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The maturity in information technology and new energy technologies enables participants to generate, buy, and sell energy in energy trading systems. Although applying blockchain technology to energy trading has solved some drawbacks in traditional centralized energy systems, the openness and transparency characteristics make the trading records stored on the blockchain vulnerable to data-mining attacks that may cause indispensable privacy leakage. Due to high efficiency and low overhead, noise-addition is an appropriate solution for privacy preservation. Nonetheless, recent research on noise-addition needs to generate massive accounts, which brings a certain amount of waste and inconvenience for later regulation and management. To avoid the aforementioned issues, this paper proposes a consortium blockchain-enabled scheme to ensure the privacy of data stored on the blockchain and resist linking attacks initiated by data mining algorithms. Our scheme utilizes a dynamic partition algorithm to leverage an account mapping algorithm and a virtual token algorithm. Specifically, the account mapping algorithm utilizes a dynamic account allocation method to hide the trading distribution of active users. Furthermore, the virtual token algorithm applies Laplace noise to hide the actual energy consumption of inactive users and curb excessive accounts generation. Finally, we formally demonstrate the privacy and effectiveness of our proposed scheme in security analysis and experiment evaluations. [ABSTRACT FROM AUTHOR]

With the proliferation of electric vehicles (EVs), private charging pile (PCP) sharing networks are likely to be an integral part of future smart cities, especially in places with limited public charging infrastructure. However, there are a number of operational challenges associated with the deployment of PCPs in such a shared and untrusted environment. For example, how do we formulate efficient PCP sharing strategies in PCP sharing networks, while also taking into consideration the dynamic charging behaviors of EVs? Therefore, in this paper, we propose an energy blockchain-based secure PCP sharing scheme (BBC) for PCP sharing networks. First, an energy blockchain-based framework is designed for PCP sharing networks to facilitate energy sharing services for EVs and PCPs, using both distributed ledgers and cryptocurrency. Then, we devise a reputation-based secure PCP sharing algorithm to improve consensus efficiency with smaller signature sizes. In addition, a distributed reputation mechanism is constructed to assess the trustworthiness of consensus nodes in blockchain, based on ratings, behaviors, and fading. We also model the interactions among EVs and cooperative PCPs as a joint coalition-matching game, and obtain the optimal strategies of PCPs and EVs by analyzing the Nash-stable coalitional structure and stable many-to-one matching pairs. Extensive simulations and the real-world implementation demonstrate that the proposed approach improves the utility of EV users and renewable energy efficiency in PCP sharing networks. [ABSTRACT FROM AUTHOR]

Growing computation requirements in emerging intelligently connected vehicle (ICV) networks has posed a big challenge for vehicles to process computation-intensive tasks on-board, and thus computation partition and offloading plays a critical role. Owing to broadcast nature of wireless channels, confidential messages without proper security protection in ICVs may be easily exposed to malicious users. In this paper, a mobile crowdsourcing (MCS) based mobile crowd computing framework for ICV networks is proposed, where multiple vehicles act as workers to provide computing services for end user (EU). In particular, artificial noise (AN) assisted physical (PHY) layer security approaches are used to enhance the security in offloading links. Ergodic secrecy rates in different offloading phases in time-varying channels are derived. In addition, an incentive-driven mechanism is introduced to encourage workers to share their idle computing resources, and an optimization problem is formulated to minimize the overall price paid for computing tasks, subject to energy consumption and delay constraints. Finally, Monte Carlo simulations verify the analysis on the ergodic secrecy rates. [ABSTRACT FROM AUTHOR]

In this paper, we propose a joint spatial division and multiplexing (JSDM) beamforming based on a neighbor scheme for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. The neighbor-based JSDM (N-JSDM) can fully utilize signal space, leading to higher spectral efficiency over the conventional JSDMs. The reason is that for the neighbor scheme, neighbors and non-neighbors are classified adaptively by the angles of departure (AoD), and the prebeamformer is designed to mitigate the non-neighbors’ interference by the statistical channel state information. The effective channel matrix after the prebeamformer then becomes a band matrix, from which the downlink training length (DTL) and the channel feedback length are much smaller than the number of antennas. Moreover, an optimal prebeamformer which is proved to be able to achieve the same system capacity as the full CSI system is proposed, followed by a suboptimal prebeamformer with constrained DTL, and a DFT-based prebeamformer. On the other hand, the neighbors’ interference is mitigated using the banded channel state information. Simulation results validate the good performance of the proposed N-JSDM. [ABSTRACT FROM AUTHOR]

