This paper undertakes a systematic review (SR) on distributed estimation techniques applied to cyber-physical systems (CPS). Even though SRs are not the common way to survey a theme in the control community, they provide a rigorous, robust and objective formula that should not be always ignored. The presented SR incorporates and adapts the guidelines recommended in other fields (mainly biosciences and computer sciences) to the field of automation and control and presents a brief description of the different phases that constitute an SR. As a result, this review compares the different techniques found in the literature in terms of: The proposed estimator (Kalman filter, Luenberger observer, Bayesian filter, etc.), the particular application within CPS, the design of the estimators (decentralized vs centralized), the amount of data required for implementation or the inclusion of experiments/simulations in the studies. Particular attention is paid to those papers that present some results in applications that include humans, animals or biological systems.

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers’ behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.

Given the need for effective crop monitoring while reducing human intervention and workload, it is necessary to implement devices that operate autonomously and are durable. These devices must be capable of operating over long distances, operate with low energy requirements, and resist climatic adversities and external factors such as dust and water to function effectively in rural areas. In this work, we introduce a low-cost autonomous IP67 IoT vision device designed and implemented in a real-world scenario. The device is used for automatic detection of wheat characteristics in order to optimise human resources when monitoring the growth and health of crops. Equipped with algorithms capable of capturing and processing images, the device has been designed to be computationally efficient, cost effective and power efficient. The device utilises the LoRaWAN communication protocol and requires 2.878W and has 3.5 months of autonomy. Furthermore, it leverages specific low computational algorithms that can operate with low-resolution images making them more effective. To test the device in a real-world scenario, an algorithm that measures the height of wheat was introduced using classical vision techniques, with 97% accuracy. Furthermore, the device incorporates an ad-hoc trained YOLOv8 and YOLOV10 object detection machine learning algorithm for the detection of spikes and stubble areas, which achieves a recall of 71.1% and a precision of 79.5% for the YOLOv8. In the case of the YOLOv10, it achieves a recall of 70% and a precision of 77%. The results are validated by expert agronomists annotations and data collected via a custom created web platform for remote visualisation and decision making tasks.

Many papers related to this topic can be found in the bibliography; however, just a modest percentage of the introduced techniques are developed to a Technology Readiness Level (TRL) sufficiently high to be implementable in industrial applications. This paper is focused precisely on the review of this specific topic. The investigation on the state of the art has been carried out as a systematic review, a very rigorous and reliable standardised scientific methodology, and tries to collect the articles which are closer to a possible implementation. This selection has been carefully done with the definition of a series of rules, drawn to represent the adequate level of readiness of fault detection techniques which the various articles propose.