Impact of interference on multi-hop wireless network performance
Wireless Networks - Special issue: Selected papers from ACM MobiCom 2003
Modeling per-flow throughput and capturing starvation in CSMA multi-hop wireless networks
IEEE/ACM Transactions on Networking (TON)
Online estimation of RF interference
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
DIRC: increasing indoor wireless capacity using directional antennas
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Interference and outage in clustered wireless ad hoc networks
IEEE Transactions on Information Theory
Multi-dimensional Conflict Graph Based Computing for Optimal Capacity in MR-MC Wireless Networks
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
Inferring and mitigating a link's hindering transmissions in managed 802.11 wireless networks
Proceedings of the sixteenth annual international conference on Mobile computing and networking
PIE in the sky: online passive interference estimation for enterprise WLANs
Proceedings of the 8th USENIX conference on Networked systems design and implementation
FLUID: improving throughputs in enterprise wireless lans through flexible channelization
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Network planning in wireless ad hoc networks: a cross-Layer approach
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
Graphs are routinely used to approximate the structure of wireless networks especially when studying connectivity or various aspects of network performance. Although not as detailed as geometrical and spatial models dealing with individual node locations, graphs can nevertheless be used to capture many of critical interactions between the nodes. In this paper we explore the use of graphs to approximate network dynamics in addition to static network structure. We show that a simple linearization of network performance functionals results in natural correlation metrics for influence of parameter changes on performance. Further, based on extensive simulations we demonstrate that these correlation metrics have a high degree of robustness against perturbations in node locations. We also discuss the relationships between the arising graph approximations of network dynamics and the commonly applied connectivity and conflict graphs. Our results indicate that graphs formed by approximating dynamics combined with connectivity structures can shed considerable insight on "gray zone" behavior commonly encountered in wireless networks. They are promising candidates for autonomous context identification.