  Understanding and predicting mobility are essential for the design and
evaluation of future mobile edge caching and networking. Consequently, research
on prediction of human mobility 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.
  Despite existing studies, more investigations are needed to capture intrinsic
mobility characteristics constraining predictability, and to explore more
dimensions (e.g. device types) and spatio-temporal granularities, especially
with the change in human behavior and technology. We analyze extensive
longitudinal datasets 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.
