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Prof. Edwin Chong
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA

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Basic Info is private.

Research Keywords & Expertise

0 Information Theory
0 sensor networks
0 Discrete Event Systems
0 Resource allocation and management
0 Systems, control, and optimization

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Wireless networks and cellular systems
Resource allocation and management
Discrete Event Systems
sensor networks
Stochastic optimization and approximation
Information Theory

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Original paper
Published: 30 June 2021 in Operational Research
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Stock-price prediction has been the focus of extensive studies. Historical price values have been proven to have power to predict future prices. At the same time, different economic variables have also been used in the literature to predict stock-price values with high accuracy. In this work, we develop a general method for stock-price prediction using multiple predictors. First, we use multichannel cross-correlation coefficient as a measure for selecting the most correlated set of variables for each stock. We then construct the temporally local covariance matrix of the data and use this as the basis for a dimension-reduction method for prediction. This method involves resolving the predictive data (predictors) onto a principal subspace and from there producing a prediction that is consistent with the resolved data. Our method is easily implemented and can accommodate an arbitrary number of predictors. We investigate the optimal number of predictors based on two performance metrics: mean squared error of the prediction and the directional change statistic. We illustrate our results based on historical daily price data for 50 companies.

ACS Style

Mahsa Ghorbani; Edwin K. P. Chong. A dimension reduction method for stock-price prediction using multiple predictors. Operational Research 2021, 1 -20.

AMA Style

Mahsa Ghorbani, Edwin K. P. Chong. A dimension reduction method for stock-price prediction using multiple predictors. Operational Research. 2021; ():1-20.

Chicago/Turabian Style

Mahsa Ghorbani; Edwin K. P. Chong. 2021. "A dimension reduction method for stock-price prediction using multiple predictors." Operational Research , no. : 1-20.

Journal article
Published: 05 April 2021 in Sensors
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In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.

ACS Style

Pranav Damale; Edwin Chong; Tian Ma. Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter. Sensors 2021, 21, 2544 .

AMA Style

Pranav Damale, Edwin Chong, Tian Ma. Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter. Sensors. 2021; 21 (7):2544.

Chicago/Turabian Style

Pranav Damale; Edwin Chong; Tian Ma. 2021. "Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter." Sensors 21, no. 7: 2544.

Journal article
Published: 09 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.

ACS Style

Christopher Robbiano; Edwin K. P. Chong; Mahmood R. Azimi-Sadjadi; Louis L. Scharf; Ali Pezeshki. Bayesian Learning of Occupancy Grids. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -12.

AMA Style

Christopher Robbiano, Edwin K. P. Chong, Mahmood R. Azimi-Sadjadi, Louis L. Scharf, Ali Pezeshki. Bayesian Learning of Occupancy Grids. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-12.

Chicago/Turabian Style

Christopher Robbiano; Edwin K. P. Chong; Mahmood R. Azimi-Sadjadi; Louis L. Scharf; Ali Pezeshki. 2020. "Bayesian Learning of Occupancy Grids." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-12.

Journal article
Published: 18 June 2020 in IEEE Control Systems Letters
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For years, there has been interest in approximation methods for solving dynamic programming problems, because of the inherent complexity in computing optimal solutions characterized by Bellman's principle of optimality. A wide range of approximate dynamic programming (ADP) methods now exists. It is of great interest to guarantee that the performance of an ADP scheme be at least some known fraction, say β, of optimal. This letter introduces a general approach to bounding the performance of ADP methods, in this sense, in the stochastic setting. The approach is based on new results for bounding greedy solutions in string optimization problems, where one has to choose a string (ordered set) of actions to maximize an objective function. This bounding technique is inspired by submodularity theory, but submodularity is not required for establishing bounds. Instead, the bounding is based on quantifying certain notions of curvature of string functions; the smaller the curvatures the better the bound. The key insight is that any ADP scheme is a greedy scheme for some surrogate string objective function that coincides in its optimal solution and value with those of the original optimal control problem. The ADP scheme then yields to the bounding technique mentioned above, and the curvatures of the surrogate objective determine the value β of the bound. The surrogate objective and its curvatures depend on the specific ADP.

ACS Style

Yajing Liu; Edwin K. P. Chong; Ali Pezeshki; Zhenliang Zhang. A General Framework for Bounding Approximate Dynamic Programming Schemes. IEEE Control Systems Letters 2020, 5, 463 -468.

