Network topology

About: Network topology is a research topic. Over the lifetime, 52259 publications have been published within this topic receiving 1006627 citations.

AIDA: Adaptive application-independent data aggregation in wireless sensor networks

Abstract: Sensor networks, a novel paradigm in distributed wireless communication technology, have been proposed for various applications including military surveillance and environmental monitoring. These systems deploy heterogeneous collections of sensors capable of observing and reporting on various dynamic properties of their surroundings in a time sensitive manner. Such systems suffer bandwidth, energy, and throughput constraints that limit the quantity of information transferred from end-to-end. These factors coupled with unpredictable traffic patterns and dynamic network topologies make the task of designing optimal protocols for such networks difficult. Mechanisms to perform data-centric aggregation utilizing application-specific knowledge provide a means to augmenting throughput, but have limitations due to their lack of adaptation and reliance on application-specific decisions. We, therefore, propose a novel aggregation scheme that adaptively performs application-independent data aggregation in a time sensitive manner. Our work isolates aggregation decisions into a module that resides between the network and the data-link layer and does not require any modifications to the currently existing MAC and network layer protocols. We take advantage of queuing delay and the broadcast nature of wireless communication to concatenate network units into an aggregate using a novel adaptive feedback scheme to schedule the delivery of this aggregate to the MAC layer for transmission. In our evaluation we show that end-to-end transmission delay is reduced by as much as 80p under heavy traffic loads. Additionally, we show as much as a 50p reduction in transmission energy consumption with an overall reduction in header overhead. Theoretical analysis, simulation, and a test-bed implementation on Berkeley's MICA motes are provided to validate our claims.

Network Observability: Identification of Observable Islands and Measurement Placement

Abstract: Two algorithms are presented; one for (i) testing the observability of a network and (ii) identifying the observable islands when the network is unobservable, and the other for selecting a minimal set of additional measurements to make the network observable. The two algorithms are based on triangular factorization of the gain matrix and are characterized by (i) being extremely simple, (ii) using, subroutines already in a state estimation program, and (iii) incurring very little extra computation. The design and testing of the algorithms are presented in this paper.

On the impact of packet spraying in data center networks

Abstract: Modern data center networks are commonly organized in multi-rooted tree topologies. They typically rely on equal-cost multipath to split flows across multiple paths, which can lead to significant load imbalance. Splitting individual flows can provide better load balance, but is not preferred because of potential packet reordering that conventional wisdom suggests may negatively interact with TCP congestion control. In this paper, we revisit this “myth” in the context of data center networks which have regular topologies such as multi-rooted trees. We argue that due to symmetry, the multiple equal-cost paths between two hosts are composed of links that exhibit similar queuing properties. As a result, TCP is able to tolerate the induced packet reordering and maintain a single estimate of RTT. We validate the efficacy of random packet spraying (RPS) using a data center testbed comprising real hardware switches. We also reveal the adverse impact on the performance of RPS when the symmetry is disturbed (e.g., during link failures) and suggest solutions to mitigate this effect.

Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks

Abstract: In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.

Application of neural networks in forecasting engine systems reliability

Abstract: This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.