Showing papers in "IEEE Systems Journal in 2015"

Design Techniques and Applications of Cyberphysical Systems: A Survey

Abstract: Cyberphysical systems (CPSs) are new class of engineered systems that offer close interaction between cyber and physical components. The field of CPS has been identified as a key area of research, and CPSs are expected to play a major role in the design and development of future systems. In this paper, we survey recent advancements made in the development and applications of CPSs. We classify the existing research work based on their characteristics and identify the future challenges. We also discuss the examples of prototypes of CPSs. The aim of this survey is to enable researchers and system designers to get insights into the working and applications of CPSs and motivate them to propose novel solutions for making wide-scale adoption of CPS a tangible reality.

Robust Biometrics-Based Authentication Scheme for Multiserver Environment

Abstract: The authentication scheme is an important cryptographic mechanism, through which two communication parties could authenticate each other in the open network environment To satisfy the requirement of practical applications, many authentication schemes using passwords and smart cards have been proposed However, passwords might be divulged or forgotten, and smart cards might be shared, lost, or stolen In contrast, biometric methods, such as fingerprints or iris scans, have no such drawbacks Therefore, biometrics-based authentication schemes gain wide attention In this paper, we propose a biometrics-based authentication scheme for multiserver environment using elliptic curve cryptography To the best of our knowledge, the proposed scheme is the first truly three-factor authenticated scheme for multiserver environment We also demonstrate the completeness of the proposed scheme using the Burrows–Abadi–Needham logic

A Review of Charge Scheduling of Electric Vehicles in Smart Grid

Abstract: Electric vehicles (EVs) are being introduced by different manufacturers as an environment-friendly alternative to vehicles with internal combustion engines, with several benefits. The number of EVs is expected to grow rapidly in the coming years. However, uncoordinated charging of these vehicles can put a severe stress on the power grid. The problem of charge scheduling of EVs is an important and challenging problem and has seen significant research activity in the last few years. This review covers the recent works done in the area of scheduling algorithms for charging EVs in smart grid. The works are first classified into two broad classes of unidirectional versus bidirectional charging, and then, each class is further classified based on whether the scheduling is centralized or distributed and whether any mobility aspects are considered or not. It then reviews the key results in this field following the classification proposed. Some interesting research challenges that can be addressed are also identified.

A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services

Abstract: In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.

Real-Time Economic Dispatch Considering Renewable Power Generation Variability and Uncertainty Over Scheduling Period

Abstract: Real-time economic dispatch (RTED) is performed every 5–15 min with the static snapshot forecast data. During the period between two consecutive schedules, generators participate in managing power imbalance, based on participation factors (PFs) from previous economic dispatch (ED). In modern power systems with considerable renewable energy sources that have high variability, this conventional approach may not adequately accommodate the economic implication of the said variability. This paper proposes the evaluation of “best-fit” PFs by taking into account the minute-to-minute variability of solar, wind, and load demand, for a scheduling period. Since “best-fit” PFs are evaluated only once, i.e., at the start of scheduling interval, the dimensionality of optimization problem remains the same as that of conventional approach. The proposed approach is suggested for sequential and dynamic variants. Results for two test systems have been obtained to verify the benefit of the proposed approach.

Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach

Abstract: In mobile ad hoc networks (MANETs), a primary requirement for the establishment of communication among nodes is that nodes should cooperate with each other. In the presence of malevolent nodes, this requirement may lead to serious security concerns; for instance, such nodes may disrupt the routing process. In this context, preventing or detecting malicious nodes launching grayhole or collaborative blackhole attacks is a challenge. This paper attempts to resolve this issue by designing a dynamic source routing (DSR)-based routing mechanism, which is referred to as the cooperative bait detection scheme (CBDS), that integrates the advantages of both proactive and reactive defense architectures. Our CBDS method implements a reverse tracing technique to help in achieving the stated goal. Simulation results are provided, showing that in the presence of malicious-node attacks, the CBDS outperforms the DSR, 2ACK, and best-effort fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet delivery ratio and routing overhead (chosen as performance metrics).

Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study

Abstract: As advanced metering infrastructure (AMI) is responsible for collecting, measuring, and analyzing energy usage data, as well as transmitting this information from a smart meter to a data concentrator and then to a headend system in the utility side, the security of AMI is of great concern in the deployment of smart grid. In this paper, we analyze the possibility of using data stream mining for enhancing the security of AMI through an intrusion detection system (IDS), which is a second line of defense after the primary security methods of encryption, authentication, authorization, etc. We propose a realistic and reliable IDS architecture for the whole AMI system, which consists of individual IDSs for three different levels of AMI's components: smart meter, data concentrator, and AMI headend. We also explore the performances of various existing state-of-the-art data stream mining algorithms on a publicly available IDS data set, namely, the KDD Cup 1999 data set. Then, we conduct a feasibility analysis of using these existing data stream mining algorithms, which exhibit varying levels of accuracies, memory requirements, and running times, for the distinct IDSs at AMI's three different components. Our analysis identifies different candidate algorithms for the different AMI components' IDSs, respectively.

Socially Aware Networking: A Survey

Abstract: The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g., opportunistic networks, mobile social networks, delay-tolerant networks, ad hoc networks, etc.) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber-physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms, and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness, and scalability are discussed.

Joint Energy and Spinning Reserve Market Clearing Incorporating Wind Power and Load Forecast Uncertainties

Abstract: This paper proposes an energy and spinning reserve market clearing (ESRMC) mechanism for wind–thermal power system, considering uncertainties in wind power and load forecasts. Two different market models for the ESRMC are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators and demand/consumers. The stochastic behavior of wind speed and wind power is represented by the Weibull probability density function (pdf), and that of the load is represented by a normal pdf. This paper considers two objectives: total cost minimization and the system-risk-level minimization. The first objective includes the cost of energy provided by thermal and wind generators, and the cost of reserves provided by thermal generators and loads. It also includes costs due to overestimation and underestimation of available wind power and load demand. The system risk level is considered as another objective as wind power is highly uncertain. Multiobjective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the ESRMC problem. The results of the IEEE 30 bus system demonstrate the utility of the proposed approach.

Energy Loss Minimization in Distribution Systems Utilizing an Enhanced Reconfiguration Method Integrating Distributed Generation

Abstract: Different types of distributed generation (DG) are broadly used and optimally placed in a distribution system to improve its performance. Since the network configuration affects the system operational conditions, the network reconfiguration and DG placement should be manipulated simultaneously. Nevertheless, the complexity of the problem may prevent from achieving the optimal solution. This paper presents a novel hybrid method of metaheuristic and heuristic algorithms, in order to boost robustness and shorten the computational runtime to achieve network minimum loss configuration in the presence of DGs. The developed backward/forward power flow is adopted to consider the PV(Q) model of DG. Moreover, different patterns of load types are taken into consideration to perform a practical study. To assess the capabilities of the proposed method, simulations are carried out on IEEE 33-bus and 83-bus practical distribution network of Taiwan Power Company. Furthermore, the proposed method is applied to a 33-bus unbalanced distribution network to verify its applicability in unbalanced distribution systems. The obtained results demonstrate the effectiveness of the proposed method to find optimal status of switches, as well as locations and sizes of DG units, in a rather shorter time than other approaches in the literature.

A Hybrid With Cross-Entropy Method and Sequential Quadratic Programming to Solve Economic Load Dispatch Problem

Abstract: This paper presents a new hybrid approach integrating the cross-entropy (CE) algorithm and the sequential quadratic programming (SQP) technique to solve the economic load dispatch (ELD) problem related to electrical power generating units. Due to the introduction of the valve-point effect in the ELD objective function, the optimization task requires tools appropriate for a nonconvex optimization landscape. In this respect, we employ the CE approach, which constructs a random sequence of solutions probabilistically converging to a near-optimal solution and, thus, facilitating the exploration capability. Additionally, to fine-tune the solution in promising basins of attraction, the SQP algorithm is invoked, which performs a local search. Despite its reliance on a global heuristic scheme, CE-SQP is vested with fast convergence capability, which may entail its use for online power dispatch. The effectiveness and the robustness of the proposed method in comparison with several state-of-the-art approaches have been demonstrated with four standard test systems that are widely reported in the ELD literature.

