Showing papers in "IEEE Systems Journal in 2021"
TL;DR: A data-flow architecture that combines the IoT with blockchain, called IoBHealth, that can be used for storing, accessing, and managing of e-healthcare data is proposed.
Abstract: Internet of Things (IoT) and blockchain technologies are being heavily exploited and used in may domains, especially for e-healthcare. In healthcare, IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed. Collected IoT data are subjected to centralized computation, processing, and storage. Such centralization can be problematic, as it can be a single point of failure, mistrust, data manipulation and tampering, and privacy evasion. Blockchain can solve such serious problems by providing decentralized computation and storage for IoT data. Therefore, the integration IoT and blockchain technologies can become a reasonable choice for the design of a decentralized IoT-based e-healthcare systems. In this article, first, we give a brief background on blockchain. Second, popular consensus algorithms used in blockchain are discussed in the context of e-health. Third, blockchain platforms are reviewed for their appropriateness in IoT-based e-healthcare. Finally, few use cases are methodologically given to show how key features of the IoT and blockchain can be leveraged to support healthcare services and ecosystems. We also propose a data-flow architecture that combines the IoT with blockchain, called IoBHealth , that can be used for storing, accessing, and managing of e-healthcare data.
179 citations
TL;DR: This article performs a comprehensive review of how blockchain technology has been, and can be, deployed in energy applications, ranging from energy management to peer-to-peer trading to electric vehicle-related applications to carbon emissions trading, and others.
Abstract: As our fossil fuel reserves are rapidly depleting, there has been an increased focus to explore the utility of renewable energy (e.g., solar energy and wind energy) in replacing fossil fuel. One resulting trend is the energy market gradually shifting toward a distributed market, where renewable energy can be traded, partly evidenced by the number of blockchain-based solutions designed for the (distributed) energy sector. The interest in blockchain is also due to blockchain's underpinning characteristics such as anonymity, decentralized, and transparency. Therefore, in this article, we perform a comprehensive review of how blockchain technology has been, and can be, deployed in energy applications, ranging from energy management to peer-to-peer trading to electric vehicle-related applications to carbon emissions trading, and others. We also study the existing architectures and solutions, and existing and emerging security and privacy challenges, as well as exploring other potential applications of blockchain in the energy sector.
99 citations
TL;DR: Simulation results demonstrate that the deployment of an intelligent reflecting surface (IRS) can help improving the performance of the DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.
Abstract: Dual-function radar and communication (DRC) system has been recently recognized as a promising approach to solve the spectrum scarcity problem. However, when the target exists within a crowded area where pathloss dominating, the performance of radar may be severely degraded. To tackle this issue, this article proposes for the first time the deployment of an intelligent reflecting surface (IRS) to help the DRC system to enhance the radar detection performance. The IRS can configure the environment around the radar by adaptively adjusting the phases of its reflecting units to strengthen the signal quality toward specific directions, mostly the target direction, and completely null-out transmissions in other directions, mostly the directions toward the communication system. Specifically, in this article, we investigate the joint optimization of the IRS passive phase-shift matrix (PSM) and precoding matrix of the radar-aided basestation for the DRC system. The optimization is carried-out through maximizing the signal-to-noise ratio (SNR) at the radar receiver under both sensing and communication constraints, which turns out to be a nonconvex problem. In order to circumvent this challenging problem, an alternation optimization approach is employed to decouple the optimization variables and split this intractable problem into two subproblems. However, it is still challenging to obtain the optimal PSM due to the high power of the objective function and the unit-modulus constraints. To solve this problem, a majorization–minimization algorithm is conceived to transform the nonconvex problem to an easy to solve quadratic constraint quadratic programming problem. Simulation results demonstrate that the IRS can help improving the performance of the DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.
98 citations
TL;DR: This article proposes an efficient certificateless aggregate signature scheme with conditional privacy preservation that is suitable for resource-constrained environments, and it is compared with related works from aspects of computation cost, communication efficiency, and security requirements.
