While wireless ad hoc networks have a wide range of applications in environment monitoring, military operations, and disaster recovery, etc, the full potential of such networks is inherently hindered by their diminishing capacity as the network size scales up. Content caching has been previously proposed to improve the availability of contents in a network and thus helps to alleviate the load on content custodians, reduce access latency, and improve the network capacity. This paper studies the scaling laws of the capacity of cache-enabled content distribution wireless ad hoc networks. We consider two basic content access schemes, namely, the Nearest Caching Node scheme where a request is satisfied by the nearest node to the requestor that has the content in its cache, and the Transparent Enroute Caching scheme where a content request is routed towards the content custodian and is satisfied by an intermediate node (or custodian) along the path that has the content in its cache. We first establish the capacity of content distribution wireless ad hoc networks without content caching as a baseline for investigating the benefit of caching. We then obtain the scaling laws of the capacity for the above two content access schemes with content caching. Based on the results we further explore their design and performance implications. Our results show that the capacity exhibits distinct scaling behaviors under different scaling regimes of the network parameters. Under certain conditions increasing the cache size of the nodes can effectively improve the capacity while under other conditions the improvement can be negligible. The characterizations of the capacity allows us to identify the bottleneck of the content access capacity for given network scenarios and choose effective approaches to improve the capacity.

Wireless researchers face the practical challenge of evaluating the performance of new routing protocols for very large-scale heterogeneous mobile systems. While simulation platforms, tools and techniques have matured, doubts remain about the broad validity of simulated results when applied to real world situations. Wireless network emulation provides a middle ground between simulations and real-world testing. However, emulation environments are complex to run and cost-prohibitive for larger-scale tests. In order to maximize fidelity while managing complexity and costs, we developed a common framework that unifies simulation-based analysis and emulation experiment tools, in order to produce calibrated and scalable performance evaluations.

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.

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.

There are several ad hoc routing algorithms that utilize position information in two dimensional (2D) coordinates to make routing decisions at each node. The well known 2D position based routing algorithm, greedy perimeter stateless routing (GPSR) does not function correctly in three dimensions (3D). We designed MLAR (multipath location aided routing), a multipath variant of LAR (location aided routing). MLAR is position based, works efficiently in both 2D and 3D and uses alternate path routing. We have implemented and validated MLAR through simulations using ns-2 and studied its efficiency, scalability and other properties. We evaluated and compared the relative performance and benefits of our MLAR approach versus LAR, AODV and AOMDV in both 2D and 3D for a range of traffic and mobility scenarios. We present a direct comparison of a multipath position based algorithm with a multipath non-position based algorithm, as well as with both their single path versions. We also extended ns-2 to support 3D mobility models and routing protocols

Mobile ad-hoc networks are infrastructure-free networks of mobile nodes that communicate with each other wirelessly. 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. We consider routing algorithms that use 3D positional information, particularly a hybrid extension to LAR that works in 3D. We propose a new hierarchical, zone-based 3D routing algorithm, based on GRID by Liao, Tseng and Sheu (2001). 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