AMA Style

Yajing Liu, Edwin K. P. Chong, Ali Pezeshki, Zhenliang Zhang. A General Framework for Bounding Approximate Dynamic Programming Schemes. IEEE Control Systems Letters. 2020; 5 (2):463-468.

Chicago/Turabian Style

Yajing Liu; Edwin K. P. Chong; Ali Pezeshki; Zhenliang Zhang. 2020. "A General Framework for Bounding Approximate Dynamic Programming Schemes." IEEE Control Systems Letters 5, no. 2: 463-468.

Journal article
Published: 15 June 2020 in IEEE Open Journal of Engineering in Medicine and Biology
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Goal: The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities— handover and cell (re)selection—used to maintain seamless coverage for mobile end-user equipment (UE) . The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation.

ACS Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities. IEEE Open Journal of Engineering in Medicine and Biology 2020, 1, 187 -189.

AMA Style

Alaa A. R. Alsaeedy, Edwin K. P. Chong. Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities. IEEE Open Journal of Engineering in Medicine and Biology. 2020; 1 ():187-189.

Chicago/Turabian Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. 2020. "Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities." IEEE Open Journal of Engineering in Medicine and Biology 1, no. : 187-189.

Journal article
Published: 29 May 2020 in IEEE Transactions on Education
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Contribution: This article presents quantitative support that the changes implemented as part of Colorado State University’s (CSU’s) Revolutionizing Engineering Departments (REDs) grant produce statistically significant positive change through a series of nonparametric analysis techniques. Additionally, the set of nonparametric analysis techniques provides a novel approach to quantitatively analyzing student data after significant pedagogical changes are made to the undergraduate curriculum. Background: As part of the grant, a series of significant pedagogical changes were made to the electrical and computer engineering (ECE) undergraduate curriculum. A large portion of these changes relates to knowledge integration techniques, which are used to highlighting the intricate relationships between the three topics of electronics, signals and systems, and electromagnetics. This article presents an analysis of the outcomes that are in part due to these changes. Intended Outcomes: As a result of the grant and the associated curriculum changes, it was anticipated that the cumulative in-major grade point average for third-year students would increase. It was also anticipated that the in-major intercourse grades would be more positively correlated. The analysis techniques that were used provide novel examples of applications to student data. Application Design: The implemented changes described in this article directly follow from the goals of the National Science Foundation’s RED program. Findings: Three nonparametric analysis techniques are performed on a collection of data from ECE undergraduates that was collected over 20 years. It is shown that the intertopical correlations between courses increase immediately following the implementation of the intervention discussed in this article, and statistically, significant evidence is presented supporting that the distribution of grades has positively changed following the intervention.

ACS Style

Christopher Robbiano; Anthony A. Maciejewski; Edwin K. P. Chong. Nonparametric Analysis of the Effect of Knowledge Integration Activities on Third-Year Undergraduate Performance. IEEE Transactions on Education 2020, 63, 305 -313.

AMA Style

Christopher Robbiano, Anthony A. Maciejewski, Edwin K. P. Chong. Nonparametric Analysis of the Effect of Knowledge Integration Activities on Third-Year Undergraduate Performance. IEEE Transactions on Education. 2020; 63 (4):305-313.

Chicago/Turabian Style

Christopher Robbiano; Anthony A. Maciejewski; Edwin K. P. Chong. 2020. "Nonparametric Analysis of the Effect of Knowledge Integration Activities on Third-Year Undergraduate Performance." IEEE Transactions on Education 63, no. 4: 305-313.

Article
Published: 05 May 2020 in Mobile Networks and Applications
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We introduce a new approach for Search-and-Rescue Operations (SAROs) to search for survivors after large-scale disasters, assuming the wireless communication network cells are partially operational and exploiting the recent trend of using Unmanned Aerial Vehicles (UAVs) as a part of the network. These SAROs are based on the idea that almost all survivors have their own wireless mobile devices, called User Equipments (UEs), which serve as human-based sensors on the ground. Our approach is aimed at accounting for limited UE battery power while providing critical information to first responders: 1) generate immediate crisis maps for the disaster-impacted areas, 2) provide vital information about where the majority of survivors are clustered/crowded, and 3) prioritize the impacted areas to identify regions that urgently need communication coverage.

