Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications

The Application of Advanced Information Technologies in Civil Infrastructure Construction and Maintenance

Information technologies have widely been used in the construction and maintenance of civil infrastructure. The advantages of information technologies provided a broader range of methods for infrastructure and enhanced its level of maintenance. However, a systematic summary of the research development of information technologies used in civil infrastructure is limited. This study aims to supplement this field by providing an objective, systematic summary of relevant literature in mainstream journals employing bibliometric retrieval and quantitative analysis from 2010 to 2020. The following results are obtained: (1) This study discusses the application of advanced information technologies in different phases and provides a critical analysis of the application of these existing information technologies, which includes wireless sensor networks (WSN), fiber optic sensing (FOS), building information modelling (BIM), radio frequency identification (RFID) and other advanced information technologies. (2) The digital twins can be used as tools for the planning and management of next-generation smart infrastructure, making the future of civil infrastructure smarter and more sustainable.

Si0.97Ge0.03 microelectronic thermoelectric generators with high power and voltage densities

Microelectronic thermoelectric generators are one potential solution to energizing energy autonomous electronics, such as internet-of-things sensors, that must carry their own power source. However, thermoelectric generators with the mm² footprint area necessary for on-chip integration made from high thermoelectric figure-of-merit materials have been unable to produce the voltage and power levels required to run Si electronics using common temperature differences. We present microelectronic thermoelectric generators using Si_0.97Ge_0.03, made by standard Si processing, with high voltage and power generation densities that are comparable to or better than generators using high figure-of-merit materials. These Si-based thermoelectric generators have <1 mm² areas and can energize off-the-shelf sensor integrated circuits using temperature differences ≤25 K near room temperature. These generators can be directly integrated with Si circuits and scaled up in area to generate voltages and powers competitive with existing thermoelectric technologies, but in what should be a far more cost-effective manner.

Thermoelectric generators with a small size are unable to produce enough high voltage and power levels to run Si integrated circuits using commonly encountered temperature differences. Here, the authors present microelectronic thermoelectric generators using Si0.97Ge0.03 to solve the problem.

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.