Where are you going next? A practical multi-dimensional look at mobility prediction

Babak Alipour, Leonardo Tonetto, Roozbeh Ketabi, Aaron Yi Ding, Jörg Ott, Ahmed Helmy

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology. We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have”on-the-go” mode of usage (and hence dubbed”Flutes”), whereas laptops are”sit-to-use” (dubbed”Cellos”). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.

Original languageEnglish
Title of host publicationMSWiM 2019 - Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
PublisherAssociation for Computing Machinery (ACM)
Pages5-12
Number of pages8
ISBN (Electronic)9781450369046
DOIs
Publication statusPublished - 2019
Event22nd ACM International Conference on Modelling, Analysis, and Simulation of Wireless and Mobile Systems, MSWiM 2019 - Miami Beach, United States
Duration: 25 Nov 201929 Nov 2019

Conference

Conference22nd ACM International Conference on Modelling, Analysis, and Simulation of Wireless and Mobile Systems, MSWiM 2019
CountryUnited States
CityMiami Beach
Period25/11/1929/11/19

Keywords

  • Device types
  • Markov chain
  • Mobility
  • Neural networks
  • Prediction
  • Wireless networks

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