Detecting and Reducing Biases in Cellular-Based Mobility Data Sets

Correctly estimating the features characterizing human mobility from mobile phone traces is a key factor to improve the performance of mobile networks, as well as for mobility model design and urban planning. Most related works found their conclusions on location data based on the cells where each user sends or receives calls or messages, data known as Call Detail Records (CDRs). In this work, we test if such data sets provide enough detail on users' movements so as to accurately estimate some of the most studied mobility features. We perform the analysis using two different data sets, comparing CDRs with respect to an alternative data collection approach. Furthermore, we propose three filtering techniques to reduce the biases detected in the fraction of visits per cell, entropy and entropy rate distributions, and predictability. The analysis highlights the need for contextualizing mobility results with respect to the data used, since the conclusions are biased by the mobile phone traces collection approach.This research was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness through TEC2017-84197-C4-1-R (Inteligencia de fuentes abiertas para redes electricas inteligentes seguras), TEC2014-54335-C4-2-R (INRISCO: INcident monitoRing In Smart COmmunities), and IPT-2011-1272-430000 (MONOLOC) projects

Entropy-Based Anomaly Detection in Household Electricity Consumption

Energy efficiency is one of the most important current challenges, and its impact at a global level is considerable. To solve current challenges, it is critical that consumers are able to control their energy consumption. In this paper, we propose using a time series of window-based entropy to detect anomalies in the electricity consumption of a household when the pattern of consumption behavior exhibits a change. We compare the accuracy of this approach with two machine learning approaches, random forest and neural networks, and with a statistical approach, the ARIMA model. We study whether these approaches detect the same anomalous periods. These different techniques have been evaluated using a real dataset obtained from different households with different consumption profiles from the Madrid Region. The entropy-based algorithm detects more days classified as anomalous according to context information compared to the other algorithms. This approach has the advantages that it does not require a training period and that it adapts dynamically to changes, except in vacation periods when consumption drops drastically and requires some time for adapting to the new situation.This work was supported by the Spanish Government under the research project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” (PID2020-113795RB-C32 MCIN/AEI/10.13039/501100011033) and the project MAGOS (TEC2017-84197-C4-1-R), and by the Comunidad de Madrid (Spain) under the projects: CYNAMON (P2018/TCS-4566), co-financed by the European Structural Funds (ESF and FEDER), and the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Characterizing and Removing Oscillations in Mobile Phone Location Data

IEEE WoWMoM 2019, 20th IEEE International symposium on a World of Wireless, Mobile and Multimedia Networks, Washington, ETATS-UNIS, 10-/06/2019 - 12/06/2019International audienceHuman mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories