Human-in-the-Loop Cyber-Physical Systems Research Articles

A unified privacy preserving model with AI at the edge for Human-in-the-Loop Cyber-Physical Systems

With the proliferation of personal Internet of Things (IoT) devices and their wide adoption among society, the original notion of IoT has been reshaped, giving rise to new IoT-based paradigms like Human-in-the-Loop Cyber-Physical Systems (HiTLCPS). While these systems can bring benefits and positively influence people, their pervasive nature raises significant privacy concerns, especially regarding the acquisition and processing of data. Although privacy-preserving mechanisms have been proposed for these implementations, the existing approaches tend to focus on either data acquisition or data processing. However, to date, no solution has encompassed the entire process. In this regard, this paper presents a unified privacy-preserving model for HiTLCPS. This approach integrates a human-centric mechanism to control and make data acquisition and sharing tasks transparent with a state inference process supported by Artificial Intelligence (AI) in the edge. A set of assessments were conducted in actual implementation to evaluate the feasibility of the model. Our findings reveal that our federated learning approach is a suitable solution compared to the traditional approach based on machine learning at the small cost of 3.27% of average accuracy. Finally, this paper concludes by providing a roadmap towards integrating HiTLCPS from diverse contexts, including a state inference process closer to the user domain.

Guest Editorial: Computational Intelligence for Human-in-the-Loop Cyber Physical Systems

The papers in this special section focus on human-in-the-loop cyber-physical systems. Recent advances in computational intelligence, real-time computing and control, have given momentum to Humanin-the-Loop Cyber Physical Systems (HitLCPS) to enable gamechanging communication and collaboration paradigms that operate in connection with humans’ natural behaviour patterns. Despite the ongoing advancement of computational intelligence techniques for analyzing the interactions between the cognitive and cyber domains, there are growing concerns regarding the security, privacy, and safety of humans when they interact with smart cyber physical environments (IoT ecosystems). The large-scale integration of heterogeneous IoT devices to manage and control a wide variety of sensors and settings will hugely increase the attack surface and the scope for misconfigurations. These could lead to unsafe or conflicting behaviour of various devices and subsystems, which in turn, can place the human in unsafe and hazardous situations, both mentally and physically. It is still unclear how to design optimized collaborative systems between people and machines in a scalable manner, how to design triggers for pro-active engagement and disengagement, and how to handle the consequences of implied actions. For example, when the system misbehaves as a result of erroneous data, it is important to have real-time rules that can guarantee a fail-safe state for the HitLCPS. The verification of operations in a large HitLCPS can be very complex due to the evolving nature of human-in-the-loop networks both in terms of physical aspects and the operational environment. Therefore, understanding the semantics of HitLCPS and the context of control behaviour is critical to detect or entirely avoid incorrect configurations and build a proactive resilience and a reactive defence against evolving threats.

Human Driver Performance Assessment based on HiLCPS Concept

The paper presents an approach for human driver performance assessment from the view of his/her abilities or limitations. The idea is based on the Human-in-the-loop Cyber-Physical Systems (HiLCPS) concept which is briefly described in the initial part of this paper. A cybernetic approach describing a human driver as a controller in cooperation with a controlled system represented by a simulated driving task is there used. In this context, a car-driving simulator based on Unreal Engine 4 was developed. A human driver controls a simulated drive via an input HCI (Human-Computer Interface) device, i.e., a joystick or a steering wheel with pedals, exploiting the perception of the current situation in the visualized scene. That simple HiLCPS system provides data representing human behavior during a specific task under defined conditions. Based on the acquired data we are able to identify the behavioral model as well as other quantities characterizing the attitude to control. These parameters then serve for describing a current state of a human driver and his/her performance which is demonstrated in the last section.

A Preliminary Investigation into Learning Behaviors in Complex Environments for Human-in-the-Loop Cyber-Physical Systems

The field of Cyber-Physical Systems (CPS) is increasingly recognizing the importance of integrating Human Factors for Human-in-the-loop CPS (HiLCPS) developments. This is because psychological, physiological, and behavioral characteristics of humans can be used to predict human-machine interactions. The goal of this pilot study is to collect initial data to determine whether driving and eye tracking metrics can provide evidence of learning for a CPS project. Six participants performed a series of 12 repeated obstacle avoidance tasks in manual driving. Lane deviations and fixation-related eye data were recorded for each trial. Overall, participants displayed either conservation/safe or aggressive/risky in their lateral position with respect to the obstacle during successive trials. Also, eye tracking metrics were not significantly affected by trial number, but observational trends suggest their potential for aiding in understanding adjustments humans make in learning. Results can inform predictive modeling algorithms that can anticipate and mitigate potential problems in real-time.

