TY - GEN
T1 - Supporting information on demand with the DisServicePro proactive peer-to-peer information dissemination system
AU - Rota, Silvia
AU - Benincasa, Giacomo
AU - Interlandi, Matteo
AU - Suri, Niranjan
AU - Bonnlander, Brian
AU - Bradshaw, Jeffrey
AU - Tortonesi, Mauro
AU - Watson, Scott
AU - Boner, Kevin
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Decision trees
KW - Dynamic information prioritization
KW - Information on-demand
KW - Peer-to-peer
KW - Pre-staging
KW - Proactive information dissemination
KW - Tactical networks
UR - https://www.scopus.com/pages/publications/79951589518
U2 - 10.1109/MILCOM.2010.5680435
DO - 10.1109/MILCOM.2010.5680435
M3 - Conference contribution
AN - SCOPUS:79951589518
SN - 9781424481804
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 561
EP - 568
BT - 2010 IEEE Military Communications Conference, MILCOM 2010
T2 - 2010 IEEE Military Communications Conference, MILCOM 2010
Y2 - 31 October 2010 through 3 November 2010
ER -