• DocumentCode
    1941871
  • Title

    Supporting information on demand with the DisServicePro Proactive peer-to-peer information dissemination system

  • Author

    Rota, Silvia ; Benincasa, Giacomo ; Interlandi, Matteo ; Suri, Niranjan ; Bonnlander, Brian ; Bradshaw, Jeffrey ; Tortonesi, Mauro ; Watson, Scott ; Boner, Kevin

  • Author_Institution
    Florida Inst. for Human & Machine Cognition, Pensacola, FL, USA
  • fYear
    2010
  • fDate
    Oct. 31 2010-Nov. 3 2010
  • Firstpage
    561
  • Lastpage
    568
  • Abstract
    Tactical networks are highly dynamic environments characterized by constrained resources, limited bandwidth, and intermittent connectivity. The limits on communication cause significant delays in the delivery of information to edge users. This paper focuses on an approach to improve the timeliness of access to information via prediction and pre-staging. The approach also incorporates a learning mechanism to dynamically adapt the information prediction algorithm. This capability has been integrated into the DisService peer-to-peer information dissemination system, which opportunistically exploits any available connectivity to address the challenging environment. The extended system, called DisServicePro (for Proactive) predicts the information needs of edge users using their mission description, including the routes that users may take as part of the mission. DisServicePro extends the capabilities of DisService by efficiently and proactively disseminating information to the edge nodes by means of replication and forwarding policies. The proactive behavior is the result of the integration of policies and a distributed learning algorithm that takes into account the history of previously requested information, along with the characteristics of the target nodes and the mission. As new information becomes available, DisServicePro matches it against the mission profile and pushes relevant information to the edge nodes. Information that is selected to be pushed is sorted based on the predicted time to use as well as the confidence value of the prediction.
  • Keywords
    learning (artificial intelligence); military computing; peer-to-peer computing; DisServicePro; distributed learning algorithm; information on demand; information prediction algorithm; proactive peer-to-peer information dissemination system; tactical network; Adaptation model; Classification algorithms; Context; Force; Machine learning algorithms; Peer to peer computing; Prediction algorithms; decision trees; dynamic information prioritization; information on-demand; peer-to-peer; pre-staging; proactive information dissemination; tactical networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    MILITARY COMMUNICATIONS CONFERENCE, 2010 - MILCOM 2010
  • Conference_Location
    San Jose, CA
  • ISSN
    2155-7578
  • Print_ISBN
    978-1-4244-8178-1
  • Type

    conf

  • DOI
    10.1109/MILCOM.2010.5680435
  • Filename
    5680435