PCFG Based Synthetic Mobility Trace Generation

Geyik, Sahin Cem; Bulut, Eyuphan; Szymanski, Boleslaw K.

doi:10.1109/glocom.2010.5683092

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Finally we look at the result stated in Lin et al [ 1 ]: mutual Information between two symbols, as a function of the number of symbols between the two, decays exponentially in any probabilistic regular grammar, but decays like a power law for a context-free grammar. This is an important observation relied upon in our work, given that human mobility has been known to follow context-free grammar [ 45 ]. Lin et al [ 1 ] further state that exponential distribution is the only continuous distribution with the memory-less property.…”

Section: Relevant Conceptsmentioning

confidence: 99%

Examining the Limits of Predictability of Human Mobility

Kulkarni¹,

Mahalunkar

Garbinato³

et al. 2019

Entropy

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We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of human-mobility on deriving this upper bound on maximum mobility predictability. By using several statistical tests on three real-world mobility datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, contrasting with the initial Markovian assumption. We show that this assumption of exponential decay of information in mobility trajectories, coupled with the inadequate usage of encoding techniques results in entropy inflation, consequently lowering the upper bound on predictability. We highlight that the current upper bound computation methodology based on Fano’s inequality tends to overlook the presence of long-range structural correlations inherent to mobility behaviors and we demonstrate its significance using an alternate encoding scheme. We further show the manifestation of not accounting for these dependencies by probing the mutual information decay in mobility trajectories. We expose the systematic bias that culminates into an inaccurate upper bound and further explain as to why the recurrent-neural architectures, designed to handle long-range structural correlations, surpass this upper limit on human mobility predictability.

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Section: Relevant Conceptsmentioning

confidence: 99%

Examining the Limits of Predictability of Human Mobility

Kulkarni¹,

Mahalunkar

Garbinato³

et al. 2019

Entropy

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show abstract

“…It cannot be captured by using a Markov Model (MM) because states are not dependent on just the previous state (e.g., the first departure from work is for lunch, while the second is for home). Therefore, it would be interesting to measure the effectiveness of different approaches for predicting the next checkin, such as using PCFG (probabilistic context free grammars) [9], or any other model richer than MM. Even though prediction and validation were not our objectives in this paper, in the future we would like to benchmark different models (HMM, PCGF, supervised learning, etc) to determine the accuracy of capturing the next location of a human being given the previous checkins as training data.…”

Section: B Future Workmentioning

confidence: 99%

Using Location-Based Social Networks to Validate Human Mobility and Relationships Models

Nguyen

Szymański

2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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We propose to use social networking data to validate mobility models for pervasive mobile ad-hoc networks (MANETs) and delay tolerant networks (DTNs). The Random Waypoint (RWP) [19] and Erdos-Renyi (ER) models have been a popular choice among researchers for generating mobility traces of nodes and relationships between them. Not only RWP and ER are useful in evaluating networking protocols in a simulation environment, but they are also used for theoretical analysis of such dynamic networks. However, it has been observed that neither relationships among people nor their movements are random. Instead, human movements frequently contain repeated patterns and friendship is bounded by distance. We used social networking site Gowalla to collect, create and validate models of human mobility and relationships for analysis and evaluations of applications in opportunistic networks such as sensor networks and transportation models in civil engineering. In doing so, we hope to provide more human-like movements and social relationship models to researchers to study problems in complex and mobile networks.

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“…Section 5 presents our extensions to the basic PCFG definition to increase PCFG usability in mobile computing and networking. Some initial applications of this kind were discussed in [1] and [2]. We demonstrate the benefits of PCFG-based mobility modeling compared to other models in terms of describing entity movements in Section 6.…”

Section: Introductionmentioning

confidence: 99%

“…In the following section, we define probabilistic context free grammars which form the foundation of our model. Next, in Section 4, we describe our grammar construction algorithm which is an improvement over our two previous publications [1] [2]. Section 5 presents our extensions to the basic PCFG definition to increase PCFG usability in mobile computing and networking.…”

Section: Introductionmentioning

confidence: 99%

Grammatical Inference for Modeling Mobility Patterns in Networks

Geyik

Bulut

Szymanski

2013

IEEE Trans. on Mobile Comput.

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Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: (i) recognizing mobility patterns of monitored agents in different event datasets collected by sensor networks, and (ii) modeling and generating node movements in mobile networks. We also discuss the model's performance in simulations utilizing both synthetic and real world mobility traces.

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PCFG Based Synthetic Mobility Trace Generation

Cited by 9 publications

References 18 publications

Examining the Limits of Predictability of Human Mobility

Examining the Limits of Predictability of Human Mobility

Using Location-Based Social Networks to Validate Human Mobility and Relationships Models

Grammatical Inference for Modeling Mobility Patterns in Networks

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