Abstract: As an extension of traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) enables information collection and dissemination, which brings a lot of convenience and benefits to the intelligent transportation systems. However, the booming IoV confronts a few challenges in the aspects of vehicle location privacy preservation and the authenticity of the transmitted information. In order to meet these challenges, we propose an efficient certificateless aggregate signature scheme with conditional privacy preservation in this article. Our scheme utilizes the technique of full aggregation to reduce the bandwidth resources and computing overhead. Besides, the conditional privacy preservation in IoV system is realized by using pseudonym mechanism. We demonstrate that the proposed scheme is secure against the Type-I and Type-II adversaries in the random oracle under the computational Diffie–Hellman assumption. In addition, the proposed scheme is compared with related works from aspects of computation cost, communication efficiency, and security requirements. The comparison results show that the proposed scheme is efficient, and it is suitable for resource-constrained environments.
91 citations
TL;DR: This article proposes a beamforming (BF) scheme for a cognitive satellite terrestrial network, where the base station and a cooperative terminal are exploited as green interference resources to enhance the system security performance in the presence of unknown eavesdroppers.
Abstract: This article proposes a beamforming (BF) scheme for a cognitive satellite terrestrial network, where the base station (BS) and a cooperative terminal (CT) are exploited as green interference resources to enhance the system security performance in the presence of unknown eavesdroppers. Different from the related works, we assume that only imperfect channel information of the mobile user (MU) and earth station (ES) is available. Specifically, we formulate an optimization problem with the objective to degrade the possible wiretap channels within the private signal beampattern region, while imposing constraints on the signal-to-interference-plus-noise ratio (SINR) at the MU, the interference level of the ES and the total transmit power budget of the BS and CT. To solve this mathematically intractable problem, we propose a joint artificial noise generation and cooperative jamming BF scheme to suppress the interception. Finally, the effectiveness and superiority of the proposed BF scheme are confirmed through computer simulations.
83 citations
TL;DR: An overview of the reported Industry 4.0 implementation challenges in the relevant literature is provided by conducting a systematic literature review and derives opportunities for overcoming them.
Abstract: Industry 4.0 is a concept aimed at achieving the integration of physical parts of the manufacturing process (i.e., complex machinery, various devices, and sensors) and cyber parts (i.e., advanced software) via networks and driven by Industry 4.0 technology categories used for prediction, control, maintenance, and integration of manufacturing processes. Industry 4.0, which is expected to have a great impact on manufacturing systems in the future, is attracting attention in both industry and academia. Although academic research on Industry 4.0 is growing exponentially, evidence of Industry 4.0 implementation in practice is still scarce. Moreover, the challenges industry faces when implementing the Industry 4.0 concept seem to be even less addressed. At the start of the present survey, a preliminary literature review identified a lack of comprehensive analysis of the Industry 4.0 implementation challenges. Thus, the purpose of the present article is to provide an overview of the reported Industry 4.0 implementation challenges in the relevant literature by conducting a systematic literature review. Specifically, while the present study differentiates between managerial and technological Industry 4.0 implementation challenges, the focus of the present article is on the managerial Industry 4.0 implementation challenges. This overview is performed by deriving an inductively coded Industry 4.0 technology framework that classifies Industry 4.0 technologies into ten categories: cyber physical systems, Internet of Things, big data analytics, cloud computing, fog and edge computing, augmented and virtual reality, robotics, cyber security, semantic web technologies, and additive manufacturing. The present article identifies, codes, and defines the managerial Industry 4.0 implementation challenges and derives opportunities for overcoming them.
78 citations
TL;DR: The comparative analysis is analyzed to check the effectiveness of the proposed hybrid control scheme with existing and adaptive control techniques in respect of power quality, better dc offset rejection, better FC and frequency extraction, and grid synchronization.
Abstract: This article investigates the power quality enhancement of a grid-tied photovoltaic (PV) distribution system by employing a fuzzy logic proportional–integrator–derivative multiple complex coefficient filter multiple second-order generalized integrator frequency-locked loop (FLPID-MCCF-MSOGI-FLL) hybrid control scheme based shunt active power filter. The MSOGI-FLL reference current generation strategy is implemented to mitigate the current harmonics by extracting the fundamental constituents (FCs) from the nonlinear load currents, whereas an MCCF is employed to separate the FC from the distorted grid voltages and eliminates the voltage harmonics during extremely polluted grid voltage condition. The main objective of using FLPID is to maintain the stable power between dc and ac sides by regulating the dc-link voltage constant under transient conditions. To track the maximum power from the PV panel under varying environmental condition, the particle swarm optimization based perturb and observe technique is used in this article. The comparative analysis is analyzed to check the effectiveness of the proposed hybrid control scheme with existing and adaptive control techniques in respect of power quality, better dc offset rejection, better FC and frequency extraction, and grid synchronization. The system with the proposed control scheme is simulated on MATLAB/Simulink and validated in a real-time field-programmable gate array platform under different test scenarios. In conclusion, the harmonic content of grid currents and voltages is found well within the IEEE-519 standard limits.