ACS Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. 5G and UAVs for Mission-Critical Communications: Swift Network Recovery for Search-and-Rescue Operations. Mobile Networks and Applications 2020, 25, 2063 -2081.

AMA Style

Alaa A. R. Alsaeedy, Edwin K. P. Chong. 5G and UAVs for Mission-Critical Communications: Swift Network Recovery for Search-and-Rescue Operations. Mobile Networks and Applications. 2020; 25 (5):2063-2081.

Chicago/Turabian Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. 2020. "5G and UAVs for Mission-Critical Communications: Swift Network Recovery for Search-and-Rescue Operations." Mobile Networks and Applications 25, no. 5: 2063-2081.

Research article
Published: 20 March 2020 in PLOS ONE
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The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. In this paper, we develop a general method for stock price prediction using time-varying covariance information. To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. Our proposed method involves a dimension-reduction operation constructed based on principle components. Projecting the noisy observation onto a principle subspace results in a well-conditioned problem. We illustrate our results based on historical daily price data for 150 companies from different market-capitalization categories. We compare the performance of our method to two other methods: Gauss-Bayes, which is numerically demanding, and moving average, a simple method often used by technical traders and researchers. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.

ACS Style

Mahsa Ghorbani; Edwin K. P. Chong. Stock price prediction using principal components. PLOS ONE 2020, 15, e0230124 .

AMA Style

Mahsa Ghorbani, Edwin K. P. Chong. Stock price prediction using principal components. PLOS ONE. 2020; 15 (3):e0230124.

Chicago/Turabian Style

Mahsa Ghorbani; Edwin K. P. Chong. 2020. "Stock price prediction using principal components." PLOS ONE 15, no. 3: e0230124.

Article
Published: 17 February 2020 in Discrete Event Dynamic Systems
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The greedy strategy is an approximation algorithm to solve optimization problems arising in decision making with multiple actions. How good is the greedy strategy compared to the optimal solution? In this survey, we mainly consider two classes of optimization problems where the objective function is submodular. The first is set submodular optimization, which is to choose a set of actions to optimize a set submodular objective function, and the second is string submodular optimization, which is to choose an ordered set of actions to optimize a string submodular function. Our emphasis here is on performance bounds for the greedy strategy in submodular optimization problems. Specifically, we review performance bounds for the greedy strategy, more general and improved bounds in terms of curvature, performance bounds for the batched greedy strategy, and performance bounds for Nash equilibria.

ACS Style

Yajing Liu; Edwin K. P. Chong; Ali Pezeshki; Zhenliang Zhang. Submodular optimization problems and greedy strategies: A survey. Discrete Event Dynamic Systems 2020, 30, 381 -412.

AMA Style

Yajing Liu, Edwin K. P. Chong, Ali Pezeshki, Zhenliang Zhang. Submodular optimization problems and greedy strategies: A survey. Discrete Event Dynamic Systems. 2020; 30 (3):381-412.

Chicago/Turabian Style

Yajing Liu; Edwin K. P. Chong; Ali Pezeshki; Zhenliang Zhang. 2020. "Submodular optimization problems and greedy strategies: A survey." Discrete Event Dynamic Systems 30, no. 3: 381-412.

Short communication
Published: 29 November 2019 in Automatica
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This paper proposes two novel observer schemes for reconstructing faults in systems where the fault enters the state and output equations via nonlinear functions, which has not been considered in the literature. Two design methods are presented: one for the case where the fault dynamics are known and can be expressed as a polynomial function of time, and another for the case where the fault dynamics are unknown. The gains of the observer are designed using linear matrix inequalities (LMIs) such that the root-mean-square (RMS) gain from the uncertainties (or disturbances) to the fault reconstruction error is bounded. Necessary conditions for the feasibility of the LMIs are presented. Finally, a simulation example is shown to demonstrate the efficacy of the proposed scheme.

ACS Style

Wen-Shyan Chua; Joseph Chang Lun Chan; Chee Pin Tan; Edwin Kah Pin Chong; Sajeeb Saha. Robust fault reconstruction for a class of nonlinear systems. Automatica 2019, 113, 108718 .

