Showing papers on "Distributed algorithm published in 2003"

An energy efficient hierarchical clustering algorithm for wireless sensor networks

Abstract: A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.

Fast linear iterations for distributed averaging

Abstract: We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nodes. When the iteration is assumed symmetric, the problem of finding the fastest converging linear iteration can be cast as a semidefinite program, and therefore efficiently and globally solved. These optimal linear iterations are often substantially faster than several simple heuristics that are based on the Laplacian matrix of the associated graph.

Bullet: high bandwidth data dissemination using an overlay mesh

Abstract: In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes self-organize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burden on the underlying network. In this paper, we target high-bandwidth data distribution from a single source to a large number of receivers. Applications include large-file transfers and real-time multimedia streaming. For these applications, we argue that an overlay mesh, rather than a tree, can deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to self-organize into a high bandwidth overlay mesh. We construct Bullet around the insight that data should be distributed in a disjoint manner to strategic points in the network. Individual Bullet receivers are then responsible for locating and retrieving the data from multiple points in parallel.Key contributions of this work include: i) an algorithm that sends data to different points in the overlay such that any data object is equally likely to appear at any node, ii) a scalable and decentralized algorithm that allows nodes to locate and recover missing data items, and iii) a complete implementation and evaluation of Bullet running across the Internet and in a large-scale emulation environment reveals up to a factor two bandwidth improvements under a variety of circumstances. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing. In a tree, it is critical that a node's parent delivers a high rate of application data to each child. In Bullet however, nodes simultaneously receive data from multiple sources in parallel, making it less important to locate any single source capable of sustaining a high transmission rate.

A utility-based power-control scheme in wireless cellular systems

Abstract: Distributed power-control algorithms for systems with hard signal-to-interference ratio (SIR) constraints may diverge when infeasibility arises. In this paper, we present a power-control framework called utility-based power control (UBPC) by reformulating the problem using a softened SIR requirement (utility) and adding a penalty on power consumption (cost). Under this framework, the goal is to maximize the net utility, defined as utility minus cost. Although UBPC is still noncooperative and distributed in nature, some degree of cooperation emerges: a user will automatically decrease its target SIR (and may even turn off transmission) when it senses that traffic congestion is building up. This framework enables us to improve system convergence and to satisfy heterogeneous service requirements (such as delay and bit error rate) for integrated networks with both voice users and data users. Fairness, adaptiveness, and a high degree of flexibility can be achieved by properly tuning parameters in UBPC.

Distributed target classification and tracking in sensor networks

Abstract: The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.

Coverage in wireless ad hoc sensor networks

Abstract: Sensor networks pose a number of challenging conceptual and optimization problems such as location, deployment, and tracking. One of the fundamental problems in sensor networks is the calculation of the coverage. In Meguerdichian et al. (2001), it is assumed that the sensor has uniform sensing ability. We provide efficient distributed algorithms to optimally solve the best-coverage problem raised in the above-mentioned article. In addition, we consider a more general sensing model: the sensing ability diminishes as the distance increases. As energy conservation is a major concern in wireless (or sensor) networks, we also consider how to find an optimum best-coverage-path with the least energy consumption and how to find an optimum best-coverage-path that travels a small distance. In addition, we justify the correctness of the method proposed above that uses the Delaunay triangulation to solve the best coverage problem and show that the search space of the best coverage problem can be confined to the relative neighborhood graph, which can be constructed locally.

Distributed wireless communication system: a new architecture for future public wireless access

Abstract: The distributed wireless communication system (DWCS) is a new architecture for a wireless access system with distributed antennas, distributed processors, and distributed control With distributed antennas, the system capacity can be expanded through dense frequency reuse, and the transmission power can be greatly decreased With distributed processors control, the system works like a software or network radio, so different standards can coexist, and the system capacity can be increased by coprocessing of signals to and from multiple antennas

Scheduling distributed applications: the SimGrid simulation framework

Abstract: Since the advent of distributed computer systems an active field of research has been the investigation of scheduling strategies for parallel applications. The common approach is to employ scheduling heuristics that approximate an optimal schedule. Unfortunately, it is often impossible to obtain analytical results to compare the efficacy of these heuristics. One possibility is to conducts large numbers of back-to-back experiments on real platforms. While this is possible on tightly-coupled platforms, it is infeasible on modern distributed platforms (i.e. Grids) as it is labor-intensive and does not enable repeatable results. The solution is to resort to simulations. Simulations not only enables repeatable results but also make it possible to explore wide ranges of platform and application scenarios. In this paper we present the SimGrid framework which enables the simulation of distributed applications in distributed computing environments for the specific purpose of developing and evaluating scheduling algorithms. This paper focuses on SimGrid v2, which greatly improves on the first version of the software with more realistic network models and topologies. SimGrid v2 also enables the simulation of distributed scheduling agents, which has become critical for current scheduling research in large-scale platforms. After describing and validating these features, we present a case study by which we demonstrate the usefulness of SimGrid for conducting scheduling research.

Distributed algorithms for transmission power control in wireless sensor networks

Abstract: In a wireless, multi-hop sensor network, choosing transmission power levels has an important impact on energy efficiency and network lifetime. Two algorithms for dynamically adjusting transmission power level on a per-node basis are proposed here. Network lifetime, convergence speed as well as resulting network connectivity are used as figures of merit for these two algorithms. They have been evaluated in an indoor sensor environment. The network lifetime metrics of these two local algorithms are also benchmarked against power control algorithms using global information. We show that these local algorithms outperform fixed power level assignment and that the lifetime achieved by them is usually within a factor of two of globally computed solution while being scalable.

Distributed algorithms for guiding navigation across a sensor network

Abstract: We develop distributed algorithms for self-organizing sensor networks that respond to directing a target through a region The sensor network models the danger levels sensed across its area and has the ability to adapt to changes It represents the dangerous areas as obstacles A protocol that combines the artificial potential field of the sensors with the goal location for the moving object guides the object incrementally across the network to the goal, while maintaining the safest distance to the danger areas We give the analysis to the protocol and report on hardware experiments using a physical sensor network consisting of Mote sensors

A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks

Abstract: We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm. While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently (C. Toh, 2001; R. Shah et al., 2002), in this paper, we propose an orthogonal approach to previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive intersensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Our simulations show the power of our proposed algorithms, revealing their potential to effect significant energy savings (from 10%-65%) for typical sensor data corresponding to a multitude of sensor modalities.

Distributed EM algorithms for density estimation and clustering in sensor networks

Abstract: The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described as a mixture of some elementary conditions. The measurements are thus statistically modeled with a mixture of Gaussians, where each Gaussian component corresponds to one of the elementary conditions. The paper presents a distributed expectation-maximization (EM) algorithm for estimating the Gaussian components, which are common to the environment and sensor network as a whole, as well as the mixing probabilities that may vary from node to node. The algorithm produces an estimate (in terms of a Gaussian mixture approximation) of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, the algorithm can be viewed as a distributed processing strategy for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the distributed EM algorithm is investigated, and simulations demonstrate the potential of this approach to sensor network data analysis.

Aggregation in sensor networks: an energy-accuracy trade-off

Abstract: Wireless ad hoc sensor networks (WASNs) are in need of the study of useful applications that will help the researchers view them as distributed physically coupled systems, a collective that estimates the physical environment, and not just energy-limited ad hoc networks. We develop this perspective using a large and interesting class of WASN applications called aggregation applications. In particular, we consider the challenging periodic aggregation problem where the WASN provides the user with periodic estimates of the environment, as opposed to simpler and previously studied snapshot aggregation problems. In periodic aggregation our approach allows the spatial-temporal correlation among values sensed at the various nodes to be exploited towards energy-efficient estimation of the aggregated value of interest. Our approach also creates a system level energy vs. accuracy knob whereby the more the estimation error that the user can tolerate, the less is the energy consumed. We present a distributed estimation algorithm that can be applied to explore the energy-accuracy subspace for a sub-class of periodic aggregation problems, and present extensive simulation results that validate our approach. The resulting algorithm, apart from being more flexible in the energy-accuracy subspace and more robust, can also bring considerable energy savings for a typical accuracy requirement (five-fold decrease in energy consumption for 5% estimation error) compared to repeated snapshot aggregations.

Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing

Abstract: Preface.1. Introduction.2. Parallel and Distributed Processing of Power Systems.3. Information System for Control Centers.4. Common Information Model and Middleware for Integration.5. Parallel and Distributed Load Flow Computation.6. Parallel and Distributed Load Flow of Distribution Systems.7. Parallel and Distributed State Estimation.8. Distributed Power System Security Analysis.9. Hierarchical and Distributed Control of Voltage/VAR.10. Transmission Congestion Management Based on Multi-Agent Theory.11. Integration, Control, and Operation of Distributed Generation.12. Special Topics in Power System Information System.Appendix A. Example System Data.Appendix B. Measurement Data for Distributed State Estimation.Appendix C. IEEE-30 Bus System Data.Appendix D. Acronyms.Bibliography.Index.