Collaborative Learning Automata-Based Routing for Rescue Operations in Dense Urban Regions Using Vehicular Sensor Networks

Abstract: In vehicular sensor networks (VSNs), an increase in the density of the vehicles on road and route jamming in the network causes delay in receiving the emergency alerts, which results in overall system performance degradation. In order to address this issue in VSNs deployed in dense urban regions, in this paper, we propose collaborative learning automata-based routing algorithm for sending information to the intended destination with minimum delay and maximum throughput. The learning automata (LA) stationed at the nearest access points (APs) in the network learn from their past experience and make routing decisions quickly. The proposed strategy consists of dividing the whole region into different clusters, based on which an optimized path is selected using collaborative LA having input parameters as vehicle density, distance from the nearest service unit, and delay. A theoretical expression for density estimation is derived, which is used for the selection of the “best” path by LA. The performance of the proposed scheme is evaluated with respect to metrics such as packet delivery delay (network delay), packet delivery ratio with varying node (vehicle) speed, transmission range, density of vehicle, and number of road side units/APs). The results obtained show that the proposed scheme performs better than the benchmark chosen in this study, as there is a 30% reduction in network delay and a 20% increase in packet delivery ratio.

Design of GPS Anti-Jamming Systems Using Adaptive Notch Filters

Abstract: Continuous wave interferences (CWIs) can reduce the positioning accuracy of a Global Positioning System (GPS) receiver and even paralyze its normal operation. Therefore, we have designed a low-cost anti-jamming system to combat CWIs for GPS receivers. The anti-jamming system comprises cascaded CWI-detectable adaptive notch filter (ANF) modules, and each module is able to mitigate one CWI. The ANF module is composed of a second-order infinite-impulse response (IIR) filter with a lattice structure. The proposed ANF detects the existence of the CWI and estimates its power by exploiting the statistic value and internal state associated with the IIR filter, respectively. The impact of the ANF module on the acquisition and tracking loops is analyzed. Properly controlling the $-$ 3-dB bandwidth of the ANF module allows its impact to be neglected. Moreover, more modules can be cascaded to deal with multiple CWIs. We have also designed an adaptation algorithm for the ANF modules and proven that the module can adaptively notch the strongest CWI at its input. This merit enables us to notch the primary CWIs with a limited number of ANF modules. Simulation results show that our approach considerably outperforms related works in both CWI detection and rejection capabilities.

Time-and-Energy-Aware Computation Offloading in Handheld Devices to Coprocessors and Clouds

Abstract: Running sophisticated software on smart phones could result in poor performance and shortened battery lifetime because of their limited resources. Recently, offloading computation workload to the cloud has become a promising solution to enhance both performance and battery life of smart phones. However, it also consumes both time and energy to upload data or programs to the cloud and retrieve the results from the cloud. In this paper, we develop an offloading framework, named Ternary Decision Maker (TDM), which aims to shorten response time and reduce energy consumption at the same time. Unlike previous works, our targets of execution include an on-board CPU, an on-board GPU, and a cloud, all of which combined provide a more flexible execution environment for mobile applications. We conducted a real-world application, i.e., matrix multiplication, in order to evaluate the performance of TDM. According to our experimental results, TDM has less false offloading decision rate than existing methods. In addition, by offloading modules, our method can achieve, at most, 75% savings in execution time and 56% in battery usage.

