Biblio

Big data which is collected by IoT devices is utilized in various businesses. For security and privacy, some data must be encrypted. IoT devices for encryption require not only to tamper resistance but also low latency and low power. PRINCE is one of the lowest latency cryptography. A glitch canceller reduces power consumption, although it affects tamper resistance. Therefore, this study evaluates the tamper resistance of dedicated hardware with glitch canceller for PRINCE by statistical power analysis and T-test. The evaluation experiments in this study performed on field-programmable gate array (FPGA), and the results revealed the vulnerability of dedicated hardware implementation with glitch canceller.

The Internet of Things (IoT) is a concept that allows the networking of various objects of everyday life and communications on the Internet without human interaction. The IoT consists of Low-Power and Lossy Networks (LLN) which for routing use a special protocol called Routing over Low-Power and Lossy Networks (RPL). Due to the resource-constrained nature of RPL networks, they may be exposed to a variety of internal attacks. Neighbor attack and DIS attack are the specific internal attacks at this protocol. This paper presents an anomaly-based lightweight Intrusion Detection System (IDS) based on threshold values for detecting attacks on the RPL protocol. The results of the simulation using Cooja show that the proposed model has a very high True Positive Rate (TPR) and in some cases, it can be 100%, while the False Positive Rate (FPR) is very low. The results show that the proposed model is fully effective in detecting attacks and applicable to large-scale networks.

This paper presents the design and VLSI implementation of a low-power HEVC main profile encoder, which is able to process up to 4096x2160@30fps 4:2:0 encoding in real-time with five-stage pipeline architecture. A pyramid ME (Motion Estimation) engine is employed to reduce search complexity. To compensate for the video sequences with fast moving objects, GME (Global Motion Estimation) are introduced to alleviate the effect of limited search range. We also implement an alternative 5x5 search along with 3x3 to boost video quality. For intra mode decision, original pixels, instead of reconstructed ones are used to reduce pipeline stall. The encoder supports DVFS (Dynamic Voltage and Frequency Scaling) and features three operating modes, which helps to reduce power consumption by 25%. Scalable quality that trades encoding quality for power by reducing size of search range and intra prediction candidates, achieves 11.4% power reduction with 3.5% quality degradation. Furthermore, a lossless frame buffer compression is proposed which reduced DDR bandwidth by 49.1% and power consumption by 13.6%. The entire video surveillance SoC is fabricated with TSMC 28nm technology with 1.96 mm2 area. It consumes 2.88M logic gates and 117KB SRAM. The measured power consumption is 103mW at 350MHz for 4K encoding with high-quality mode. The 0.39nJ/pixel of energy efficiency of this work, which achieves 42% $\backslash$textasciitilde 97% power reduction as compared with reference designs, make it ideal for real-time low-power smart video surveillance applications.

We present an adaptive multi-rate ray sampling algorithm targeting mobile ray-tracing GPUs. We efficiently combine two existing algorithms, adaptive supersampling and undersampling, into a single framework targeting ray-tracing GPUs and extend it to a new multi-rate sampling scheme by utilizing tile-based rendering and frame-to-frame coherency. The experimental results show that our implementation is a versatile solution for future ray-tracing GPUs as it provides up to 2.98 times better efficiency in terms of performance per Watt by reducing the number of rays to be fed into the dedicated hardware and minimizing the memory operations.