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Correct and timely responses to abnormal conditions in the power systems are crucial to their sound operation. In order for the operator or the automated response system to take prompt measures during system contingencies, it is critical to facilitate an accurate mechanism for the classification of the events and disturbances in the power grid. The massive amount of time-synchronized data recorded by the phasor measurement units can be combined with logs from other components in the power grid to create datasets for event and intrusion detection. This paper presents the results of applying deep learning techniques on open datasets recorded from a power system testbed to classify contingencies and cyber-attacks. Three different designs of recurrent neural networks (RNN) are investigated and tested for discriminating binary and multiclass events. Experiment results show 100% and 99.99% accuracy when applying the proposed classifiers on large scale binary and multiclass datasets respectively. It is also shown that one can improve the efficiency of the scheme by selectively eliminating 75% of the features in the dataset while maintaining as high as 99.96% accuracy in classifying multiclass events. Additionally, the feasibility of the design is validated by the low classification latency recorded on the low-end embedded system Jetson Nano. These promising results demonstrate the potential of employing RNN techniques in developing event and intrusion detection systems for power grids.

The Internet of Things provides visions of innovative services and domain-specific applications. With the development of Internet of Things services, various structural data need to be transferred over the Internet. However, protecting structural information that contains sensitive data has raised concerns against Internet of Things services. For a publish/subscribe scenario consisting of sensors, fog nodes, and subscribers, we propose a model that (1) expands the present XML Encryption standard for data with string and numeric types implemented in the sensors, (2) efficiently and discreetly filters matched streaming data and performs summation in the fog nodes, and (3) decrypts the filtered and aggregated data in the subscribers without revealing privacy data. The experimental results of the performance on fog node implemented by PC or Raspberry Pi show that the proposed model can rapidly process multiple encrypted XML streams generated by sensors in a parallel manner without revealing privacy data to subscribers. Therefore, the proposed model is a solution to the fog computing applications in which the privacy preservation of sensor data is of great concern.

Capacity has been an important issue for many wireless backhaul networks. Both the multihop nature and the large per packet channel access overhead can lead to its low channel efficiency. The problem may get even worse when there are many applications transmitting packets with small data payloads, e.g., Voice over Internet Protocol (V0IP). Previously, the use of multiple parallel channels and employing packet concatenation were treated as separate solutions to these problems. However, there is no available work on the integrated design and performance analysis of a complete scheduler architecture combining these two schemes. In this paper, we propose a scheduler that concatenates small packets into large frames and sends them through multiple parallel channels with an intelligent channel selection algorithm between neighboring nodes. Besides the expected capacity improvements, we also derive delay bounds for this scheduler. Based on the delay bound formula, call admission control (CAC) of a broad range of scheduling algorithms can be obtained. We demonstrate the significant capacity and resequencing delay improvements of this novel design with a voice-data traffic mixing example, via both numerical and simulation results. It is shown that the proposed packet concatenation and channel selection algorithms greatly outperform the round-robin scheduler in a multihop scenario. [ABSTRACT FROM AUTHOR]