AMA Style

Wen-Shyan Chua, Joseph Chang Lun Chan, Chee Pin Tan, Edwin Kah Pin Chong, Sajeeb Saha. Robust fault reconstruction for a class of nonlinear systems. Automatica. 2019; 113 ():108718.

Chicago/Turabian Style

Wen-Shyan Chua; Joseph Chang Lun Chan; Chee Pin Tan; Edwin Kah Pin Chong; Sajeeb Saha. 2019. "Robust fault reconstruction for a class of nonlinear systems." Automatica 113, no. : 108718.

Review
Published: 29 November 2019 in International Journal of Network Management
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Mobility management (MM) in Long‐Term Evolution (LTE) networks is a vital process to keep an individual User Equipment (UE) connected while moving within the network coverage area. MM Entity (MME) is the LTE component responsible for tracking and paging procedures and controlling the corresponding signaling between the UE and its serving cell, which is necessary for data‐packet exchange. Because of the massive increase in the density of mobile UEs, MME is burdened by the high volume signaling load, especially because most of that load comes from Tracking Area Update (TAU) and Paging messages, which are essential to exchange UE‐specific information with the network. To achieve cost‐efficient resource provisioning, many solutions have been proposed for TAU and Paging management to optimize not only UE experience (ie, battery power consumption) but also network resources (ie, bandwidth). In this paper, we discuss various solution schemes for TAU and Paging in terms of complexity, latency, and computation costs. Also, this review discusses the adverse effects of these solutions on the LTE Key Performance Indicators (KPIs). Furthermore, we present a new trend of MM solutions in LTE networks, called software‐defined network (SDN) and software‐defined virtualization (SDNV). To this end, we examine the existing schemes and challenges in the literature toward next‐generation wireless networks (eg, 5G, Internet‐of‐Things [IoT], and machine to machine [M2M] communications), and we describe user mobility models that are used to analyze the network performance.

ACS Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. A review of mobility management entity in LTE networks: Power consumption and signaling overhead. International Journal of Network Management 2019, 30, 1 .

AMA Style

Alaa A. R. Alsaeedy, Edwin K. P. Chong. A review of mobility management entity in LTE networks: Power consumption and signaling overhead. International Journal of Network Management. 2019; 30 (1):1.

Chicago/Turabian Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. 2019. "A review of mobility management entity in LTE networks: Power consumption and signaling overhead." International Journal of Network Management 30, no. 1: 1.

Preprint
Published: 02 October 2019
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Critical infrastructure systems such as electric power networks, water networks, and transportation systems play a major role in the welfare of any community. In the aftermath of disasters, their recovery is of paramount importance; orderly and efficient recovery involves the assignment of limited resources (a combination of human repair workers and machines) to repair damaged infrastructure components. The decision maker must also deal with uncertainty in the outcome of the resource-allocation actions during recovery. The manual assignment of resources seldom is optimal despite the expertise of the decision maker because of the large number of choices and uncertainties in consequences of sequential decisions. This combinatorial assignment problem under uncertainty is known to be \mbox{NP-hard}. We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices. Our framework is closed-loop, and naturally incorporates all the attractive features of such a decision-making system. In contrast to myopic approaches, which do not account for the future effects of the current choices, our methodology has an anticipatory learning component that effectively incorporates \emph{lookahead} into the solutions. To this end, we leverage the theory of regression analysis, Markov decision processes (MDPs), multi-armed bandits, and stochastic models of community damage from natural disasters to develop a method for near-optimal recovery of communities. Our method contributes to the general problem of MDPs with massive action spaces with application to recovery of communities affected by hazards.

ACS Style

Yugandhar Sarkale; Saeed Nozhati; Edwin K. P. Chong; Bruce R. Ellingwood. Decision Automation for Electric Power Network Recovery. 2019, 1 .

AMA Style

Yugandhar Sarkale, Saeed Nozhati, Edwin K. P. Chong, Bruce R. Ellingwood. Decision Automation for Electric Power Network Recovery. . 2019; ():1.

Chicago/Turabian Style

Yugandhar Sarkale; Saeed Nozhati; Edwin K. P. Chong; Bruce R. Ellingwood. 2019. "Decision Automation for Electric Power Network Recovery." , no. : 1.

