Showing papers by "Samir R. Das published in 2008"

Minimum Interference Channel Assignment in Multiradio Wireless Mesh Networks

Abstract: In this paper, we consider multihop wireless mesh networks, where each router node is equipped with multiple radio interfaces, and multiple channels are available for communication. We address the problem of assigning channels to communication links in the network with the objective of minimizing the overall network interference. Since the number of radios on any node can be less than the number of available channels, the channel assignment must obey the constraint that the number of different channels assigned to the links incident on any node is at most the number of radio interfaces on that node. The above optimization problem is known to be NP-hard. We design centralized and distributed algorithms for the above channel assignment problem. To evaluate the quality of the solutions obtained by our algorithms, we develop a semidefinite program and a linear program formulation of our optimization problem to obtain lower bounds on overall network interference. Empirical evaluations on randomly generated network graphs show that our algorithms perform close to the above established lower bounds, with the difference diminishing rapidly with increase in number of radios. Also, ns-2 simulations, as well as experimental studies on testbed, demonstrate the performance potential of our channel assignment algorithms in 802.11-based multiradio mesh networks.

Efficient gathering of correlated data in sensor networks

Abstract: In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network.We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique—even in dynamic conditions.

A measurement study of interference modeling and scheduling in low-power wireless networks

Abstract: Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20-node TelosB motes testbeds -- one indoor and the other outdoor -- to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hop-based, range-based, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90-percentile error about 20-25% (and 80 percentile error 7-12%), depending on the scenario. The accuracy of the other models is worse and scenario-specific. The second best model trails the physical model by roughly 12-18 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model -- 'thresholded' (conservative, but typically considered in literature) and 'graded' (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve `expected throughput' over the thresholded version by scheduling imperfect links.

Benefit-Based Data Caching in Ad Hoc Networks

Abstract: Data caching can significantly improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. However, designing efficient distributed caching algorithms is nontrivial when network nodes have limited memory. In this article, we consider the cache placement problem of minimizing total data access cost in ad hoc networks with multiple data items and nodes with limited memory capacity. The above optimization problem is known to be NP-hard. Defining benefit as the reduction in total access cost, we present a polynomial-time centralized approximation algorithm that provably delivers a solution whose benefit is at least 1/4 (1/2 for uniform-size data items) of the optimal benefit. The approximation algorithm is amenable to localized distributed implementation, which is shown via simulations to perform close to the approximation algorithm. Our distributed algorithm naturally extends to networks with mobile nodes. We simulate our distributed algorithm using a network simulator (ns2) and demonstrate that it significantly outperforms another existing caching technique (by Yin and Cao [33]) in all important performance metrics. The performance differential is particularly large in more challenging scenarios such as higher access frequency and smaller memory.

Drive-By Localization of Roadside WiFi Networks

Abstract: We use a steerable beam directional antenna mounted on a moving vehicle to localize roadside WiFi access points (APs), located outdoors or inside buildings. Localizing APs is an important step towards understanding the topologies and network characteristics of large scale WiFi networks that are deployed in a chaotic fashion in urban areas. The idea is to estimate the angle of arrival of frames transmitted from the AP using signal strength information on different directional beams of the antenna - as the beam continuously rotates while the vehicle is moving. This information together with the GPS locations of the vehicle are used in a triangulation approach to localize the APs. We show how this method must be extended using a clustering approach to account for multi-path reflections in cluttered environments. Our technique is completely passive requiring minimum effort beyond driving the vehicle around in the neighborhood where the APs need to be localized, and is able to improve the localization accuracy by an order of magnitude compared with trilateration approaches using omnidirectional antennas, and by a factor of two relative to other known techniques using directional antennas.

Near-Optimal Dynamic Spectrum Allocation in Cellular Networks

Abstract: In this paper, we address the spectrum allocation problem in cellular networks under the coordinated dynamic spectrum access (CDSA) model. In this model, a centralized spectrum broker owns a part of the spectrum and issues dynamic spectrum leases to competing base stations in the region it controls. We consider a dynamic auction based approach where the base stations bid for channels depending on their demands. The broker allocates channels to them with an objective to maximize the overall revenue generated subject to wireless interference in the network. This problem is known to be NP-hard and has been addressed before in limited context. We address this problem in a very generic context where (i) interference in the network is modeled using pairwise and physical interference models and (ii) base stations can bid for heterogeneous channels of different width using generic bidding functions. We propose efficient approximation algorithms that give near optimal solutions with provable analytical bounds. Detailed simulation studies using randomly generated and real base station networks show that our algorithms scale very well for large network sizes.

