Efficient Sensor Placement Optimization for Early Detection of Contagious Outbreaks in Mobile Social Networks

Abstract

In this paper, we investigate the problem of placing sensors in a mobile social network to get quickly informed about contagious outbreaks, i.e., placing k sensors in a network in order to minimize the time until a contaminant - starting from a random node in the network - is detected. We aim to optimize the Sensor Placement from two complementary directions. One is to improve the original greedy algorithm and its extensions [13] to reduce sensor selection time, and the other is to propose a new Quickest Path heuristic that can shorten the detection time. We test and compare our algorithms with previous algorithms on four real data sets. Experimental results show that 1) the new greedy algorithm is more efficient than existing greedy algorithms in terms of selection time, 2) the quickest path heuristic obtains less detection time than centrality-based heuristics, and is as effective as the greedy algorithms, and 3) the new heuristic has the potential to scale well to large networks, having low detection time and selection time.