Journal article
Published: 17 September 2019 in IEEE Transactions on Automatic Control
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We formulate the optimal dynamic sampling allocation decision problem for feasibility determination as a stochastic control problem in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulations. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single feature of the value function one step ahead. Numerical results demonstrate the efficiency of the proposed method.

ACS Style

Yijie Peng; Jie Song; Jie Xu; Edwin K. P. Chong. Stochastic Control Framework for Determining Feasible Alternatives in Sampling Allocation. IEEE Transactions on Automatic Control 2019, 65, 2647 -2653.

AMA Style

Yijie Peng, Jie Song, Jie Xu, Edwin K. P. Chong. Stochastic Control Framework for Determining Feasible Alternatives in Sampling Allocation. IEEE Transactions on Automatic Control. 2019; 65 (6):2647-2653.

Chicago/Turabian Style

Yijie Peng; Jie Song; Jie Xu; Edwin K. P. Chong. 2019. "Stochastic Control Framework for Determining Feasible Alternatives in Sampling Allocation." IEEE Transactions on Automatic Control 65, no. 6: 2647-2653.

Journal article
Published: 26 July 2019 in IEEE Transactions on Vehicular Technology
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We study the problem of base station (BS) dynamic switching for energy efficient design of fifth generation (5G) cellular networks and beyond. We formulate this problem as a Markov decision process (MDP) and use an approximation method known as policy rollout to solve it. This method employs Monte Carlo sampling to approximate the Q-value. In this paper, we introduce a novel approach to design an energy efficient BS control algorithm. We design an MDP-based algorithm to control the ON/OFF switching of BSs in real-time; we exploit user mobility and location information in the selection of the optimal control actions. We start our formulation with the simple case of one-user one-ON. We then gradually and systematically extend this formulation to the multi-user multi-ON scenario. Simulation results show the potential of our novel approach of exploiting user mobility information within the MDP framework to achieve significant energy savings while providing quality-of-service guarantees.

ACS Style

Fateh Elsherif; Edwin K. P. Chong; Jeong-Ho Kim. Energy-Efficient Base Station Control Framework for 5G Cellular Networks Based on Markov Decision Process. IEEE Transactions on Vehicular Technology 2019, 68, 9267 -9279.

AMA Style

Fateh Elsherif, Edwin K. P. Chong, Jeong-Ho Kim. Energy-Efficient Base Station Control Framework for 5G Cellular Networks Based on Markov Decision Process. IEEE Transactions on Vehicular Technology. 2019; 68 (9):9267-9279.

Chicago/Turabian Style

Fateh Elsherif; Edwin K. P. Chong; Jeong-Ho Kim. 2019. "Energy-Efficient Base Station Control Framework for 5G Cellular Networks Based on Markov Decision Process." IEEE Transactions on Vehicular Technology 68, no. 9: 9267-9279.

Journal article
Published: 03 June 2019 in IEEE Internet of Things Journal
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In mobile wireless networks, Mobility Management (MM) is an important process to track and locate User Equipments (UEs), including IoT devices, while moving throughout the network. In LTE and 5G wireless networks, the two MM procedures are known as Tracking Area Update (TAU) and Paging, which are burdensome for both mobile IoT/UEs and network—the IoT/UEs and network always initiate the TAU and Paging, respectively. Because of potentially very high-volume traffic and increasing density of high-mobility IoT/UEs, the TAU/Paging procedure increases the accompanied signaling overhead and the power consumption in the battery-limited IoT/UEs. Hence, this problem will become even worse in 5G because the latter is expected to accommodate exceptional services (e.g., longer IoT/UE battery lifetime). We propose a new solution to solve this problem, named gNB-based UE Mobility Tracking (gNB-based UeMT). This solution has four features achieving 5G goals. First, the mobile IoT/UEs will no longer trigger the TAU to report their location changes, giving much higher power savings with no signaling overhead. Instead, second, the network elements, gNBs, take over the responsibility of Tracking and Locating these IoT/UEs, giving always-known IoT/UE locations. Third, our Paging procedure is markedly improved over the conventional one, providing very fast IoT/UE reachability with no Paging messages being sent simultaneously. Fourth, this solution guarantees lightweight signaling overhead with very low Paging delay; it achieves about 92% reduction in the corresponding signaling overhead. To this end, our solution adds no implementation complexity; instead, it exploits the already existing LTE/5G communication protocols, functions, and measurement reports.