Minimum energy disjoint path routing in wireless ad-hoc networks

Abstract: We develop algorithms for finding minimum energy disjoint paths in an all-wireless network, for both the node and link-disjoint cases. Our major results include a novel polynomial time algorithm that optimally solves the minimum energy 2 link-disjoint paths problem, as well as a polynomial time algorithm for the minimum energy k node-disjoint paths problem. In addition, we present efficient heuristic algorithms for both problems. Our results show that link-disjoint paths consume substantially less energy than node-disjoint paths. We also found that the incremental energy of additional link-disjoint paths is decreasing. This finding is somewhat surprising due to the fact that in general networks additional paths are typically longer than the shortest path. However, in a wireless network, additional paths can be obtained at lower energy due to the broadcast nature of the wireless medium. Finally, we discuss issues regarding distributed implementation and present distributed versions of the optimal centralized algorithms presented in the paper.

Small worlds in wireless networks

Abstract: In this study, the concept of small worlds is investigated in the context of wireless networks. Wireless networks are spatial graphs that tend to be much more clustered than random networks and have much higher path length characteristics. We observe that by adding a few short cut links, path length of wireless networks is reduced drastically. More interestingly, such short cut links need not be random but may be confined to a limited number of hops; a fraction of the network diameter. This facilitates the design of practical distributed algorithms, based on contacts, to improve performance of resource discovery in wireless networks.

A collaborative approach to in-place sensor calibration

Abstract: Numerous factors contribute to errors in sensor measurements. In order to be useful, any sensor device must be calibrated to adjust its accuracy against the expected measurement scale. In large-scale sensor networks, calibration will be an exceptionally difficult task since sensor nodes are often not easily accessible and manual device-by-device calibration is intractable. In this paper, we present a two-phase post-deployment calibration technique for large-scale, dense sensor deployments. In its first phase, the algorithm derives relative calibration relationships between pairs of co-located sensors, while in the second phase, it maximizes the consistency of the pair-wise calibration functions among groups of sensor nodes. The key idea in the first phase is to use temporal correlation of signals received at neighboring sensors when the signals are highly correlated (i.e. sensors are observing the same phenomenon) to derive the function relating their bias in amplitude. We formulate the second phase as an optimization problem and present an algorithm suitable for localized implementation. We evaluate the performance of the first phase of the algorithm using empirical and simulated data.

A distributed self spreading algorithm for mobile wireless sensor networks

Abstract: Sensor deployment is an important problem in mobile wireless sensor networks. This paper presents a distributed self deployment algorithm for mobile sensors. Performance metrics to evaluate algorithm performance are coverage, uniformity, time and distance traveled till the algorithm converges. Our algorithm is compared with a simulated annealing based algorithm for deployment and is shown to exhibit excellent performance.

DOLPHIN: an autonomous indoor positioning system in ubiquitous computing environment

Abstract: Determining physical location of indoor objects is one of the key issues in development of context-aware applications in ubiquitous computing environments. This is mainly because context information obtained from sensor networks is meaningful only when the physical location of the context information source is determined. Recently, several indoor location information systems, such as Active Bat and Cricket, have been developed for precise indoor object localization. However, to provide accurate physical location tracking in large-scale space, those systems requires a lot of manual configuration for all the ultrasonic sensor nodes. To reduce configuration costs, we developed a new indoor positioning system called DOLPHIN. The DOLPHIN system consists of distributed wireless sensor nodes which are capable of sending and receiving RF and ultrasonic signals. These nodes are attached to various indoor objects. And using a novel distributed positioning algorithm in the nodes, DOLPHIN enables autonomous positioning of the objects with minimal manual configuration. This paper describes the design and implementation of the DOLPHIN system, and evaluates basic performance through several experiments in an indoor environment.

Distributed Search and Rescue with Robot and Sensor Teams

Abstract: We develop a network of distributed mobile sensor systems as a solution to the emergency response problem. The mobile sensors are inside a building and they form a connected ad-hoc network. We discuss cooperative localization algorithms for these nodes. The sensors collect temperature data and run a distributed algorithm to assemble a temperature gradient. The mobile nodes are controlled to navigate using this temperature gradient. We also discuss how such networks can assist human users to find an exit. We have conducted an experiment to at a facility used to train firefighters to understand the environment and to test component technology. Results from experiments at this facility as well as simulations are presented here.

