MeshMon project (2007-2011)

This project is no longer active; this page is no longer updated.

Related keywords: [wifi]

Summary

Wireless mesh networks provide Wi-Fi service to mobile clients, much like an infrastructure wireless network, but the backhaul connection between access points is itself an ad hoc wireless network. One large challenge in mesh networks is management. We developed the Mesh-Mon system, which can inform a sysadmin about the health of the mesh network and help diagnose any problems with the network. The system and results are best described by Nanda's dissertation [nanda:thesis], though aspects are covered by the other papers listed below.

Mesh-Mon was a scalable, distributed and decentralized management system in which mesh nodes cooperate in a proactive manner to help detect, diagnose and resolve network problems automatically. Mesh-Mon was independent of the routing protocol used by the mesh routing layer and can function even if the routing protocol fails. We demonstrated this feature by running Mesh-Mon on two versions of our local mesh network, one running on AODV (a reactive mesh routing protocol) and the second running on OLSR (a proactive mesh routing protocol) in separate experiments.

We developed methods to identify critical nodes in the network, introducing two new metrics based on social-network analysis: the Localized Bridging Centrality (LBC) metric and the Localized Load-aware Bridging Centrality (LLBC) metric, that can identify critical nodes efficiently and in a fully distributed manner.

Mesh-Mon solves several management challenges in a scalable manner, and is a useful and effective tool for monitoring and managing real-world mesh networks.

People

Soumendra Nanda and David Kotz.

Funding and acknowledgements

This research program was a part of the Institute for Security Technology Studies (ISTS), supported by a gift from Intel Corporation, the US Department of Homeland Security (Science and Technology Directorate) award 2000-DT-CX-K001, and the US Department of Justice (Bureau of Justice Assistance) award 2005-DD-BX-1091.

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Papers (tagged 'meshmon')

[Also available in BibTeX]

Papers are listed in reverse-chronological order; click an entry to pop up the abstract. For full information and pdf, please click Details link. Follow updates with RSS.

2011:
Soumendra Nanda and David Kotz. Social Network Analysis Plugin (SNAP) for Mesh Networks. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC). March 2011. [Details]

In a network, bridging nodes are those nodes that from a topological perspective, are strategically located between highly connected regions of nodes. Thus, they have high values of the Bridging Centrality (BC) metric. We recently introduced the Localized Bridging Centrality (LBC) metric, which can identify such nodes via distributed computation, yet has an accuracy equal to that of the centralized BC metric. The LBC and BC metrics are based on the Social Network Analysis (SNA) metric “betweenness centrality”. We now introduce a new SNA metric that is more suitable for use in wireless mesh networks: the Localized Load-aware Bridging Centrality (LLBC) metric. The LLBC metric improves upon LBC by detecting critical bridging nodes while taking into account the actual traffic flows present in a mesh network. We only use local information from surrounding nodes to compute the LLBC metric, thus our LLBC metric is designed for scalable distributed computation and distributed network analysis. We developed the SNA Plugin (SNAP) for the Optimized Link State Routing (OLSR) protocol to study the potential use of LBC and LLBC in improving multicast communications. We present some promising initial results for SNAP from real and emulated mesh networks. SNAP is open source and free for academic use.

2009:
Soumendra Nanda, Zhenhui Jiang, and David Kotz. A Combined Routing Method for Ad Hoc Wireless Networks. Technical Report, February 2009. [Details]

Several simulation and real world studies show that certain ad hoc routing protocols perform better than others under specific mobility and traffic patterns. In order to exploit this phenomena, we propose a novel approach to adapt a network to changing conditions; we introduce “a combined routing method” that allows the network to seamlessly swap from one routing protocol to another protocol dynamically, while routing continues uninterrupted. By creating a thin new virtual layer, we enable each node in the ad hoc wireless network notify each other about the protocol swap and we do not make any changes to existing routing protocols. To ensure that routing works efficiently after the protocol swap, we reuse information from the previous protocol’s routing table while initializing the data structures for the new routing protocol. We study the feasibility of our technique and the overheads incurred while swapping between AODV, ODMRP and APRL under different network topologies and traffic patterns through detailed simulations. Our results show that the swap latency is related to the nature of the destination protocol and the topology of the network. We also find that the control packet ratio of a routing protocol during and after a swap is close to that of the protocol running before a swap, thus indicating that our approach does not add excessive overhead.

2008:
Soumendra Nanda and David Kotz. Localized Bridging Centrality for Distributed Network Analysis. Proceedings of the International Conference on Computer Communications and Networks (ICCCN). August 2008. [Details]

Centrality is a concept often used in social network analysis to study different properties of networks that are modeled as graphs. We present a new centrality metric called Localized Bridging Centrality (LBC). LBC is based on the Bridging Centrality (BC) metric that Hwang et al. recently introduced. Bridging nodes are nodes that are strategically located in between highly connected regions. LBC is capable of identifying bridging nodes with an accuracy comparable to that of the BC metric for most networks. As the name suggests, we use only local information from surrounding nodes to compute the LBC metric, whereas, global knowledge is required to calculate the BC metric. The main difference between LBC and BC is that LBC uses the egocentric definition of betweenness centrality to identify bridging nodes, while BC uses the sociocentric definition of betweenness centrality. Thus, our LBC metric is suitable for distributed or parallel computation and has the benefit of being an order of magnitude faster to calculate in computational complexity. We compare the results produced by BC and LBC in three examples. We applied our LBC metric for network analysis of a real wireless mesh network. Our results indicate that the LBC metric is as powerful as the BC metric at identifying bridging nodes. The LBC metric is thus an important tool that can help network administrators identify critical nodes that are important for the robustness of the network in a distributed manner.

