The expansion of monitoring systems for power grids from the traditional transmission lines to include the distribution grid - closer to the end-user - is largely due to the advance in electrical monitoring devices and their increasing affordability. This paradigm-shift benefits from new algorithms that can be implemented at the grid edge (as opposed to the transmission grid) to detect anomalies faster, more efficiently, and in real-time. Micro-PMUs (μ-PMUs) are phasor measurement units that can be placed on the distribution lines to provide real-time data about the state of the grid and can detect fast transients. In this paper, we use data from (μ-PMUs to engineer physics-based features for a novel anomaly detection algorithm that detects anomalies based on the transient properties of the power grid. This allows for a more capable, agile, and reliable anomaly scoring system that can learn normal behavior patterns and detect anomalous behavior efficiently. We show that by combining the classical data-based approaches with physics-based features in the anomaly detection algorithm, the machine learning algorithm proposed is capable of detecting different classes of anomalies (such as edge devices on-off attacks, single line-to-ground (SLG) faults, etc.). Simulation results on IEEE 34-node test feeder show that we can achieve better detection performance than traditional techniques for a broader class of anomalies.

Data offloading from the cellular network to low-cost WiFi has been the subject of several research works in the last years. In-network caching has also been studied as an efficient means to further reduce cellular network traffic. In this paper we consider a scenario where mobile users can download popular contents (e.g., maps of a city, shopping information, social media, etc.) from WiFi-enabled caches deployed in an urban area. We study the optimal distribution of contents among the caches (i.e., what contents to put in each cache) to minimize users' access cost in the whole network. We argue that this optimal distribution does not necessarily provide geographic fairness, i.e., users at different locations can experience highly variable performance. In order to mitigate this problem, we propose two different cache coordination algorithms based on gossiping. These algorithms achieve geographic fairness while preserving the minimum access cost for end users.

Clustering of a graph is the task of grouping its nodes in such a way that the nodes within the same cluster are well connected, but they are less connected to nodes in different clusters. In this paper we propose a clustering metric based on the random walks' properties to evaluate the quality of a graph clustering. We also propose a randomized algorithm that identifies a locally optimal clustering of the graph according to the metric defined. The algorithm is intrinsically distributed and asynchronous. If the graph represents an actual network where nodes have computing capabilities, each node can determine its own cluster relying only on local communications. We show that the size of clusters can be adapted to the available processing capabilities to reduce the algorithm's complexity.