Measurement driven deployment of a two-tier urban mesh access network

Abstract

Multihop wireless mesh networks can provide Internet access over a wide area with minimal infrastructure expenditure. In this work, we present a measurement driven deployment strategy and a data-driven model to study the impact of design and topology decisions on network-wide performance and cost. We perform extensive measurements in a twotier urban scenario to characterize the propagation environment and correlate received signal strength with application layer throughput. We find that well-known estimates for pathloss produce either heavily overprovisioned networks resulting in an order of magnitude increase in cost for high pathloss estimates or completely disconnected networks for low pathloss estimates. Modeling throughput with wireless interface manufacturer specifications similarly results in severely underprovisioned networks. Further, we measure competing, multihop flow traffic matrices to empirically define achievable throughputs of fully backlogged, rate limited, and web-emulated traffic. We find that while fully backlogged flows produce starving nodes, rate-controlling flows to a fixed value yields fairness and high aggregate throughput. Likewise, transmission gaps occurring in statistically multiplexed web traffic, even under high offered load, remove starvation and yield high performance. In comparison, we find that well-known noncompeting flow models for mesh networks over-estimate network-wide throughput by a factor of 2. Finally, our placement study shows that a regular grid topology achieves up to 50 percent greater throughput than random node placement.

On max–min fair flow optimization in wireless mesh networks

“…Several works have addressed how to take advantage of the multi-channel and multi-rate capabilities of wireless network interfaces used in WMNs, and exploit the channel and path diversity [10,17,20]. All these studies point out that the transmission rate used by the wireless interfaces is one of the most important factors that influence the WMN performance.…”

“…The reason is that in an optimal dual solution all variables β m vi can be made equal to 0. Solving (20) is much more complicated than solving (17), because it consists of binary variables and in consequence requires the branch-and-price approach [6,39]. When at a certain node of the branch-and-bound tree a variable x m v becomes fixed (to 0 or 1) then all the compatible sets used in the corresponding branch-and-bound subtree must obey this.…”

“…Thus, the integrity of all other variables, i.e., X vt , x m vt , Z m v , and z evt , can be relaxed. The branch-and-price approach for solving (20) was implemented in C++. The branch-andprice tree was traversed using the depth-first search (DFS) algorithm.…”

Sustainable Forest Management Techniques

“…However, it is still a great challenge to design a wireless sensor network system for rare event detection; where network lifetime and robustness is a major concern. Some of the recent developments include campus-wide and community-wide wireless mesh networks (Bicket et al, 2005; UCSD Active Campus; Camp et al, 2006), and real-world sensor network deployments in environments as diverse as forests, active volcanoes, and bridges. WSN system design for forest monitoring involves:  Sensors  Design of low power wireless communication module  Simulation and implementation of energy efficient protocol  Deployment strategies  Middleware…”

Adding Capacity Points to a Wireless Mesh Network Using Local Search

“…We compare our calculation with measured throughput data from the operational TFA mesh network, in order to show that a better placement as per our capacity calculation is also a better placement as per measurements. TFA is a multitier mesh network providing Internet access in a densely populated, single-family residential, urban neighborhood with 18 deployed mesh nodes [3]. In the topology, two mesh nodes are connected if their link is on average usable at greater than 1 Mbps.…”