**Abstract**

Recent developments in low-cost sensors, wireless network communication, and computational tools have paved the way for applications like monitoring with the high spatial and temporal resolution for example in the context of air quality. However, the reduced quality of sensing units necessitates robust drift detection and calibration schemes. The few existing methods are variants of outlier detection algorithms. We presented an optimization-based clustering algorithm that first smooths the data and then performs clustering for drift detection. We present the detection efficiency of the algorithm with a simulated dataset where the proposed algorithm detects sensor failures like random walks, reduced sensitivity and changes in bias. The system we consider consists of pumps delivering water to different reservoirs in a network, with each reservoir catering to time varying demand. Pumps and ON/OFF valves are used as manipulated variables to control the flow and pressure. The decision variables are the number of pumps to be turned on and the state of the valves in the network over a given horizon and the objective is to minimize energy consumption while meeting the time varying demand. Given the nonlinear nature of the pump operating curve and the hydraulics, this results in a Mixed Integer NonLinear Program (MINLP). We propose to solve by decomposing it into series of sub-problems that can be solved efficiently. Application of these ideas to distribution networks reveals potential significant savings in energy or improvement in supply. Experimental results will be shared followed by our field implementations. ** The Speaker **

Alexandre Reiffers is a post-doctoral fellow at Robert Bosch Centre for Cyber-Physical Systems. He received the B.Sc. degree in mathematics (2010) from the University of Marseille, the master degree in applied mathematics (2012) from the University of Pierre et Marie CURIE and the Ph.D. degree in computer science (January 2015) from the INRIA (National research institute in computer science and control) and the University of Avignon. His supervisors were Eitan Altman and Yezekael Hayel. From July 2016 to December 2017, Alexandre Reiffers was a researcher at SafranTech where he was working on comparison of maintenance strategies. Most of his research projects concern the application of mathematical tools (game theory, optimization, stochastic process and machine learning) for a better understanding of real-world problems. The different issues that he studies touch topics such as social networks, speech between human and computer, economy and manufacturing.