78 citations
TL;DR: A maiden attempt of the ICA is proposed to optimize the gains of the ITDF controller utilizing the integral time absolute error criterion, and its outcomes are contrasted with two existing optimization strategies, namely the genetic algorithm and the particle swarm optimization.
Abstract: The multimicrogrid system is a complicated nonlinear system, which brings performance degradation due to deficient damping under the unexpected fluctuation in power generation due to the presence of renewable sources, dynamically changing loading conditions, and parameter variations. Owing to this, to provide consistent electric power with superlative attribute, sturdy and intelligent control techniques are amazingly imperative in the automatic generation control of microgrid (MG). The application of imperialist competitive algorithm (ICA)-based fractional-order integral proportional derivative with filter (IPDF), i.e., integral tilt derivative with filter controller (ITDF) controller in frequency control in two areas interconnected MG (isolation mode) with renewable penetration, is a novel work. A maiden attempt of the ICA is proposed to optimize the gains of the ITDF controller utilizing the integral time absolute error criterion. To demonstrate the supremacy of ICA, its outcomes are contrasted with two existing optimization strategies, namely the genetic algorithm and the particle swarm optimization. The effectiveness of the proposed controller is revealed by contrasting the dynamic responses of multi microgrid (MMG) with proportional integral derivative with filter (PIDF) and tilt integral derivative with filter (TIDF) controllers. At last, a sensitivity investigation is performed to exhibit the power of the studied strategy to wide variations in the MG parameters, magnitude as well as the location of step/random load disturbance. The proposed MMG is simulated in MATLAB/Simulink environment.
75 citations
TL;DR: The result proves that the proposed P&O MPPT technique can track the MPP accurately under various operating conditions and is enhanced by including the change in current, in addition to the changes in output voltage and output power of the PV module.
Abstract: The primary concerns in the practical photovoltaic (PV) system are the power reduction due to the change in operating conditions, such as the temperature or irradiance, the high computation burden due to the modern maximum power point tracking (MPPT) mechanisms, and to maximize the PV array output during the rapid change in weather conditions. The conventional perturb and observation (P&O) technique is preferred in most of the PV systems. Nevertheless, it undergoes false tracking of maximum power point (MPP) during the rapid change in solar insolation due to the wrong decision in the duty cycle. To avoid the computational burden and drift effect, this article presents a simple and enhanced P&O MPPT technique. The proposed technique is enhanced by including the change in current ( dI ), in addition to the changes in output voltage and output power of the PV module. The effect of including the dI profile with the traditional method is explained with the fixed and variable step-size methods. The mathematical expression for the drift-free condition is derived. The traditional boost converter is considered for validating the effectiveness of the proposed methods by employing the direct duty cycle technique. The proposed algorithm is simulated using MATLAB/Simulink and validated under various scenarios with the developed laboratory prototype in terms of drift-free characteristics and tracking efficiency. The result proves that the proposed technique can track the MPP accurately under various operating conditions.
72 citations
TL;DR: In this paper, the authors summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions, and discuss a number of topics worth pursuing in the future.
Abstract: Agile satellites with advanced attitude maneuvering capability are the new generation of earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this article are, thus, to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multiobjective function and autonomous model. A detailed literature review from 1997–2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic, and machine learning. Finally, we discuss a number of topics worth pursuing in the future.
70 citations
TL;DR: The features of seven prominent IoT frameworks are investigated for the purpose of simplifying the selection of a suitable framework for an industrial application and a technical comparison of their features and characteristics is provided.
Abstract: The Internet of Things (IoT) has gained popularity and is increasingly used in large scale deployments for industrial applications. Such deployments rely on the flexibility and scalability of systems and devices. Heterogeneous systems need to be interoperable and work together seamlessly. In order to manage such system of systems, it is important to work with a framework that not only supports the flexible nature of IoT systems but also provides adequate support for industrial requirements, such as real-time and runtime features, architectural approaches, hardware constraints, standardization, industrial support, interoperability, and security. The selection of an appropriate framework results difficult due to the rising number of available frameworks and platforms, which offer different support for the aforementioned requirements. Therefore, this article investigates the features of seven prominent frameworks for the purpose of simplifying the selection of a suitable framework for an industrial application. The aim of this article is to present the recent developments and state-of-the-art of industrial IoT frameworks and provide a technical comparison of their features and characteristics.
