Showing papers in "IEEE Systems Journal in 2017"
TL;DR: The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
Abstract: The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
682 citations
TL;DR: A novel sequential Monte-Carlo-based time-series simulation model is introduced to assess power system resilience and the concept of fragility curves is used for applying weather- and time-dependent failure probabilities to system's components.
Abstract: Electrical power systems have been traditionally designed to be reliable during normal conditions and abnormal but foreseeable contingencies. However, withstanding unexpected and less frequent severe situations still remains a significant challenge. As a critical infrastructure and in the face of climate change, power systems are more and more expected to be resilient to high-impact low-probability events determined by extreme weather phenomena. However, resilience is an emerging concept, and, as such, it has not yet been adequately explored in spite of its growing interest. On these bases, this paper provides a conceptual framework for gaining insights into the resilience of power systems, with focus on the impact of severe weather events. As quantifying the effect of weather requires a stochastic approach for capturing its random nature and impact on the different system components, a novel sequential Monte-Carlo-based time-series simulation model is introduced to assess power system resilience. The concept of fragility curves is used for applying weather- and time-dependent failure probabilities to system's components. The resilience of the critical power infrastructure is modeled and assessed within a context of system-of-systems that also include human response as a key dimension. This is illustrated using the IEEE 6-bus test system.
415 citations
TL;DR: A new anonymous authentication scheme for WBANs is proposed and it is proved that it is provably secure and overcomes the security weaknesses in previous schemes but also has the same computation costs at a client side.
Abstract: Advances in wireless communications, embedded systems, and integrated circuit technologies have enabled the wireless body area network (WBAN) to become a promising networking paradigm. Over the last decade, as an important part of the Internet of Things, we have witnessed WBANs playing an increasing role in modern medical systems because of its capabilities to collect real-time biomedical data through intelligent medical sensors in or around the patients’ body and send the collected data to remote medical personnel for clinical diagnostics. WBANs not only bring us conveniences but also bring along the challenge of keeping data’s confidentiality and preserving patients’ privacy. In the past few years, several anonymous authentication (AA) schemes for WBANs were proposed to enhance security by protecting patients’ identities and by encrypting medical data. However, many of these schemes are not secure enough. First, we review the most recent AA scheme for WBANs and point out that it is not secure for medical applications by proposing an impersonation attack. After that, we propose a new AA scheme for WBANs and prove that it is provably secure. Our detailed analysis results demonstrate that our proposed AA scheme not only overcomes the security weaknesses in previous schemes but also has the same computation costs at a client side.
374 citations
TL;DR: It is shown how normal operations of power networks can be statistically distinguished from the case under stealthy attacks, and two machine-learning-based techniques for stealthy attack detection are proposed.
Abstract: Aging power industries, together with the increase in demand from industrial and residential customers, are the main incentive for policy makers to define a road map to the next-generation power system called the smart grid. In the smart grid, the overall monitoring costs will be decreased, but at the same time, the risk of cyber attacks might be increased. Recently, a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine-learning-based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a distributed support vector machine (SVM). The design of the distributed SVM is based on the alternating direction method of multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects the deviation in measurements. In both methods, principal component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on IEEE standard test systems demonstrate the effectiveness of both schemes.
363 citations
TL;DR: This work explores and discusses how the various enabling technologies can be efficiently deployed to achieve a green IoT environment, and identifies some of the emerging challenges that need to be addressed in the future to enable agreen IoT.
Abstract: Recent technological advances have led to an increase in the carbon footprint. Energy efficiency in the Internet of Things (IoT) has been attracting a lot of attention from researchers and designers over the last couple of years, paving the way for an emerging area called green IoT. There are various aspects (such as key enablers, communications, services, and applications) of IoT, where efficient utilization of energy is needed to enable a green IoT environment. We explore and discuss how the various enabling technologies (such as the Internet, smart objects, sensors, etc.) can be efficiently deployed to achieve a green IoT. Furthermore, we also review various IoT applications, projects and standardization efforts that are currently under way. Finally, we identify some of the emerging challenges that need to be addressed in the future to enable a green IoT.