Current Status and Issues of Concern for the Integration of Distributed Generation Into Electricity Networks

Abstract: As existing distribution networks were designed to deliver unidirectional power to consumers and require minimal control intervention, they result in largely passive infrastructures The installation of distributed generation (DG) units with significant capacity in these passive networks can cause reverse power flows, which will result in some conflicts with the operation of the existing protection system In this context, utilities around the world have started establishing requirements to ensure safe and reliable interconnection of generators in low- and medium-voltage networks The technical grid code requirements and regulations vary considerably from country to country However, any standard should address the critical need to make the DG marketable by providing uniform criteria and requirements relevant to the performance, operation, and safety This paper presents a critical review of the requirements adopted by distribution companies in selected countries, such as the US, the UK, Germany, and Australia, to facilitate the connection of DG The main problems, such as voltage regulation, islanding operation, and dynamic interactions among DG and loads, are discussed to identify a few points where attention is still needed to improve the reliability of distribution systems

A Local Search-Based Multiobjective Optimization Algorithm for Multiobjective Vehicle Routing Problem With Time Windows

Abstract: Vehicle routing problem with time windows (VRPTW) is an important logistics problem, which appears to be multiobjective in real world. Recently, a general multiobjective VRPTW (MOVRPTW) with five objectives has been defined, and a set of MOVRPTW problem instances based on data from real world have been proposed. These instances indicate more truly multiobjective nature and represent more realistic and challenging MOVRPTW cases. In this paper, a local search-based multiobjective optimization algorithm is proposed for the real-world MOVRPTW instances. Considering the problem structure of MOVRPTW, we design different local search methods for different objectives. These simple but effective local search methods cooperate to optimize different objectives simultaneously. More problem-specific knowledge can be extracted by using objectivewise local search components, and thus, high-quality solutions are expected to be generated. The proposed algorithm is tested on 45 realistic and challenging MOVRPTW benchmark instances from real world. Experimental results show that the proposed algorithm can obtain better solutions than the previous evolutionary algorithm-based multiobjective algorithm on new MOVRPTW cases. Additional results on 56 Solomon instances show the stability of the proposed algorithm across data sets.

MoMoRo: Providing Mobility Support for Low-Power Wireless Applications

Abstract: Recently, mobile devices have been introduced in various wireless sensor network (WSN) applications in order to solve complex tasks or to increase the data collection efficiency. However, the current generation of low-power WSN protocols is mainly designed to support data collection and address application-specific challenges without any particular considerations for mobility. In this paper, we introduce MoMoRo, a mobility support layer that can be easily applied to existing data collection protocols, thereby enabling mobility support in the network. MoMoRo robustly collects neighborhood information and uses a fuzzy estimator to make link quality estimations. This fuzzy estimator continuously reconfigures its thresholds for determining the fuzzy sets, allowing MoMoRo to easily adapt to changing channel environments. Furthermore, MoMoRo includes an active destination search scheme that allows disconnected mobile nodes with sparse traffic to quickly reconnect if there are packets in the network destined to this mobile node. We evaluate MoMoRo both indoor and outdoor and show that a continuously moving device in a MoMoRo-enabled RPL (i.e., IPv6 Routing Protocol for Low-Power and Lossy Networks) network can achieve a high packet reception ratio of up to 96% and stay connected in areas where RPL alone cannot and with less than half the packet overhead needed by the well-known Ad hoc On-Demand Distance Vector routing protocol.

Real-Time Coordination of Electric Vehicles to Support the Grid at the Distribution Substation Level

Abstract: In this paper, the charging stations (CSs) of electric vehicles (EVs) and their coordination at the substation level are presented. It is considered that the EVs of a particular area arrive at the CS in their idle time to charge their batteries. Fuzzy logic controllers (FLCs) have been designed at the substation and the CS level. The FLC at the substation level decides the amount of power to be compensated by the entire CSs, and the FLC at the CS level determines the power to be exchanged by individual CS. The aggregator at the substation level will distribute the power among the CSs connected to different subfeeders. Also, every subfeeder has an aggregator which distributes the power among different CSs connected to the same subfeeder. Batteries of EVs have been modeled which can handle the capacity loss at different charging/discharging rates $(C_{\rm rate})$ . The $C_{\rm rate} $ of the battery is controlled to achieve the desired rate of power flow between the grid and the EV battery. The main focus of this paper is to coordinate the EVs, present at the CSs, and support the grid by peak shaving and valley filling.

