SLAW: A New Mobility Model for Human Walks

By Kyunghan Lee, Seongik Hong, Seong Joon Kim, Injong Rhee, Song Chong. IEEE INFOCOM 2009, 2009

560 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/5e5419ac4cdba366361a7f00475286f17604f888

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Guohong Cao
2. Mani B. Srivastava
3. Deborah Estrin
4. Guangjie Han
5. Trung Quang Duong
6. Jinfang Jiang
7. Sasank Reddy
8. Injong Rhee

In Electrical and Electronic Engineering

1. Deborah Estrin
2. Guohong Cao
3. Mani B. Srivastava
4. Guangjie Han
5. Trung Quang Duong
6. Jinfang Jiang
7. George K. Karagiannidis
8. Gao Cong

In Transportation

1. Aixin Sun
2. Quan Yuan
3. Giusy Di Lorenzo
4. Song Chong
5. Kyunghan Lee
6. Carlo Ratti
7. Gao Cong
8. Deborah Estrin

Citations in the corpus

• Time-aware point-of-interest recommendation (2013, background): “Spatial behavior: Spatial influence is another important factor in analyzing users’ daily activities [13, 23].”
• Recruitment Framework for Participatory Sensing Data Collections (2010, methodology): “The Mobile AdHoc Networking (MANET) community has used mobility models that simulate the movements of individuals to test out performance of networking protocols [20, 21].”
• On the Levy-Walk Nature of Human Mobility (2008, background): “We leave the work of developing accurate humanmobilitymodelsforfuturework.Thereadersarereferred to [ 29 ] for our preliminary work on this topic, where the comparisons with various existing mobility models are presented.”
• Achieving k-anonymity in privacy-aware location-based services (2014, methodology): “The Levy walk model [32], which has been shown to better describe the mobility patterns of human being [33], is used to generate synthetic user contact events.”
• Understanding intra-urban trip patterns from taxi trajectory data (2012, background): “A number of mobility models have been proposed, including random way point, random direction, Brownian motion, random walk, and obstacle model for describing human movement (Lee et al. 2009).”
• A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks (2016, background): “Hybrid mobility models are synthetic mobility models which have features extracted from mobility models based on traces [55]–[59].”
• Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data (2010, background): “[17] develop a mobility model that captures the effect of human mobility patterns characterized by some fundamental statistical functions.”