Education 4.0 is being empowered more and more by artificial intelligence (AI) methods. We observe a continuously growing demand for adaptive and personalized Education. In this paper we present an innovative approach to promoting AI in Education 4.0. Our first contribution is AI assisted Higher Education Process with smart sensors and wearable devices for self-regulated learning. Secondly we describe our first results of Education 4.0 didactic methods implemented with learning analytics and machine learning algorithms. The aim of this case study is to predict the final score of students before participating in final examination. We propose an Early Recognition System equipped with real data captured in a blended learning course with a personalized test at the beginning of the semester, an adaptive learning environment based on Auto Tutor by N. A. Crowder theory with adaptive self-assessment feedback. It is obvious that focusing on students' success and their experiences is a win-win scenario for students and professors as well as for the administration.

Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success

LSDAR: A light-weight structure based data aggregation routing protocol with secure internet of things integrated next-generation sensor networks

Smart sensors are defined by the IEEE 1451 standard as sensors with small memory and standardized physical connection to enable the communication with processor and data network. Beyond this definition, smart sensors are defined as the combination of a sensor with signal conditioning, embedded algorithms and digital interface. They are currently highly adopted in mobile and portable devices like phones and tablets. Such types of sensors respond to the issues of power consumption, data communication and system integration at the sensor level and for predefined use cases. Some limitations of smart sensors are the lack of flexibility, absence of customization, narrow spectrum of applications, and the basic communication protocol. Moreover, there is a growing request of new and broader applications for individual sensors while integrating an increasing number of different types of sensors. Therefore, to overcome these limitations and address the new challenges, the next generation of sensors is proposed: the intelligent sensor platform. It is defined as the combination of sensor and processing with a dedicated architecture to aggregate external sensor data. The main advantages are reviewed and an implementation of an intelligent sensor platform embedding a MEMS accelerometer with a 32-bit microcontroller is described.

Next smart sensors generation

Power management techniques in smartphone-based mobility sensing systems: A survey

The development of high-speed intelligent rail transit has increased the number of sensors applied on trains. These play an important role in train state control and monitoring. These sensors generally work in a severe environment, so the key problem for sensor data acquisition is to ensure data accuracy and reliability. In this paper, we follow the sequence of sensor signal flow, present sensor signal sensing technology, sensor data acquisition, and processing technology, as well as sensor fault diagnosis technology based on the voltage, current, speed, and temperature sensors which are commonly used in train traction systems. Finally, intelligent sensors and future research directions of rail transit train sensors are discussed.

https://www.mdpi.com/1424-8220/17/6/1356/pdf

Review on the Traction System Sensor Technology of a Rail Transit Train.

An innovative product type is enabling wireless sensor networks (WSNs) to become pervasive: The intelligent sensor hub This device type integrates one or more sensor die with a 32-bit microcontroller and memory in an architecture optimized to manage, process and fusion a cluster of different sensor types Integrated analog and digital interfaces allow connections to a wide spectrum of external sensors and systems Sensor networks can be efficiently managed at system and sub-system levels, allowing very low-power consumption, high flexibility through software, and adaptability by managing new kinds of sensors An implementation proposal for a WSN demonstrates the key benefits of this concept at the node and system levels This includes minimizing data transfers and intelligent interaction between the different nodes Interfaces within a wireless network, like ZigBee™, are also becoming seamless and modular Moreover, the small size −3 by 3 millimeter — and the low-cost of such new solutions are required for WSNs to become widely adopted in many applications

Stephane Gervais-Ducouret

Papers

Next smart sensors generation

Intelligent sensor hub benefits for wireless sensor networks

Next Generation of Smart Sensors