Soumendra Nanda and David Kotz. Mesh-Mon: A Multi-Radio Mesh Monitoring and Management System. Computer Communications. May 2008. [Details]

Mesh networks are a potential solution for providing communication infrastructure in an emergency. They can be rapidly deployed by first responders in the wake of a major disaster to augment an existing wireless or wired network. We imagine a mesh node with multiple radios embedded in each emergency vehicle arriving at the site to form the backbone of a mobile wireless mesh. The ability of such a mesh network to monitor itself, diagnose faults and anticipate problems are essential features for its sustainable operation. Typical SNMP-based centralized solutions introduce a single point of failure and are unsuitable for managing such a network. Mesh-Mon is a decentralized monitoring and management system designed for such a mobile, rapidly-deployed, unplanned mesh network and works independently of the underlying mesh routing protocol. Mesh-Mon nodes are designed to actively cooperate and use localized algorithms to predict, detect, diagnose and resolve network problems in a scalable manner. Mesh-Mon is independent of the underlying routing protocol and can operate even if the mesh routing protocol completely fails. A novel aspect of our approach is that we employ mobile users of the mesh, running software called Mesh-Mon-Ami to ferry management packets between physically-disconnected partitions in a delay-tolerant network manner. The main contributions of this paper are the design, implementation and evaluation of a comprehensive monitoring and management architecture that helps a network administrator proactively identify, diagnose and resolve a range of issues that can occur in a dynamic mesh network. In experiments on Dart-Mesh, our 16-node indoor mesh testbed, we found Mesh-Mon to be effective in quickly diagnosing and resolving a variety of problems with high accuracy, without adding significant management overhead.

Soumendra Nanda. Mesh-Mon: a Monitoring and Management System for Wireless Mesh Networks. PhD thesis, May 2008. Available as Dartmouth Computer Science Technical Report TR2008-619. [Details]

A mesh network is a network of wireless routers that employ multi-hop routing and can be used to provide network access for mobile clients. Mobile mesh networks can be deployed rapidly to provide an alternate communication infrastructure for emergency response operations in areas with limited or damaged infrastructure.

In this dissertation, we present Dart-Mesh: a Linux-based layer-3 dual-radio two-tiered mesh network that provides complete 802.11b coverage in the Sudikoff Lab for Computer Science at Dartmouth College. We faced several challenges in building, testing, monitoring and managing this network. These challenges motivated us to design and implement Mesh-Mon, a network monitoring system to aid system administrators in the management of a mobile mesh network. Mesh-Mon is a scalable, distributed and decentralized management system in which mesh nodes cooperate in a proactive manner to help detect, diagnose and resolve network problems automatically. Mesh-Mon is independent of the routing protocol used by the mesh routing layer and can function even if the routing protocol fails. We demonstrate this feature by running Mesh-Mon on two versions of Dart-Mesh, one running on AODV (a reactive mesh routing protocol) and the second running on OLSR (a proactive mesh routing protocol) in separate experiments.

Mobility can cause links to break, leading to disconnected partitions. We identify critical nodes in the network, whose failure may cause a partition. We introduce two new metrics based on social-network analysis: the Localized Bridging Centrality (LBC) metric and the Localized Load-aware Bridging Centrality (LLBC) metric, that can identify critical nodes efficiently and in a fully distributed manner.

We run a monitoring component on client nodes, called Mesh-Mon-Ami, which also assists Mesh-Mon nodes in the dissemination of management information between physically disconnected partitions, by acting as carriers for management data.

We conclude, from our experimental evaluation on our 16-node Dart-Mesh testbed, that our system solves several management challenges in a scalable manner, and is a useful and effective tool for monitoring and managing real-world mesh networks.

Soumendra Nanda and David Kotz. Localized Bridging Centrality for Distributed Network Analysis. Technical Report, January 2008. [Details]

Centrality is a concept often used in social network analysis to study different properties of networks that are modeled as graphs. We present a new centrality metric called Localized Bridging Centrality (LBC). LBC is based on the Bridging Centrality (BC) metric that Hwang et al. recently introduced. Bridging nodes are nodes that are located in between highly connected regions. LBC is capable of identifying bridging nodes with an accuracy comparable to that of the BC metric for most networks. As the name suggests, we use only local information from surrounding nodes to compute the LBC metric, while, global knowledge is required to calculate the BC metric. The main difference between LBC and BC is that LBC uses the egocentric definition of betweenness centrality to identify bridging nodes, while BC uses the sociocentric definition of betweenness centrality. Thus, our LBC metric is suitable for distributed computation and has the benefit of being an order of magnitude faster to calculate in computational complexity. We compare the results produced by BC and LBC in three examples. We applied our LBC metric for network analysis of a real wireless mesh network. Our results indicate that the LBC metric is as powerful as the BC metric at identifying bridging nodes that have a higher flow of information through them (assuming a uniform distribution of network flows) and are important for the robustness of the network.

2007:
Soumendra Nanda, Zhenhui Jiang, and David Kotz. A Combined Routing Method for Ad hoc Wireless Networks. Technical Report, June 2007. [Details]

To make ad hoc wireless networks adaptive to different mobility and traffic patterns, this paper proposes an approach to swap from one protocol to another protocol dynamically, while routing continues. By the insertion of a thin new layer, we were able to make each node in the ad hoc wireless network notify each other about the protocol swap. To ensure that routing works efficiently after the protocol swap, we initialized the destination routing protocol’s data structures and reused the previous routing information to build the new routing table. We also tested our approach under different network topologies and traffic patterns in static networks to learn whether the swap was fast and whether the swap incurred too much overhead. We found that the swap latency was related to the nature of the destination protocol and the topology of the network. We also found that the control packet ratio after swap was close to that of the protocol running without swap, which indicates that our method does not incur too much overhead for the swap.

[Kotz research]