The quantification of an operating power grid’s resilience is highly significant today, given its criticality as an enabler of other infrastructures, complexity, and the threat it faces due to a wide range of detrimental events, from extreme climate to cyber attacks. Currently, there exist no standardized definitions and metrics for measuring the resilience of an operating grid. In this paper, we introduce a novel resilience quantification framework and demonstrate a method to measure the flexibility towards topological/structural changes due to potential failures in the power grid to assess operational resilience. We start with the state estimation data from a large utility and use the graph analysis methods and power flow simulation tools to compute the identified resilience parameters.

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

The adoption of health information technology (HIT) has facilitated efforts to increase the quality and efficiency of health care services and decrease health care overhead while simultaneously generating massive amounts of digital information stored in electronic health records (EHRs). However, due to patient safety issues resulting from the use of HIT systems, there is an emerging need to develop and implement hazard detection tools to identify and mitigate risks to patients. This paper presents a new methodological framework to develop hazard detection models and to demonstrate its capability by using the US Department of Veterans Affairs' (VA) Corporate Data Warehouse, the data repository for the VA's EHR. The overall purpose of the framework is to provide structure for research and communication about research results. One objective is to decrease the communication barriers between interdisciplinary research stakeholders and to provide structure for detecting hazards and risks to patient safety introduced by HIT systems through errors in the collection, transmission, use, and processing of data in the EHR, as well as potential programming or configuration errors in these HIT systems. A nine-stage framework was created, which comprises programs about feature extraction, detector development, and detector optimization, as well as a support environment for evaluating detector models. The framework forms the foundation for developing hazard detection tools and the foundation for adapting methods to particular HIT systems.

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

As the licensing and deployment of long term evolution (LTE) systems are ramping up, the study of cellular activity in LTE systems is essential to better understand the spectrum needs and make informed decisions on mobile device density over time. Meanwhile, there are limited tools to non-intrusively monitor mobile device density in real-time. Cellular carriers gather such data through their base stations; however, this data is proprietary with restricted access due to privacy concerns. In this paper, we present a toolset that non-intrusively monitors the cellular activity and senses the mobile device density from over-the-air LTE radio measurements without violating the privacy of mobile users. This information can be passively extracted from the downlink control channel information messages in the LTE physical layer protocol. A hardware prototype has been developed in our laboratory to measure and analyze the downlink LTE radio signals and provide real-time insights to the cellular activity and mobile device count.

It is estimated that 50% of the global population lives in urban areas occupying just 0.4% of the Earth's surface. Understanding urban activity constitutes monitoring population density and its changes over time, in urban environments. Currently, there are limited mechanisms to non-intrusively monitor population density in real-time. The pervasive use of cellular phones in urban areas is one such mechanism that provides a unique opportunity to study population density by monitoring the mobility patterns in near real-time. Cellular carriers such as AT&T harvest such data through their cell towers; however, this data is proprietary and the carriers restrict access, due to privacy concerns. In this work, we propose a system that passively senses the population density and infers mobility patterns in an urban area by monitoring power spectral density in cellular frequency bands using periodic beacons from each cellphone without knowing who and where they are located. A wireless sensor network platform is being developed to perform spectral monitoring along with environmental measurements. Algorithms are developed to generate real-time fine-resolution population estimates.

The use of state-of-the-art 3G cellular CDMA technologies in a utility owned AMI network results in a large amount of control traffic relative to data traffic, increases the average packet delay and hence are not an appropriate choice for smart grid distribution applications. Like the CDG, we consider a utility owned cellular like CDMA network for smart grid distribution applications and classify the distribution smart grid data as scheduled data and random data. Also, we propose SMAC protocol, which changes its mode of operation based on the type of the data being collected to reduce the data collection latency and control overhead when compared to 3G cellular CDMA2000 MAC. The reduction in the data collection latency and control overhead aids in increasing the number of smart meters served by a base station within the periodic data collection interval, which further reduces the number of base stations needed by a utility or reduces the bandwidth needed to collect data from all the smart meters. The reduction in the number of base stations and/or the reduction in the data transmission bandwidth reduces the CAPital EXpenditure CAPEX and OPerational EXpenditure OPEX of the AMI network. The proposed SMAC protocol is analyzed using markov chain, analytical expressions for average throughput and average packet delay are derived, and simulation results are also provided to verify the analysis.

The selection of the appropriate communication technology for different smart grid applications has drawn a great attention in the recent past. In this paper, we propose a Hybrid Spread Spectrum (HSS) based Advanced smart Metering Infrastructure (AMI) that reduces the overhead and latency in data transfer when compared to the use of 3G/4G technologies for smart meter data collection. We present a preliminary PHY and MAC layer design of a HSS based AMI network and evaluate their performance using matlab and NS2 simulations.

In sensor networks, collaborative processing between multiple sensor nodes is essential in order to complement for each others sensing capability, tolerate faults, and provide reliable information. The client/server-based paradigm is typical for distributed processing. However, it is not the most efficient in the context of sensor networks. In this paper, we present a mobile agent-based paradigm to carry out collaborative processing, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. This approach has great potential in providing energy-efficient and scalable collaborative processing with low latency. We design two metrics (execution time and energy consumption) and use simulation tools to quantitatively measure the performance of different computing models in collaborative processing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we develop a cluster-based hybrid computing paradigm to combine the advantages of both paradigms. We analyze two different scenarios in hybrid computing and simulation results show that there is always one scenario that performs better than either the client/server- or mobile-agent-based paradigm.