296 citations
TL;DR: A multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers and shows cost reduction, convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions.
Abstract: The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach.
243 citations
TL;DR: This paper proposes a cloud-supported cyber–physical localization system for patient monitoring using smartphones to acquire voice and electroencephalogram signals in a scalable, real-time, and efficient manner and uses Gaussian mixture modeling for localization to outperform other similar methods in terms of error estimation.
Abstract: The potential of cloud-supported cyber–physical systems (CCPSs) has drawn a great deal of interest from academia and industry. CCPSs facilitate the seamless integration of devices in the physical world (e.g., sensors, cameras, microphones, speakers, and GPS devices) with cyberspace. This enables a range of emerging applications or systems such as patient or health monitoring, which require patient locations to be tracked. These systems integrate a large number of physical devices such as sensors with localization technologies (e.g., GPS and wireless local area networks) to generate, sense, analyze, and share huge quantities of medical and user-location data for complex processing. However, there are a number of challenges regarding these systems in terms of the positioning of patients, ubiquitous access, large-scale computation, and communication. Hence, there is a need for an infrastructure or system that can provide scalability and ubiquity in terms of huge real-time data processing and communications in the cyber or cloud space. To this end, this paper proposes a cloud-supported cyber–physical localization system for patient monitoring using smartphones to acquire voice and electroencephalogram signals in a scalable, real-time, and efficient manner. The proposed approach uses Gaussian mixture modeling for localization and is shown to outperform other similar methods in terms of error estimation.
237 citations
TL;DR: An energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation, which allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application.
Abstract: Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.
228 citations
TL;DR: The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
Abstract: This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
201 citations
TL;DR: A working prototype of the SeDaSC methodology is implemented and its performance is evaluated based on the time consumed during various operations to show that Se daSC has the potential to be effectively used for secure data sharing in the cloud.
Abstract: Cloud storage is an application of clouds that liberates organizations from establishing in-house data storage systems. However, cloud storage gives rise to security concerns. In case of group-shared data, the data face both cloud-specific and conventional insider threats. Secure data sharing among a group that counters insider threats of legitimate yet malicious users is an important research issue. In this paper, we propose the Secure Data Sharing in Clouds (SeDaSC) methodology that provides: 1) data confidentiality and integrity; 2) access control; 3) data sharing (forwarding) without using compute-intensive reencryption; 4) insider threat security; and 5) forward and backward access control. The SeDaSC methodology encrypts a file with a single encryption key. Two different key shares for each of the users are generated, with the user only getting one share. The possession of a single share of a key allows the SeDaSC methodology to counter the insider threats. The other key share is stored by a trusted third party, which is called the cryptographic server. The SeDaSC methodology is applicable to conventional and mobile cloud computing environments. We implement a working prototype of the SeDaSC methodology and evaluate its performance based on the time consumed during various operations. We formally verify the working of SeDaSC by using high-level Petri nets, the Satisfiability Modulo Theories Library, and a Z3 solver. The results proved to be encouraging and show that SeDaSC has the potential to be effectively used for secure data sharing in the cloud.
184 citations
TL;DR: This paper collects and analyzes behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days, and considers four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS or WiFi.
Abstract: Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.
TL;DR: Experimental results show that the proposed MDPDA algorithm can efficiently reduce the memory access cost and extend the lifetime of MRAM, and a novelmultidimensional dynamic programming data allocation (MDPDA) algorithm to strategically allocate data blocks to each memory.