ACS Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead. IEEE Internet of Things Journal 2019, 6, 8237 -8247.

AMA Style

Alaa A. R. Alsaeedy, Edwin K. P. Chong. Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead. IEEE Internet of Things Journal. 2019; 6 (5):8237-8247.

Chicago/Turabian Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. 2019. "Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead." IEEE Internet of Things Journal 6, no. 5: 8237-8247.

Journal article
Published: 14 November 2018 in IEEE Transactions on Aerospace and Electronic Systems
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We develop polynomial-time heuristic methods to solve unimodular quadratic program (UQP) approximately, which is a known NP-hard problem. Several problems in active sensing and wireless communication applications boil down to UQPs. First, we derive a performance bound for a known UQP approximation method called dominant-eigenvectormatching heuristic. Next, we present two new polynomial-time heuristic methods inspired from the greedy strategy, and we provide performance guarantees for these methods with respect to the optimal objective.

ACS Style

Shankarachary Ragi; Edwin K. P. Chong; Hans D. Mittelmann. Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees. IEEE Transactions on Aerospace and Electronic Systems 2018, 55, 2118 -2127.

AMA Style

Shankarachary Ragi, Edwin K. P. Chong, Hans D. Mittelmann. Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees. IEEE Transactions on Aerospace and Electronic Systems. 2018; 55 (5):2118-2127.

Chicago/Turabian Style

Shankarachary Ragi; Edwin K. P. Chong; Hans D. Mittelmann. 2018. "Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees." IEEE Transactions on Aerospace and Electronic Systems 55, no. 5: 2118-2127.

Proceedings article
Published: 09 November 2018 in Volume 5: Engineering Education
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This paper discusses the results of a literature search on implementing novel approaches to teaching engineering design as well as the need for teaching Systems Engineering (SE) at an undergraduate level. In addition, the paper presents the results and lessons learned by assigning a capstone project requiring students to develop a conveyor system using the 8-phase SE process and a project based collaborative design methodology. The instructor teaches the fundamentals of systems engineering, the concept of synthesis, and the basics of trade-off studies. Students learn how to use functional modeling and the proper use of a functional flow block diagram to transform design requirements into failure modes. Students perform traditional failure mode calculations, using a strength-resistance approach, on machine components such as shafts, bearings, gears, belts, chains, keyways, splines, clutches, springs, brakes, and bolts for the conveyor system’s transmission. The instructor assigns a conveyor system and its systems requirements and students must demonstrate their understanding of the SE process as well as being able to perform design calculations on various machine components. The students demonstrate their understanding of SE and failure modes by taking part in design reviews throughout the semester and a final engineering report.

ACS Style

Anthony D’Angelo; Edwin K. P. Chong. Application of Systems Engineering to Machine Design for Technology Students. Volume 5: Engineering Education 2018, 1 .

AMA Style

Anthony D’Angelo, Edwin K. P. Chong. Application of Systems Engineering to Machine Design for Technology Students. Volume 5: Engineering Education. 2018; ():1.

Chicago/Turabian Style

Anthony D’Angelo; Edwin K. P. Chong. 2018. "Application of Systems Engineering to Machine Design for Technology Students." Volume 5: Engineering Education , no. : 1.

Proceedings article
Published: 09 November 2018 in Volume 13: Design, Reliability, Safety, and Risk
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This paper establishes the baseline for incorporating the Internet of Things (IoT) into the Reliability-Risk model. The authors developed the original Reliability-Risk model as a “trade-off” tool for ranking conceptual designs as a function of reliability. We summarize the original Reliability-Risk model and algorithm and discuss the process of updating the standard Integration Definition Function Modeling (IDEF0) technique with the IoT. Inserting the updated IDEF0 into the Reliability-Risk modeling framework creates a dynamic closed-loop system. We identified a concept for using a probabilistic workflow to automate the new closed-loop system and discuss a Reliability-Risk sensitivity approach. The Reliability-Risk model ranked five conceptual packaging designs against 17 criteria for incorporation into the supply chain. The authors use a Multi-Criteria-Decision System (MCDS) to establish the rankings. The paper re-visits the original example to include data (the IoT) such as shock, temperature, and humidity obtained from various nodes in the logistics cycle. After the sensor data are incorporated, updated systems specification and reliability models resulted in a new ranking. We will discuss the results of the rankings. Current research in developing the Digital Twin and Digital Thread are lacking in the area of logistics modeling. The incorporation of Discrete Event Simulation models to simulate transportation, handling, and storage shows promise to address these shortcomings. Therefore, we will briefly discuss our approach on incorporating Discrete Event Simulation modeling into the Reliability-Risk-IoT model to create a “logistics twin.”

