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Context Dissemination for Dynamic Urban-Scale Applications

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Abstract

Realising the “smart city” vision requires applications that can efficiently disseminate context among millions of potentially mobile nodes. Numerous context dissemination algorithms exist based on flooding-, gossip- and overlay-based approaches. However, due to their message transmission (flooding, gossip) or control (overlay) overhead, they cannot support the amount of mobile nodes envisaged in urban-scale scenarios. This paper describes Adaptive Context Tries (ACT), a decentralised context dissemination middleware that balances message transmission and control overhead to support urban-scale context-aware applications. ACT achieves scalability using a dynamically constructed virtual overlay, structured as a retrieval tree (trie) on node identifiers (IDs), avoiding continuous overlay rebuilds due to mobility or nodes changes by removing the need for subscriptions. Through formal analysis and extensive large-scale simulations we show that unlike existing context dissemination algorithms ACT can handle dynamic context requirements in urban-scale scenarios.

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Notes

  1. This typically takes the form of a network address of some kind, depending on the underlying network type. For example, an IPv4 or IPv6 address and an UDP port number

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Authors and Affiliations

  1. Distributed Systems Group, School of Computer Science and Statistics, Trinity College, Dublin, Ireland

    Alistair Morris, Mélanie Bouroche & Vinny Cahill

  2. Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou str., Pireas, Greece

    Constantinos Patsakis

Authors
  1. Alistair Morris
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  2. Constantinos Patsakis
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  3. Mélanie Bouroche
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  4. Vinny Cahill
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Corresponding author

Correspondence to Alistair Morris.

Additional information

This papers denotes an extention of our previous work with ACT that extends on findings in [27, 28] and [29].

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Morris, A., Patsakis, C., Bouroche, M. et al. Context Dissemination for Dynamic Urban-Scale Applications. Mobile Netw Appl 22, 305–317 (2017). https://doi.org/10.1007/s11036-017-0809-x

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