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.

Abstract — As wireless access technologies improve in data rates, the problem focus is shifting towards providing adequate backhaul from the wireless access points to the Internet. Existing wired backhaul technologies such as copper wires running at DSL, T1, or T3 speeds can be expensive to install or lease, and are becoming a performance bottleneck as wireless access speeds increase. Longhaul, non-line-of-sight wireless technologies such as WiMAX (802.16d) hold the promise of enabling a high speed wireless backhaul as a cost-effective alternative. However, the biggest challenge in building a wireless backhaul is achieving guaranteed performance (throughput and delay) that is typically provided by a wired backhaul. This paper explores the problem of efficiently designing a multihop wireless backhaul to connect multiple wireless access points to a wired gateway. In particular, we provide a generalized link activation framework for scheduling packets over this wireless backhaul, such that any existing wireline scheduling policy can be implemented locally at each node of the wireless backhaul. We also present techniques for determining good interferencefree routes within our scheduling framework, given the link rates and cross-link interference information. When a multihop wireline scheduler with worst case delay bounds (such as WFQ or Coordinated EDF) is implemented over the wireless backhaul, we show that our scheduling and routing framework guarantees approximately twice the delay of the corresponding wireline topology. Finally, we present simulation results to demonstrate the low delays achieved using our framework. I.

Abstract. We introduce the concept of a trust network—a decentralized payment infrastructure in which payments are routed as IOUs between trusted entities. The trust network has directed links between pairs of agents, with capacities that are related to the credit an agent is willing to extend another; payments may be routed between any two agents that are connected by a path in the network. The network structure introduces group budget constraints on the payments from a subset of agents to another on the trust network: this generalizes the notion of individually budget constrained bidders. We consider a multi-unit auction of identical items among bidders with unit demand, when the auctioneer and bidders are all nodes on a trust network. We define a generalized notion of social welfare for such budgetconstrained bidders, and show that the winner determination problem under this notion of social welfare is NP-hard; however the flow structure in a trust network can be exploited to approximate the solution with a factor of 1 − 1/e. We then present a pricing scheme that leads to an incentive compatible, individually rational mechanism with feasible payments that respect the trust network’s payment constraints and that maximizes the modified social welfare to within a factor 1 − 1/e. 1

This thesis presents a measurement-parameterized performance study of deploy-ment factors in wireless mesh networks using four performance metrics: client cov-erage area, backhaul tier connectivity, protocol-dependent throughput, and per-user fair rates. For each metric, I identify and study deployment factors which strongly influence mesh performance via an extensive set of Monte Carlo simulations capturing realistic physical layer behavior. My findings include: (i) A random topology is un-suitable for a large-scale mesh deployment due to doubled node density requirements, yet a moderate level of perturbations from ideal grid placement has minor impact. (ii) Multiple backhaul radios per mesh node is a cost-effective deployment strategy as it leads to mesh deployments costing 50 % less than with a single-radio architecture. This work adds to the understanding of mesh deployment factors and their general impact on performance, providing further insight into practical mesh deployments. Acknowledgments First and foremost, I would like to thank my advisor, Dr. Edward Knightly, for the guidance, support, and opportunities he has provided me. He has been a

Abstract — Wireless mesh network deployments are popular as a cost-effective means to provide broadband connectivity to large user populations. As the network usage grows, network planners need to evolve an existing mesh network to provide additional capacity. In this paper, we study the problem of adding new capacity points (e.g., gateway nodes) to an existing mesh network. We first present a new technique for calculating gateway-limited fair capacity as a function of the contention at each gateway. Then, we present two online gateway placement algorithms that use local search operations to maximize the capacity gain on an existing network. A key challenge is that each gateway’s capacity depends on the locations of other gateways and cannot be known in advance of determining a gateway placement. We address this challenge with two placement algorithms with different approaches to estimating the unknown gateway capacities. Our first placement algorithm, MinHopCount, is adapted from a solution to the facility location problem. MinHopCount minimizes path lengths and iteratively estimates the wireless capacity of each gateway location. Our second algorithm, MinContention, is adapted from a solution to the uncapacitated k-median problem and minimizes average contention on mesh nodes, i.e. the number of links in contention range of a mesh node and the number of routes using each link. We show that our gateway placement algorithms outperform a greedy heuristic by up to 64 % on realistic topologies. For an example topology, we study the set of all possible gateway placements and find that there is large capacity gain between near-optimal and optimal placements, but the near-optimal placements found by local search are similar in configuration to the optimal. I.

