Wireless mesh network

About: Wireless mesh network is a research topic. Over the lifetime, 13600 publications have been published within this topic receiving 221035 citations. The topic is also known as: WMN.

Ad Hoc UAV Ground Network (AUGNet)

Abstract: *† ‡ § , This paper describes an implementation of a wireless mobile ad hoc network with radio nodes mounted at fixed sites, on ground vehicles, and in small (10kg) UAVs. The ad hoc networking allows any two nodes to communicate either directly or through an arbitrary number of other nodes which act as relays. We envision two sce narios for this type of network. In the first, the UAV acts as a prominent radio node that connects disconnected ground radios. In the second, the networking enables groups of UAVs to communicate with each other to extend small UAVs' operational scope and range. The network consists of mesh network radios assembled from low -cost commercial off the shelf components. The radio is an IEEE 802.11b (WiFi) wireless interface and is controlled by an embedded computer. The network protocol is an implementation of the Dynamic Source Routing ad hoc networking protocol. The radio is mounted either in an environmental enclosure for outdoor fixed and vehicle mounting or directly in our custom built UAVs. A monitoring architecture has been embedded into the radios for de tailed performance characterization and analysis. This paper describes these components and performance results me asured at an outdoor test range .

Capacity of a wireless ad hoc network with infrastructure

Abstract: In this paper we study the capacity of wireless ad hoc networks with infrastructure support of an overlay of wired base stations. Such a network architecture is often referred to as hybrid wireless network or multihop cellular network. Previous studies on this topic are all focused on the two-dimensional disk model proposed by Gupta and Kumarin their original work on the capacity of wireless ad hoc networks. We further consider a one-dimensional network model and a two-dimensional strip model to investigate the impact of network dimensionality and geometry on the capacity of such networks. Our results show that different network dimensions lead to significantly different capacity scaling laws. Specifically, for a one-dimensional network of n nodes and b base stations, even with a small number of base stations, the gain in capacity is substantial, increasing linearly with the number of base stations as long as b log b ≤ n. However, a two-dimensional square (or disk) network requires a large number of base stations b = Ω(√n) before we see such a capacity increase. For a 2-dimensional strip network, if the width of the strip is at least on the order of the logarithmic of its length, the capacity follows the same scaling law as in the 2-dimensional square case. Otherwise the capacity exhibits the same scaling behavior as in the 1-dimensional network. We find that the different capacity scaling behaviors are attributed to the percolation properties of the respective network models.

Method and arrangement for providing a wireless mesh network

Abstract: A method and an arrangement for providing a wire-free mesh network are provided. An approval procedure is carried out in situations in which a subscriber who is registering on the mesh network transmits an MAC address which already exists in the mesh network, such that two different subscribers within the mesh network never have identical MAC addresses.

Engineering wireless mesh networks: joint scheduling, routing, power control, and rate adaptation

Abstract: We present a number of significant engineering insights on what makes a good configuration for medium- to large-size wireless mesh networks (WMNs) when the objective function is to maximize the minimum throughput among all flows. For this, we first develop efficient and exact computational tools using column generation with greedy pricing that allow us to compute exact solutions for networks significantly larger than what has been possible so far. We also develop very fast approximations that compute nearly optimal solutions for even larger cases. Finally, we adapt our tools to the case of proportional fairness and show that the engineering insights are very similar.