Introduction to algorithms
Computing the block triangular form of a sparse matrix
ACM Transactions on Mathematical Software (TOMS)
International migration and air travel: global smoothing and estimation
Applied Mathematics and Computation
Topology control for wireless sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Event-driven, role-based mobility in disaster recovery networks
Proceedings of the second ACM workshop on Challenged networks
Modelling mobility in disaster area scenarios
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Ballooned Wireless Mesh Network for Emergency Information System
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
Teletraffic modeling for personal communications services
IEEE Communications Magazine
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Public safety organizations increasingly rely on wireless technology to provide effective communications during emergency and disaster response operations. This paper presents a comprehensive study on dynamic placement of relay nodes (RNs) in a disaster area wireless network. It is based on our prior work of mobility model that characterizes the spatial movement of the first responders as mobile nodes (MNs) during their operations. We first investigate the COverage-oriented Relay Placement (CORP) problem that is to maximize the total number of MNs connected with the relays. Considering the network throughput, we then study the CApacity-oriented Relay Placement (CARP) problem that is to maximize the aggregated data rate of all MNs. For both coverage and capacity studies, we provide each the optimal and the greedy algorithms with computational complexity analysis. Furthermore, simulation results are presented to compare the performance between the greedy and the optimal solutions for the CORP and CARP problems, respectively. It is shown that the greedy algorithms can achieve near optimal performance but at significantly lower computational complexity.