Abstract: Resource scheduling is one of the most important issues in mobile cloud computing due to the constraints in memory, CPU, and bandwidth. High energy consumption and low performance of memory accesses have become overwhelming obstacles for chip multiprocessor (CMP) systems used in cloud systems. In order to address the daunting “memory wall” problem, hybrid on-chip memory architecture has been widely investigated recently. Due to its advantages in size, real-time predictability, power, and software controllability, scratchpad memory (SPM) is a promising technique to replace the hardware cache and bridge the processor–memory gap for CMP systems. In this paper, we present a novel hybrid on-chip SPM that consists of a static random access memory (RAM), a magnetic RAM (MRAM), and a zero-capacitor RAM for CMP systems by fully taking advantages of the benefits of each type of memory. To reduce memory access latency, energy consumption, and the number of write operations to MRAM, we also propose a novel multidimensional dynamic programming data allocation (MDPDA) algorithm to strategically allocate data blocks to each memory. Experimental results show that the proposed MDPDA algorithm can efficiently reduce the memory access cost and extend the lifetime of MRAM.
TL;DR: This paper presents a mathematical formulation of sensor cloud, which is very important for studying the behavior of WSN-based applications in the sensor- cloud platform, and suggested a paradigm shift of technology from traditional WSNs to sensor-cloud architecture.
Abstract: This paper focuses on the theoretical modeling of sensor cloud, which is one of the first attempts in this direction. We endeavor to theoretically characterize virtualization, which is a fundamental mechanism for operations within the sensor-cloud architecture. Existing related research works on sensor cloud have primarily focused on the ideology and the challenges that wireless sensor network (WSN)-based applications typically encounter. However, none of the works has addressed theoretical characterization and analysis, which can be used for building models for solving different problems to be encountered in using sensor cloud. We present a mathematical formulation of sensor cloud, which is very important for studying the behavior of WSN-based applications in the sensor-cloud platform. We also suggested a paradigm shift of technology from traditional WSNs to sensor-cloud architecture. A detailed analysis is made based on the performance metrics, i.e., energy consumption, fault tolerance, and lifetime of a sensor node. A thorough evaluation of the cost effectiveness of sensor cloud is also done by examining the cash inflow and outflow characteristics from the perspective of every actor of the sensor cloud. Analytical results show that the sensor-cloud architecture outperforms a traditional WSN, by increasing the sensor lifetime by 3.25% and decreasing the energy consumption by 36.68%. We also observe that the technology shift to sensor cloud reduces the expenditure of an end user by 14.72%, on average.
TL;DR: This paper defines dynamic machine models along with their parameters for each IEEE test bed system, thus producing full dynamic models for all test systems.
Abstract: Transient stability analysis is performed to assess the power system's condition after a severe contingency and is carried out using simulations. To adequately assess the system's transient stability, the correct dynamic models for the machines (i.e., generators, condensers, and motors) along with their dynamic parameters must be defined. The IEEE test systems contain the data required for steady-state studies. However, neither the dynamic model of the machines nor their specific parameters have been established for transient studies. As a result, there is a demand for test bed systems suitable for transient analysis. This paper defines dynamic machine models along with their parameters for each IEEE test bed system, thus producing full dynamic models for all test systems. It is important to mention that the parameters of the proposed dynamic models are based on typical data. The test systems are subjected to large disturbances, and a case study for each test system, which examines the frequency, angle, and voltage stability, is presented. Furthermore, the proposed dynamic IEEE test systems, implemented in PowerWorld, are available online.
TL;DR: A system model is proposed for cooperative centralized and distributed spectrum sensing in vehicular networks to minimize both the spectral scarcity and high mobility issues and the results show that the cooperative cognitive model is more suitable for Vehicular networks that minimize interference and hidden PU problem.
Abstract: To resolve the contradictions between the increasing demand of vehicular wireless applications and the shortage of spectrum resources, high mobility, short link lifetime, and spectrum efficiency, a novel cognitive radio (CR) and efficient management of spectrum in vehicular communication is required. Therefore, to exhibit the importance of spectral efficiency, a system model is proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. The proposed architecture is used to minimize both the spectral scarcity and high mobility issues. Furthermore, we analyze the decision fusion techniques in cooperative spectrum sensing for vehicular networks. In addition, a system model is designed for decision fusion techniques using renewal theory, and then, we analyze the probability of detection of primary channel and the average waiting time for CR user or secondary user in PU transmitter. Finally, mathematical analysis is performed to check the probability of detection and false alarm. The results show that the cooperative cognitive model is more suitable for vehicular networks that minimize interference and hidden PU problem.