ACS Style

Anthony D’Angelo; Edwin K. P. Chong. A Systems Engineering Approach to Incorporating the Internet of Things to Reliability-Risk Modeling for Ranking Conceptual Designs. Volume 13: Design, Reliability, Safety, and Risk 2018, 1 .

AMA Style

Anthony D’Angelo, Edwin K. P. Chong. A Systems Engineering Approach to Incorporating the Internet of Things to Reliability-Risk Modeling for Ranking Conceptual Designs. Volume 13: Design, Reliability, Safety, and Risk. 2018; ():1.

Chicago/Turabian Style

Anthony D’Angelo; Edwin K. P. Chong. 2018. "A Systems Engineering Approach to Incorporating the Internet of Things to Reliability-Risk Modeling for Ranking Conceptual Designs." Volume 13: Design, Reliability, Safety, and Risk , no. : 1.

Article
Published: 29 October 2018 in Mobile Networks and Applications
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The 5th Generation wireless systems (5G) is expected to accommodate exceptional services beyond current cellular systems. To achieve this goal, however, ongoing studies are still developing new schemes to provide seamless connections to the ever increasing density of high-mobility User Equipments (UEs). That means that the network needs to track all UEs while moving throughout the coverage area for the purpose of data-packet delivery. The two Mobility Management (MM) procedures that are essential to localize a specific UE and deliver data packets to that UE are known as Tracking Area Update (TAU) and Paging, which are burdensome to the system because of very high-volume traffic. Therefore, MM will become a crucial problem for 5G requirements; how to support real-time applications and provide close-to-zero latency for life-critical systems? This paper addresses a variety of problems that should be faced and discusses various solution schemes in terms of implementation complexity, latency, and computation overhead for both the TAU and Paging. Because 5G systems will work in conjunction with current Long Term Evolution (LTE) systems and the latter is retuned to use as a base design for future 5G, our discussion starts from current LTE solutions towards 5G MM improvements. In this context, this paper emphasizes a new key design for 5G and explains the challenges that impact both the network performance and UE experience (e.g., power saving). Next, we critically discuss the applicability of current LTE solution schemes (in terms of TAU and Paging costs) and evaluate them for 5G use cases. To the best of our knowledge, this paper is the first study that emphasizes and gives a critique on using of different types of UE mobility models (based on the given studies), which are used to analyze the network performance that interacts with the UE movements. In this context, some 5G improvement schemes are discussed.

ACS Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. Tracking Area Update and Paging in 5G Networks: a Survey of Problems and Solutions. Mobile Networks and Applications 2018, 24, 578 -595.

AMA Style

Alaa A. R. Alsaeedy, Edwin K. P. Chong. Tracking Area Update and Paging in 5G Networks: a Survey of Problems and Solutions. Mobile Networks and Applications. 2018; 24 (2):578-595.

Chicago/Turabian Style

Alaa A. R. Alsaeedy; Edwin K. P. Chong. 2018. "Tracking Area Update and Paging in 5G Networks: a Survey of Problems and Solutions." Mobile Networks and Applications 24, no. 2: 578-595.

Journal article
Published: 20 September 2018 in Reliability Engineering & System Safety
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The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances.

ACS Style

Saeed Nozhati; Yugandhar Sarkale; Bruce Ellingwood; Edwin K.P. Chong; Hussam Mahmoud. Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management. Reliability Engineering & System Safety 2018, 181, 116 -126.

AMA Style

Saeed Nozhati, Yugandhar Sarkale, Bruce Ellingwood, Edwin K.P. Chong, Hussam Mahmoud. Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management. Reliability Engineering & System Safety. 2018; 181 ():116-126.

Chicago/Turabian Style

Saeed Nozhati; Yugandhar Sarkale; Bruce Ellingwood; Edwin K.P. Chong; Hussam Mahmoud. 2018. "Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management." Reliability Engineering & System Safety 181, no. : 116-126.