Epidemic Information Dissemination in Mobile Social Networks With Opportunistic Links

Xu, Qiuliang; Su, Zhou; Zhang, Kuan; Ren, Pinyi; Shen, Xuemin

doi:10.1109/tetc.2015.2414792

Cited by 133 publications

(66 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nevertheless, the counterparts on land-based network could lay down a solid foundation for our work [22][23][24][25][26][27][28]. A theoretical model to compute the throughput of cooperative content distribution in vehicular ad hoc networks is conceived in [29].…”

Section: Related Workmentioning

confidence: 99%

Perceiving who and when to leverage data delivery for maritime networks: An optimal stopping view

Yang

Feng

et al. 2015

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

The advances in the integration of wireless communication and sensor technologies have stimulated an innovative paradigm named Crowd Sensing Networks, which caters to the exponential growth of service demands on the sea and drives the development of potential maritime wideband networks. This paper investigates the issue of sensed traffic data scheduling between vessels, combining Time Division Long Term Evolution (TD-LTE) and delaytolerant networks (DTNs) on the sea. Specially, we propose a unique network topology which combines maritime crowd sensing network and delay tolerant networks, i.e., a storecarry-and-forward routing topology is explored to address the intermittent network connectivity in maritime context. Notably, the alternative eco-friendly green energy in maritime environment will make the scheduling issue more challenging. To the best of our knowledge, this is the first Tingting Yang work to do such investigation with the goal of minimizing the costs associated with end-to-end delay of data delivery and energy consumption of DTN throw box. On this basis, we design the scheme of data cooperation transmission between vessels that the hosting vessel decides which DTN throw box to store the data, and when a vessel arrives, the DTN throw box determines whether to stop, i.e., let the arriving vessel carry the data, or skip it and continue to wait for other vessels. A Two-step Time and Energy Oriented Optimal-stopping (TTEOO) algorithm leveraging backward induction method is proposed, based on the optimal stopping rules. Simulation results are presented to show the effectiveness of the proposed method, in terms of consumption cost and data delivery ratio.

show abstract

Section: Related Workmentioning

confidence: 99%

Perceiving who and when to leverage data delivery for maritime networks: An optimal stopping view

Yang

Feng

et al. 2015

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

show abstract

“…Analytical models can avoid these drawbacks providing a fast and broader performance evaluation. Two classes of analytical models have been proposed for modelling this network dynamics: Markovian models [5,6,7,8,9,10] and deterministic models based 35 on Ordinary Differential Equations (ODEs) [6,11,12,13]. Analytical models require anyway a precise and concise description of the mobility scenario, that usually assume that the inter-contact times distribution between pairs of nodes are exponentially distributed with a given contact rate [5].…”

mentioning

confidence: 99%

“…In [13] the authors propose a detailed analytical model to analyse the epidemic information dissemination in mobile social networks. It is also based on SIR models including rules that concern user's behaviour, especially when their 55 interests change according to the information type, and it can have a considerable impact on the dissemination process.…”

mentioning

confidence: 99%

Analytical evaluation of the performance of contact-Based messaging applications

et al. 2016

View full text Add to dashboard Cite

Communications in mobile opportunistic networks, instead of using the Internet infrastructure, take place upon the establishment of ephemeral contacts among mobile nodes using direct communication. In this paper, we analytically model the performance of mobile opportunistic networks for contact-based messaging applications in city squares or gathering points, a key challenging topic that is required for the effective design of novel services. We take into account several social aspects such as: the density of people, the dynamic of people arriving and leaving a place, the size of the messages and the duration of the contacts. We base our models on Population Processes, an approach commonly used to represent the dynamics of biological populations. We study their stable equilibrium points and obtain analytical expressions for their resolution.The evaluations performed show that these models can reproduce the dynamics of message diffusion applications. We demonstrate that when the density of people increases, the effectiveness of the diffusion is improved. Regarding the arrival and departure of people, their impact is more relevant when the density of people is low. Finally, we prove that for large message sizes the effectiveness of the epidemic diffusion is reduced, and novel diffusion protocols should be considered.

show abstract

“…These models are considered macroscopic models, where we can identify the key mechanisms underlying the information diffusion. Two main classes of analytical models have been proposed for modeling this network dynamics: Markovian models [52,53,144,56,80,164] and deterministic models based on population or epidemic processes [53,172,33,169,61].…”

Section: Performance Evaluation Of Opportunistic Networkmentioning

confidence: 99%

“…They noticed that, to improve diffusion performance, a node should first diffuse the most similar data according to their common interests when meeting a friend, and it should first diffuse the most dissimilar data according to their common interests when meeting a stranger. In [168] a detailed analytical model is proposed to analyze the epidemic information dissemination in mobile social networks. This model is based on rules concerning user behavior, especially when their interests change according to the information type, and this can have a considerable impact on the dissemination process.…”

Section: Overviewmentioning

confidence: 99%

Improving Message Dissemination in Opportunistic Networks

Tapia¹

View full text Add to dashboard Cite

Epidemic Information Dissemination in Mobile Social Networks With Opportunistic Links

Cited by 133 publications

References 38 publications

Perceiving who and when to leverage data delivery for maritime networks: An optimal stopping view

Perceiving who and when to leverage data delivery for maritime networks: An optimal stopping view

Analytical evaluation of the performance of contact-Based messaging applications

Improving Message Dissemination in Opportunistic Networks

Contact Info

Product

Resources

About