Kinetic data structures: a state of the art report
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Data structures for mobile data
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Introduction to Algorithms
Maintaining Minimum Spanning Trees in Dynamic Graphs
ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
Parametric and Kinetic Minimum Spanning Trees
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
A data structure for dynamic trees
STOC '81 Proceedings of the thirteenth annual ACM symposium on Theory of computing
Algorithm Design
Spatio-temporal network databases and routing algorithms: a summary of results
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Minimum spanning tree on spatio-temporal networks
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
A novel approach to reconnaissance using cooperative mobile sensor nodes
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
A centralized energy-efficient routing protocol for wireless sensor networks
IEEE Communications Magazine
Minimum spanning tree on spatio-temporal networks
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
A critical-time-point approach to all-start-time lagrangian shortest paths: a summary of results
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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Given a spatio-temporal network whose edge properties vary with time, a time-sub-interval minimum spanning tree (TSMST) is a collection of minimum spanning trees where each tree is associated with one or more time intervals; during these time intervals, the total cost of this spanning tree is the least among all spanning trees. The TSMST problem aims to identify a collection of distinct minimum spanning trees and their respective time-sub-intervals. This is an important problem in spatio-temporal application domains such as wireless sensor networks (e.g., energy-efficient routing). As the ranking of candidate spanning trees is non-stationary over a given time interval, computing TSMST is challenging. Existing methods such as dynamic graph algorithms and kinetic data structures assume separable edge weight functions. In contrast, we propose novel algorithms to find TSMST for large networks by accounting for both separable and non-separable piecewise linear edge weight functions. The algorithms are based on the ordering of edges in edge-order-intervals and intersection points of edge weight functions.