A Review of Research on Privacy Protection of Internet of Vehicles Based on Blockchain

Numerous academic and industrial fields, such as healthcare, banking, and supply chain management, are rapidly adopting and relying on blockchain technology. It has also been suggested for application in the internet of vehicles (IoV) ecosystem as a way to improve service availability and reliability. Blockchain offers decentralized, distributed and tamper-proof solutions that bring innovation to data sharing and management, but do not themselves protect privacy and data confidentiality. Therefore, solutions using blockchain technology must take user privacy concerns into account. This article reviews the proposed solutions that use blockchain technology to provide different vehicle services while overcoming the privacy leakage problem which inherently exists in blockchain and vehicle services. We analyze the key features and attributes of prior schemes and identify their contributions to provide a comprehensive and critical overview. In addition, we highlight prospective future research topics and present research problems

Sequential Geometric Approach for Device-Free Localization with Outlier Link Rejection

Device-free localization (DFL) is an emerging technique for estimating the location of the target that does not attach any electronic equipment. Wireless devices are needed to perform as transmitters or receivers. The location of the target is estimated by detecting the changes of the received signal strength (RSS) measurements of the wireless links formed by wireless transmitters and receivers. Due to the uncertainty of the wireless channel, certain links may be polluted seriously, resulting in error detection. In this paper, we propose a novel sequential geometric approach with outlier link rejection (SGOLR) for DFL. It consists of three sequential strategies, including (1) affected link identification by differential RSS detection; (2) outlier link rejection via clustering algorithm by intersection of link (IoL) calculation from the affected links; and (3) density based IoL selection and target location estimation from the remained IoLs. Experimental results show that SGOLR is robust to the fluctuation of the wireless signals with superior localization accuracy compared with the existing Radio Tomographic Imaging (RTI) approach

Induction of the HIV-1 Tat co-factor cyclin T1 during monocyte differentiation is required for the regulated expression of a large portion of cellular mRNAs

BACKGROUND: P-TEFb, a general RNA polymerase II elongation factor, is composed of CDK9 (cyclin-dependent kinase 9) as a catalytic unit and either cyclin T1, T2 or K as a regulatory subunit. The cyclin T1/P-TEFb complex is targeted by HIV to mediate Tat transactivation. Cyclin T1 protein expression is induced during early macrophage differentiation, suggesting a role in regulation of mRNA expression during the differentiation process. To study the functional significance of cyclin T1 induction during differentiation, we utilized the human Mono Mac 6 (MM6) monocytic cell line. RESULTS: We found that cyclin T1 protein expression is induced by a post-transcriptional mechanism following PMA treatment of MM6 cells, similar to its induction in primary monocytes and macrophages. Also in agreement with findings in primary cells, cyclin T2a is present at relatively high levels in MM6 cells and is not induced by PMA. Although the knock-down of cyclin T1 in MM6 cells by shRNA inhibited HIV-1 Tat transactivation, MM6 cell growth was not affected by the depletion of cyclin T1. Using DNA microarray technology, we found that more than 20% of genes induced by PMA require cyclin T1 for their normal level of induction, and approximately 15% of genes repressed by PMA require cyclin T1 for their normal level of repression. Gene ontology analysis indicates that many of these cyclin T1-dependent genes are related to immune response and signal transduction. CONCLUSION: These results suggest that cyclin T1 serves a critical role in the program of macrophage differentiation, and this raises questions about the feasibility of cyclin T1 serving as an antiviral therapeutic target

Mobile Sensing and Simultaneously Node Localization in Wireless Sensor Networks for Human Motion Tracking

This paper exploits optimal position of the mobile sensor to improve the target tracking performance of wireless sensor networks and simultaneously localize both of the static sensor nodes and mobile sensor nodes when tracking the human motion. In our approach, mobile sensors collaborate with static sensors and move optimally to achieve the required detection performance. The accuracy of final tracking result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. Specifically, we can simultaneously localize the mobile sensor and static sensors position when localizing the human’s position based on augmented extended Kalman filters (EKF). In the algorithm, we develop a sensor movement optimization algorithm that achieves near-optimal system tracking performance. We also presented an sensor nodes management scheme in order to deduce the computation complexity when localizing the static sensor nodes. The effectiveness of our approach is validated by extensive simulations using the simulations

A framework for learning analytics using commodity wearable devices

We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner’s physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55)

Communication and Innovation in Cooperatives

Cooperatives differ in their intensity of horizontal and vertical communications, their innovation policies, and their centralization of decision-making power. We aim to establish relationships between these communication, innovation, and decision-making aspects of cooperatives, and to identify the circumstances when a particular configuration adds most value. Horizontal and vertical communications are analyzed in a decentralized and centralized cooperative. Horizontal communication (HC) is characterized as the exchange of information between farmers in the society of members. It is associated with process innovation. Vertical communication (VC) is the exchange of information between a member and the CEO of the cooperative enterprise. It is associated with product innovation. The CEO decides regarding the deliveries of the member and the level of vertical communication in the centralized cooperative, while these decisions are taken by the members in the decentralized cooperative. We establish that the decentralized cooperative is efficient at an intermediate level of the VC cost coefficient and when the HC cost coefficient is above a certain level, while the centralized cooperative is efficient in the other cases

Knowledge-driven Meta-learning for CSI Feedback

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.Comment: arXiv admin note: text overlap with arXiv:2301.1347