Internet of Things (IoT) devices regularly process sensitive data, including personal information. Therefore, ensuring their security is crucial to avoid damage and prevent data breaches. The Advanced Encryption Standard (AES) is generally regarded as one of the most popular cryptographic algorithms for ensuring data security. Typical lightweight implementations of the algorithm published in the literature focus on area and power optimization, while neglecting the performance. This paper presents a novel lightweight approach for the AES algorithm and considers both encryption and decryption. In terms of performance per unit area and performance per unit power, our 32-bit design outperforms the state-of-the-art by 1.69x and 1.27x, respectively. These improvements become even larger when implementing higher data-path designs, such as 64-bit or 128-bit designs. Our non-DOM AES design is secure against Correlation Power Analysis (CPA) but vulnerable to Template Based Attack (TBA) when more than 1500 traces are considered. To enhance its resilience against side channel attacks (SCAs), we modified our design by adopting and further improving on the most recent countermeasure, i.e., Domain-Oriented Masking (DOM). The results demonstrate that incorporating DOM into our design enables it to withstand against both CPA and TBA. Besides, our simplified eight-stage and five-stage 1st-order DOM SBOX designs achieve a reduction in area of 9.9% and 6.9% compared to the original proposed designs, respectively.

Over the last decade, the recognition of the potential value of augmented reality (AR) and other human-machine interfaces has been growing. These applications are all based on depth sensing technologies. Among various depth sensing technologies, the Time-of-Flight (ToF) approach is emerging as a widely applicable method because it has the potential of reaching much longer distances at higher speed and accuracy, is natively suitable for mobile phones or AR.
One obvious problem is that the dToF system mainly targets the automotive industry or 3D imaging in close range, which means different power, accuracy, and fewer area requirements than mobile phone or AR applications. The application of the dToF system in the mobile phone or AR industry needs to be tested. The data volume is another problem of dToF. Usually, in the read out part, the dToF time-to digital converter (TDC)’s timestamps will all be saved into a histogram. The peak value of the histogram is the detected result. However, in a large pixels scenario, the area cost will be too much for mobile phone or AR applications if the whole histogram for all pixels is saved. Hence, an algorithm that can save histogram partially or find out the peak value without saving histogram is needed.
This thesis proposed two novel algorithms for the dToF system’s read out part and tested three other algorithms’ functions in mobile phone or AR applications. With the Lambertian model and probabilistic theory, a module of the dToF system is built using MATLAB to generate a testing dataset. Besides, introductions to depth-sensing technologies, single-photon avalanche diode (SPAD) sensors, and dToF systems will be given before the core chapters of the thesis.