Inertial navigation system (INS) is widely equipped in various vehicles for overcoming GPS-challenging environments. The thermal-magnetic coupling heading-effect drift (thermal-magnetic drift) of INS is a multi-physics coupling error related to carrier maneuver, which cannot be compensated well by traditional methods. In this paper, the source of gyro thermal-magnetic drift are analyzed and an Ensemble Learning (EL) and Sage-Husa adaptive Kalman filter (SHAKF) based integration scheme for the thermal-magnetic drift compensation in high-grade INS is established. In the integrated system, the geomagnetic field strength calculated by International Geomagnetic Reference Field (IGRF) model and temperature information measured by sensors are selected as input of the thermal-magnetic drift prediction model. The gyro biases estimated by exponential fading SHAKF are taken as target in training phase. The EL algorithms, LSBoost and Bagging, are used for training when GPS data is available. The gyro thermal-magnetic drift is predicted and compensated by the trained model during GPS outages. Land and sea tests show its effectiveness on compensate the gyro thermal-magnetic drift of the proposed method. [ABSTRACT FROM AUTHOR]

This article aims to provide a comprehensive overview on PHY layer security assisted ICV networks. Beginning with a discussion from a general perspective of the ICV network architecture, this work summarizes the major security threats faced by ICV networks. To achieve confidential data transmission with a low latency and a high data rate, several physical layer security schemes are surveyed in this article, which include RRM, cooperative jamming, multi-antenna technologies, and PHY-layer key generation. In addition, the performances of the aforementioned schemes are compared in terms of different aspects, followed by a discussion on some major challenges that need to be tackled in the implementation of ICV networks. [ABSTRACT FROM AUTHOR]

This paper proposes a brain–robot interface (BRI)-based control strategy in combination with the simultaneous localization and mapping (SLAM) to achieve the navigation and control of a mobile robot in uncertain environments. The BRI is based on steady state visually evoked potentials, utilizing the multivariate synchronization index classification algorithm to analyze the human electroencephalograph (EEG) signals in such a manner that human intentions can be recognized and motion commands can be produced for the brain controlled robot. The entire system is semi-autonomous since the navigation of mobile robot is commanded by the BRI, and the low-level motion of the mobile robot is autonomous with a designed kinematic controller. By utilizing vanishing points and door plates as the environmental features, a global metric map of the environment has been built by a sequential SLAM algorithm. The main contribution of this paper is the combination of an artificial potential field (APF) and the brain signals, which builds up the relationship between the strength of EEG signals and the intensity of the potential field. Through the proposed EEG-APF method, motion commands that would plan an obstacle-free trajectory in un-structured environments, can be obtained. The entire system has been tested with eight volunteer subjects, and all subjects are able to successfully fulfill manipulating mobile robot in the experiments. [ABSTRACT FROM AUTHOR]

In device-to-device (D2D) underlaying vehicle-to-vehicle (V2V) communications, radio resource blocks (RBs) are allocated to primary cellular users, which yields sub-optimal rates and a long access latency for secondary D2D-enabled vehicles. This work investigates a joint radio resource and power management (RRPM) problem for secure cellular underlaying V2V communications, where cellular users and vehicles have the same priority, giving vehicles more opportunities to access the RBs. Specifically, we aim to maximize secrecy rates of V2V channels under the condition that eavesdropper has an adaptive receiving detection vector to maximize the received signal-to-interference-plus-noise ratio (SINR) from vehicles. For a single pair of a vehicle and a cellular user, the closed-form optimal power allocation expressions are derived for the interference-limited scenario and the noise-limited scenario, respectively. Moreover, for multiple pairs of vehicles and cellular users, a 3-partite hypergraph based 3-dimensional matching approach is proposed to solve a mixed-integer and non-convex problem, which achieves a near-optimal result with an $O(n^4)$ time complexity. Simulations in different scenarios show that the secrecy rate of the proposed scheme can be improved by 50% if compared to existing schemes. [ABSTRACT FROM AUTHOR]

