Conference Papers

Self-organized traffic control

Ferreira M., Fernandes R., Conceicao H., Viriyasitavat W., Tonguz O.K.

Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

2010

Abstract:

In this paper we propose and present preliminary results on the migration of traffic lights as roadside-based infrastructures to in-vehicle virtual signs supported only by vehicle- to-vehicle communications. We design a virtual traffic light protocol that can dynamically optimize the flow of traffic in road intersections without requiring any roadside infrastructure. Elected vehicles act as temporary road junction infrastructures and broadcast traffic light messages that are shown to drivers through in-vehicle displays. This approach renders signalized control of intersections truly ubiquitous, which significantly increases the overall traffic flow. We pro- vide compelling evidence that our proposal is a scalable and cost-effective solution to urban traffic control.

Sensor placement for real-time dynamic state estimation in power systems: Structural systems approach

Rocha P., Pequito S., Kar S., Aguiar A.P., Rocha P.

Conference Record - Asilomar Conference on Signals, Systems and Computers

2015

Abstract:

This paper studies the problem of sensor placement design for efficient dynamic real-time state estimation in electric power networks. Given a (linearized) dynamic physical model of the power system, efficient sensor placement strategies are proposed that minimize the observability index of the system. The observability index plays a key role in determining the minimum window length of filters that guarantee stable estimation error and minimizing this index allows the design of memory and computationally efficient filtering schemes with performance guarantees. Specifically, given the system dynamics, the paper addresses the following two sensor placement design problems: (1) determining the minimal number and placement of sensors that achieves a certain desired system observability index, and (2) given the number of sensors to be deployed, obtaining the placement achieving minimal system observability index. These problems are addressed in a structural systems framework, i.e., the placement strategies are obtained on the basis of the sparsity pattern (location of zeroes/non-zeroes) of the system coupling matrix, and the design guarantees hold for almost all numerical parametric realizations of the system. Finally, an example is provided which illustrates the analytical findings.

Sensor selection for event detection in wireless sensor networks

Bajovic D., Sinopoli B., Xavier J.

IEEE Transactions on Signal Processing

2011

Abstract:

We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of p out of n sensors that yields the best detection performance. As the sensor selection optimality criteria, we propose the Kullback-Leibler and Chernoff distances between the distributions of the selected measurements under the two hypothesis. We formulate the maxmin robust sensor selection problem to cope with the uncertainties in distribution means. We prove that the sensor selection problem is NP hard, for both Kullback-Leibler and Chernoff criteria. To (sub)optimally solve the sensor selection problem, we propose an algorithm of affordable complexity. Extensive numerical simulations on moderate size problem instances (when the optimum by exhaustive search is feasible to compute) demonstrate the algorithm’s near optimality in a very large portion of problem instances. For larger problems, extensive simulations demonstrate that our algorithm outperforms random searches, once an upper bound on computational time is set. We corroborate numerically the validity of the Kullback-Leibler and Chernoff sensor selection criteria, by showing that they lead to sensor selections nearly optimal both in the Neyman-Pearson and Bayes sense.

Sensor selection for hypothesis testing in wireless sensor networks: A Kullback-Leibler based approach

Bajovic D., Sinopoli B., Xavier J.

Proceedings of the IEEE Conference on Decision and Control

2009

Abstract:

We consider the problem of selecting a subset of p out of n sensors for the purpose of event detection, in a wireless sensor network (WSN). Occurrence of the event of interest is modeled as a binary Gaussian hypothesis test. In this case sensor selection consists of finding, among all (p n) combinations, the one maximizing the Kullback-Leibler (KL) distance between the induced p-dimensional distributions under the two hypotheses. An exhaustive search is impractical if n and p are large, as the resulting optimization problem is combinatorial. We propose a suboptimal approach with computational complexity of order O(n3p). This consists of relaxing the 0/1 constraint on the entries of the selection matrices to let the optimization problem search over the set of Stiefel matrices. Although finding the Stiefel matrix is a nonconvex problem, we provide an algorithm that is guaranteed to produce a global optimum for p = 1, through a series of judicious problem reformulations. The case p > 1 is tackled by an incremental, greedy approach. The obtained Stiefel matrix is then used to determine the sensor selection matrix which best approximates its range space. Extensive simulations are used to assess near optimality of the proposed approach. They also show how the proposed approach performs better than exhaustive searches once an upper bound on the computation time is set.