TL;DR: An alternating optimization algorithm with guaranteed convergence is developed to minimize the maximum computation delay among IoT devices with the joint scheduling for association control, computation task allocation, transmission power and bandwidth allocation, UAV computation resource, and deployment position optimization.
Abstract: Space-aerial-assisted computation offloading has been recognized as a promising technique to provide ubiquitous computing services for remote Internet of Things (IoT) applications, such as forest fire monitoring and disaster rescue. This article considers a space-aerial-assisted mixed cloud-edge computing framework, where the flying unmanned aerial vehicles (UAVs) provide IoT devices with low-delay edge computing service and satellites provide ubiquitous access to cloud computing. We aim to minimize the maximum computation delay among IoT devices with the joint scheduling for association control, computation task allocation, transmission power and bandwidth allocation, UAV computation resource, and deployment position optimization. Through exploiting block coordinate descent and successive convex approximation, we develop an alternating optimization algorithm with guaranteed convergence, to solve the formulated problem. Extensive simulation results are provided to demonstrate the remarkable delay reduction of the proposed scheme than existing benchmark methods.
TL;DR: The key mechanism of the proposed JTARO strategy is to employ the optimization technique to jointly optimize the target-to-radar assignment, revisit time control, bandwidth, and dwell time allocation subject to several resource constraints, while achieving better tracking accuracies of multiple targets and low probability of intercept (LPI) performance of phased array radar network.
Abstract: In this article, a joint target assignment and resource optimization (JTARO) strategy is proposed for the application of multitarget tracking in phased array radar network system. The key mechanism of our proposed JTARO strategy is to employ the optimization technique to jointly optimize the target-to-radar assignment, revisit time control, bandwidth, and dwell time allocation subject to several resource constraints, while achieving better tracking accuracies of multiple targets and low probability of intercept (LPI) performance of phased array radar network. The analytical expression for Bayesian Cramer–Rao lower bound with the aforementioned adaptable parameters is calculated and subsequently adopted as the performance metric for multitarget tracking. After problem partition and reformulation, an efficient three-stage solution methodology is developed to resolve the underlying mixed-integer, nonlinear, and nonconvex optimization problem. To be specific, in Step 1, the revisit time for each target is determined. In Step 2, we implement the joint signal bandwidth and dwell time allocation for fixed target-to-radar assignments, which combine the cyclic minimization algorithm and interior point method. In Step 3, the optimal target-to-radar assignments are obtained, which results in the minimization of both the tracking accuracy for multiple targets and the total dwell time consumption of the network system. Simulation results are provided to demonstrate the advantages of the presented JTARO strategy, in terms of the achievable multitarget tracking accuracy and LPI performance of phased array radar network.
TL;DR: This article proposes a new Identity-based RDIC scheme that makes use of homomorphic verifiable tag to decrease the system complexity and is proved secure under the assumption of computational Diffie–Hellman problem.
Abstract: Although cloud storage service enables people easily maintain and manage amounts of data with lower cost, it cannot ensure the integrity of people's data. In order to audit the correctness of the data without downloading them, many remote data integrity checking (RDIC) schemes have been presented. Most existing schemes ignore the important issue of data privacy preserving and suffer from complicated certificate management derived from public key infrastructure. To overcome these shortcomings, this article proposes a new Identity-based RDIC scheme that makes use of homomorphic verifiable tag to decrease the system complexity. The original data in proof are masked by random integer addition, which protects the verifier from obtaining any knowledge about the data during the integrity checking process. Our scheme is proved secure under the assumption of computational Diffie–Hellman problem. Experiment result exhibits that our scheme is very efficient and feasible for real-life applications.
TL;DR: This article considers the use of maximum-ratio transmission to beamform DL pilots in the DL beamforming training (BT) phase and develops two successive approximation algorithms to improve the sum spectral efficiency (SE) and total energy efficiency (EE).
