A cluster-based approach for routing in dynamic networks
ACM SIGCOMM Computer Communication Review
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Smooth is better than sharp: a random mobility model for simulation of wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
A Map-Based Dead-Reckoning Protocol for Updating Location Information
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
High-performance spatial indexing for location-based services
WWW '03 Proceedings of the 12th international conference on World Wide Web
Journal of Computer and System Sciences - Special issue on PODS 2000
Efficient constraint processing for location-aware computing
Proceedings of the 6th international conference on Mobile data management
A Threshold-Based Algorithm for Continuous Monitoring of k Nearest Neighbors
IEEE Transactions on Knowledge and Data Engineering
k-Closest Pair Query Monitoring Over Moving Objects
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
ACM SIGMOBILE Mobile Computing and Communications Review
Adaptive location constraint processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Extending the LBS-framework TraX: Efficient proximity detection with dead reckoning
Computer Communications
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Clique detection is a base mechanism of proactive multitarget Location-based Services (LBSs), ranging from mobile social network services to logistics. A clique is a set of n mobile targets which are pairwise located within spatial proximity range. Clique detection refers to automatically detecting such cliques within a set S of size s ≥ n of tracked targets. Assuming terminal-based positioning like GPS, the paper presents an efficient clique detection strategy for reducing the message exchange between the targets' devices and a central location server for correlating the targets' positions. The basic idea is to prove the non-existence of a clique as long as it does not exist. Based on conclusions from graph theory, this is achieved by distributing the members of S into n -- 1 so-called independent sets, which are sets of targets known to be pairwise not within proximity range. For maintaining the independent sets, proximity detection between two targets, for which efficient strategies already exist, is dynamically applied to selected pairs of targets. Based on simulations it turns out that, compared to a rudimentary strategy, the achieved message savings are substantial.