7275848
Contour maps: Monitoring and diagnosis in sensor networks
Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink. In this paper, we propose an efficient data-collection scheme that can be used for event monitoring or network-wide diagnosis. Our scheme relies on the well-known representation of data--contour maps, which trade off accuracy with the amount of samples. The scheme consists of three novel algorithms to build contour maps: distributed spatial and temporal data suppression, contour reconstruction at the sink via interpolation and smoothing, and an efficient mechanism to convey routing information over multiple hops. By reducing the number of transmissions required to convey relevant information to the sink, the proposed contour mapping scheme saves energy and improves network lifetime. In a sharp contrast to related work in this area, the scheme does not require all nodes to explicitly share information.The contour mapping scheme can be applied for tasks such as: (1) presenting a global picture of the network in both temporal and spatial domains, (2) being used as a diagnosis tool, e.g., to detect faulty sensors and to scan for residual energy, (3) working in concert with in-network aggregation schemes to further reduce the communication overhead of aggregation schemes. The proposed scheme imposes little processing and storage overhead, allowing for the sensor networking paradigm of 'dumb sensor, smart sink' which enables economical deployment of large-scale sensor networks.Simulation results show that our scheme is resilient to both high packet loss rate and measurement noise. The design is also energy efficient, resulting in up to an-order-of-magnitude power savings when compared with the base line scheme where every sensor sends its report to the sink.
11410811
Fair scheduling in wireless packet networks
Fair scheduling of delay and rate-sensitive packet flows over a wireless channel is not addressed effectively by most contemporary wireline fair-scheduling algorithms because of two unique characteristics of wireless media: (1) bursty channel errors and (2) location-dependent channel capacity and errors. Besides, in packet cellular networks, the base station typically performs the task of packet scheduling for both downlink and uplink flows in a cell; however, a base station has only a limited knowledge of the arrival processes of uplink flows. We propose a new model for wireless fair-scheduling based on an adaptation of fluid fair queueing (FFQ) to handle location-dependent error bursts. We describe an ideal wireless fair-scheduling algorithm which provides a packetized implementation of the fluid mode, while assuming full knowledge of the current channel conditions. For this algorithm, we derive the worst-case throughput and delay bounds. Finally, we describe a practical wireless scheduling algorithm which approximates the ideal algorithm. Through simulations, we show that the algorithm achieves the desirable properties identified in the wireless FFQ model.
8338982
Self-coordinating localized fair queueing in wireless ad hoc networks
Distributed fair queueing in a multihop, wireless ad hoc network is challenging for several reasons. First, the wireless channel is shared among multiple contending nodes in a spatial locality. Location-dependent channel contention complicates the fairness notion. Second, the sender of a flow does not have explicit information regarding the contending flows originated from other nodes. Fair queueing over ad hoc networks is a distributed scheduling problem by nature. Finally, the wireless channel capacity is a scarce resource. Spatial channel reuse, i.e., simultaneous transmissions of flows that do not interfere with each other, should be encouraged whenever possible. In this paper, we reexamine the fairness notion in an ad hoc network using a graph-theoretic formulation and extract the fairness requirements that an ad hoc fair queueing algorithm should possess. To meet these requirements, we propose maximize-local-minimum fair queueing (MLM-FQ), a novel distributed packet scheduling algorithm where local schedulers self-coordinate their scheduling decisions and collectively achieve fair bandwidth sharing. We then propose enhanced MLM-FQ (EMLM-FQ) to further improve the spatial channel reuse and limit the impact of inaccurate scheduling information resulted from collisions. EMLM-FQ achieves statistical short-term throughput and delay bounds over the shared wireless channel. Analysis and extensive simulations confirm the effectiveness and efficiency of our self-coordinating localized design in providing global fair channel access in wireless ad hoc networks.
1919911
Robust rate adaptation for 802.11 wireless networks
Rate adaptation is a mechanism unspecified by the 802.11 standards, yet critical to the system performance by exploiting the multi-rate capability at the physical layer.I n this paper, we conduct a systematic and experimental study on rate adaptation over 802.11 wireless networks. Our main contributions are two-fold. First, we critique five design guidelines adopted by most existing algorithms. Our study reveals that these seemingly correct guidelines can be misleading in practice, thus incur significant performance penalty in certain scenarios. The fundamental challenge is that rate adaptation must accurately estimate the channel condition despite the presence of various dynamics caused by fading, mobility and hidden terminals. Second, we design and implement a new Robust Rate Adaptation Algorithm (RRAA)that addresses the above challenge. RRAA uses short-term loss ratio to opportunistically guide its rate change decisions, and an adaptive RTS filter to prevent collision losses from triggering rate decrease. Our extensive experiments have shown that RRAA outperforms three well-known rate adaptation solutions (ARF, AARF, and SampleRate) in all tested scenarios, with throughput improvement up to 143%.
23906830
PEAS: a robust energy conserving protocol for long-lived sensor networks
Small, inexpensive sensors with limited memory, computing power and short battery lifetimes are turning into reality. Due to adverse conditions such as high noise levels, extreme humidity or temperatures, or even destructions from unfriendly entities, sensor node failures may become norms rather than exceptions in real environments. To be practical, sensor networks must last for much longer times than that of individual nodes, and have yet to be robust against potentially frequent node failures. This paper presents the design of PEAS, a simple protocol that can build a long-lived sensor network and maintain robust operations using large quantities of economical, short-lived sensor nodes. PEAS extends system functioning time by keeping only a necessary set of sensors working and putting the rest into sleep mode. Sleeping ones wake up now and then, probing the local environment and replacing failed ones. The sleeping periods are self-adjusted dynamically, so as to keep the sensors' wakeup rate roughly constant, thus adapting to high node densities.