Abstract: In this article, we investigate the downlink (DL) of a cell-free massive multiple-input multiple-output (MIMO) system over spatially correlated Rician fading, in which many distributed access points (APs) equipped with multiple antennas serve single-antenna users. The APs apply minimum mean-square error channel estimation to obtain the uplink channel state information (CSI). Furthermore, in order to obtain DL CSI at users, this article considers the use of maximum-ratio transmission to beamform DL pilots in the DL beamforming training (BT) phase. For such a system, we derive the closed-form expressions of the sum spectral efficiency (SE) and total energy efficiency (EE). Based on the obtained closed-form expressions, we develop two successive approximation algorithms to improve the sum SE and total EE by optimizing the power control coefficients of DL data and pilot. Numerical results are provided to demonstrate the superiority of the proposed algorithms in improving the sum SE and total EE. In addition, the numerical results also show that the sum SE of a cell-free massive MIMO system with exploiting the BT scheme can be significantly improved over the system without employing the BT scheme.
TL;DR: A joint target assignment and power allocation (TAPA) strategy is developed for multiple distributed MIMO radar networks in cluttered environment using the DTFR mode to achieve the better system tracking accuracy under the constraints of receive beam direction capability and power budget.
Abstract: The “defocused transmit-focused receive” (DTFR) mode in the distributed multiple-input multiple-output (MIMO) radar network is very effective in multitarget tracking. In this mode, a completely defocused beam is transmitted and a focused receive beam is synthesized so that the MIMO radar is capable of tracking targets independently. A joint target assignment and power allocation (TAPA) strategy is developed for multiple distributed MIMO radar networks in cluttered environment using the DTFR mode. Our aim is to achieve the better system tracking accuracy under the constraints of receive beam direction capability and power budget. We derive the posterior Cramer-Rao lower bound (PCRLB) and adopt it as the objective function, since it quantifies the precision of target state estimates. It is shown that the TAPA problem is a mixed integer programming and NP-hard problem, where two involved parameters, i.e., the target-radar assignment and power allocation, are both coupled in the objective and in the constraints. By introducing an intermediate variable, we propose an efficient two-step-based solution for solving this problem. The simulation results show the superior performance and adaptivity compared with existing algorithms.
TL;DR: Using elliptic curve cryptography, a new authentication scheme is proposed to secure the communication between a user and a drone flying in some specific flying zone to enhance comfort in many applications.
Abstract: The continuous innovation and progression in hardware, software and communication technologies helped the expansion and accelerated growth in Internet of Things based drone networks (IoD), for the devices, applications and people to communicate and share data. IoD can enhance comfort in many applications including, daily life, commercial, and military/rescue operations in smart cities. However, this growth in infrastructure smartness is also subject to new security threats and the countermeasures require new customized solutions for IoD. Many schemes to secure IoD environments are proposed recently; however, some of those were proved as insecure and some degrades the efficiency. In this article, using elliptic curve cryptography, we proposed a new authentication scheme to secure the communication between a user and a drone flying in some specific flying zone. The security of the proposed scheme is solicited using formal Random oracle method along with a brief discussion on security aspects provided by proposed scheme. Finally, the comparisons with some related and latest schemes is illustrated.
TL;DR: This article addresses the problem of designing the joint subcarrier selection and power allocation scheme to minimize the power consumption of aDFRC system, under a general scenario in which the DFRC system is capable of performing a primary radar purpose and a secondary communications purpose in the meantime.
Abstract: Dual-function radar-communications (DFRC) system has been recognized as a promising solution to alleviate the radio frequency spectrum congestion and the shortage of spectrum resources. In this article, we address the problem of designing the joint subcarrier selection and power allocation scheme to minimize the power consumption of a DFRC system, under a general scenario in which the DFRC system is capable of performing a primary radar purpose and a secondary communications purpose in the meantime. In particular, the key mechanism is to minimize the total radiated power of the multicarrier DFRC system by jointly selecting the best possible subcarriers for radar and communications purposes in sequence and allocating the optimal power resource on the corresponding subcarriers, under the constraints of a predefined mutual information for target characterization and a desired communications data rate for information transmission. The resulting problem is formulated as a two-variable nonconvex optimization problem, one for subcarrier selection and the other for power allocation. Then, after convex relaxation reformulation and problem partition, an efficient three-step solution technique is developed for the joint optimization scheme, which combines the cyclic minimization algorithm and Karush–Kuhn–Tuckers optimality conditions. Finally, numerical results are provided to validate the theoretical findings and to verify the effectiveness of the proposed joint optimization scheme.
