The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today's world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people's lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.

The sensor nodes in IoT are usually supplied by energy using batteries with limited capacity. Therefore, saving energy as much as possible is important for increasing their lifetime and thus allowing their use in real applications. Because radio communication is usually the major reason of energy consumption, among the most commonly adopted energy-saving methods is to reduce data transmission / reception, for example, by compressing data. Taking advantage of the high correlation which usually occurs between successive samples captured by IoT sensor nodes and using the entropy principle in compression, a simple lossless algorithm for compression built on Differential Pulse Code Modulation (DPCM) was suggested in this paper, that is especially useful for IoT sensor nodes that characterized by reduced memory and computational resources. Compared to the state-of-the-art approaches, the findings of our simulation experiments showed that the suggested solution substantially decreased the consumption of energy and enhanced the network lifetime for all sensor nodes.

The wireless sensor devices of the Internet of Things (IoT) networks will represent one of the most providers of the big data on the network because it is implemented in the widespread of real-world applications. The large volume of gathered data from the sensor devices leads to increase the communication overhead and thus decrease the limited lifetime of the sensor devices of IoT. Therefore, it is necessary to clean and reduce the redundant sensed data to minimize the cost of communication and save the energy of sensor devices. In this paper, a Data Reduction and Cleaning Approach (DaReCA) for Energy-saving in Wireless Sensor Networks (WSNs) of IoT is proposed. This approach is based on two-level of data cleaning and reduction: the sensor level and the aggregator level. In the latter, we implement a divide and conquer method to merge the near similar data sets which are received from the sensor devices and reduce the transmitted data sets to the sink. In the former, the sensor node will employ a cleaning algorithm based on the leader cluster algorithm to remove redundant data from the sensed data before sending them to the aggregator. The proposed approach is evaluated and implemented using real sensed data of wireless sensor devices with the OMNeT ++ network simulator. The proposed DaReCA approach can clean and reduce the sensed data and save energy whilst keeping suitable data accuracy.

The devices that interconnected to the Internet of Things (IoT) will continue to grow exponentially, and in addition, the amount of data that they report. Sensor nodes (SNs) that arranged in WSNs will create some of IoT data and transmit their readings to Gateway (GW), which driving the sensor nodes to quick expenditure their energy and storage. The low costs of SNs impose a restriction on their energy and storage. To handle these problems it's prefer to carry out reduction on data at the source nodes to reduce both of utilized storage and consumed energy. A large portion of proposed solutions implement data reduction just at one level of the IoT design (e.g. at gateways). A Two-Tier Data Reduction (TTDR) technique is proposed to work at two tier of the network that are: sensor nodes and the gateway. At the sensor node tier we use a simple and suitable data compression methods for constrained IoT sensor nodes. The techniques exploit the temporal correlation in sensor data and use Delta Encoding followed by Run-Length Encoding (RLE). At the gateway tier we apply the hierarchical clustering for grouping data sets received from sensor nodes dependent on the Minimum Description Length (MDL) principle. If any pairs of received data sets can be compressed by the MDL principle, they will be combined into one cluster. Consequently, the amount of data sets is decreased gradually, and the merging of sets in clusters is stopped if the discovery of any match of sets to compress is impossible. Finally, the TTDR performance is evaluated based on real sensory data and using OMNeT++ simulator. The acquired outcomes illustrate the proficiency of the proposed system in regarding data transmission and energy.

In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the big data contributors due to the wide range of real-life applications that use this type of networks. The data volume increases in unexpected ratio. The dense WSN can lead to an increase in the redundant data in the gathered measures of the sensor node. Therefore, it is essential to apply energy-efficient data aggregation to remove the data redundancy and maintain a suitable rate of accuracy. This paper proposes an Integrated Divide and Conquer with Enhanced K-means technique (IDiCoEK) for energy-saving data aggregation in WSNs. The IDiCoEK aggregates the measures in two levels: the node and cluster head levels. A divide and conquer algorithm is applied at the sensor node to remove the redundant data from the collected measures and then send it to the cluster head. The cluster head applies an enhanced K-means approach for clustering the received data sets from the sensor nodes into groups of near similar sets and then the best representative set will be sent to the base station from each group. The IDiCoEK performance is assessed using OMNeT++ network simulator with real data readings of sensor nodes. Results demonstrate that our IDiCoEK technique can save energy by decreasing the measures sent to the sink whilst conserving a suitable level of data accuracy at the sink node.