Exploiting path diversity in the link layer in wireless ad hoc networks

Abstract: We develop an anycast mechanism at the link layer for wireless ad hoc networks. The goal is to exploit path diversity in the link layer by choosing the best next hop to forward packets when multiple next hop choices are available. Such choices can come from a multipath routing protocol, for example. This technique can reduce transmission retries and packet drop probabilities in the face of channel fading. We develop an anycast extension of the IEEE 802.11 MAC layer based on this idea. We implement the protocol in an experimental proof-of-concept testbed using the Berkeley motes platform and S-MAC protocol stack. We also implement it in the popular ns-2 simulator and experiment with the AOMDV multipath routing protocol and Ricean fading channels. We show that anycast performs significantly better than 802.11 in terms of packet delivery, particularly when the path length or effect of fading is large. Further we experiment with anycast in networks that use multiple channels and those that use directional antennas for transmission. In these networks, deafness and hidden terminal problems are the main source of packet loss. We implemented anycast as extension of 802.11 like protocols that were proposed for these special networks. We are able to show that anycast is capable of enhancing the performance of these protocols by simply making use of the path diversity whenever it is available.

RFID-based networks: exploiting diversity and redundancy

Abstract: In this article, we outline a research agenda for developing protocols and algorithms for densely populated RFID based systems covering a wide geographic area. This will need multiple readers collaborating to read RFID tag data. We consider cases where the tag data is used for identification, or for sensing environmental parameters. We address performance issues related to 'accuracy' and 'efficiency' in such systems by exploiting 'diversity' and 'redundancy'. We discuss how tag multiplicity can be used to improve accuracy. In a similar fashion, we explore how reader diversity, achieved by using multiple readers with potentially partially overlapping coverage areas, can be exploited to improve accuracy and efficiency. Finally, we show how multiple antennas in a reader can be used to improve accuracy and access rates by utilizing antenna diversity. RFID tag/sensor data can be highly redundant for the purpose of answering a higher level query. For example, often the higher level query needs to compute a statistic or a function on the sensory data obtained by the RFID sensors, and does not need all the individual sensor readings. We outline the need for efficient tag-to-reader communication, and reader-to-reader coordination to effectively compute such functions with low overhead.

A measurement study of inter-vehicular communication using steerable beam directional antenna

Abstract: We provide a measurement study of a single vehicle-to-vehicle (V2V) link using 802.11b as the link layer technology. Our goal is to investigate practical usage of steerable beam directional antennas to improve V2V communications. We conduct extensive experiments using commercially available phased-array antennas mounted on cars in two different environments -- suburban roads and highways, with various drive patterns. It is observed that directional beamforming improves the link SNR significantly, that translates to significant range improvements. However, to achieve this performance gain both antenna beams must be steered appropriately in the right direction. We observe that often the best beams indeed point directly to each other (called `LOS beams'), in spite of various sources of reflections that could be present in the environment. We develop and evaluate a simple beam steering approach that uses LOS beams for communication. We present experimental data, demonstrating the performance gains (in terms of SNR and PHY-layer data rates) achieved by this approach. While we have studied a single V2V link, this method can be extended to a multihop V2V network.

On estimating joint interference for concurrent packet transmissions in low power wireless networks

Abstract: In a wireless network it is important to understand the nature of the joint interference generated at a receiver by multiple concurrent transmitters. This understanding helps developing packet scheduling algorithms. Prior experimental work using older generation mote-class radios (CC1000) have showed systematic deviations between estimation and direct measurement of the joint interference power, thus questioning whether the standard assumption that received signal powers are additive is applicable in practice. We, however, show via extensive experimentation that on newer generation radios (CC2420), the additive assumption is valid, particularly at the low power end.

Measurement-based approaches for accurate simulation of 802.11-based wireless networks

Abstract: In this work, we address the issue of unrealistic simulations of wireless networks using a measurement-based approach. The idea is to use empirical modeling using measurement data as a mechanism to model physical layer behavior. We demonstrate the power of this approach for 802.11-based networks using ns2, a packet-level network simulator. Specifically, we develop two versions of the ns2 simulator that model the wireless physical layer with different levels of fidelity. In both versions, the deferral and reception model are built using measurements. For propagation modeling, one version uses direct measurements and the other uses an empirically derived model. In validation experiments with a 12-node mesh testbed, both these versions were found to be reasonably accurate (85 percentile errors within about 10% of the capacity) relative to regular simulations (85 percentile errors within 50% of capacity).

A framework for joint scheduling and diversity exploitation under physical interference in wireless mesh networks

Abstract: Recently, interest has arisen in use of realistic interference models for transmission scheduling in wireless multihop networks, particularly in mesh networks where throughput is a major concern. In this work, we use the SINR-based physical interference model and develop a uniform framework for transmission scheduling when diverse wireless resources can be exploited. The factors considered are multiple (possibly overlapped) channels, directional antennas, and transmit power control. We develop an efficient heuristic for computing a diversity exploiting schedule based on a new network saturation metric. We prove that, under uniform random node distributions, the schedule produced by our heuristic is within a poly-log factor from optimal with a probability that approaches one as network size increases. Through simulation, we demonstrate the ability of our algorithm to achieve up to a 10-fold throughput improvement with respect to networks without diversity. Our analysis also reveals a number of insights on the ability of diversity exploitation to reduce or eliminate interference.