Electromechanical Dynamics of Controlled Variable-Speed Wind Turbines

Abstract: Variable-speed wind turbines are increasingly penetrating into the electrical grid, replacing the conventional synchronous-generator-based power plants and thus decreasing the available inertial response for primary frequency stability. This paper offers a deeper understanding of variable-speed wind turbine generators (WTGs) in the context of maximum power point tracking and obtaining primary frequency response. Linearized models have been obtained between the wind velocity and the system frequency versus the power output. System complexity has been studied from the point of view of modal analysis of a two-mass drive train model of a WTG, as well as Hankel singular values. Finally, individual WTG models have been combined to form wind farms, whose complexity has again been found to depend on the nature of modeling of the WTG drive trains.

Multisatellite Formation Control for Remote Sensing Applications Using Artificial Potential Field and Adaptive Fuzzy Sliding Mode Control

Abstract: The formation control of satellites for remote sensing applications has received considerable attention during the past decade. This work deals with the development of a formation control strategy for the circular formation of a group of satellites. In this paper, artificial potential field method is used for path planning, and sliding mode control (SMC) technique is used for designing a robust controller. A fuzzy inference mechanism is utilized to reduce the chattering phenomenon inherent in the conventional SMC. An adaptive tuning algorithm is also derived based on Lyapunov stability theory to tune the fuzzy parameter. The proposed fuzzy-SMC-based technique is intended to compensate for the modeling uncertainties existing in practical applications. The results of simulations done for a group of five satellites making a circular formation confirm the stability and robustness of the present scheme.

HERO: Hierarchical Energy Optimization for Data Center Networks

Abstract: The rapid escalating power consumption has become critically important to modern data centers. Existing works on reducing the power consumption of network elements formulate the power optimization problem for a general network topology and require a centralized controller. As the scale of data centers increases, the complexity of solving this optimization problem increases rapidly. Inspired from the hierarchical data center network (DCN) topologies and data center traffic patterns, we design a two-level, pod-level, and core-level power optimization model, namely, Hierarchical EneRgy Optimization (HERO), to reduce the power consumption of network elements by switching off network switches and links while still guaranteeing full connectivity and maximizing link utilization. Given a physical DCN topology and a traffic matrix, we illustrate that two-level power optimizations in HERO fall in the class of capacitated multicommodity minimum cost flow (CMCF) problem, which is NP-hard. Therefore, we design several heuristic algorithms based on different switch elimination criteria to solve the proposed HERO optimization problem. The power-saving performance of the proposed HERO model is evaluated by several experiments with different traffic patterns. Our simulations demonstrate that HERO can reduce power consumptions of network elements effectively with reduced complexity.

A Combined Approach of Using an SDBR and a STATCOM to Enhance the Stability of a Wind Farm

Abstract: This paper presents a method to enhance the stability of a grid-connected wind farm composed of a fixed-speed wind turbine generator system (WTGS) using a combination of a small series dynamic braking resistor (SDBR) and static synchronous compensator (STATCOM). The SDBR and STATCOM have active and reactive power control abilities, respectively, and a combination of these units paves the way to stabilize well the fixed-speed wind farm. In this paper, a centralized control scheme of using an SDBR and a STATCOM together is focused, which can be easily integrated with a wind farm. Different types of symmetrical and unsymmetrical faults are considered to evaluate the transient performance of the proposed control scheme, applicable to a grid-connected wind farm. The effect of a multimass drive train of a fixed-speed WTGS in fault analysis, along with its importance in determining the size of the SDBR to augment the transient stability of a wind farm, is investigated. Extensive simulation analyses are performed to determine the approximate sizes of both SDBR and STATCOM units. Dynamic analysis is performed using real wind speed data. A salient feature of this work is that the effectiveness of the proposed system to minimize the blade–shaft torsional oscillation of a fixed-speed WTGS is also analyzed. Simulation results show that a combination of a small SDBR and STATCOM is an effective means to stabilize the wind farm composed of a fixed-speed WTGS.