Constant-time distributed dominating set approximation

Abstract: Finding a small dominating set is one of the most fundamental problems of traditional graph theory. In this paper, we present a new fully distributed approximation algorithm based on LP relaxation techniques. For an arbitrary parameter k and maximum degree Δ, our algorithm computes a dominating set of expected size O(kΔ2/k log Δ|DSOPT|) in O(k2) rounds where each node has to send O(k2Δ) messages of size O(logΔ). This is the first algorithm which achieves a non-trivial approximation ratio in a constant number of rounds.

DFuse: a framework for distributed data fusion

Abstract: Simple in-network data aggregation (or fusion) techniques for sensor networks have been the focus of several recent research efforts, but they are insufficient to support advanced fusion applications. We extend these techniques to future sensor networks and ask two related questions: (a) what is the appropriate set of data fusion techniques, and (b) how do we dynamically assign aggregation roles to the nodes of a sensor network. We have developed an architectural framework, DFuse, for answering these two questions. It consists of a data fusion API and a distributed algorithm for energy-aware role assignment. The fusion API enables an application to be specified as a coarse-grained dataflow graph, and eases application development and deployment. The role assignment algorithm maps the graph onto the network, and optimally adapts the mapping at run-time using role migration. Experiments on an iPAQ farm show that, the fusion API has low-overhead, and the role assignment algorithm with role migration significantly increases the network lifetime compared to any static assignment.

Hundreds of impossibility results for distributed computing

Abstract: We survey results from distributed computing that show tasks to be impossible, either outright or within given resource bounds, in various models The parameters of the models considered include synchrony, fault-tolerance, different communication media, and randomization The resource bounds refer to time, space and message complexity These results are useful in understanding the inherent difficulty of individual problems and in studying the power of different models of distributed computing There is a strong emphasis in our presentation on explaining the wide variety of techniques that are used to obtain the results described

A game-theoretic approach to power allocation in frequency-selective gaussian interference channels

Abstract: This paper defines a rate maximization power allocation game in a frequency selective Gaus- sian interference channel, after assuming a suboptimal but pragmatic multi-user coding scheme. We show that the Nash equilibrium always exists in this game. We consider a distributed power allocation scheme (l, Section IV.), and provide a sufficient condition for the convergence of the distributed algorithm. The condi- tion for the convergence is also a sufficient condition under which the Nash equilibrium is unique. I. PROBLEM FORMULATION

Distributed construction of random expander networks

Abstract: A novel distributed algorithm for constructing random overlay networks that are composed of d Hamilton cycles is presented. The protocol is completely decentralized as no globally-known server is required. The constructed topologies are expanders with O(log/sub d/ n) diameter with high probability. Our construction is highly scalable because both the processing and the space requirements at each node grow logarithmically with the network size. A new node can join the network in O(log/sub d/ n) time with O(d log/sub d/ n) messages. A node can leave in O(1) time with O(d) messages. The protocol is robust against an offline adversary selecting the sequence of the join and leave operations. We also discuss a layered construction of the random expander networks in which any node can be located in O(log n) time. The random expander networks have applications in community discovery, distributed lookup service, and dynamic connectivity.

Event-based motion control for mobile-sensor networks

Abstract: In many sensor networks, considerably more units are available than necessary for simple coverage of the space. Augmenting sensor networks with motion can exploit this surplus to enhance sensing while also improving the network's lifetime and reliability. Sensor mobility allows better coverage in areas where events occur frequently. Another use of mobility comes about if the specific area of interest (within a larger area) is unknown during deployment. We've developed distributed algorithms for mobile-sensor networks to physically react to changes or events in their environment or in the network itself. Distribution supports scalability and robustness during sensing and communication failures. We present two classes of motion-control algorithms that let sensors converge on arbitrary event distributions. These algorithms trade off the amount of required computation and memory with the accuracy of the sensor positions. We also present three algorithms that let sensor networks maintain coverage of their environment. These algorithms work alongside either type of motion-control algorithm such that the sensors can follow the control law unless they must stop to ensure coverage.

A framework for event composition in distributed systems

Abstract: For large-scale distributed applications such as internet-wide or ubiquitous systems, event-based communication is an effective messaging mechanism between components. In order to handle the large volume of events in such systems, composite event detection enables application components to express interest in the occurrence of complex patterns of events. In this paper, we introduce a general composite event detection framework that can be added on top of existing middleware architectures -- as demonstrated in our implementation over JMS. We argue that the framework is flexible, expressive, and easy to implement. Based on finite state automata extended with a rich time model and support for parameterisation, it provides a decomposable core language for composite event specification, so that composite event detection can be distributed throughout the system. We discuss the issues associated with automatic distribution of composite event expressions. Finally, tests of our composite event system over JMS show reduced bandwidth consumption and a low notification delay for composite events.