Mesh-Mon: a Monitoring and Management System for Wireless Mesh Networks

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

Spatial Multipath Location Aided Routing

Mobile ad-hoc networks (MANETs) are infrastructure-free networks of mobile nodes that communicate with each other wirelessly. There are several routing schemes that have been proposed and several of these have been already extensively simulated or implemented as well. The primary applications of such networks have been in disaster relief operations, military use, conferencing and environment sensing. There are several ad hoc routing algorithms at present that utilize position information (usually in two dimensional terms) to make routing decisions at each node. Our goal is to utilize three-dimensional (3D) position information to provide more reliable as well as efficient routing for certain applications. We thus describe extensions to various location aware routing algorithms to work in 3D. We propose a new hierarchical, zone-based 3D routing algorithm, based on GRID by Liao, Tseng and Sheu. Our new algorithm called Hyper-GRID is a hybrid algorithm that uses multipath routing (alternate path caching) in 3D. We propose replacing LAR with Multipath LAR (MLAR) in GRID. We have implemented MLAR and are validating MLAR through simulation using ns-2 and studying its efficiency, scalability and other properties. We use a random waypoint mobility model and compare our MLAR approach versus LAR, AODV and AOMDV in both 2D and 3D for a range of traffic and mobility scenarios. Our simulation results demonstrate the performance benefits of MLAR over LAR and AODV in most mobility situations. AOMDV delivers more packets than MLAR consistently, but does so at the cost of more frequent flooding of control packets and thus higher bandwidth usage than MLAR

Localized Bridging Centrality for Distributed Network Analysis

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

Social Network Analysis Plugin (SNAP) for Mesh Networks

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

A Combined Routing Method for Wireless Ad Hoc Networks

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\u27s 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

Congestion-aware caching and search in information-centric Networks

ABSTRACT The performance of in-network caching in informationcentric networks, and of cache networks more generally, is typically characterized by network-centric performance metrics such as hit rate and hop count, with approaches to locating and caching content evaluated and optimized for these metrics. We believe that user-centric performance metrics, in particular the delay from when a content request is made by the user to the time at which the requested content has been completely downloaded, are also important. For such metrics, performance is often determined by link capacity constraints and network congestion. We investigate network cache management and search policies that account for path-level (content-server to content-requestor) congestion and file popularity in order to directly minimize user-centric, content-download delay. Through simulation, we find that our policies yield significantly better download delay performance than existing policies, even though these existing policies provide better performance according to traditional metrics such as cache hit rate and hop count

Social Network Analysis Plugin (SNAP) for Mesh Networks

Abstract—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. I

Localized Bridging Centrality for Distributed Network Analysis

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 compu-tation 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

A Combined Routing Method for Wireless Ad Hoc Networks

Abstract — 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. I