Learning in the presence of concept drift and hidden contexts
Machine Learning
ACM Transactions on Computer Systems (TOCS)
The broadcast storm problem in a mobile ad hoc network
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Sending messages to mobile users in disconnected ad-hoc wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Computers and Intractability: A Guide to the Theory of NP-Completeness
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INS/Twine: A Scalable Peer-to-Peer Architecture for Intentional Resource Discovery
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IEEE Transactions on Knowledge and Data Engineering
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MSWiM '04 Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Publish/subscribe in a mobile environment
Wireless Networks - Special issue: Pervasive computing and communications
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WMCSA '04 Proceedings of the Sixth IEEE Workshop on Mobile Computing Systems and Applications
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Snapshot Queries: Towards Data-Centric Sensor Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
HYPER: A Hybrid Approach to Efficient Content-Based Publish/Subscribe
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
PeopleNet: engineering a wireless virtual social network
Proceedings of the 11th annual international conference on Mobile computing and networking
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Localized routing trees for query processing in sensor networks
Proceedings of the 14th ACM international conference on Information and knowledge management
Searching Local Information in Mobile Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Search-and-Discover in Mobile P2P Network Databases
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Towards lightweight information dissemination in inter-vehicular networks
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CarTel: a distributed mobile sensor computing system
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ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Gossiping on MANETs: the beauty and the beast
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Mobile Peer-to-Peer Data Dissemination with Resource Constraints
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Autonomic Communication
Beyond average: toward sophisticated sensing with queries
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
IQN routing: integrating quality and novelty in P2P querying and ranking
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Learning the relevance of parking information in VANETs
Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking
Buffer dimensioning of delay-tolerant network nodes - a large deviations approach
ICDCN'12 Proceedings of the 13th international conference on Distributed Computing and Networking
Impact of persistent storage on the DTN routing performance
ICDCN'12 Proceedings of the 13th international conference on Distributed Computing and Networking
Intelligent search in social communities of smartphone users
Distributed and Parallel Databases
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In this article we study the data dissemination problem in which data items are flooded to all the moving objects in a mobile ad hoc network by peer-to-peer transfer. We show that if memory and bandwidth are bounded at moving objects, then the problem of determining whether a set of data items can be disseminated to all the moving objects is NP-complete. For a heuristic solution we postulate that a moving object should save and transmit the data items that are most likely to be new (i.e., previously unknown) to future encountered moving objects. We propose a method to be used by each moving object to prioritize data items based on their probabilities of being new to future receivers. The method employs a machine learning system for estimation of the novelty probability and the machine learning system is progressively trained by received data items. Through simulations based on real mobility traces, we show the superiority of the method against some natural alternatives.