Collaborative information processing and query evaluation in wireless sensor networks

Abstract: Data-centric is one of the most important features that make wireless sensor networks distinct from other types of communication networking systems. A sensor network usually generates massive amount of data, but users only query quite high-level summarized information. Thus, information processing and query evaluation become fundamental problems in sensor networks. The goal of this dissertation is to explore the potentials of sensor networks as collaborative data processing engines. It is expected that in the near future, sensor networks will reactively impact the physical world and interact with end users, who stay in the same physical domain and query the sensor network anytime anywhere. In that context, it requires that sensor nodes collaboratively process information in an ad hoc manner rather than resorting to a centralized base station for post-processing. We investigate essential grand challenges of collaborative processing and query evaluation in wireless sensor networks, and aim to improve the accessibility, interactivity and shareability of sensor data. The first key problem of information processing is how to link users’ selective queries with relevant information. It is challenging because both queries and related data can appear anytime anywhere in the network. Furthermore, a complex query may depend on multi-dimensional data collected by different types of sensors, which themselves can be distributed far apart. Thus, how to match those different types of data is the other aspect of this brokerage problem we need to handle. We proposed algorithms for in-network join of multiple data streams in a sensor network, based on the observation that a sensor network can be viewed as a distributed database system. One of our proposed approaches, viz., the Perpendicular Approach, is load-balanced, and results in substantially prolonging the network lifetime. The Perpendicular Approach is further extended to a general double ruling scheme for information brokerage. The second challenge of information processing comes from the diversity of queries. Some queries request explicit information, e.g., temperature at a particular location. Some may ask for more implicit information, e.g., is there a traffic-free path. Different queries request different processing techniques. Queries for implicit information is especially challenging to be answered, since they usually require global knowledge that is hard to be obtained through sensor’s local view. We investigated on a group tracking problem as a specific example of processing implicit queries. We proposed a light-weight contour tracking algorithm to process implicit contour information and its topological features. This algorithm performs a foundation for further information processing of spatial sensor data. Thirdly, the underlying deployment environment has fundamental effects on high level tasks. Designing protocols for a specific deployment is expensive and time-consuming. Thus, it is highly desirable to have a generic approach to handle sensor fields with complex shapes, and make the design of new protocols transparent to the deployment specifics. We proposed a segmentation algorithm that partitions an irregular sensor field into nicely shaped pieces such that existing algorithms and protocols can be reusable inside each piece. Across the segments, problem dependent structures specify how the segments and data collected in these segments are integrated. The ultimate goal of information processing is to return useful information to users. Thus, it is essential to provide friendly programming paradigm. We propose a deductive framework for programming and querying sensor networks. In this framework, sensor networks work as collaborative data processing engines and allow users to specify with ease the high-level functionality of an application, while hid from the low-level details. All of the above proposed collaborative processing techniques can be fundamentalblocks and integrated into this framework.

Measurement-based modeling of interference in wi-fi networks: techniques and applications

Abstract: Characterizing interference is critical to understanding the performance of a wireless network. Many protocol and algorithmic work fundamentally depend on such characterization. However, current research considers interference models that are either over-simplified or too abstract with unknown parameters limiting their use in practice. We address this issue in connection with WiFi networks (i.e., IEEE 802.11-based) due to their widespread use. We first develop a practical, measurement-based model to estimate the capacity of any given link in the presence of any given number of interfering links in an actual deployed 802.11 network, carrying any specified amount of offered load. For a network with N nodes, only O(N) measurement steps are needed to gather metrics for individual links that seed the model. We provide two solution approaches: one based on direct simulation (slow, but accurate) and the other based on analytical methods (faster, but approximate). We also show that as a by-product of our research we can create a highly accurate simulation model (e.g., using a packet level simulator such as ns2) of a real deployed network by seeding the simulator with measurement data. In an application of the above-mentioned capacity model, we address the issue of supporting voice-over-IP (VoIP) calls in a wireless mesh network. Specifically, we propose solutions for call admission control (CAC) and route selection for VoIP calls. Call admission decisions are made by using the capacity model to predict whether the capacity constraints at various nodes will be satisfied if a new call is admitted with a given route. We also develop a polynomial-time algorithm to search for feasible routes. In addition to studying feasibility, we study several routing metrics such as shortest feasible path and maximum residual feasible path. The above modeling approach requires active measurements. Also, it requires instrumentation access to network nodes. These could be impractical in many deployment scenarios. To address this issue, we develop an approach to estimate the interference between nodes and links in a live 802.11 network by passive monitoring of wireless traffic using a distributed set of sniffers. We model the 802.11 protocol as a Hidden Markov Model (HMM), and use a machine learning approach to learn the state transition probabilities in this model using the observed wireless traffic traces. This in turn helps us to deduce the interference relationships. We show the effectiveness of this approach via simulations and real experiments.