Client-side Computational Optimization

Abstract

Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by mobile platforms. In this article, we report on some exploratory experience in this area. We illustrate some practical, real-world cases where running optimization programs on mobile or embedded devices can be useful, with particular emphasis on matheuristics approaches. Then, we discuss a practical use case involving the feasibility version of the generalized assignment problem (GAP). We present a JavaScript implementation of a GAP solver that can be executed inside an ordinary browser supporting ECMAScript. We tested the code on different smartphones of varying age and power, as well as on desktop PCs and other embedded devices. Our experiments confirm the viability of mobile devices for computational intensive tasks.

Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context

“…This solution only worked in scenarios with few devices and it could not be scalable in any way. In this sense, we aimed to change the weights of our architecture ( fat client–thin server ) by shifting the computation to a client-embedded device: a design choice that was well supported by current architectures, even for computationally demanding tasks [ 51 , 52 ], while maintaining a good level of accuracy for the prediction of the number of people in a classroom [ 17 ]. In this way, this method is expected to produce many benefits: Higher scalability: fat clients can complete jobs independently from other clients and then send their results to the server; Working semi-offline: in this way, it is possible to predict the number of people in a scene and store that result directly on a single-board computer without the need to send the data immediately; Higher availability: instead of having a single point of failure, there are different clients that work independently.…”