TL;DR: The proposed EGTran model could be utilized by grid operators for the short-term commitment and dispatch of power systems in highly interdependent conditions with relatively large natural gas-fired generating units.
Abstract: This paper proposes a coordinated stochastic model for studying the interdependence of electricity and natural gas transmission networks (referred to as EGTran). The coordinated model incorporates the stochastic power system conditions into the solution of security-constrained unit commitment problem with natural gas network constraints. The stochastic model considers random outages of generating units and transmission lines, as well as hourly forecast errors of day-ahead electricity load. The Monte Carlo simulation is applied to create multiple scenarios for the simulation of the uncertainties in the EGTran model. The nonlinear natural gas network constraints are converted into linear constraints and incorporated into the stochastic model. Numerical tests are performed in a six-bus system with a seven-node gas transmission network and the IEEE 118–bus power system with a ten-node gas transmission network. Numerical results demonstrate the effectiveness of EGTran to analyze the impact of random contingencies on power system operations with natural gas network constraints. The proposed EGTran model could be utilized by grid operators for the short-term commitment and dispatch of power systems in highly interdependent conditions with relatively large natural gas-fired generating units.
TL;DR: This paper presents a survey and taxonomy for server consolidation techniques in cloud data centers, special attention has been devoted to the parameters and algorithmic approaches used to consolidate VMs onto PMs.
Abstract: Data centers and their applications are exponentially growing. Consequently, their energy consumption and environmental impacts have also become increasingly more important. Virtualization technologies are widely used in modern data centers to ease the management of the data center and to reduce its energy consumption. Data centers that employ virtualization technologies are typically called virtualized or cloud data centers . Virtualization technologies enable virtual machine (VM) live migration, which allows the VMs to be freely moved among physical machines (PMs) with negligible downtime. Thus, several VMs can be packed on a single PM so as to let the PM run in its more energy-efficient working condition. This technique is called server consolidation and is an effective and widely used approach to reduce total energy consumption in data centers. Server consolidation can be done in various ways and by considering various parameters and effects. This paper presents a survey and taxonomy for server consolidation techniques in cloud data centers. Special attention has been devoted to the parameters and algorithmic approaches used to consolidate VMs onto PMs. In this end, we also discuss open challenges and suggest areas for further research.
TL;DR: Experimental results showed that the VMD-based GRNN ensemble forecasting paradigm could be a promising methodology for California electricity and Brent crude oil price prediction.
Abstract: Recently, variational mode decomposition (VMD) has been proposed as an advanced multiresolution technique for signal processing. This study presents a VMD-based generalized regression neural network ensemble learning model to predict California electricity and Brent crude oil prices. Its performance is compared to that of the empirical mode decomposition (EMD) based generalized regression neural network (GRNN) ensemble model. Particle swarm optimization is used to optimize each GRNN initial weight within the ensemble system. Experimental results showed that the VMD-based ensemble outperformed EMD-based ensemble forecasting system in terms of mean absolute error, mean absolute percentage error, and root mean-squared error. It also outperformed the conventional auto-regressive moving average model used for comparison purpose. As a result, the VMD-based GRNN ensemble forecasting paradigm could be a promising methodology for California electricity and Brent crude oil price prediction.
TL;DR: A novel Cloud-assisted Message Downlink dissemination Scheme (CMDS), with which the safety messages in the cloud server are first delivered to the suitable mobile gateways on relevant roads with the help of cloud computing, and then being disseminated among neighboring vehicles via vehicle-to-vehicle (V2V) communication.
