A Novel Approach for Deploying Minimum Sensors in Smart Buildings

Buildings, viewed as cyber-physical systems, become smart by deploying Building Management Systems (BMS). They should be aware about the state and environment of the building. This is achieved by developing a sensing system that senses different interesting factors of the building, called as “facets of sensing.” Depending on the application, different facets need to be sensed at various locations. Existing approaches for sensing these facets consist of deploying sensors at all the places so they can be sensed directly. But installing numerous sensors often aggravate the issues of user inconvenience, cost of installation and maintenance, and generation of e-waste. This article proposes how intelligently using the existing information can help to estimate the facets in cyber-physical systems like buildings, thereby reducing the sensors to be deployed. In this article, an optimization framework has been developed, which optimally deploys sensors in a building such that it satisfies BMS requirements with minimum number of sensors. The proposed solution is applied to real-world scenarios with cyber-physical systems. The results indicate that the proposed optimization framework is able to reduce the number of sensors by 59% and 49% when compared to the baseline and heuristic approach, respectively.

A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters

In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed-integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.

Parallel simulation and optimization framework of supplies production processes for unconventional emergencies

The outbreak of unconventional emergencies leads to a surge in demand for emergency supplies. How to effectively arrange emergency production processes and improve production efficiency is significant. The emergency manufacturing systems are typically complex systems, which are difficult to be analyzed by using physical experiments. Based on the theory of Random Service System (RSS) and Parallel Emergency Management System (PeMS), a parallel simulation and optimization framework of production processes for surging demand of emergency supplies is constructed. Under this novel framework, an artificial system model paralleling with the real scenarios is established and optimized by the parallel implementation processes. Furthermore, a concrete example of mask shortage, which occurred at Huoshenshan Hospital in the COVID-19 pandemic, verifies the feasibility of this method.