared with the traditional sensor networks, sensors in UWSNs consume more energy due to the acoustic technology used in under water communications. Node clustering is a common method to organize data traffic and reduce in-network communications while improving scalability and energy consumption. In this paper, we present a new clustering method to handle the spatial similarity between node readings. We suppose that readings are sent periodically from sensor nodes to their appropriate cluster heads (CHs). Then, a two-tier data aggregation technique is proposed. At the first level, each node periodically cleans its readings in order to eliminate redundancies before sending its data set to its CH. Once the CH receives all data sets, it applies an enhanced K-means algorithm based on a one-way ANOVA model to identify nodes generating identical data sets and to aggregate these sets before sending them to the sink. Our proposed approach is validated via experiments on real sensor data and comparison with other existing clustering and data aggregation techniques.

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 Huffman 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 first step. Various measures implemented to show the effectiveness of the suggested approach and the comparison results confirm that the KCHE reduces the amount of EEG data transmitted to the Fog and Cloud platform and produces a suitable detection of an epileptic seizure. The average of compression power of the proposed KCHE is four times the average of compression power of other methods for all EEG records (Z, F, N, O, S). Furthermore, the proposed ESDNB outperforms the other methods in terms of accuracy, where it provides accuracy from 99.53 % up to 99.99 % using the dataset of Bonn University.

Liesbet Van Bulck, Raphaël Couturier, Philip Moons; Applications of artificial intelligence for nursing: has a new era arrived?, European Journal of Cardiovascu