In this article, we present implementations for Addition, Rotation, and eXclusive-or (ARX)-based block ciphers, including LEA and HIGHT, on IoT devices, including 8-bit AVR, 16-bit MSP, 32-bit ARM, and 32-bit ARM-NEON processors. We optimized 32-/8-bitwise ARX operations for LEA and HIGHT block ciphers by considering variations in word size, the number of general purpose registers, and the instruction set of the target IoT devices. Finally, we achieved the most compact implementations of LEA and HIGHT block ciphers. The implementations were fairly evaluated through the Fair Evaluation of Lightweight Cryptographic Systems framework, and implementations won the competitions in the first and the second rounds.

In this paper, a computationally efficient orientation estimation algorithm using an inertial/magnetic sensor is presented for ambulatory real-time human motion tracking. Based on a quaternion formulation, the proposed algorithm is designed to have two main steps that are connected in feedback relationship: a quaternion measurement step with a vector selector scheme and a Kalman filter (KF) step. This allows us to choose only the quaternion as the state and measurement vectors in our KF design. Thus, the KF has a minimum-order structure (i.e., 4th-order), which decreases the computational cost. The estimated orientation accuracy is validated experimentally by using an optical tracking system.

This paper describes a new quaternion-based Kalman filter (KF) for estimating human body orientation using an inertial/magnetic sensor. The proposed algorithm is comprised of a quaternion measurement step and a KF step that are connected in feedback relationship. This allows the algorithm to have a minimum-order structure (i.e., fourth order) that is computationally very efficient. Furthermore, to offer more reliable information to the quaternion measurement step, a vector selector scheme is adopted, which effectively adds the gyro measurement to the so-called Wahba's problem that conventionally uses only the accelerometer and magnetometer measurements. This protects the algorithm against undesirable conditions such as fast movements and temporary magnetic disturbances, enabling it to compute an accurate orientation estimate. Due to the computational efficiency of the algorithm, it is suitable for real-time ambulatory human motion tracking applications that require multiple and untethered inertial/magnetic sensors with low-cost onboard processing.