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Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects.

Due to the technical characteristics and application scenarios of distributed networks, their nodes can easily be invaded and compromised. It will result in information being forged or tampered without difficulty. An effective scheme to guarantee the authenticity and integrity of information is judging how trustworthy nodes are in terms of transmission. In D-S evidence theory (DST), the uncertainty can be expressed to solve the trust fusion issue for multiple nodes. In this paper, for reviewing the DST-based trust evaluation and decision and providing their future research directions systematically. Meanwhile, the DST is briefly reviewed, and two improvements in DST are categorically described. The role and mechanism in DST-based trust models are compared and analyzed. The valuable research directions in the near future are represented. Our contributions could solve the trust problem in resource constrained sensor nodes and improve the decision reliability of network.

This paper proposes a new distributed AC state estimation method. Different from the popular distributed state estimation (DSE) methods based on area partitioning method, the proposed method is a truly distributed method in which the power system is not required to be divided into smaller areas and a centralized state estimator in each area is not needed. In order to achieve fully DSE, the information propagation algorithm is introduced in this paper to help the distributed local state estimators share the measurement data. The information propagation algorithm is developed based on consensus protocol. The proof of the convergence of the information propagation algorithm is provided in this paper. Then, the AC state estimation method is integrated with the information propagation algorithm to realize the proposed method. The proposed method is tested in different standard power system models. The results show that the proposed method reaches the similar accuracy as the traditional centralized state estimation methods and performs faster and more accurate than the existing DSE methods.

As a lightweight method, runtime verification aims to check whether one program execution satisfies a desired property. For online runtime verification, the approach efficiency and property expressiveness are two key points restricting its wide application. In this paper, we propose a distributed network-based parallel runtime verification approach to verifying full regular temporal properties for a suitable subset of C (named by Xd-C) programs in an online manner. With this approach, an Xd-C program is translated into an equivalent Modeling, Simulation and Verification Language (MSVL) program, and a desired property is specified as a Propositional Projection Temporal Logic (PPTL) formula; during the program execution, segments of the generated state sequence are verified in parallel by distributed multi-core machines. Experimental results show that, our approach has a speedup of 2.5X-5.0X over the state-of-art runtime verification approaches and supports full regular temporal properties, meaning that our approach can not only take full advantage of computing and storage resources in a distributed network, but also support more expressive properties. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) provides a promising and novel direction to design future time-varying wireless networks by leading to significantly superior performances compared to conventional methods. In addition, the advanced deployment of unmanned aerial vehicles (UAVs) has boosted extensive novel research results and industrial products in terms of aerial-ground networks. However, with the rapid development of mobile networks and growing requirements for low-latency services, the conventional centralized aerial-ground network has failed to meet the time-varying expectations of mobile users in the dynamic network environment. To cope with the problems, the marriage of the aerial-ground network and innovative AI techniques, i.e., distributed artificial intelligence enabled aerial-ground network (DAIAGN), is proposed in this article, which consists of three vital components: deep reinforcement learning enabled distributed information sharing, edge intelligence enabled distributed security management, and multi-agent reinforcement learning enabled distributed decision making. The functions of the three components are elaborated, and recent related advances are surveyed in detail. A specific case study is also provided with respect to multi-agent reinforcement learning enabled distributed decision making. Furthermore, key challenges and open issues are also discussed to provide some guidances for potential future directions.

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely, privacy and anonymity, in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal. [ABSTRACT FROM AUTHOR]

Most of the existing studies mainly consider the process and measurement noise as Gaussian white noises, while a large number of systems are influenced by colored noises in the practical applications. In this brief, a distributed state estimator with colored noises is proposed. Since every moment of colored noise sequence is related, we first make the colored noises equivalent to Gaussian white noises by augmenting the state matrix. Then, we design a state estimator with colored noises, and further analyze the convergence property of the estimation error covariance. Ultimately, simulation results indicate that the designed distributed state estimator can effectively address colored noises and ensure high accuracy and performance of state estimation, and demonstrate the relationship between colored noise parameters and estimation performance as well. [ABSTRACT FROM AUTHOR]