TL;DR: A fresh three-factor authentication scheme providing session keys for WSNs, using NS-3 for simulation shows that the scheme can run in IoT environment normally and has practical perspective.
Abstract: As an important topic of IoT, wireless sensor network (WSN) data transmission is popular nowadays. It is widely accepted that the wireless channel is hazard, and multifactor authentication schemes are proposed to save the hazard of wireless communication circumstance. To overcome the problems, we give a fresh three-factor authentication scheme providing session keys for WSNs. Formal verification given by Proverif illustrates that the new scheme keeps security properties. At the same time, the informal analysis also denotes that the proposed scheme is practical and satisfies general needs, such as counteraction against various attacks and meeting security properties. Compared to some recent similar schemes, the proposed scheme performs better in security and is suitable for application. At last, we use NS-3 for simulation. The results from the simulation show that the scheme can run in IoT environment normally and has practical perspective.
TL;DR: A new two-stage hybrid stochastic–information gap-decision theory (IGDT) based on the network-constrained unit commitment framework is proposed for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems.
Abstract: This article proposes a new two-stage hybrid stochastic–information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.
TL;DR: This article proposes a covert communication model combined with smart contracts to covertly transfer information in the blockchain environment that has tamper resistance and low complexity, and it is feasible to use this model for covert communication.
Abstract: The traditional covert communication channel relying on a third-party node is vulnerable to attack. The data are easily tampered with and the identity information of the communication party is fragile. Blockchain has the characteristics of decentralization and tamper resistance, which can effectively solve the above problems. In addition, some confidential information needs to be transmitted covertly in the transparent blockchain. A smart contract deployed in the blockchain to automatically realize its function can replace a centralized node to provide credible guarantee for communication. The diversity of parameters, data redundancy, and code programmability of smart contract make it an excellent carrier for covert communication under blockchain. In this article, we propose a covert communication model combined with smart contracts to covertly transfer information in the blockchain environment. To implement this model, we use the parameters in the contract to map the secret information sequence, and call the contract to transfer message. Voting contract and secret bidding contract are combined to instantiate the proposed model, and optimized versions of the two contracts are also proposed to reduce costs. Moreover, we use encryption algorithms and two-round protocols to ensure data privacy and design corresponding information embedding and transmission methods for different scenarios. To improve the concealment of communication, redundant options, effective price ranges, and invalid bids are set in two contracts, respectively. The experimental results show that the proposed model has tamper resistance and low complexity, and it is feasible to use this model for covert communication.
TL;DR: This article presents a comprehensive review of robust control methods for microgrids, including AC, DC, and hybrid microGrids, with different topologies and different types of interconnection to conventional power systems based on recently published research studies.
Abstract: Microgrids consisting of photovoltaic (PV) power plants and wind farms have been widely accepted in power systems for reliability enhancement and power loss reduction. Microgrids are capable of providing voltage and frequency support, improving power quality, and achieving proper power-sharing. To achieve such goals and deal with the nonlinear behavior in such systems, appropriate robust control strategies are required to be adopted. This article presents a comprehensive review of robust control methods for microgrids, including AC, DC, and hybrid microgrids, with different topologies and different types of interconnection to conventional power systems based on recently published research studies. The main control objectives, along with proposed control methods, are comparatively discussed for different types of microgrids. Furthermore, several research gaps in this area related to the scalability, robustness assessment, and evaluation approach are discussed. Recommendations are made that can potentially open new research lines to enhance the effectiveness of robust controllers for AC, DC, and hybrid microgrids.
TL;DR: A hybrid MPPT-algorithm integrating of Modified Invasive Weed Optimization and Perturb & Observe technique under rapid weather change and partial shading scenarios for efficient extraction of the maximum power from the standalone PV-based hybrid system is introduced.
