The deployment of online e-learning can lead to many security risks, such as confidentiality loss, exposure of critical data, availability and destruction of publicly available information services. Security and proper authentication is critical in any online learning environment because any flaws can affect perceptions of its trustworthiness. Biometric authentication is increasingly being used in the newer generation of online learning environments for authentication of remote learners. Biometrics scan unique physiological characteristics in humans to identify people. These include fingerprints, iris, retina, voice, face, gait, and odor. The authors look at the state of biometric authentication techniques applicable to online learning environments and provide a more in-depth examination of face- and iris-based authentication systems for proper identification of learners.
The deployment of online e-learning can lead to many security risks, such as confidentiality loss, exposure of critical data, availability and destruction of publicly available information services. Security and proper authentication is critical in any online learning environment because any flaws can affect perceptions of its trustworthiness. Biometric authentication is increasingly being used in the newer generation of online learning environments for authentication of remote learners. Biometrics scan unique physiological characteristics in humans to identify people. These include fingerprints, iris, retina, voice, face, gait, and odor. The authors look at the state of biometric authentication techniques applicable to online learning environments and provide a more in-depth examination of face- and iris-based authentication systems for proper identification of learners.
Tracking people indoors can be valuable in smart living scenarios such as tracking shoppers in a mall or in healthcare situations when tracking the movement of elderly patients can allow them to remain more independent. Determining accurate movement of people indoors is problematic however as there is no universal tracking system such as GPS which works indoors. Instead, a range of techniques are used based on technologies such as cameras, radio frequency identification, WiFi, Bluetooth, pressure pads and radar are used to track people and objects within indoor environments. The most common technologies for tracking are Bluetooth and WiFi. Many Internet of Things (IoT) devices support these protocols and can therefore act as beacons and hubs for movement detection indoor. We provide here an overview of an IoT focused framework which allows the plug and play of Bluetooth and WiFi devices in addition to integrating passive and active approaches to determining the movement of people indoors.
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