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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.

Side-channel attacks (SCA) are capable of bypassing the theoretical protection of cryptographic schemes and revealing secret information in hardware devices. They have become a serious threat to the hardware security of the cryptographic modules. Entering the Internet of Things (IoT) era, it is crucial to verify the IoT devices' ability to thwart potential attacks from stealing confidential data. In this work, we set out to design and implement a framework for efficient SCA resistance verification. By integrating trace measurement equipment and GPU-accelerated analyzing engines, we have successfully built an automatic system which greatly simplifies the test procedure and enables on-the-fly SCA tests. Experiment results on common IoT development boards show both promising strength and flexibility of our framework to meet different testing requirements smoothly.

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.

Geographic routing yields scalable operation in Wireless Multihop Networks. Nodes forward packets to the destination either in a greedy manner, or along arbitrary curves from source to destination. The latter allows for higher robustness, as paths are adjusted to avoid, for instance, congested areas or non-cooperative nodes. However, determining working curves is either based on unreliable trial-and-error approaches or requires global network knowledge. We propose two local schemes that dynamically adapt the curve defining the packet forwarding at node level. Our first procedure uses cached curves, and the second one builds local maps at each node. We implement them for the case of planar graph routing with guaranteed delivery but they can also be easily adapted to greedy routing. We evaluate our schemes by means of simulation in constrained multihop networks, and show that they reduce transmissions up to 74% compared to existing solutions.

Cloud storage is one of the most rapidly growing cloud services, which at the same time also faces serious security challenges. Recently, several cloud-storage service providers started to provide encryption protection to client data in the cloud. However, encryption imposes significant limits on data use. In this paper, we report our design and implementation of an encrypted cloud storage system that supports multi-user secure indices, allowing efficient search among encrypted documents of multiple users. Experiment results show that keyword search can be performed in real time. We believe that our system represent a first step toward providing secure and useful cloud-storage services in practice.

Carrier aggregation in LTE-Advanced provides the capability to uncoordinatedly deploy home eNodeBs with the opportunity to autonomously mitigate the interferences. Among many improvement approaches, the autonomous component carrier selection (ACCS) scheme to improve the performance of the cell edge users worth our attentions. However, a poorly ordered execution of the carrier selection can lead to waste of radio resources in a typical home Internet access scenario. In order to address this issue, we propose a component carrier reselection scheme which ameliorates the total throughput of the eNodeBs by reselecting the carriers according to an intelligently planned order. Additionally, we also propose to improve the performance of the cell edge users with the low-complexity fractional power suppression technique. System-level simulation of the proposed schemes has shown substantial improvement over the original ACCS scheme.

Both the femtocell technology and the concept of mobile virtual network operators (MVNO) are believed to be key directions for future mobile network evolutions. This paper proposes a detailed model for the femtocell-based MVNO and analyzes the dynamics between the femto base station transmit power and its absorbing effect on the macrocell users via game theoretic techniques. The results show that the power settings given by the Nash equilibrium maintain the required QoS level without causing excessive interferences. Such numerical results should help the feasibility of large-scale deployment of femtocell-based MVNOs.