Electrical Microgrid Optimization via a New Recurrent Neural Network

Abstract: This paper presents the development and implementation of a new recurrent neural network for optimization as applied to optimal operation of an electrical microgrid, which is interconnected to the utility grid; moreover, it incorporates batteries, for energy storing and supplying, and an electric car. The proposed neural network determines the optimal amount of power over a time horizon of one week for wind, solar, and battery systems, including that of the electric car, in order to minimize the power acquired from the utility grid and to maximize the power supplied by the renewable energy sources. Simulation results illustrate that generation levels for each energy source over a time horizon can be reached in an optimal form.

Power-Efficient Provisioning for Online Virtual Network Requests in Cloud-Based Data Centers

Abstract: A cloud computing paradigm enables users to access services, applications, and infrastructure resources by using thin clients anywhere and at any time. In this paradigm, multiple users can share cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and can be submitted to the cloud-based data centers. How to map a VN onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Thus, efficient mapping techniques that intelligently use the resources of cloud infrastructure are important and necessary. Current research on VN mapping and design focuses on resource-efficient VN mapping or cost-efficient VN mapping. However, there is another important issue in cloud-based data centers that we must pay attention to, i.e., the amount of power or energy that is consumed by a data center. The power consumption in data centers can be a significant percentage of the total power consumption, and it not only leads to a higher data center operating cost but also contributes to carbon emissions and the greenhouse effect. In this paper, we propose a power-efficient resource provisioning technique in cloud-based data centers while complying with service level agreements. We first model a power-efficient VN provisioning problem as a mathematical optimization problem, with the objective of minimizing the power consumption by employing mixed-integer programming. We then propose a heuristic algorithm to efficiently solve this model since this optimization problem is NP-hard. We validate and evaluate our framework and algorithm by conducting extensive simulations on different cloud infrastructure networks under various scenarios. The simulation results show that our approach performs well.

Optimal Posturing in Day-Ahead Market Clearing for Uncertainties Considering Anticipated Real-Time Adjustment Costs

Abstract: This paper proposes a market-clearing mechanism which explicitly takes into account the impact of uncertainties in wind power generation and load forecast. Since market clearing is a multisettlement process—day ahead and real time (RT), a strategy is proposed, which provides best-fit day-ahead (DA) schedule, which minimizes the twin (both DA and RT adjustment) costs/maximizes social welfare, under all possible scenarios in RT. This two-stage optimization strategy consists of a genetic algorithm (GA) based DA market clearing and a two-point estimate-based probabilistic RT optimal power flow (OPF). The former generates sample schedules, while the latter provides mean adjustment costs. Two commonly employed standard market practices to incorporate wind energy into wholesale electricity markets have been presented. The results for a sample system with GA and two-point estimate OPF, and GA and Monte Carlo simulation have been obtained to ascertain the effectiveness of the proposed method.

Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid

Abstract: In this paper, a hierarchical smart grid architecture is presented. The concept of smart home is extended in two aspects: 1) from traditional households with smart devices, such as advanced metering infrastructure, to intelligent entities with instantaneous and distributive decision-making capabilities; and 2) from individual households to general customer units of possibly large scales. We then develop a hidden mode Markov decision process (HM-MDP) model for a customer real-time decision-making problem. This real-time decision-making framework can effectively be integrated with demand response schemes, which are prediction based and therefore inevitably lead to real-time power-load mismatches. With the Baum–Welch algorithm adopted to learn the nonstationary dynamics of the environment, we propose a value iteration (VI)-based exact solution algorithm for the HM-MDP problem. Unlike conventional VI, the concept of parsimonious sets is used to enable a finite representation of the optimal value function. Instead of iterating the value function in each time step, we iterate the representational parsimonious sets by using the incremental pruning algorithm. Although this exact algorithm leads to optimal policies giving maximum rewards for the smart homes, its complexity suffers from the curse of dimensionality. To obtain a low-complexity real-time algorithm that allows adaptively incorporating new observations as the environment changes, we resort to Q-learning-based approximate dynamic programming. Q-learning offers more flexibility in practice because it does not require specific starting and ending points of the scheduling period. Performance analysis of both exact and approximate algorithms, as compared with the other possible alternative decision-making strategies, is presented in simulation results.

Profiling-Based Indoor Localization Schemes

Abstract: In this paper, we consider the indoor localization problem, i.e., identifying the Cartesian coordinates of an object or a person under the roof. To solve this problem we consider an RF-based localization method called profiling , a two-step process, where a radio map of the monitored area is first constructed by collecting signal strength from known locations. An unknown location is then predicted using this radio map as a reference. In this paper, we first propose a K nearest neighbor (KNN) profiling-based localization method dubbed LEMON (location estimation by mining oversampled neighborhoods). It is based on a low-cost, low-power wireless devices and ensures good accuracy compared to the state-of-the-art. We then propose a variant of LEMON called combinatorial localization which exhaustively searches for the best possible set of nearest neighbors. We further define a Bayesian network model for the same localization problem. The performance of these methods is evaluated through extensive experiments in various indoor areas. We found an interesting outcome that the simple KNN-based approach can offer better localization accuracy compared to other complex localization methods. Thus we further enhance the performance of the KNN-based approach using multiple RF channels.

Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning

Abstract: Remaining useful life (RUL) estimations obtained from a prognostics and health monitoring (PHM) system can be used to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to rush to replace a component that would not affect immediately the operation of the system under consideration. This paper presents a methodology for decision support in maintenance planning with application in aeronautical systems. The proposed methodology combines system architecture information and RUL estimations for all components in the system under study, allowing the estimation of an overall system-level RUL (S-RUL). The S-RUL information can be used to support maintenance decisions regarding the replacement of multiple components. For this purpose, the decision problem can be cast into an optimization framework involving the minimization of the component replacement cost under a safety constraint. Two case studies are used to illustrate the S-RUL concept, as well as the proposed optimization methodology.

Distributed Intelligent MEMS: Progresses and Perspectives

Abstract: Microelectromechanical systems (MEMS) research has, until recently, focused mainly on the engineering process, resulting in interesting products and a growing market. To fully realize the promise of MEMS, the next step is to add embedded intelligence. With embedded intelligence, the scalability of manufacturing will enable distributed intelligent MEMS systems, consisting of thousands or millions of units that can work together to achieve a common goal. However, before such systems can become a reality, we must come to grips with the challenge of scalability, which will require paradigm shifts in both hardware and software. Furthermore, the need for coordinated actuation, programming, communication, and mobility management raises new challenges in both control and programming. The objective of this paper is to report on the progress and remaining challenges in these areas by examining two examples.

Measuring the Resilience of Supply Chain Systems Using a Survival Model

Abstract: Disruptions at any stage of a supply chain system can cause mammoth operational and financial losses to a firm. When there is a disruption with a supply chain system, it is highly desired that the system quickly recover. The ability of recovery is, in short, called resilience. This paper proposes a new measure of the resilience of a supply chain system based on the concept of survival and, subsequently, a survival model [Cox proportional hazard (Cox-PH) model]. The survival model represents a time interval or period from the time the system failed to function to the time the system gets back with its function (i.e., recovery). The input to the model is, thus, a failure event; the output from the model is the recovery time. This model has been implemented. There is a case study to illustrate how the model is used to give a quantitative measurement of resilience, in terms of recovery time.