Abstract
Human monitoring and trailing in a blocked or closed environment such as a
jail or psychological shelter is an important research concern. Industry 4.0 has enabled
the monitoring of physically or mentally challenged people in asylums and criminals
who are sentenced to serve their terms in jails with various tools such as sensors,
wireless systems and sophisticated cameras. The hidden nature of monitoring and
reporting in closed environments without any new technologies such as IoT, RFID,
etc., may lead to ill-treatment of the inmates in the above-mentioned places. The
traditional physical monitoring system can end up with wrong reports about the
inmates and can hide the real scenarios. Personal opinions and characteristics of
officials as well as the prisoners may vary based on their health and behavioral
patterns. The automation of human monitoring involves monitoring of security,
activity, fitness, and health factors of the inmates in the closed environment. The
human-activity monitoring is carried out by acquiring and analyzing the body signals
of the inmates. Passive tags are attached to the wristband of each person in the RFID
human monitoring systems. Minimal human intervention and effort is one of the
biggest advantages of the human monitoring system. Authentication, intelligent
decision making and minimum use of resources are the main challenges in designing a
human monitoring system. Intelligent decision making algorithms are applied to predict
human behavioral patterns. This work gives a summary of different authentication
protocols and methodologies used with the Internet of Things (IoT) and RFID devices
in human monitoring systems. It presents the components and infrastructure of a typical
human monitoring system and summarizes the sensors and IoT devices used for the
same. A wide investigation is conducted on security and privacy issues while storing
the private and confidential details of the inmates. A comprehensive survey on different
authentication techniques and data security issues in closed human monitoring is
presented in this work.