IMF-PR: An Improved Morton-Filter-Based Pseudonym-Revocation Scheme in VANETs.

Vehicle ad hoc networks (VANETs) are special wireless networks which help vehicles to obtain continuous and stable communication. Pseudonym revocation, as a vital security mechanism, is able to protect legal vehicles in VANETs. However, existing pseudonym-revocation schemes suffer from the issues of low certificate revocation list (CRL) generation and update efficiency, along with high CRL storage and transmission costs. In order to solve the above issues, this paper proposes an improved Morton-filter-based pseudonym-revocation scheme for VANETs (IMF-PR). IMF-PR establishes a new distributed CRL management mechanism to maintain a low CRL distribution transmission delay. In addition, IMF-PR improves the Morton filter to optimize the CRL management mechanism so as to improve CRL generation and update efficiency and reduce the CRL storage overhead. Moreover, CRLs in IMF-PR store illegal vehicle information based on an improved Morton filter data structure to improve the compress ratio and the query efficiency. Performance analysis and simulation experiments showed that IMF-PR can effectively reduce storage by increasing the compression gain and reducing transmission delay. In addition, IMF-PR can also greatly improve the lookup and update throughput on CRLs.

Theory of Coupled Electrochemistry and Piezoelectricity in a Porous Medium.

We find that a porous piezoelectric medium stabilizes electrodeposition and suppresses dendrite. The effect is 6 orders of magnitude larger than mechanical blocking. We develop a theory integrating electrochemistry, piezoelectricity, and mechanics. A piezoelectric overpotential is derived, which reveals a fundamental relation to surface charge density, dielectric property of the medium, electrolyte concentration and diffusivity, and the reaction coefficient. The simulations show that piezoelectric medium suppresses electrodeposition on any protrusion, leading to a flat, dendrite-free surface.

Optical three-dimensional shape measurement based on structured light and a binocular vision system.

Three-dimensional shape measurement based on structured light is affected by two factors: the number of fringe patterns and the phase unwrapping process. Although one-shot technology can get the wrapped phase, it is not suitable for measuring complex surface. Moreover, phase unwrapping also affects measurement speed and accuracy. To overcome these problems, a two-dimensional wavelet transform with binocular vision system is proposed. Wavelet transform is used to get the wrapped phase based on the Morlet wavelet. In order to get a three-dimensional shape without phase unwrapping, a binocular vision system is used. The increase matching accuracy, the preliminary disparity, and the sub-pixel optimization are calculated, respectively. Based on the calibration parameters, three-dimensional information can be obtained directly from the wrapped phase. In addition, the average phase is calculated based on ambient pixels to confirm wrapped phase boundary. Experimental results demonstrate the feasibility and advantage of the proposed method. Compared with traditional methods, both measurement accuracy and measurement speed can be increased.

A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries.

With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.

PCP: A Pseudonym Change Scheme for Location Privacy Preserving in VANETs.

In vehicular ad hoc networks (VANETs), pseudonym change is considered as the vital mechanism to support vehicles' anonymity. Due to the complicated road conditions and network environment, it is a challenge to design an efficient and adaptive pseudonym change protocol. In this paper, a pseudonym change protocol for location privacy preserving (PCP) is proposed. We first present the requirements of pseudonym change in different scenarios. According to variable network states and road conditions, vehicles are able to take different pseudonym change strategies to resist the tracking by global passive adversaries. Furthermore, the registration protocol, authentication protocol, pseudonym issuance protocol, and pseudonym revocation protocol are introduced for the pseudonym management mechanism. As a consequence, it is not feasible for global passive adversaries to track a vehicle for a long time and obtain the trajectory of the vehicle. The analysis results show that the security and performance of PCP are improved compared with the traditional ones.

A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT).

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

Machine learning toward advanced energy storage devices and systems.

Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

Piezoelectric Mechanism and a Compliant Film to Effectively Suppress Dendrite Growth.

We show a piezoelectric mechanism that effectively suppresses dendrite growth using a compliant piezoelectric film as a separator or coating. When an electrode surface starts to lose stability upon lithium deposition, any protrusion causes film stretching, generating a local piezoelectric overpotential that suppresses deposition on the protrusion. Lithium ions thus spontaneously deposit to a flat surface. By proposing a theory that couples electrochemistry and piezoelectricity, we quantify the suppression effect and growth morphology. We find that the dendrite-suppression capability is over 5 × 105 stronger than the limit of mechanical blocking by any separators or solid-state electrolytes. Surprisingly, the mechanism ensures deposition to form a flat surface even if the initial substrate surface has significant protrusions, suggesting its robustness and effectiveness against manufacturing defects. We show that the mechanism is so strong that even a weak piezoelectric material is highly effective, opening up a wide range of materials.