STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
An O(log*n) approximation algorithm for the asymmetric p-center problem
Journal of Algorithms
A randomized art-gallery algorithm for sensor placement
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Two O (log* k)-Approximation Algorithms for the Asymmetric k-Center Problem
Proceedings of the 8th International IPCO Conference on Integer Programming and Combinatorial Optimization
A Faster All-Pairs Shortest Path Algorithm for Real-Weighted Sparse Graphs
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Asymmetric k-center is log* n-hard to approximate
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
Locating sensors in paths and cycles: The case of 2-identifying codes
European Journal of Combinatorics
RAID '08 Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
Joint Monitoring and Routing in Wireless Sensor Networks Using Robust Identifying Codes
Mobile Networks and Applications
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Spread of information in a social network using influential nodes
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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We consider the problem of placing sensors in a network to detect and identify thesource of any contamination. We consider two variants of this problem:0(1)sensor-constrained: we are allowed a fixed number of sensors and want to minimize contaminationdetection time; and (2)time-constrained: we must detect contamination within a given time limit and want to minimize the number of sensors required. Our main results are as follows. First, we give a necessary and sufficient condition for source identification.Second, we show that the sensor and time constrained versions of the problem are polynomially equivalent. Finally, we show that the sensor-constrained version of the problem is polynomially equivalent to the asymmetric k-center problem and that the time-constrained version of the problem is polynomially equivalent to the dominating set problem.