The existing investigations on artificial noise (AN) security systems assumed that only null spaces are used to send AN signals, and all eigen-subchannels should be used to transmit messages. Our previous work proposed an AN scheme that allocates some of eigen-subchannels to transmit AN signals for improving secrecy rates. Nevertheless, our previous work considered only uncorrelated MIMO Rayleigh fading channels. In fact, the correlations among antennas exist in realistic scattering channel environments. In this paper, we extend our previous AN scheme to spatially correlated Rayleigh fading channels at both legitimate receiver and eavesdropper sides and derive an exact theoretical expression for the ergodic secrecy rate of the AN scheme, along with an approximate analysis. Both numerical and simulation results show that the proposed AN scheme offers a higher ergodic secrecy rate than the existing schemes, revealing a fact that the correlation among eavesdropper’s antennas can potentially improve the secrecy rate of an MIMO system. [ABSTRACT FROM AUTHOR]

To achieve the better navigation performance of a mobile robot in the unknown environments, a novel human–robot hybrid system incorporating a motor-imagery (MI)-based brain teleoperation control is presented in this paper, where a deep-learning-based active perception is developed in the simultaneous localization and mapping (SLAM) framework. Using the deep-learning-based object recognition in the red–green–blue-depth (RGB-D) data acquisition process, the designed SLAM approach can select the valid feature points effectively, and the speed of displacement tracking can be improved by combining the oriented FAST and rotated BRIEF (ORB) SLAM algorithm with the optical flow method. The global trajectory map can also be mended using graph-based nonlinear error optimization. In addition, to build the connection between human intentions and the robot control commands flexibly in the developed mobile robot, a common spatial pattern (CSP)-based support vector machine (SVM) classification algorithm is proposed so that the control commands can be obtained directly from the human electroencephalograph (EEG) signals, which are preanalyzed and classified using the phenomena of event-related synchronization/desynchronization (ERS/ERD). Experiments involving several operators have verified the effectiveness of the proposed framework in the actual unstructured environments. Note to Practitioners—This paper is motivated by the exploration of the mobile robots remotely controlled by the disables in an unstructured environment that includes some unknown moving objects in the background. In the conventional approaches, the image feedback and the brain–computer interface-based EEG signals invoked by the visual stimulus are used to guide the motion of the robots. In this paper, the MI of left or right hand is adopted as an input of the brain teleoperation system, and the EEG signals are classified into two categories representing two robot control commands through the designed algorithm. Besides, a deep-learning-based SLAM is designed to analyze the image and depth of the environmental information provided by the robot RGB-D sensor, and the results can be transferred into a 3-D map locating the robot and assisting the operator to understand the operating environment. It has been verified that the deep-learning-based SLAM presented is more efficient and robust than traditional SLAM. The feasibility of the system is demonstrated by a set of experiments in the corridor environment. [ABSTRACT FROM AUTHOR]

Dual-axis rotational inertial navigation system (RINS) has been widely used in long-term marine navigation for recent years. With all constant biases averaged out, those gyro biases related to geomagnetic or gravitational field, which cannot be averaged out, become the main error source. In this paper, the geographic-frame-equivalent (G-F-E) gyro bias is defined by integrating the gyro biases caused by all kinds of external fields. A practicable online calibration method is proposed by deducing the error propagation rules. The calibration equation is established by observing position errors at three points, and the optimal solution of the G-F-E gyro bias is obtained using the least square method. Static and sailing experimental results show that the G-F-E gyro bias can be calibrated during the 6-h initial alignment. The proposed calibration method can further improve the position accuracy of a dual-axis RINS by 30% compared with the previous method when the G-F-E gyro bias changes. [ABSTRACT FROM AUTHOR]

Normally, authentication in vehicular ad hoc networks (VANETs) uses public key infrastructure to verify the integrity of messages and the identity of message senders. The issues considered in the authentication schemes include the level of security and computational efficiency in the verification processes. Most existing schemes mainly focus on assuring the security and privacy of VANET information. However, these schemes may not work well in VANET scenarios. For instance, it is difficult for a roadside unit (RSU) to verify each vehicle's signature sequentially when a large number of vehicles emerge in the coverage areas of an RSU. To reduce the computational overhead of RSUs, we propose a proxy-based authentication scheme (PBAS) using distributed computing. In the PBAS, proxy vehicles are used to authenticate multiple messages with a verification function at the same time. In addition, the RSU is able to independently verify the outputs from the verification function of the proxy vehicles. We also design an expedite key negotiation scheme for transmitting sensitive messages. It is shown from the analysis and simulations that an RSU can verify 26 500 signatures per second simultaneously with the help of the proxy vehicles. The time needed to verify 3000 signatures in the PBAS can be reduced by 88% compared with existing batch-based authentication schemes. [ABSTRACT FROM PUBLISHER]