Abstract: To augment the photovoltaic (PV) power generation conversion, a maximum power point tracking (MPPT) technique plays a very significant role. This article introduces a hybrid MPPT algorithm integrating modified invasive weed optimization (MIWO) and perturb and observe (P&O) technique under the rapid weather change and partial shading scenarios for the efficient extraction of the maximum power from the standalone PV-based hybrid system. MIWO handles the initial stages of MPPT followed by the application of the P&O algorithm at the final stages in view of acquiring the rapid global peak and maximal PV power. The studied microgrid comprises of the PV system, battery, electrolyzer, fuel cell, and load. A coordinated dc-voltage regulation and power management strategy between each subsystem of the hybrid microgrid is implemented to save the battery from the undesirable charging/discharging operation. Additionally, with the monitoring of dc voltage, the dc/dc converter associated with the battery and dc link plays as an MPPT circuit of the PV without the requirement of an extra dedicated circuit. Takagi–Sugeno (TS) fuzzy controller is adopted for suppressing/mitigating the voltage oscillations of the microgrid during the variations in the solar irradiance/temperature and power demand. The results clearly exhibit the superior performance of the proposed methodology compared with some of the existing techniques.
TL;DR: An enhanced grey wolf algorithm (EGWA) is proposed which is coordinated with the capacitor banks (CBs) and the voltage regulators (VRs) to achieve the target and great reduction of power losses is achieved with high improvement of the minimum voltage and loading capacity.
Abstract: Enhancing the distribution systems performance is an important target for system operators. This article proposes an enhanced grey wolf algorithm (EGWA) for allocating the distributed generation units (DGUs) which is coordinated with the capacitor banks (CBs) and the voltage regulators (VRs) to achieve the target. Diversified tasks aim at minimizing the investment expenses of the coordinated equipment, and maximizing the benefits resulted from power losses reduction and the purchased power from the grid. In the technical direction, it is investigated through ameliorating the voltage profile and the loading capacity. Also, the loading variations are incorporated via light, shoulder, and peak levels of demand. A dynamic adaptation mechanism is used for updating the control parameters of the GWA. The proposed EGWA is employed for solving the optimal allocation problem (OAP) for two Egyptian distribution systems. Simulation results declare the proposed EGWA capability for solving the coordinated allocation of CBs, DGUs, and VRs. Great reduction of power losses is achieved with high improvement of the minimum voltage and loading capacity. Also, a comparative and statistical analysis is executed for the application of the proposed EGWA with different optimization techniques, which derives superior capabilities of the proposed EGWA over the others in the literature.
TL;DR: The proposed ID-2PAKA protocol is analyzed in the random oracle model to achieve provable security based on the hardness assumptions of computational Diffie–Hellman and bilinear Diffie-Hellman problems.
Abstract: With the significant development of the Internet, Internet of Things (IoT) has become an emerging technology in many industries To support security and privacy in the Industrial IoT environment, a user may interact with another user on the Internet to share confidential information, which requires an authenticated communication channel To meet this demand, in this article the authors developed an identity-based two-party authenticated key agreement (ID-2PAKA) protocol that allows two users to communicate securely and share sensitive data across IoT-enabled regions Similar protocols found in the literature either proven to be insecure or carry the burden of high communication and computational costs The proposed ID-2PAKA protocol is analyzed in the random oracle model to achieve provable security based on the hardness assumptions of computational Diffie–Hellman and bilinear Diffie–Hellman problems The performance analysis of the proposed ID-2PAKA protocol is performed using the pairing-based cryptography library The comparative results from the perspective of the computation and communication costs against the competing protocols showed that the proposed ID-2PAKA protocol is secure and efficient
TL;DR: In this article, a decentralized and privacy-preserving charging scheme for EVs is proposed, which is based on blockchain and fog computing, and a flexible consortium blockchain architecture is proposed.
Abstract: A distributed charging system based on the Internet of Things can provide important supports to ensure the safe and sustainable operation of electric vehicles (EVs). Usually, drivers prefer to use local charging piles by querying the remote cloud server. Frequent communication with the cloud server will not only produce an unnecessary communication overhead but also increase the latency of response. More seriously, the cloud-based centralized management mode is vulnerable to cyber-attacks, which usually leads to privacy leakage. However, previous studies seldom focus on the privacy issue of the charging system for EVs. In this article, a decentralized and privacy-preserving charging scheme for EVs is proposed, which is based on blockchain and fog computing. In this scheme, fog computing is introduced to provide local computing with low latency. Specifically, a fog computing network, which is composed of fog computing nodes (FCNs), is used to provide localized services. Besides, a flexible consortium blockchain architecture is proposed. The blockchain system is deployed on the distributed FCNs, providing a decentralized and secure storage environment. By combining mutual authentication, smart contract, and blockchain-based storage, the security of privacy in the charging process can be ensured. The theoretical analysis and experiments demonstrate the advantages of the proposed scheme.