1236934
A Packet Scheduling Approach to QoS Support in Multihop Wireless Networks
Providing packet-level quality of service (QoS) is critical to support both rate-sensitive and delay-sensitive applications in bandwidth-constrained, shared-channel, multihop wireless networks. Packet scheduling has been a very popular paradigm to ensure minimum throughput and bounded delay access for packet flows. This work describes a packet scheduling approach to QoS provisioning in multihop wireless networks. Besides minimum throughput and delay bounds for each flow, our scheduling disciplines seek to achieve fair and maximum allocation of the shared wireless channel bandwidth. However, these two criteria can potentially be in conflict in a generic-topology multihop wireless network where a single logical channel is shared among multiple contending flows and spatial reuse of the channel bandwidth is possible. In this paper, we propose a new scheduling model that addresses this conflict. The main results of this paper are the following: (a) a two-tier service model that provides a minimum “fair” allocation of the channel bandwidth for each packet flow and additionally maximizes spatial reuse of bandwidth, (b) an ideal centralized packet scheduling algorithm that realizes the above service model, and (c) a practical distributed backoff-based channel contention mechanism that approximates the ideal service within the framework of the CSMA/CA protocol.
7682350
Incremental genetic K-means algorithm and its application in gene expression data analysis
BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
14619760
A New Inlining Algorithm for Mapping XML DTDs to Relational Schemas
XML is rapidly emerging on the World Wide Web as a standard for representing and exchanging data. It is critical to have efficient mechanisms to store and query XML documents to exploit the full power of this new technology. While one approach is to develop native XML repositories that support XML data models and query languages directly, the other approach is to take advantage of the mature technologies that are provided by current relational or object-relational DBMSs. There is active research along both approaches and it is still not clear which one is better than the other. We continue our effort on the second approach. In particular, we have developed an efficient algorithm which takes an XML DTD as input and produces a relational schema as output for storing and querying XML documents conforming to the input DTD. Our algorithm features several significant improvements over the shared-inlining algorithm including overcoming its incompleteness, eliminating redundancies caused by shared elements, performing optimizations and enhancing efficiency.
24216233
CAQ: A machine learning tool for engineering
Abstract Machine learning algorithms developed from artificial intelligence (AI) research are rapidly finding useful applications in engineering domains. They are joining a family of tools that can help engineers summarize (or synthesize) massive amounts of data to support decision making. Traditional tools in this family, such as various statistical-based techniques, deal mostly with continuous (real) valued attributes — their results are relatively accurate, but often hard to interpret. On the other hand, logic-based learning tools, such as Michalski's AQ series of programs for learning concepts from examples, deal only with discrete valued attributes — their results are easier to comprehend, but not accurate for engineering applications which rarely deal solely with discrete valued attributes. Ideally, engineers need algorithms that work with a mix of attributes including those that take continuous values and those that take discrete values. Engineering problems also demand that learning algorithms be able to handle noise and produce concepts that are comprehensible. CAQ (Continuous AQ) is an algorithm designed to meet engineering requirements. This paper explains how CAQ combines continuous and discrete values, and shows that handling continuous attributes as real numbers instead of forcing them into a discrete representation leads to more efficient concept formation. Data from a turning process simulator used in support of machine operation planning in manufacturing are used to demonstrate the new algorithm.
13902125
GCA: A Coclustering Algorithm for Thalamo-Cortico-Thalamic Connectivity Analysis
The reciprocal connectivity between the cerebral cortex and the thalamus in a human brain is involved in consciousness and related to various brain disorders, thus, in-vivo analysis of this connectivity is critically important for brain diagnosis and surgery planning. While existing work either focuses on fiber tracking analysis or on thalamic nuclei segmentation, to our best knowledge, no techniques yet exist for performing in-vivo analysis of thalamo-cortico-thalamic connectivity. In this paper, (i) we propose a new partitioning paradigm, called coclustering, to model this problem. In contrast to the traditional clustering paradigm, a coclustering procedure not only simultaneously partitions cortical voxels and thalamic voxels into groups, but also identifies the corresponding strong connectivities between the two classes of groups; (ii) we develop the first coclustering algorithm, genetic coclustering algorithm (GCA), to solve the coclustering problem; and (iii) we apply GCA to perform in-vivo analysis of the thalamo-cortico-thalamic connectivity and produce a strikingly clear 3D visualization of the seven thalamic nuclei groups as well as their connectivities to the corresponding cortical regions of a human brain
620324
Relational Nested Optional Join for Efficient Semantic Web Query Processing
Increasing amount of RDF data on the Web drives the need for its efficient and effective management. In this light, numerous researchers have proposed to use RDBMSs to store and query RDF annotations using the SQL and SPARQL query languages. The first few attempts at SPARQL-to-SQL translation revealed non-trivial challenges related to correctness and efficiency of such translation in the presence of nested optional graph patterns. In this paper, we propose to extend relational databases with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional graph patterns. We design three efficient algorithms to implement the new operator in relational databases: (1) nested-loops NOJ algorithm, NL-NOJ, (2) sort-merge NOJ algorithm, SM-NOJ, and (3) simple hash NOJ algorithm, SH-NOJ. Based on a real life RDF dataset, we demonstrate the efficiency of our algorithms by comparing them with the corresponding left outer join implementations.