Abstract: In vehicular ad hoc networks (VANETs), efficient message dissemination is critical to road safety and traffic efficiency. Since many VANET-based schemes suffer from high transmission delay and data redundancy, the integrated VANET–cellular heterogeneous network has been proposed recently and attracted significant attention. However, most existing studies focus on selecting suitable gateways to deliver safety message from the source vehicle to a remote server, whereas rapid safety message dissemination from the remote server to a targeted area has not been well studied. In this paper, we propose a framework for rapid message dissemination that combines the advantages of diverse communication and cloud computing technologies. Specifically, we propose a novel Cloud-assisted Message Downlink dissemination Scheme (CMDS), with which the safety messages in the cloud server are first delivered to the suitable mobile gateways on relevant roads with the help of cloud computing (where gateways are buses with both cellular and VANET interfaces), and then being disseminated among neighboring vehicles via vehicle-to-vehicle (V2V) communication. To evaluate the proposed scheme, we mathematically analyze its performance and conduct extensive simulation experiments. Numerical results confirm the efficiency of CMDS in various urban scenarios.
TL;DR: An automated system that is capable of detecting insider threats within an organization is described with a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then uses this to obtain a consistent representation of features that provide a rich description of the user's behavior.
Abstract: Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. This could range from financial theft and intellectual property theft to the destruction of property and business reputation. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. In this paper, we describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using ten synthetic data-driven scenarios and found that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst.
TL;DR: Using the proposed distributed scheme, i.e., HoMeS, the earned profit of the grid improves up to 55%, and the customers consume almost 30.79% higher amount of energy, which, in turn, increases the utilization of the generated energy by the microgrids.
Abstract: In this paper, the problem of distributed home energy management system with storage ( HoMeS ) in a coalition, which consists of multiple microgrids and multiple customers, is studied using the multiple-leader–multiple-follower Stackelberg game theoretic model—a multistage and multilevel game. The microgrids, which act as the leaders, need to decide on the minimum amount of energy to be generated with the help of a central energy management unit and the optimum price per unit energy to maximize their profit. On the other hand, the customers, which act as the followers, need to decide on the optimum amount of energy to be consumed, including the energy to be requested for storage. Using the proposed distributed scheme, i.e., HoMeS , the earned profit of the grid improves up to 55%, and the customers consume almost 30.79% higher amount of energy, which, in turn, increases the utilization of the generated energy by the microgrids.
TL;DR: Various ways, in which electrical distance might be defined for empiric power systems are proposed, and how well each candidate distance measure may be embedded in two dimensions are proposed.
Abstract: Recent work, using electrical distance metrics and concepts from graph theory, has revealed important results about the electrical connectivity of empiric power systems. Such structural features are not widely understood or portrayed. Power systems are often depicted using unenlightening single-line diagrams, and the results of loadflow calculations are often presented without insightful elucidation, lacking the necessary context for usable intuitions to be formed. For system operators, educators, and researchers alike, a more intuitive and accessible understanding of a power system's inner electrical structure is called for. Data visualization techniques offer several paths toward realizing such an ideal. This paper proposes various ways, in which electrical distance might be defined for empiric power systems, and records how well each candidate distance measure may be embedded in two dimensions. The resulting 2-D projections form the basis for new visualizations of empiric power systems and offer novel and useful insights into their electrical connectivity and structure.
TL;DR: An evolutionary game model is put forward to investigate the evolution and risk analysis of cooperation under the spatial public goods game (PGG), in which the individual reputation is obviously utilized to cut down the individual risk of being exploited during the evolution of cooperation.
Abstract: In this paper, an evolutionary game model is put forward to investigate the evolution and risk analysis of cooperation under the spatial public goods game (PGG), in which the individual reputation is obviously utilized to cut down the individual risk of being exploited during the evolution of cooperation. In this model, based on the individual utility, the strategy state will be asynchronously updated according to the Fermi-like rule. Among them, each individual will be initially endowed with an integral reputation value, and then it evolves during the evolution of strategy; while for the individual utility, it is characterized as the product of the game payoff and a power function of reputation value. Monte Carlo simulation (MCS) method is adopted here to verify the system’s evolutionary characteristics, and large quantities of simulations demonstrate that the cooperation behavior can be greatly varied and enhanced when the reputation is incorporated into the utility evaluation. Detailed strategy distribution proves that the individual with large reputation value renders the cooperators to lower their risks to be reaped by defectors, and dominates the evolution of cooperation within the whole population. In addition, the whole cooperation phase diagrams show that the coexistence region of cooperators and defectors becomes narrower and narrower as the reputation is introduced more and more. Meanwhile, it is also displayed that the reputation effect favors the evolution of cooperation, and greatly fosters the cooperators to form the compact clusters so as to reduce the risk of being invaded by defectors. To summarize, current results are conducive to making a deeper insight into the evolution of collective cooperation within many real-world biological and man-made systems.
