Wireless Sensor Networks (WSNs) constitute a very dynamic research area. The constraints related to deployment, topology, and energy motivates much of the research activities related to the WSNs. In a network of thous...
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ISBN:
(纸本)9781509003044
Wireless Sensor Networks (WSNs) constitute a very dynamic research area. The constraints related to deployment, topology, and energy motivates much of the research activities related to the WSNs. In a network of thousands of sensor nodes, routing management and data exchange are expensive in terms of energy consumption and storage capacity. In fact, a sensor needs to store a lot of information to perform data routing. A lot of researchers have proposed clustering to minimize energy consumption by cutting the network into groups and routing captured information at different hierarchical levels. Clustering has been deeply investigated for energy savings in the WSNs. It has also the advantage of being an alternative to address scalability problems. However, building and maintaining a cluster structure requires an additional cost compared to flat networks. In this paper, we address the problem of maximizing the network lifetime by means of cluster formation and by assigning sensor nodes to cluster heads. We propose a multi-objective model for clustering that guarantees an optimal selection of cluster heads among sensor nodes with the highest residual energy while simultaneously minimizing the intra cluster distance. The numerical results demonstrate the effectiveness of our model compared to existing ones.
Network emulation is a valuable, though potentially resource intensive, method for virtualizing networks for analysis or testing. Though high-powered servers are becoming increasingly accessible, the size and complexi...
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Network emulation is a valuable, though potentially resource intensive, method for virtualizing networks for analysis or testing. Though high-powered servers are becoming increasingly accessible, the size and complexity of physical networks have increased in a similar fashion, thereby limiting the type and size of networks that can be emulated on a single physical machine. In this thesis, we present a tool that allows the developers of ground truth topologies to distribute the emulation requirements across multiple physical machines, thereby increasing the size of networks that can be emulated. First, we reexamine existing tools to discover current methods for emulating synthetic and physical networks. Then we modify an existing platform to enable execution on multiple machines, while increasing flexibility for future extensions. We then develop methods for efficiently distributing the topology among the available resources in order to maximize the potential scale. Finally, we run a series of scenarios simulating real world events, such as a Border Gateway Protocol (BGP) hijack attack, in order to demonstrate the utility and efficiency of the system.
The distinguished econometrician Ragnar Frisch (1895-1973) also played an important role in optimization theory. In fact, he was a pioneer of interior-point methods. This note reconsiders his contribution, relating it...
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The distinguished econometrician Ragnar Frisch (1895-1973) also played an important role in optimization theory. In fact, he was a pioneer of interior-point methods. This note reconsiders his contribution, relating it to history and modern developments.
In this paper we introduce the circuit diameter of polyhedra, which is always bounded from above by the combinatorial diameter. We consider dual transportation polyhedra defined on general bipartite graphs. For comple...
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In this paper we introduce the circuit diameter of polyhedra, which is always bounded from above by the combinatorial diameter. We consider dual transportation polyhedra defined on general bipartite graphs. For complete MxN bipartite graphs the Hirsch bound (M-1)(N-1) on the combinatorial diameter is a known tight bound [Math. Oper. Res., 9 (1984), pp. 629-633]. For the circuit diameter we show the much stronger bound M+N-2 for all dual transportation polyhedra defined on arbitrary bipartite graphs with M+N nodes.
The vector autoregressive (VAR) model is a powerful tool in learning complex time series and has been exploited in many fields. The VAR model poses some unique challenges to researchers: On one hand, the dimensionalit...