The use of distributed generation has received wide attention due to low maintenance costs, reduced transmission line losses and network congestion, as well as minimal impact on climate change and global warming. However, the distributed generation integrated to the distribution network introduces various protection problems that cannot be solved by conventional protection systems. These obstacles include the bi-directional power flow and the variation in the fault current level during the topology change. Thus, appropriate fault detection and protection scheme are strongly recommended to increase safety and reliability of the distributed network. This paper presents a comprehensive overview of the distributed network fault detection and protection strategies incorporated with distributed generation. This review also investigates the various fault detection approaches concerning types, communication methods, operation mode, constraints and benefits. Additionally, numerous island detection techniques are explored, focusing on generation types, parameters, cost and advantages. Moreover, the review outlines the various protection schemes highlighting categories, operation, constraints, strength and shortcomings. The key issues and challenges are discussed along with selective proposals for future research. All the highlighted viewpoints of this research will hopefully be beneficial to power system engineers and researchers for the advancement of distributed network fault and protection strategies for suitable operation and management of future distributed generation systems.

In this paper, a new fully distributed state estimation (DSE) based on weighted least square (WLS) method and graph theory is proposed for power system. The proposed method is fully distributed so that the centralized facilities, e.g., supervisory control and data acquisition (SCADA) and centralized estimators, are not required. Also, different from the existing DSE methods, the proposed method is a bus-level DSE method, in which the power system is not required to be partitioned into several areas. In order to realize the proposed fully distributed DSE method, a novel information propagation algorithm is developed in this paper. This algorithm has great potential in future applications since it is useful to broadcast the local information of the nodes to the entire system in a fully distributed network. The proposed DSE method is compared with the conventional centralized state estimation method and existing multi-area DSE method in different models in this paper. The results show that the proposed method has better performance than the traditional methods.

With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PG&E 69-node system.

We present an open-loop microwave distributed beamforming system using a self-mixing circuit for wireless frequency synchronization between two transmitting nodes in relative motion. Distributed beamforming on arrays of wireless nodes requires accurate coordination of the electrical states of each node to ensure that transmitted signals arrive at the desired destination with sufficient phase alignment for high signal gain. Of the necessary coordination parameters, synchronizing the frequencies of the internal oscillators is among the most crucial to ensure distributed coherence. In this work, we demonstrate distributed beamforming between separate microwave wireless transmitters using wireless frequency synchronization in a hierarchical coordination topology that is directly scalable to larger array sizes. The primary node transmits a spectrally sparse two-tone waveform that is received by a self-mixing circuit at the secondary node. A 10-MHz frequency reference of the primary node is modulated onto the tone separation of the waveform; upon self-mixing, the demodulated 10-MHz frequency reference is passed to a phase-locked loop to discipline the oscillator of the secondary node. We demonstrate beamforming at a 1.5-GHz carrier frequency between the two nodes, performed while moving the primary node, demonstrating the ability to maintain greater than 90% ideal distributed beamforming gain. [ABSTRACT FROM AUTHOR]

Distributed wireless mobile ad hoc network has superiority of flexible networking, powerful extensibility, and low cost of operation and maintenance. Compared with the centralized ones, the distributed network can effectively solve a series of problems, such as the increase in operation and maintenance costs, the complex setting of center nodes, performance deterioration in large-scale scenarios, and hard to optimize routing. It has broad application prospects in ultra-dense networks of fifth-generation, distributed device-to-device communication, and industrial wireless sensor networks. Due to the absence of a control center, interference coordination in distributed network can only rely on the maintenance of neighbors. This paper first establishes the model of link interference and successful transmission probability under different hops of neighbor maintenance. We discover that the existing protocols with two-hop neighbor maintenance have serious inter-area interference which can jeopardize the performance, and maintaining three-hop neighbors have a 6-dB gain of receivers’ signal interference ratio. Hereby, this paper proposes and verifies a scheduling and channel reuse protocol under three-hop neighbor maintenance, laying a solid foundation for the practical application of distributed network.