Analysis of Student Academic Performance Using Human-in-the-Loop Cyber-Physical Systems

Is it possible to analyze student academic performance using Human-in-the-Loop Cyber-Physical Systems (HiLCPS) and offering personalized learning methodologies? Taking advantage of the Internet of Things (IoT) and mobile phone sensors, this article presents a system that can be used to adapt pedagogical methodologies and to improve academic performance. Thus, in this domain, the present work shows a system capable of analyzing student behavior and the correlation with their academic performance. Our system is composed of an IoT application named ISABELA and a set of open-source technologies provided by the FIWARE Project. The analysis of student performance was done through the collection of data, during 30 days, from a group of Ecuadorian university students at “Escuela Politécnica Nacional” in Quito, Ecuador. Data gathering was carried out during the first period of classes using the students’ smartphones. In this analysis, we found a significant correlation between the students’ lifestyle and their academic performance according to certain parameters, such as the time spent on the university campus, the students’ sociability, and physical activity, etc.

ISABELA – A Socially-Aware Human-in-the-Loop Advisor System

Currently, we are surrounded by all sorts of smart devices. Nevertheless, surprisingly, apart from e-health systems, few applications consider humans as active system players, as most applications/systems have the sole objective of providing some service to humans without considering their intents, actions, emotions, or social context. In this respect, they can be looked at as open loop, rather than integrating humans in a feedback loop, with potential to react to and change the environment. Systems that do consider human feedback are called Human-in-the-Loop Cyber-Physical-Systems (HiTLCPS), and these are now looked at as essential components of intelligent advisor systems (AS), i.e., systems that can take decisions and provide advice based the user environment and mood. In this paper, we present a HITLCPS AS system that combines online social networks (OSN) data with a variety of other data, such as environmental data and personal mobile devices data, in order to provide user advice. The paper details the system architecture, presents a data analysis from the collected dataset, provides results for some of the built models, and discusses future research directions.

Adaptive Human Control Model and its Usability in Modeling of Human-in-the-loop Cyber Physical Systems

This paper discusses usability of an adaptive human control model in the context of a simple Human-in-the-loop Cyber Physical Systems (HiLCPS). The first part of the paper characterizes human factor assessment with an emphasis on the modeling of human behavior. This section is followed by a description of the human control model structure, including a definition of the individual components and their roles during the control phases. Exploiting relevant theoretical background, the authors present an example of a human controller comprising the feedback component and two different implementations of a pursuit controller. The parameters of the controllers are obtained from data measured on a flight simulator controlled by a pilot during the defined task. The first results presented in this paper indicate the possibility of use the described human control structure in HiLCPS modeling and its potential for other, more complex experiments within human factor assessment.

Human-in-the-Loop Issue in Context of the Cyber-Physical Systems

This paper presents the introduction to the Human-in-the-loop issue connected to the concept of the Cyber-physical systems (CPS). In this context, the brief overview of CPS is provided, with emphasis on the role of human in cooperation with CPS. Such systems are often called Human-in-the-Loop Cyber-Physical Systems (HiLCPSs). The following part of this paper brings the short background of HiLCPS and approaches for human performance assessment. The last part deals with example of human performance assessment based on the real data. For these purposes, several approaches is used, e.g. evaluation of measured human responses or evaluation of human dynamic properties based on the modelling of human behavior. The input data was collected using measurements with real human/pilots. Due to many realized tests with pilots in the different stage of training, there is statistically relatively significant set of input data.

Study on High Resilient Structures for IoT Systems to Detect Accidents

In order to operate the Internet of Things (IoT) or Cyber Physical Systems (CPS) in the real world, the system needs to be structured to have people in the real world incorporated as a part of its process: Human-in-the-Loop CPS (HITLCPS). With people in the real world incorporated as a part of its process, the system must have a secure structure to be able to continue operating normally. With sensors, actuators and other devices connected in a network, it becomes vulnerable to cyberattacks; hence, its framework must be resilient and secure in order to ensure its safety in the face of any disturbances. In this paper, we describe a safety-based secure system structure, using a STAMP model and a covariance structure.