TL;DR: A new metric, called “QoI satisfaction ratio,” is introduced to quantify how much collected sensory data can satisfy a multidimensional task's QoI requirements in terms of data granularity and quantity.
Abstract: Mobile crowd sensing systems have been widely used in various domains but are currently facing new challenges. On one hand, the increasingly complex services need a large number of participants to satisfy their demand for sensory data with multidimensional high quality-of-information (QoI) requirements. On the other hand, the willingness of their participation is not always at a high level due to the energy consumption and its impacts on their regular activities. In this paper, we introduce a new metric, called “QoI satisfaction ratio,” to quantify how much collected sensory data can satisfy a multidimensional task's QoI requirements in terms of data granularity and quantity. Furthermore, we propose a participant sampling behavior model to quantify the relationship between the initial energy and the participation of participants. Finally, we present a QoI-aware energy-efficient participant selection approach to provide a suboptimal solution to the defined optimization problem. Finally, we have compared our proposed scheme with existing methods via extensive simulations based on the real movement traces of ordinary citizens in Beijing. Extensive simulation results well justify the effectiveness and robustness of our approach.
TL;DR: Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for CPS, as compared to the critical path scheduling method and the parallelism-based scheduling algorithm.
Abstract: The smartphone is a typical cyberphysical system (CPS). It must be low energy consuming and highly reliable to deal with the simple but frequent interactions with the cloud, which constitutes the cloud-integrated CPS. Dynamic voltage scaling (DVS) has emerged as a critical technique to leverage power management by lowering the supply voltage and frequency of processors. In this paper, based on the DVS technique, we propose a novel Energy-aware Dynamic Task Scheduling (EDTS) algorithm to minimize the total energy consumption for smartphones, while satisfying stringent time constraints and the probability constraint for applications. Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for CPS, as compared to the critical path scheduling method and the parallelism-based scheduling algorithm.
TL;DR: A novel communication framework for on-the-move EV charging scenario, based on the Publish/Subscribe (P/S) mechanism for disseminating necessary CS information to EVs, in order for them to make optimized decisions on where to charge.
Abstract: Motivated by alleviating CO2 pollution, electric vehicle (EV)-based applications have recently received wide interests from both commercial and research communities by using electric energy instead of traditional fuel energy. Although EVs are inherently with limited traveling distance, such limitation could be overcome by deploying public charging stations (CSs) to recharge EVs' battery during their journeys. In this paper we propose a novel communication framework for on-the-move EV charging scenario, based on the Publish/Subscribe (P/S) mechanism for disseminating necessary CS information to EVs, in order for them to make optimized decisions on where to charge. A core part of our communication framework is the utilization of roadside units (RSUs) to bridge the information flow from CSs to EVs, which has been regarded as a type of cost-efficient communication infrastructure. Under this design, we introduce two complementary communication modes of signal protocols, namely, push and pull modes, in order to enable the required information dissemination operation. Both analysis and simulation show the advantage of the pull mode, in which the information is cached at RSUs to support asynchronous communication. We further propose a remote reservation service based on the pull mode such that the CS-selection decision making can utilize the knowledge of EVs' charging reservation, as published from EVs through RSUs to CSs. Results show that both performances at CS and EV sides are further improved based on using this anticipated information.
TL;DR: A small-signal stability/eigenvalue analysis of a grid-connected PV system with the complete linearized model is performed to assess the robustness of the controller and the decoupling character of the grid- connected PV system.
