Implications For Network Design Research Articles

Curators and sojourners in learning networks: Practices for transformation

Networks are a promising mechanism for creating systemic change. Studies abound, but do not examine roles and practices to reveal the processes through which networks create transformative capacity. This participatory single case study provides a framework to examine structures, roles and practices. The framework details common network substructures, across which critical practices occur, and provides a typology of specific network practices of curators and sojourners. The primary claim is that a variety of structures and interdependent roles and practices work in concert to support transformation across sites and scales. Results have implications for network design, leadership, practice, and evaluation.

Inferential ecosystem models, from network data to prediction

Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations "expensive"; networks will typically be deployed in remote locations without access to infrastructure (e.g., power). The capacity to sample intensively makes sensor networks valuable, but high-frequency data are informative only at specific times and locations. Sampling intervals will range from meters and seconds to landscapes and years, depending on the process, the current states of the system, the uncertainty about those states, and the perceived potential for rapid change. Given that intensive sampling is sometimes critical, but more often wasteful, how do we develop tools to control the measurement and transmission processes? We address the potential of data collection controlled and/or supplemented by inferential ecosystem models. In a given model, the value of an observation can be evaluated in terms of its contribution to estimates of state variables and important parameters. There will be more than one model applied to network data that will include as state variables water, carbon, energy balance, biogeochemistry, tree ecophysiology, and forest demographic processes. The value of an observation will depend on the application. Inference is needed to weigh the contributions against transmission cost. Network control must be dynamic and driven by models capable of learning about both the environment and the network. We discuss application of Bayesian inference to model data from a developing sensor network as a basis for controlling the measurement and transmission processes. Our examples involve soil moisture and sap flux, but we discuss broader application of the approach, including its implications for network design.

Sublogarithmic approximation for telephone multicast

Consider a network of processors modeled by an n-vertex graph G = ( V , E ) . Assume that the communication in the network is synchronous, i.e., occurs in discrete “rounds,” and in every round every processor is allowed to pick one of its neighbors, and to send it a message. The telephone k-multicast problem requires to compute a schedule with minimal number of rounds that delivers a message from a given single processor, that generates the message, to all the processors of a given set T ⊆ V , | T | = k , whereas the processors of V ∖ T may be left uninformed. The case T = V is called broadcast problem. Several approximation algorithms with a polylogarithmic ratio were suggested for these problems, and the upper bound on their approximation threshold stands currently on O ( log k ) and O ( log n ) , respectively. In this paper we devise an O ( log k log log k ) -approximation algorithm for the k-multicast problem, and, consequently, an O ( log n log log n ) -approximation algorithm for the broadcast problem. Even stronger than that, whenever an instance of the k-multicast problem admits a schedule of length br ∗ , our algorithm guarantees an approximation ratio of O ( log k log br ∗ ) . As br ∗ is always at least log k , the ratio of O ( log k log log k ) follows. In addition, whenever br ∗ = Ω ( k δ ) for some constant δ > 0 , we obtain a constant O ( 1 / δ ) -approximation ratio for the problem. Our results have implications for network design. The poise of a spanning tree is the sum of its depth and maximum degree [R. Ravi, Rapid rumor ramification: Approximating the minimum broadcast time, in: Proceedings of the IEEE Symposium on Foundations of Computer Science, FOCS '94, 1994, pp. 202–213]. We improve the O ( log k ) approximation algorithm [A. Bar-Noy, S. Guha, J. Naor, B. Schieber, Multicasting in heterogeneous networks, SIAM J. Comput. 30 (2) (2000) 347–358] for the poise problem to O ( log k / log k log k ) , and obtain an improved ( O ( log k / log log k ) , O ( log k / log k log k ) ) bicriteria approximation for the depth-degree problem. We also derive results concerning the edge-dependent heterogeneous k-multicast problem.

Spatial Correlation of Automatic Air Quality Monitoring at Urban Background Sites: Implications for Network Design

Measurements of carbon monoxide, nitrogen oxides, PM10, sulphur dioxide and ozone from four UK National Automatic Urban Network Stations have been analysed for the year 1994. All of the sites are urban background, with two in the same city (Birmingham Centre and Birmingham East) and the other two also in cities in the centre of England, Leicester and Leeds, respectively 58 and 154 km from Birmingham. Expressed as cumulative frequency distributions and median concentrations, the pollution climates of the four sites are relatively similar, with differences between Birmingham Centre and Birmingham East as large as those between Birmingham and the other cities. Sulphur dioxide, which does not have an appreciable road traffic source, behaves differently, especially in Leeds, which is adjacent to a number of major point sources. However, when monthly hourly maxima and exceedences of health-related threshold concentrations are examined, the sites behave differently and relationships between them are much poorer, even for the two sites within the same city. It is concluded that network design needs to compromise between two extreme views. On the one hand, very high site density would be necessary to reflect all local conditions and provide adequate public information. On the other hand, concentrations are so spatially variable that the view may be taken that it is impossible to provide sufficient stations to monitor adequately, and that very few stations are all that is needed to give a general view of the pollution climate. Provided an adequate safety margin is built into ambient air quality standards, a high network density is not essential due to the generally strong correlation of measurements at adjacent sites.

Long-range dependence in variable-bit-rate video traffic

We analyze 20 large sets of actual variable-bit-rate (VBR) video data, generated by a variety of different codecs and representing a wide range of different scenes. Performing extensive statistical and graphical tests, our main conclusion is that long-range dependence is an inherent feature of VBR video traffic, i.e., a feature that is independent of scene (e.g., video phone, video conference, motion picture video) and codec. In particular, we show that the long-range dependence property allows us to clearly distinguish between our measured data and traffic generated by VBR source models currently used in the literature. These findings give rise to novel and challenging problems in traffic engineering for high-speed networks and open up new areas of research in queueing and performance analysis involving long-range dependent traffic models. A small number of analytic queueing results already exist, and we discuss their implications for network design and network control strategies in the presence of long-range dependent traffic. >