Smartphones and smartwatches, which include powerful sensors, provide a readily available platform for implementing and deploying mobile motion-based behavioral biometrics. However, the few studies that utilize these commercial devices for motion-based biometrics are quite limited in terms of the sensors and physical activities that they evaluate. In many such studies, only the smartwatch accelerometer is utilized and only one physical activity, walking, is investigated. In this study we consider the accelerome-ter and gyroscope sensor on both the smartphone and smartwatch, and determine which combination of sensors performs best. Furthermore, eighteen diverse activities of daily living are evaluated for their bio-metric efficacy and, unlike most other studies, biometric identification is evaluated in addition to biometric authentication. The results presented in this article show that motion-based biometrics using smartphones and/or smartwatches yield good results, and that these results hold for the eighteen activities. This suggests that zero-effort continuous biometrics based on normal activities of daily living is feasible, and also demon-strates that certain easy-to-perform activities, such as clapping, may be a viable alternative (or supplement) to gait-based biometrics.

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients’ health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is comprised of four layers: IoT network sensors/devices, Fog layers, Cloud computing layer, and healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: light-weight FPGA hardware-based cipher algorithm and secret cipher share algorithm, respectively. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients’ data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients’ personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.

Bluetooth technology is a key component of wireless communications. It provides a low-energy and low-cost solution for short-range radio transmissions. Bluetooth, more specifically Bluetooth Low Energy (BLE) has become the predominant technology for connecting IoT (Internet of Things). It can be found in cell phones, headsets, speakers, printers, keyboards, automobiles, children’s toys, and medical devices, as well as many other devices. The technology can also be found in automated smart homes, to provide monitors and controls for lights, thermostats, door locks, appliances, security systems, and cameras. Bluetooth offers convenience and ease of use, but it lacks a centralized security infrastructure. As a result, it has serious security vulnerabilities, and the need for awareness of the security risks are increasing as the technology becomes more widespread. This paper presents an overview of Bluetooth technology in IoT including its security, vulnerabilities, threats, and risk mitigation solutions, as well as real-life examples of exploits. Our study highlights the importance of understanding attack risks and mitigation techniques involved with using Bluetooth technology on our devices. Real-life examples of recent Bluetooth exploits are presented. Several recommended security measures are discussed to secure Bluetooth communication.