Human mobility models for opportunistic networks

By Dmytro Karamshuk, Chiara Boldrini, Marco Conti, Andrea Passarella. 2011

246 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Transportation

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

In Transportation

Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “Results obtained from these subjects provide clues for improving transportation system [194], urban planning [195], and communication network [196].”
A Survey on Deep Learning for Human Mobility (2020, methodology): “First, synthetic trajectories are useful to the performance analysis of networking protocols such as mobile ad hoc networks, where the displacements of network users are exploited to route and deliver the messages [78, 96, 190].”
Mobile Crowd Sensing and Computing (2015, methodology): “In addition, since human mobility plays a major role in governing the network dynamic and behavior in MCSC, recent research efforts have been devoted to mobility modeling and new routing schemes based on human mobility [Karamshuk et al. 2011; Pasquale and Silvestri 2013].”
D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration (2016, unknown): “many real-world user mobility traces [35].”
Mobile ad hoc networking: milestones, challenges, and new research directions (2014, background): “Thus, opportunistic-network experiments, necessarily, involves real users mobility, with the aim of providing a characterization of the temporal properties devices/humans mobility.”
Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things (2013, background): “models [17, 18], co-location patterns [19, 20], and social network structure [21] as key pieces of human behavior/context information to predict nodes’ activeness and estimate”
Understanding Urban Human Mobility through Crowdsensed Data (2018, background): “…public transportation [1]; studying contacts between residents on their daily routes helps simulate the dynamics of disease transmission [2]; and understanding patterns of human movements and connectivity helps establish opportunistic networks which improves the connectivity of mobile devices [3].”