Abstract: For utility-scale photovoltaic (PV) systems, the control objectives, such as maximum power point tracking, synchronization with grid, current control, and harmonic reduction in output current, are realized in single stage for high efficiency and simple power converter topology. This paper considers a high-power three-phase single-stage PV system, which is connected to a distribution network, with a modified control strategy, which includes compensation for grid voltage dip and reactive power injection capability. To regulate the dc-link voltage, a modified voltage controller using feedback linearization scheme with feedforward PV current signal is presented. The real and reactive powers are controlled by using $dq$ components of the grid current. A small-signal stability/eigenvalue analysis of a grid-connected PV system with the complete linearized model is performed to assess the robustness of the controller and the decoupling character of the grid-connected PV system. The dynamic performance is evaluated on a real-time digital simulator.
TL;DR: This paper investigates the problem of user association in a heterogeneous network with massive MIMO and small cells, where the macro base station is equipped with a massive M IMO, and the picocell BSs are equipped with regular MIMOs.
Abstract: Massive multiple-input–multiple-output (MIMO) and small cell are both recognized as key technologies for the future fifth-generation wireless systems. In this paper, we investigate the problem of user association in a heterogeneous network (HetNet) with massive MIMO and small cells, where the macro base station (BS) is equipped with a massive MIMO, and the picocell BSs are equipped with regular MIMOs. We first develop centralized user association algorithms with proven optimality, considering various objectives such as rate maximization, proportional fairness, and joint user association and resource allocation. We then develop a repeated game model, which leads to distributed user association algorithms with proven convergence to the Nash equilibrium. We demonstrate the efficacy of these optimal schemes by comparing with several greedy algorithms through simulations.
TL;DR: It is shown that the autonomously controlled hybrid microgrid fails to operate following variations in the power generation characteristics of local DG units, so a centralized control strategy is proposed and compared to the autonomous scheme.
Abstract: This paper addresses power management and control strategies of a hybrid microgrid system that comprises ac and dc subgrids. Each subgrid consists of multiple distributed generation (DG) units and local loads. Both entities are interconnected by voltage source converters (VSCs) to facilitate a bidirectional power flow and increase the system reliability. The control of the interconnecting VSC can be achieved autonomously. However, it is shown that the autonomously controlled hybrid microgrid fails to operate following variations in the power generation characteristics of local DG units (such as droop coefficients, set points, or loss/connection of DG units, etc.) A centralized controller is therefore proposed and compared to the autonomous scheme. The centralized control strategy provides an accurate and optimized power exchange between both subgrids. Parallel operation of multiple interconnecting VSCs is considered so that the transmitted power is shared according to their power ratings. Small-signal stability analysis is conducted to investigate the influence of the communication delays on the system stability. A hierarchical control strategy has been proposed by setting the autonomous controller in a primary layer whereas the centralized controller is set into a secondary layer to generate a compensation signal. Time-domain simulations results are presented to show the effectiveness of the proposed techniques and the drawbacks of the conventional scheme.
TL;DR: The main merits of the proposed enhanced-location-privacy-preserving scheme include the following: 1) no fully trusted entities are required, and 2) each user can obtain accurate points of interest while preserving location privacy.
Abstract: With the increasing popularity of mobile communication devices loaded with positioning capabilities (e.g., GPS), there is growing demand for enjoying location-based services (LBSs). An important problem in LBSs is the disclosure of a user's real location while interacting with the location service provider (LSP). To address this issue, existing solutions generally introduce a trusted Anonymizer between the users and the LSP. However, the introduction of an Anonymizer actually transfers the security risks from the LSP to the Anonymizer. Once the Anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose an enhanced-location-privacy-preserving scheme for the LBS environment. Our scheme employs an entity, termed Function Generator, to distribute the spatial transformation parameters periodically, with which the users and the LSP can perform the mutual transformation between a real location and a pseudolocation. Without the transforming parameters, the Anonymizer cannot have any knowledge about a user's real location. The main merits of our scheme include the following: 1) no fully trusted entities are required, and 2) each user can obtain accurate points of interest while preserving location privacy. The efficiency and effectiveness of the proposed scheme are validated by extensive experiments. The experimental results show that the proposed scheme preserves location privacy at low computational and communication cost.