Smart factory is a typical application scene of Internet of Things and wireless terminal devices naturally compose a three-dimensional (3-D) industrial wireless network. A primary requirement in the network is delivering packets from source node to destination node. Most geographic routing algorithms are designed for planar networks and they do not suit 3-D networks. In this paper, we extend a greedy perimeter stateless routing algorithm (GPSR) into three dimensions named GPSR-3D. In GPSR-3D, each node decides next hop of a packet by cooperating with only local neighbors and hence this algorithm is totally distributed. GPSR-3D comprises two packet forwarding patterns named greedy forwarding pattern (GFP) and surface forwarding pattern (SFP). In GFP, a node always sends the packet to a neighbor closest to destination and when it fails, SFP is employed for recovery. In SFP, we first divide the whole network space into a set of subspaces based on a novel 3-D geometric structure. Then, a parallel polyhedron traverse algorithm is proposed to recover local minima. A flowchart of GPSR-3D is given to clearly present the process of delivering a packet based on GFP and SFP. Simulation results show that GPSR-3D is of great reliability, energy efficiency and storage efficiency. Specifically, data transmission amount in GPSR-3D is about 67% and 71% to that of multihop Delaunay triangulation (MDT) and GDSTR-3D on average. Moreover, GPSR-3D performs much better than MDT and GDSTR-3D in terms of average storage cost and the average storage space in GPSR-3D is about 48% and 26% to that of MDT and GDSTR-3D, respectively. [ABSTRACT FROM AUTHOR]

The cyber security of modern power systems has drawn increasing attention in both academia and industry. Many detection and defense methods for cyber-attacks have therefore been proposed to enhance robustness of modern power systems. In this paper, we propose a new, distributed blockchain-based protection framework to enhance the self-defensive capability of modern power systems against cyber-attacks. We present a comprehensive discussion on how blockchain technology can be used to enhance the robustness and security of the power grid, by using meters as nodes in a distributed network which encapsulates meter measurements as blocks. Effectiveness of the proposed protection framework is demonstrated via simulation experiments on the IEEE-118 benchmark system. [ABSTRACT FROM AUTHOR]

Distributed estimation, where a set of nodes collaboratively estimate some parameters of interest from noisy measurements, has received much attention in both science and engineering. Recent studies mainly focus on distributed non-blind or training-based estimation, that is, a training signal and its desired output are both known to the distributed receivers. However, in some applications, it is physically difficult, if not impossible, to get a training signal in prior. Besides, the use of training signals consumes much channel bandwidth. So, it is more preferable for the receivers to perform distributed estimation without the assistance and expense of training sequences, i.e., distributed blind estimation. In this paper, the problem of distributed blind estimation over sensor networks is considered, and a kind of distributed diffusion generalized Sato algorithm is proposed to design a blind equalizer for channel equalization and source signal estimation. The stability of the proposed method in mean and mean-square senses is analyzed theoretically, and its performance is verified numerically by a series of simulations.

As one of the signed variants of the diffusion least mean square (DLMS) algorithm over networks, the diffusion sign error algorithm has been presented in previous reference. In this paper, we propose two novel signed variants of the DLMS algorithm, i.e., the diffusion signed regressor algorithm and the diffusion sign-sign algorithm. Moreover, this paper analyzes the performance of these three signed variants of the DLMS algorithm for cyclostationary white Gaussian inputs which have periodically time-varying variances. It is assumed that the distributed algorithms are in non-stationary environments. Specifically, the unknown parameter to be identified is time-varying according to the standard random walk model. The analysis models in terms of mean weight behavior and mean square performance are provided, in which, we can find some interesting results. Finally, simulations are carried out to verify the correctness of the proposed analysis model.

This paper introduces a new consensus-based control scheme for distributed power systems to solve the distributed economic dispatch (DED) problem. With the development of renewable energy, smart grid, and microgrid, more and more distributed generators will be connected to power systems. Thus the distributed control schemes become more and more important for power systems to control and manage the distributed equipment. Therefore, some consensus-based methods are published previously to solve the DED problem. But the challenge to apply the consensus-based method is to meet the power balance constraint without the centralized facility, since the value of power mismatch is hard to be obtained for a distributed control system. To solve the problem, this paper proposes the method to combine the frequency control methods with the consensus protocol from graph theory to develop the DED control method, so that the real power between the load and the generation can be balanced during the economic dispatch. With this method, a totally distributed optimal control scheme is obtained. The PI frequency controller and neural network frequency controller are employed to develop the DED control method. Then the communication failure problem is considered in this paper. The robust DED control method is proposed to improve the robustness of this distributed control method, allowing the system to stay at the optimal dispatching even if the communication is failed. In addition, the model of transmission line losses is incorporated in the DED problem to reduce the line losses. Finally, several case studies are given in this paper to verify the proposed method. [ABSTRACT FROM AUTHOR]

