A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus.

Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them.

Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach.

In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources.

Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios.

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs).

BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility.

This article develops the design, installation, exploitation, and final utilization of intelligent techniques, hardware, and software for understanding mobility in a modern city. We focus on a smart-campus initiative in the University of Malaga as the scenario for building this cyber⁻physical system at a low cost, and then present the details of a new proposed evolutionary algorithm used for better training machine-learning techniques: BiPred. We model and solve the task of reducing the size of the dataset used for learning about campus mobility.