Sequential observer selection for source localization

Zejnilovic S., Gomes J., Sinopoli B.

2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

2016

Abstract:

Identifying the source of network diffusion is an important task in applications such as epidemics management and understanding the trend propagation over social networks. As observing each node carries a cost, we study the problem of sequential selection of observed nodes from two aspects: which nodes to observe such that the source is localized with the lowest cost, and for a pre-specified number of time-steps, which nodes to observe such that the resulting number of possible source candidates is the lowest. We show that both problems can be framed, under a simple propagation scenario, as dynamic programing with imperfect state knowledge. The proposed approach is optimal, but computationally intensive, hence we propose two simple greedy strategies. Using adaptive submodularity, we provide performance guarantees for one greedy algorithm. We evaluate the proposed approaches through simulation.

Short-term real-time traffic prediction methods: A survey

Barros J., Araujo M., Rossetti R.J.F.

2015 International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015

2015

Abstract:

Short-term traffic prediction provides tools for improved road management by allowing the reduction of delays, incidents and other unexpected events. Different real-time approaches provide traffic managers with varying but valuable information. This paper reviews the literature regarding model-driven and data-driven approaches focusing on short-term realtime traffic prediction. We start by analyzing real-time traffic data collection, referring network state acquisition and description methods which are used as input to predictive algorithms. According to the input variables available, we describe common and useful traffic prediction outputs that should contribute to understand the panorama verified on a road network. We then discuss metrics commonly used to assess prediction accuracy, in order to understand a standardized way to compare the different approaches. We list, detail and compare existing model-driven and data-driven approaches that provide short-term real-time traffic predictions. This research leads to an understanding of the many advantages, disadvantages and trade-offs of the approaches studied and provides useful insights for future development. Despite the predominance of model-driven solutions for the last years, data-driven approaches also present good results suitable for Traffic Management usage.

Significance of early tagged contextual graphemes in grapheme based speech synthesis and recognition systems

Anumanchipalli G.K., Prahallad K., Black A.W.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

2008

Abstract:

n this paper we present our argument that context information could be used in early stages i.e., during the definition of mapping of the words into sequence of graphemes. We show that the early tagged contextual graphemes play a significant role in improving the performance of grapheme based speech synthesis and speech recognition systems.

Small signal stability and centralized control design of modern distribution systems

Honarvar Nazari M.

NAPS 2011 - 43rd North American Power Symposium

2011

Abstract:

This paper investigates small signal stability and designing advanced centralized control to enhance robustness and dynamic performance of a modern distribution system with high penetration of distributed generators (DG). The paper mainly concerns comparison of classical, H 2 and H ∞ control strategies on system stability. The target performance is to satisfy stability of the system and improve nominal performance of DGs. Therefore, first we design a base controller for the system using classic control. Then, H 2 and H ∞ control systems are designed in order to warrant both nominal stability and dynamic performance of the system. Finally, the results of three control strategies are compared and the general findings of the paper are discussed.

Small-signal stability and decentralized control design for electric energy systems with a large penetration of distributed generators

Honarvar Nazari M., Ilic M., Pecas Lopes J.

Control Engineering Practice

2012

Abstract:

This paper investigates small-signal stability and decentralized control design for distribution electric energy systems with a large penetration of distributed generators. Two real world distribution systems are studied in this paper. The first system is the IEEE 30-node distribution system and the second one is the distribution system on Flores Island, one of the western group islands of the Azores Archipelago. The Block Gerschgorin Theorem and Liapunov function-based stability criteria are applied to formally state sufficient conditions for small-signal stability. The results illustrate that when the governor control of distributed generators is designed without considering interactions between generators, small-signal instability could occur in the system and even the sufficient conditions for stability would not be satisfied. In the next step, the paper assesses control design to stabilize potentially unstable distribution systems. The main focus is on designing enhanced decentralized control based on the introduced stability criteria. The findings illustrate that implementing the enhanced decentralized control could ensure stability and support a large penetration of distributed generators.