Distributed energy resources installed in the residential areas enable energy consumers to participate in the demand response of distribution systems. In the literature, the algorithms used for the decision-making process of an energy consumer are often centralized and require a dedicated communication link with the power utility. In this paper, a novel decision-making algorithm known as the auction decision process has been proposed. It helps energy consumers to independently meet the demand response in a decentralized manner. The resultant demand response operates in a distributed manner, where the limited information is transmitted among the neighboring consumers using the short-range ZigBee communication. This minimizes the required initial bulk investment for a power utility compared to using a centralized communication system. The data privacy is also improved when compared to the centralized approach. For this work, finding a path with the least transmission loss is selected as the desired constraint for the decision-making process of the demand response. The proposed method has been demonstrated using the IEEE 33 bus systems. [ABSTRACT FROM AUTHOR]

This paper proposes a priority-based energy scheduling mechanism for a distributed network with multiple microgrids. Based on energy supply and demand mismatch, a role of buyer or seller is assigned to a microgrid. In the proposed mechanism, the distribution network management system (DNMS) gathers surplus energy from all sellers and distributes it among the buyers based on their optimal strategies. A novel method for calculating priority index (PI) is proposed in which the past contributions made by a microgrid and local load demand are considered as vital parameters. Each buyer decides its optimal strategy for buying energy from DNMS using optimal buyer strategy algorithm which gives Nash equilibrium solution of a noncooperative energy competition game. The DNMS allocates optimal energy to each buyer microgrid using an optimal DNMS decision algorithm. Energy allocated by the DNMS is directly proportional to the PI of the buyer. The proposed system is stable enough for real-time implementation in the electricity market. [ABSTRACT FROM PUBLISHER]

This letter proposes a fast retry limit adaptation algorithm for the 802.11e distributed networks. Different from existing solutions, which manage a unique access category, the presented method jointly estimates the retry limits associated with both voice and video packets. The algorithm that operates in saturated and non-saturated traffic conditions is validated adopting an 802.11n physical layer and comparing its performance with a proper extension of an existing solution. [ABSTRACT FROM PUBLISHER]

An improved high-frequency small-signal model for SiGe HBTs under the off-state is presented in this paper. The proposed model takes into account the distribution characteristics of the intrinsic transistor, link base region under spacer, and extrinsic base–collector junction. The equivalent circuit for each region is separately derived using the transmission line equation with reasonable approximations. Being different from previous models, the intrinsic base resistance in the proposed model is pushed inside the internal base node and added to the components of collector and emitter resistance. To extract all the parameters for the proposed model, a novel extraction technique based on rational function fitting over the whole range of frequencies is developed. After the rational function fitting to related admittance parameters, a number of coefficients are accurately obtained and then all the model parameters are directly extracted without any special test structure or numerical optimization. The proposed model and extraction technique are validated with a series of sized SiGe HBTs from 100 MHz to 20.89 GHz at a wide range of bias points. An excellent agreement is obtained between the measured and simulated S-parameters. [ABSTRACT FROM PUBLISHER]