TL;DR: This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs) to extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT).
Abstract: In the aftermath of disasters, localization of trapped victims is imperative to ensure their safety and rescue This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs) The UAVs can extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT) The one-hop neighbor information corresponds to range measurements, which may or may not contain noise For the noiseless case, we present a customized online graph traversal approach that minimizes the search energy of the UAV and the number of unlocalized nodes The lower limits on the various performance aspects of this joint approach are presented For a noiseless case, the results of UAV travel distance and cells searched show a decreasing trend with an increase in the number of maximum neighbors These curves approximately approach their corresponding lower limits when the number of maximum neighbors is increased beyond 9 For the case of noisy range measurements, using the same objective function and graph traversal algorithm, the probabilistic region for search is determined that gives the least probability of flip errors To this end, we further optimize the UAV flight path and its search energy in the probabilistic region through clustering The proposed method is able to achieve linear scaling of the area searched with respect to the noise level For a given noise level and increasing number of nodes, the UAV search energy with clustering can reduce the energy cost to 70%
TL;DR: A comprehensive review of the marine optimization-based power/energy management system is provided and the future trends of PMS/EMS in ship power systems are discussed.
Abstract: The increasing demands for reducing greenhouse emissions and improving fuel efficiency of marine transportation have presented opportunities for electric ships. Due to the complexity of multiple power resources coordination, varied propulsion loads, changeable economical, and environmental requirements, power/energy management system (PMS/EMS) becomes essential in both designing and operational processes. The existing literature on PMS/EMS can be categorized into rule-based and optimization-based approaches. Compared to the rule-based PMS/EMS, which relies heavily on human expertise, as well as predefined strategies and priorities, the optimization-based approaches can offer more efficient solutions and are more widely used nowadays. This article provides a comprehensive review of the marine optimization-based power/energy management system and discusses the future trends of PMS/EMS in ship power systems.
TL;DR: A localized protection scheme for dc microgrids with radial configuration under the impact of constant power loads (CPLs) to determine the location of faults accurately and can efficiently and reliably estimate the location and resistance of faults with high accuracy and acceptable error margin is proposed.
Abstract: This article proposes a localized protection scheme for dc microgrids with radial configuration under the impact of constant power loads (CPLs) to determine the location of faults accurately. The proposed fault location scheme is primarily designed for fault location of CPLs in dc microgrids. First, a local protection relay for CPL is designed based on the transient behavior of the current and voltage in the main distribution line. Then, the estimation of the fault resistance is formulated based on the power sharing in the system to improve the accuracy of the protection system. To realize a robust protection scheme considering the variation of fault resistance, a fault resistance estimation procedure is employed to design a system that locates both low- and high-impedance faults. Finally, the effectiveness of the proposed strategy is evaluated based on offline digital time-domain simulations in Digsilent PowerFactory software environment and experimentally verified by implementing on a laboratory scale hardware setup. The obtained simulation and experimental test results, and comparison with other methods prove that the proposed scheme is immune against these disturbances and can efficiently and reliably estimate the location and resistance of faults with high accuracy and acceptable error margin.
TL;DR: A comprehensive study on the feasibility of integrating EVs into the existing distribution system, considering both slow and fast charging schemes, and using a novel metaheuristic optimization called the water cycle algorithm finds the node voltage variation is found within the permissible limits.
Abstract: In the future, electric vehicles (EVs) are anticipated to dominate the urban transportation sector. To lessen the operational risk of existing distribution networks due to massive deployment of EVs, optimal planning and operation are vital as EVs elevate new charging demand. This article conducts a comprehensive study on the feasibility of integrating EVs into the existing distribution system, considering both slow and fast charging schemes. The problem is formulated as an objective function that minimizes the total price of charging incurred by the charging stations and the peak-to-average ratio. To diminish the negative impact of EVs on the node voltage in the distribution system, EVs are also modeled as a reactive power compensating device. Simulations are conducted on the IEEE 33-bus distribution network and an in-depth study is presented on the maximum feasible EV penetration to the existing system without augmentation. The problem is solved using a novel metaheuristic optimization called the water cycle algorithm. The proposed charging/discharging strategy works well and results in the housing of higher penetration of EVs in the distribution network along with a reduction in the charging price. Moreover, with the proposed coordinated strategy, the node voltage variation is found within the permissible limits.