Routing in multi-radio, multi-hop wireless mesh networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Jigsaw: solving the puzzle of enterprise 802.11 analysis
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
IQU: practical queue-based user association management for WLANs
Proceedings of the 12th annual international conference on Mobile computing and networking
Automating cross-layer diagnosis of enterprise wireless networks
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Understanding handoffs in large ieee 802.11 wireless networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
SCUBA: focus and context for real-time mesh network health diagnosis
PAM'08 Proceedings of the 9th international conference on Passive and active network measurement
Internet usage and performance analysis of a rural wireless network in Macha, Zambia
Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions
Scaling end-to-end measurements in heterogeneous wireless mesh networks
Proceedings of the 8h ACM symposium on QoS and security for wireless and mobile networks
Routing Cost and Latency in the VillageTelco Wireless Mesh Network
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Monitoring and troubleshooting a large wireless mesh network presents several challenges. Diagnosis of problems related to wireless access in these networks requires a comprehensive set of metrics and network monitoring data. Collection and offloading of a large amount of data is infeasible in a bandwidth constrained mesh network. Additionally, the processing required to analyze data from the entire network restricts the scalability of the system and impacts the ability to perform real-time fault diagnosis. To this end, we propose MeshMon, a network monitoring framework that includes a multi-tiered method of data collection. MeshMon dynamically controls the granularity of data collection based on observed events in the network, thereby achieving significant bandwidth savings and enabling real-time automated management. Our evaluation of MeshMon on a real testbed shows that we can diagnose a majority (87%) of network faults with a 66% savings in bandwidth required for network monitoring.