A privacy-preserving traffic monitoring scheme via vehicular crowdsourcing

The explosive number of vehicles has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles’ speed information is an effective way to monitor the traffic conditions and avoid vehicles’ congestion, however it may threaten vehicles’ location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle’s speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles’ speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles’ speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles’ privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles’ identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme.This research was supported by the National Natural Science Foundation of China (Grant Nos. 61402037, 61872041, 61272512)

Resource Cube:Multi-Virtual Resource Management for Integrated Satellite-Terrestrial Industrial IoT Networks

Industrial Internet of Things (IIoT) has found wider research, and satellite-terrestrial network (STN) can provide large-scale seamless connections for IIoT. With virtualization, we design resource cube to describe the integration and state of multi-dimensional virtual resources. To achieve higher resource utilization and smarter connections, we design a matching considered preferences (MCPR) algorithm to match IIoT nodes with service sides. The matching design considers the resource cube (MCRC) algorithm based on MCPR algorithm to lower the total system delay. In addition, in order to simplify the analysis of resource management, we adopt a layered architecture and multiple M/M/1 queuing models. We analyze the resource utilization and the total system delay for three different combinations of arrival rate and service rate of each resource cube. With MCRC algorithm, the utilization of resources is slightly reduced, while the total system delay is greatly reduced compared with MCPR algorithm. © 1967-2012 IEEE

Guest Editorial Special Section on AI-Driven Developments in 5G-Envisioned Industrial Automation: Big Data Perspective

[No abstract available

AI technique for load flow planning and contingency analysis

An outline is presented of the development process of the intelligent load flow engine. Building the engine proceeds from simple to hard tasks by incrementally improving the organization and the representation of the knowledge base. Interface programs are designed to provide the communication from the engine to a load flow and contingency analysis progra

Intent-driven Closed-Loop Control and Management Framework for 6G Open RAN

Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of quality of experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure towards communication service providers (CSPs). In the 6th generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closed-loop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in OpenAirInterface platform and realizing the CL control and management with near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate

Blockchain-based secure and efficient secret image sharing with outsourcing computation in wireless networks

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the ( k, n ) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks

Digital Twin-enabled IoMT System for Surgical Simulation using rAC-GAN

A digital twin-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier generative adversarial network (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the digital twin-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92, and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for digital twin-based surgical implementation

CO2 gasification of chars prepared from wood and forest residue

The CO2 gasification of chars prepared from Norway spruce and its forest residue was investigated in a thermogravimetric analyzer (TGA) at slow heating rates. The volatile content of the samples was negligible; hence the gasification reaction step could be studied alone, without the disturbance of the devolatilization reactions. Six TGA experiments were carried out for each sample with three different temperature programs in 60 and 100% CO2. Linear, modulated, and constant-reaction rate (CRR) temperature programs were employed to increase the information content available for the modeling. The temperatures at half of the mass loss were lower in the CRR experiments than in the other experiments by around 120 degrees C. A relatively simple, well-known reaction kinetic equation described the experiments. The dependence on the reacted fraction as well as the dependence on the CO2, concentration were described by power functions (n-order reactions). The evaluations were also carried out by assuming a function of the reacted fraction that can mimic the various random pore/random capillary models. These attempts, however, did not result in an improved fit quality. Nearly identical activation energy values were obtained for the chars made from wood and forest residues (221 and 218 kJ/mol, respectively). Nevertheless, the forest residue char was more reactive; the temperatures at half of the mass loss showed 20-34 degrees C differences between the two chars at 10 degrees C/min heating rates. The assumption of a common activation energy, E, and a common reaction order, v, on the CO2, concentration for the two chars had only a negligible effect on the fit quality

Comparison of diet consumption, body composition and lipoprotein lipid values of Kuwaiti fencing players with international norms

<p>Abstract</p> <p>Background</p> <p>No published data is currently available that describes the dietary patterns or physiological profiles of athletes participating on the Kuwaiti national fencing team and its potential impact on health and physical performance. The purpose of this investigation was to: 1) collect baseline data on nutrient intake 2) collect, analyze and report baseline for body composition, plasma lipid and lipoprotein concentrations during the competitive season, 3) compare the results with the international norms, 4) and provide necessary health and nutritional information in order to enhance the athletes' performance and skills.</p> <p>Methods</p> <p>Fifteen national-class fencers 21.5 ± 2.6 years of age participated in this study. Food intake was measured using a 3-day food record. Body composition was estimated using both the BOD POD and Body Mass Index (BMI). Total blood lipid profiles and maximum oxygen consumption was measured for each of the subjects during the competitive season.</p> <p>Results</p> <p>The results of the present study showed significant differences in dietary consumption in comparison with the recommended dietary allowances (RDA). The blood lipids profile and body composition (BMI and % body fat) were in normal range in comparison with international norms However, the average VO_{2 max}value was less than the value of the other fencers.</p> <p>Conclusion</p> <p>Due to the results of the research study, a dietary regimen can be designed that would better enhance athletic performance and minimize any health risks associated with nutrition. Percent body fat and BMI will also be categorized for all players. In addition, the plasma blood tests will help to determine if any of the players have an excessive level of lipids or any blood abnormalities. The outcomes of present study will have a direct impact on the players health and therefore their skills and athletic performance.</p