Special Interest Messaging: A Comparison of IGM Approaches

Cutting, Daniel; Quigley, Aaron; Landfeldt, Bjorn

doi:10.1093/comjnl/bxm076

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Our approach is scalable to a significantly large sized network without relying on any centralized architecture and relevant information flow between and within the groups/peers in the network. Cutting et al (2007) propose implicit group definition based on tags used by users to identify interesting content. Peers interested in the same content tags are considered to belong to the same implicit group.…”

Section: Results Of Abstracted Simulation and Discussionmentioning

confidence: 99%

Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation

Paridel

Mantadelis

Yasar

et al. 2012

J Ambient Intell Human Comput

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Vehicle-to-vehicle and vehicle-to-infrastructure communication systems enable vehicles to share information captured by their local sensors with other interested vehicles. To ensure that this information is delivered at the right time and location, context-aware routing is vital for intelligent inter-vehicular communication. Traditional network addressing and routing schemes do not scale well for large vehicular networks. The conventional network multicasting and broadcasting cause significant overhead due to a large amount of irrelevant and redundant transmissions. To address these challenges, we first take into account contextual properties such as location, direction, and information interest to reduce the network traffic overhead. Second, to improve the relevancy of the received information we leverage the mobility patterns of vehicles and the road layouts to further optimize the peer-to-peer routing of the information. Third, to ensure our approach is scalable, we propose a context-based grouping mechanism in which relevant information is shared in an intelligent way within and between the groups. We evaluate our approach based on groups with common spatio-temporal characteristics. Our simulation experiments show that our context-based routing scheme and grouping mechanism significantly reduces the propagation of irrelevant and redundant information.

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Section: Results Of Abstracted Simulation and Discussionmentioning

confidence: 99%

Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation

Paridel

Mantadelis

Yasar

et al. 2012

J Ambient Intell Human Comput

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“…Cutting et al [13] propose implicit group definition based on tags used by users to identify interesting content. Peers interested on the same content tags are considered to belong to the same implicit group.…”

Section: B Results and Analysismentioning

confidence: 99%

Optimizing information dissemination in large scale mobile peer-to-peer networks using context-based grouping

Yasar

Vanrompay

Preuveneers

et al. 2010

13th International IEEE Conference on Intelligent Transportation Systems

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Nowadays novel embedded computing devices enable vehicles to form large scale mobile peer-to-peer networks in which they can assist each other to improve their driving experience. Therefore context-aware communication is considered to be vital for inducing inter-vehicular intelligence between groups of vehicles with similar interests. However, traditional network addressing schemes are not well suited for group-based communication in large scale vehicular networks. The classical network paradigms of multicasting and broadcasting to define groups are too limited. First, there is no way to optimize network traffic based on the contextual characteristics of the nodes. Second, the groups of nodes are highly dynamic with vehicles randomly joining and leaving multiple groups. We propose an information dissemination approach based on context grouping in which only relevant information is shared among nodes. We evaluate our approach in a large scale vehicular network where groups are formed based on the location and shared interests of the nodes. The experiments show that by inducing our context-based grouping mechanism we can significantly eliminate irrelevant information and reduce overall network traffic in a scalable way.

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