The Online Broadcast Range-Assignment Problem

Let $P=\{p_0,\ldots,p_{n-1}\}$ be a set of points in $\mathbb{R}^d$, modeling devices in a wireless network. A range assignment assigns a range $r(p_i)$ to each point $p_i\in P$, thus inducing a directed communication graph $G_r$ in which there is a directed edge $(p_i,p_j)$ iff $\textrm{dist}(p_i, p_j) \leq r(p_i)$, where $\textrm{dist}(p_i,p_j)$ denotes the distance between $p_i$ and $p_j$. The range-assignment problem is to assign the transmission ranges such that $G_r$ has a certain desirable property, while minimizing the cost of the assignment; here the cost is given by $\sum_{p_i\in P} r(p_i)^{\alpha}$, for some constant $\alpha>1$ called the distance-power gradient. We introduce the online version of the range-assignment problem, where the points $p_j$ arrive one by one, and the range assignment has to be updated at each arrival. Following the standard in online algorithms, resources given out cannot be taken away -- in our case this means that the transmission ranges will never decrease. The property we want to maintain is that $G_r$ has a broadcast tree rooted at the first point $p_0$. Our results include the following. - For $d=1$, a 1-competitive algorithm does not exist. In particular, for $\alpha=2$ any online algorithm has competitive ratio at least 1.57. - For $d=1$ and $d=2$, we analyze two natural strategies: Upon the arrival of a new point $p_j$, Nearest-Neighbor increases the range of the nearest point to cover $p_j$ and Cheapest Increase increases the range of the point for which the resulting cost increase to be able to reach $p_j$ is minimal. - We generalize the problem to arbitrary metric spaces, where we present an $O(\log n)$-competitive algorithm.Comment: Preliminary version in ISAAC 202

ENERGY-EFFICIENT PROTOCOL DESIGN AND ANALYSIS FOR WIRELESS SENSOR NETWORKS

Wireless sensor networks are an emerging technology which has the promise of revolutionizing the way of collecting, processing and disseminating information. Due to the small sizes of sensor nodes, resources like battery capacity, memory and processing power are very limited. Wireless sensor networks are usually unattended oncedeployed and it is infeasible to replace batteries. Designing energy-efficient protocols to prolong the network life without compromising too much on the network performance is one of the major challenges being faced by researchers.Data generation in wireless sensor networks could be bursty as it is dictated by the presence or absence of events of interest that generate these data. Therefore sensor nodes stay idle for most of the time. However, idle listening consumes as much energy as receiving. To save the unnecessary energy consumption due to idlelistening, sensor nodes are usually put into sleep.MAC protocols coordinate data communications among neighboring nodes. We designed an energy-efficient MAC protocol called PMAC in which sleep-awake schedules are determined through pattern exchange. PMAC also adapts to different traffic conditions.To handle bursty traffic and meanwhile preserve energy, dual radio interfaces with different ranges, capacity and power consumption can be employed on each individual sensor node. We designed a distributed routing-layer switch agent which intelligently directs traffic between the dual radios. The low-power radio will be used for light traffic load to preserve energy. The high-power radio is turned on only when the traffic load becomes heavy or the end-to-end delay exceeds a certain threshold. Each radio has its own routing agent so that a better path can be found when the high-power radio is in use.Data gathering is a typical operation in wireless sensor networks where data flow through a data gathering tree towards a sink node. DMAC is a popular energyefficient MAC protocol specifically designed for data gathering in wireless sensor networks. It employs staggered sleep-awake schedules to enable continuous data forwarding along a data gathering tree, resulting in reduced end-to-end delays and energy consumption. we have analyzed end-to-end delay and energy consumption with respect to the source node for both constant bit rate traffic and stochastic traffic following a Poisson process. The stochastic traffic scenario is modeled as a discrete time Markov chain and expressions for state transition probabilities, the average delay and average energy consumption are developed and are evaluated numerically. Simulations are carried out with various parameters and the results are in line with the analytical results.Lots of work had been done on constructing energy-efficient data gathering trees at the routing layer. We proposed a sleep scheme at the routing layer called DGSS which could be incorporated into different data gathering tree formation algorithms. Unlike DMAC, in which nodes are scanned level by level, DGSS starts scanningfrom the leaf nodes and shrinks inward towards the sink node. Simulation shows that DGSS can achieve better energy efficiency than DMAC at relatively higher data rates

Interference Management in Dense 802.11 Networks

Wireless networks are growing at a phenomenal rate. This growth is causing an overcrowding of the unlicensed RF spectrum, leading to increased interference between co-located devices. Existing decentralized medium access control (MAC) protocols (e.g. IEEE 802.11a/b/g standards) are poorly designed to handle interference in such dense wireless environments. This is resulting in networks with poor and unpredictable performance, especially for delay-sensitive applications such as voice and video. This dissertation presents a practical conflict-graph (CG) based approach to designing self-organizing enterprise wireless networks (or WLANs) where interference is centrally managed by the network infrastructure. The key idea is to use potential interference information (available in the CG) as an input to algorithms that optimize the parameters of the WLAN.We demonstrate this idea in three ways. First, we design a self-organizing enterprise WLAN and show how the system enhances performance over non-CG based schemes, in a high fidelity network simulator. Second, we build a practical system for conflict graph measurement that can precisely measure interference (for a given network configuration) in dense wireless environments. Finally, we demonstrate the practical benefits of the conflict graph system by using it in an optimization framework that manages associations and traffic for mobile VoIP clients in the enterprise. There are a number of contributions of this dissertation. First, we show the practical application of conflict graphs for infrastructure-based interference management in dense wireless networks. A prototype design exhibits throughput gains of up to 50% over traditional approaches. Second, we develop novel schemes for designing a conflict graph measurement system for enterprise WLANs that can detect interference at microsecond-level timescales and with little network overhead. This allows us to compute the conflict graph up to 400 times faster as compared to the current best practice proposed in the literature. The system does not require any modifications to clients or any specialized hardware for its operation. Although the system is designed for enterprise WLANs, the proposed techniques and corresponding results are applicable to other wireless systems as well (e.g. wireless mesh networks). Third, our work opens up the space for designing novel fine-grained interference-aware protocols/algorithms that exploit the ability to compute the conflict graph at small timescales. We demonstrate an instance of such a system with the design and implementation of an architecture that dynamically manages client associations and traffic in an enterprise WLAN. We show how mobile clients sustain uninterrupted and consistent VoIP call quality in the presence of background interference for the duration of their VoIP sessions