The smart grid is a new paradigm that aims to modernize the legacy power grid. It is based on the integration of ICT technologies, embedded system, sensors, renewable energy and advanced algorithms for management and optimization. The smart grid is a system of systems in which communication technology plays a vital role. Safe operations of the smart grid need a careful design of the communication protocols, cryptographic schemes, and computing technology. In this article, the authors describe current communication technologies, recently proposed algorithms, protocols, and architectures for securing smart grid communication network. They analyzed in a unifying approach the three principles pillars of smart-gird: Sensors, communication technologies, and security. Finally, the authors elaborate open issues in the smart-grid communication network

The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P₀ versus P₄) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approach.

A structural health-monitoring system needed to come out from the problem associated due to the rapidly growing population of elderly and the health care demand. The paper discussed the consumer's electricity usage data, from the smart meter, how to support the healthcare sector by load profiling the normal or abnormal energy consumption. For this work, the measured dataset is taken from 12 households and collected by the smart meter with an interval of an hour for one month. The dataset is grouped according to the features pattern, reduced by matrix-based analysis and classified with K-Means algorithm data mining clustering method. We showed how the clustering result of the Sum Square Error (SSE) has connection trend to indicate normal or abnormal behavior of electricity usage and leads to determine the assumption of the consumer's health status.

In this paper, we present and discussed Internet of Things (IoT) based smart grid (SG) using smart meter (SM) for energy management techniques on Field Programmable Gate Arrays (FPGA). The SG allows for reliable communication and an efficient energy management system. IoT-based SG technology can manage and monitor remotely using Home Area Networks (HANs) for home energy management. HAN is equipped with SM to collect a huge amount of data from appliances to report their energy consumption. Home Energy Management’s (HEM’s) have the advantage of offering real-time energy usage to the consumer by limiting the power requirement to the SG. The paper addresses HAN system devices using ZigBee makes wireless communication effective for monitoring consumer's daily routine and by alerting security alarms with lower cost. The ZigBee network in connection with the microcontroller and interfaced with FPGA on HAN advanced the existing remote energy management system and the relatability and confidentiality of the SGs on the home energy management system.