Trace- and Social-Based Modeling of Human Mobility Patterns

Trace- and Social-Based Modeling of Human Mobility Patterns

Ali Diab (Al-Baath University, Syria & Ilmenau University of Technology, Germany) and Andreas Mitschele-Thiel (Ilmenau University of Technology, Germany)
DOI: 10.4018/978-1-5225-0239-5.ch012
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Abstract

The 5th Generation (5G) of mobile communication networks is being developed to address the demands and business contexts of 2020 and beyond. Its vision is to enable a fully mobile and connected society and also to trigger socio-economic transformations in ways eventually unimagined today. This means that the physical world to be covered with planned 5G networks including communication networks, humans and objects is becoming a type of an information system. So as to improve the experience of individuals, communities, societies, etc. within such systems, a thorough comprehension of intelligence processes responsible of generating, handling and controlling those data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have crucial role in forthcoming communication technologies. Human mobility patterns models can be categorized into synthetic, trace-based and community-based models. Synthetic models are largely preferred and widely applied to simulate mobile communication networks. They try to capture the patterns of human movements by means of a set of equations. These models are traceable, however, not capable of generating realistic mobility models. The key idea of trace-based models is the exploitation of available measurements and traces achieved in deployed systems to reproduce synthetic traces characterized by the same statistical properties of real traces. A main drawback of trace-based modeling of human patterns is the tight coupling between the trace-based model and the traces collected, the network topology deployed and even the geographic location, where the traces were collected. This is why the results of various trace-based models deviate clearly from each other. Sure, this prohibits the generalization of trace-based models. When one also considers that the traces themselves are rarely available, one can understand why synthetic models are preferred over trace-based ones. Community-based modeling of human movements depends on the fact stating that mobile devices are usually carried by humans, which implies that movement patterns of such devices are necessarily related to human decisions and socialization behaviors. So, human movement routines heavily affect the overall movement patterns resulting. One of the major contributions in this context is the application of social networks theory to generate more realistic human movement patterns. The chapter highlights the state of art and provides a comprehensive investigation of current research efforts in the field of trace- and social-based modeling of human mobility patterns. It reviews well-known approaches going through their pros and cons. In addition, the chapter studies an aspect that heavily relates to human mobility patterns, namely the prediction of future locations of users.
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2. Trace-Based Modeling Of Human Mobility Patterns

Trace-based mobility models, also termed empirical models, characterize movement patterns based on connectivity information that represent the distribution of users’ locations within the environment and the movements of humans carrying wireless devices. These models depend on traces gathered by already deployed cellular networks or GPS-enabled devices to obtain information about the locations of users. A well-known open-source project issued to hold such sets of traces is the Community Resource for Archiving Wireless Data At Dartmouth (CRAWDAD) (Kotz, Henderson & Abyzov, 2015). The project is one of the largest projects in this scope. It contains significant traces that are considered by many researchers. Another relevant set of traces were gathered by the Networking Research Lab of North Carolina State University (NCSU) from 100 volunteers in five different sites (two university campuses, New York City, Disney World, and North Carolina state fair) in the duration between 2006 and 2008 (Networking Research Lab, 2015). Out of these traces, two main properties were extracted, namely the flight length and pause time, see (Ribeiro, 2011).

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