The diversity of the deployment settings of sensor networks is naturally inherited from the diversity of geographical features of the embedded environment, and greatly influences network design. Many sensor network protocols in the literature implicitly assume that sensor nodes are deployed inside a simple geometric region, without considering possible obstacles and holes in the deployment environment. When the real deployment setting deviates from that, we often observe degraded performance. Thus, it is highly desirable to have a generic approach to handle sensor fields with complex shapes. In this paper, we propose a segmentation algorithm that partitions an irregular sensor field into nicely shaped pieces such that algorithms and protocols that assume a nice sensor field can be applied inside each piece. Across the segments, problem dependent structures specify how the segments and data collected in these segments are integrated. Our segmentation algorithm does not require any extra knowledge (e.g., sensor locations) and only uses network connectivity information. This unified spatial-partitioning approach makes the protocol design become flexible and independent of deployment specifics. Existing protocols are still reusable with segmentation, and the development of new topology-adaptive protocols becomes much easier. We verified the correctness of the algorithm on various topologies and evaluated the performance improvements by integrating shape segmentation with several fundamental problems in network design.

Abstract — The performance of conventional gateway access optimization techniques deteriorate dramatically when traffic load is dynamic. In this article, we propose a novel gateway access algorithm called ‘Cog Gap’, which is a cognitive method and is designed for Wireless Mesh Networks to maximize the network utilization. The proposed Cog Gap utilizes a destination-hub access model, where multiple gateway nodes are connected by wired links, and packets from a source node can be sent from any connected gateway nodes to increase transmission opportunities. In Cog Gap, we use the Hidden Markov Model (HMM) and the expectation maximization method to handle uncertain traffic pattern and the loss of probing results. A traffic allocation algorithm is then proposed to optimize dynamic multi-gateway access. By modeling the route state determination and transition, we transform the opportunistic gateway access problem into a Markov decision process (MDP) problem. A heuristic and adaptive algorithm named hindsight optimal is used in solving MDP. Simulation results have proven that the proposed Cog Gap algorithm can make full use of the transmission opportunities and does not incur noticeable protocol overhead.

In this paper we consider a generalization of the machine activation problem introduced recently [“Energy efficient scheduling via partial shutdown ” by Khuller, Li and Saha (ACM-SIAM 2010 Symp. on Discrete Algorithms)] where the unrelated parallel machine scheduling problem is studied with machine activation cost. This is the standard unrelated parallel machine scheduling problem with a machine dependent activation cost that is incurred, if any job is assigned to the machine. The problem asks for a choice of machines to activate, and a schedule of all jobs on the active machines subject to the makespan constraint. The goal is to minimize the total activation cost. Our main generalization consists of a general activation cost model, where the activation cost for a machine is a non-decreasing function of its load. We develop a greedy algorithm that yields a fractional assignment of jobs, such that at least n − ɛ jobs are assigned fractionally and the total cost is at most 1 + ln(n/ɛ) times the optimum. Combining with standard rounding methods yields improved bounds for several machine activation problems. In addition, we study the machine activation problem with d linear constraints (these could model makespan constraints, as well as other types of constraints). Our method yields a schedule with machine activation cost of O ( 1 ɛ log n) times the optimum and a constraint violation by a factor of 2d + ɛ. This result matches our previous bound for the case d = 1. As a by-product, our method also yields a ln n + 1 approximation factor for the non-metric universal facility location problem for which the cost of opening a facility is an arbitrary non-decreasing function of the number of clients assigned to it. This gives an affirmative answer to the open question posed in earlier work on universal facility location.

Rapport de recherche n ° 5649 — Août 2005 — 22 pages Abstract: The increased popularity of IEEE 802.11 WLANs has led to dense deployments in urban areas. Such high density leads to sub-optimal performance unless the interfering networks learn how to optimally share the spectrum. This paper proposes a set of novel fully distributed algorithms that allow (i) multiple interfering 802.11 WLANs to select their operating frequency in a way that minimizes global interference, and (ii) clients to choose their Access Point so that the bandwidth of all interfering networks is shared optimally. The proposed algorithms rely on Gibbs ’ sampler and optimize global network performance based on local information. They do not require explicit coordination among the wireless devices. We establish the mathematical properties of the proposed algorithms and study their performance using analytical, eventdriven simulations. Our results strongly motivate the need for self-organization strategies in wireless access networks. We discuss implementation requirements and show that significant benefits can be gained even within incremental deployments and in the presence of non-cooperating wireless clients.