We study the compressive diffusion strategies over distributed networks based on the diffusion implementation and adaptive extraction of the information from the compressed diffusion data. We demonstrate that one can achieve a comparable performance to the full information exchange configurations, even if the diffused information is compressed into a scalar or a single bit, i.e., a tremendous reduction in the communication load. To this end, we provide a complete performance analysis for the compressive diffusion strategies. We analyze the transient, the steady-state and the tracking performances of the configurations in which the diffused data is compressed into a scalar or a single-bit. We propose a new adaptive combination method improving the convergence performance of the compressive diffusion strategies further. In the new method, we introduce one more freedom-of-dimension in the combination matrix and adapt it by using the conventional mixture approach in order to enhance the convergence performance for any possible combination rule used for the full diffusion configuration. We demonstrate that our theoretical analysis closely follow the ensemble averaged results in our simulations. We provide numerical examples showing the improved convergence performance with the new adaptive combination method while tremendously reducing the communication load. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to investigate the model, control, and implementation of a multifunction grid-interfaced inverter with output LCL filter, which can provide high performance active power current and compensate the existent harmonics simultaneously in a distributed network. Equipped with LCL filter, the proposed multifunction inverter offers reduced switching harmonics and superior output current shapes. However, the multifunction inverter with output LCL filter brings some challenges, including LCL resonance, phase lag, and complexity in system design. To address these issues, this paper analyzes and develops general Thevenin/Norton models for the multifunction grid inverters with LCL filter. Based on the general models of system, this paper presents guidelines of the control and the procedure of filter design for this application. In particular, a proportional-resonant (PR) plus odd-harmonic repetitive control (OHRC) scheme is designed for the outer current loop. The phase compensation method and detailed design criteria for the proposed control scheme are presented. Furthermore, the proposed OHRC scheme is compared with the previous multiple resonant control (MRC) based on their internal relationship. Simulation and experimental results are provided to validate the effectiveness and advantages of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In mobile ad hoc networks (MANETs), tasks are conducted based on the cooperation of nodes in the networks. However, since the nodes are usually constrained by limited computation resources, selfish nodes may refuse to be cooperative. Reputation systems and price-based systems are two main solutions to the node noncooperation problem. A reputation system evaluates node behaviors by reputation values and uses a reputation threshold to distinguish trustworthy nodes and untrustworthy nodes. A price-based system uses virtual cash to control the transactions of a packet forwarding service. Although these two kinds of systems have been widely used, very little research has been devoted to investigating the effectiveness of the node cooperation incentives provided by the systems. In this paper, we use game theory to analyze the cooperation incentives provided by these two systems and by a system with no cooperation incentive strategy. We find that the strategies of using a threshold to determine the trustworthiness of a node in the reputation system and of rewarding cooperative nodes in the price-based system may be manipulated by clever or wealthy but selfish nodes. Illumined by the investigation results, we propose and study an integrated system. Theoretical and simulation results show the superiority of the integrated system over an individual reputation system and a price-based system in terms of the effectiveness of cooperation incentives and selfish node detection. [ABSTRACT FROM PUBLISHER]

Symmetric key agreement is significant to security provisioning in sensor networks with resource limitations. A number of pairwise key pre-distribution protocols have been proposed, but their scalability is often constrained by the conflict between the desired probability of sharing keys between neighboring nodes and the resilience against node capture attacks under a given budget for storing keying information within each sensor. In this paper, we propose LKE, a self-configuring in-situ key establishment scheme targeting large-scale sensor networks. LKE employs location information for a deterministic key space generation and keying information distribution. For uniformly distributed networks, LKE exhibits strong resilience against node capture attacks and achieves a high key-sharing probability (close to 1) at the expense of a small amount of memory overhead. An improvement over LKE, termed as iLKE, is also proposed. iLKE is topology-adaptive, and therefore works well for both uniform and non-uniform network models. We conduct both theoretic analysis and simulation study to evaluate the performances of LKE and iLKE. [ABSTRACT FROM PUBLISHER]

Today's Internet is a massive, distributed network which continues to explode in size as e-commerce and related activities grow. The heterogeneous and largely unregulated structure of the Internet renders tasks such as dynamic routing, optimized service provision, service-level verification, and detection of anomalous/malicious behavior increasingly challenging tasks. The problem is compounded by the fact that one cannot rely on the cooperation of individual servers and routers to aid in the collection of network traffic measurements vital for these tasks. In many ways, network monitoring and inference problems bear a strong resemblance to other "inverse problems" in which key aspects of a system are not directly observable. Familiar signal processing problems such as tomographic image reconstruction, system identification, and array processing all have interesting interpretations in the networking context. This article introduces the new field of network tomography, a field which we believe will benefit greatly from the wealth of signal processing theory and algorithms [ABSTRACT FROM PUBLISHER]

Wireless Sensor Networks (WSNs) are at the forefront of emerging technologies due to the recent advances in Microelectromechanical Systems (MEMSs). The inherent multidisciplinary nature of WSN attracted scientists coming from different areas stemming from networking to robotics. WSNs are considered to be unattended systems with applications ranging from environmental sensing, structural monitoring, and industrial process control to emergency response and mobile target tracking. Most of these applications require basic services such as self-localization or time synchronization. The distributed nature and the limited hardware capabilities of WSN challenge the development of effective applications. In this paper, the self-localization problem for sensor networks is addressed. A distributed formulation based on the Information version of the Kalman Filter is provided. Distribution is achieved by neglecting any coupling factor in the system and assuming an independent reduced-order filter running onboard each node. The formulation is extended by an interlacement technique. It aims to alleviate the error introduced by neglecting the cross-correlation terms by "suitably” increasing the noise covariance matrices. Real experiments involving MICAz Mote platforms produced by Crossbows along with simulations have been carried out to validate the effectiveness of the proposed self-localization technique. [ABSTRACT FROM PUBLISHER]