Biblio

The major challenge of Real Time Protocol is to balance efficiency and fairness over limited bandwidth. MPTCP has proved to be effective for multimedia and real time networks. Ideally, an MPTCP sender should couple the subflows sharing the bottleneck link to provide TCP friendliness. However, existing shared bottleneck detection scheme either utilize end-to-end delay without consideration of multiple bottleneck scenario, or identify subflows on switch at the expense of operation overhead. In this paper, we propose a lightweight yet accurate approach, EMPTCP, to detect shared bottleneck. EMPTCP uses the widely deployed ECN scheme to capture the real congestion state of shared bottleneck, while at the same time can be transparently utilized by various enhanced MPTCP protocols. Through theory analysis, simulation test and real network experiment, we show that EMPTCP achieves higher than 90% accuracy in shared bottleneck detection, thus improving the network efficiency and fairness.

Dynamic security assessment provides system operators with vital information for possible preventive or emergency control to prevent security problems. In some cases, power system topology change deteriorates intelligent system-based online stability assessment performance. In this paper, we propose a new online assessment scheme to improve classification performance reliability of dynamic transient stability assessment. In the new scheme, we use an intelligent system consisting an ensemble of neural networks based on extreme learning machine. A new feature selection algorithm combining filter type method RRelief-F and wrapper type method Sequential Floating Forward Selection is proposed. Boosting learning algorithm is used in intelligent system training process which leads to higher classification accuracy. Moreover, we propose a new classification rule using weighted outputs of predictors in the ensemble helps to achieve 100% transient stability prediction in our case study.

Dynamic security assessment provides system operators with vital information for possible preventive or emergency control to prevent security problems. In some cases, power system topology change deteriorates intelligent system-based online stability assessment performance. In this paper, we propose a new online assessment scheme to improve classification performance reliability of dynamic transient stability assessment. In the new scheme, we use an intelligent system consisting an ensemble of neural networks based on extreme learning machine. A new feature selection algorithm combining filter type method RRelief-F and wrapper type method Sequential Floating Forward Selection is proposed. Boosting learning algorithm is used in intelligent system training process which leads to higher classification accuracy. Moreover, we propose a new classification rule using weighted outputs of predictors in the ensemble helps to achieve 100% transient stability prediction in our case study.

This work presents a highly reliable and tamper-resistant design of Physical Unclonable Function (PUF) exploiting Resistive Random Access Memory (RRAM). The RRAM PUF properties such as uniqueness and reliability are experimentally measured on 1 kb HfO2 based RRAM arrays. Firstly, our experimental results show that selection of the split reference and offset of the split sense amplifier (S/A) significantly affect the uniqueness. More dummy cells are able to generate a more accurate split reference, and relaxing transistor's sizes of the split S/A can reduce the offset, thus achieving better uniqueness. The average inter-Hamming distance (HD) of 40 RRAM PUF instances is 42%. Secondly, we propose using the sum of the read-out currents of multiple RRAM cells for generating one response bit, which statistically minimizes the risk of early retention failure of a single cell. The measurement results show that with 8 cells per bit, 0% intra-HD can maintain more than 50 hours at 150 °C or equivalently 10 years at 69 °C by 1/kT extrapolation. Finally, we propose a layout obfuscation scheme where all the S/A are randomly embedded into the RRAM array to improve the RRAM PUF's resistance against invasive tampering. The RRAM cells are uniformly placed between M4 and M5 across the array. If the adversary attempts to invasively probe the output of the S/A, he has to remove the top-level interconnect and destroy the RRAM cells between the interconnect layers. Therefore, the RRAM PUF has the “self-destructive” feature. The hardware overhead of the proposed design strategies is benchmarked in 64 × 128 RRAM PUF array at 65 nm, while these proposed optimization strategies increase latency, energy and area over a naive implementation, they significantly improve the performance and security.