Deep Neural Networks for Indentifying Non-Recurring Traffic Congestion
Congratulations Fangzhou for getting the paper on non-recurring congestion accepted in Special Session on Intelligent Data Mining at IEEE Big Data
Paper on the Design of Communication and Transaction Anonymity in Blockchain-Based Transactive Microgrids
In this paper, we extend a recently developed trading workflow called PETra and describe our solution for communication and transactional anonymity.
Privacy Preserving Energy Transactions (PETRA)
We will be presenting the work on using blockchains for transactive energy platforms at IoT 2017 in October. In the paper we describe Privacy-preserving Energy Transactions (PETra), which is a secure and safe solution for transactive microgrids that enables consumers to trade energy without sacrificing their privacy. PETra builds on distributed ledgers, such as blockchains, and provides anonymity for communication, bidding, and trading.
The SpeedPro Project
These slides describe a describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.
Slides from FMEC 2017
As the number of low cost computing devices at the edge of communication network increase, there are greater opportunities to enable innovative capabilities, especially in cyber-physical systems. For example, micro-grid power systems can make use of computing capabilities at the edge of a Smart Grid to provide more robust and decentralized control. However, the downside to distributing intelligence to the edge away from the controlled environment of the data centers is the increased risk of failures. The paper introduces a framework for handling these challenges. The contribution of this framework is to support strategies to (a) tolerate the transient faults as they appear due to network fluctuations or node failures, and to (b) systematically reconfigure the application if the faults persist.
Slides from ISORC 2017
The emerging Fog Computing paradigm is providing an additional computational layer that enables new capabilities in real-time data-driven applications. The application of Fog Computing is especially interesting in the domain of Smart Grid where it can be used to prove a decentralized application framework that reflects the ongoing trend of distribution of intelligence in Smart Systems. These slides describe a component-based decentralized computation platform called RIAPS which provides an application architecture for such systems.
The Overview of the Transit-Hub Project
These slides describe the transit hub project
The Microgrid Control Application Demonstration Using RIAPS Software
Our collaborators at North Carolina State University recently developed and demonstrated A distributed power system application is running on 4 RIAPS nodes that shows the initial capabilities of RIAPS platform services. The demonstration shows how distributed synchronization can be implemented. Three out of the four nodes are connected to a real-time simulator simulating the micro grid. The fourth node is used for logging data. This demo also uses the C37 device actor developed by the Vanderbilt University team as part of the initial capability implementation of RIAPS
Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles
The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at time points that improves the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.