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The vector autoregressive (VAR) model is a powerful tool in learning complex time series and has been exploited in many fields. The VAR model poses some unique challenges to researchers: On one hand, the dimensionality, introduced by incorporating multiple numbers of time series and adding the order of the vector autoregression, is usually much higher than the time series length;On the other hand, the temporal dependence structure naturally present in the VAR model gives rise to extra difficulties in data analysis. The regular way in cracking the VAR model is via "least squares" and usually involves adding different penalty terms (e.g., ridge or lasso penalty) in handling high dimensionality. In this manuscript, we propose an alternative way in estimating the VAR model. The main idea is, via exploiting the temporal dependence structure, formulating the estimating problem to a linear program. There is instant advantage of the proposed approach over the lasso type estimators: The estimation equation can be decomposed to multiple sub-equations and accordingly can be solved efficiently using parallel computing. Besides that, we also bring new theoretical insights into the VAR model analysis. So far the theoretical results developed in high dimensions (e.g., Song and Bickel, 2011 and Kock and Callot, 2015) are based on stringent assumptions that are not transparent. Our results, on the other hand, show that the spectral norms of the transition matrices play an important role in estimation accuracy and build estimation and prediction consistency accordingly. Moreover, we provide some experiments on both synthetic and real-world equity data. We show that there are empirical advantages of our method over the lasso-type estimators in parameter estimation and forecasting.
New electricity generation technologies are often assessed using simple metrics such as average return, break-even energy prices or levelised cost of electricity. These simple metrics do not always capture the full ec...
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New electricity generation technologies are often assessed using simple metrics such as average return, break-even energy prices or levelised cost of electricity. These simple metrics do not always capture the full economic value of a technology, particularly those that can react quickly and efficiently to changes in demand. In a wholesale spot market, opportunities exist to capture greater revenues, as currently achieved by peak power plants. This study demonstrates the use of two complementary methods to determine how storage should be operated to maximise revenue. First, the authors formulate and solve the problem as a linear program. The results indicate that there are distinct control modes. They then use Pontryagin's principle to confirm that the optimal operating strategy has three distinct control modes: (i) store all collected power, without generating, (ii) generate using collected power only and (iii) generate at maximum capacity using both collected and stored power. The mode that should be used at any instant depends only on the spot price of electricity relative to a pair of critical prices. These critical prices depend on the total energy that will be collected and the turnaround efficiency of the storage system.
State-of-the-art systems analysis techniques focus on efficiently finding optimal solutions. Yet an optimal solution is optimal only for the modeled issues and managers often seek near-optimal alternatives that addres...
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State-of-the-art systems analysis techniques focus on efficiently finding optimal solutions. Yet an optimal solution is optimal only for the modeled issues and managers often seek near-optimal alternatives that address unmodeled objectives, preferences, limits, uncertainties, and other issues. Early on, Modeling to Generate Alternatives (MGA) formalized near-optimal as performance within a tolerable deviation from the optimal objective function value and identified a few maximally different alternatives that addressed some unmodeled issues. This paper presents new stratified, Monte-Carlo Markov Chain sampling and parallel coordinate plotting tools that generate and communicate the structure and extent of the near-optimal region to an optimization problem. Interactive plot controls allow users to explore region features of most interest. Controls also streamline the process to elicit unmodeled issues and update the model formulation in response to elicited issues. Use for an example, single-objective, linear water quality management problem at Echo Reservoir, Utah, identifies numerous and flexible practices to reduce the phosphorus load to the reservoir and maintain close-to-optimal performance. Flexibility is upheld by further interactive alternative generation, transforming the formulation into a multiobjective problem, and relaxing the tolerance parameter to expand the near-optimal region. Compared to MGA, the new blended tools generate more numerous alternatives faster, more fully show the near-optimal region, and help elicit a larger set of unmodeled issues.
Electricity markets have evolved over the years leading to sophisticated spot markets for contingency reserve in operation in a number of countries. This paper proposes a security-constrained optimal power flow based ...
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Electricity markets have evolved over the years leading to sophisticated spot markets for contingency reserve in operation in a number of countries. This paper proposes a security-constrained optimal power flow based co-optimization of energy and reserve services. The proposed paradigm improves on the existing market clearing engines because it enables an endogenous determination of reserve requirements, allocation of reserve to generators and unbundled energy/reserve prices, to accurately reflect the impact of underlying line/generator contingencies. We highlight the salient aspects of the proposed formulation using a set of numerical examples. Copyright (c) 2013 John Wiley & Sons, Ltd.
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