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A 6G network is not only faster, but also offers a wider range of services than 5G networks, which will lead to an increased number of participants. However, combined with the Internet of Everything, 6G networks face security issues such as man-in-the-middle hijacking and bypass listening. Because covert transmission is inherently highly concealed and difficult to detect, a 6G network based on information hiding has become a new paradigm to solve such security problems. In this article, we propose a large-capacity local multi-dimensional information hiding method based on orthogonal space and spread transform. By extending the quantization index modulation hiding approach, the proposed method hides the multi-layer information in digital signals' multi-dimensional space using orthogonal space and spread transform. In theory, this method can hide information in multiple layers at the same location while allowing the extraction of information from multiple layers independently. This proposed method offers a much higher hiding capacity than prior work. Through performance analysis and comparison against existing advanced methods, we demonstrate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the wide application of Internet-of-Medical-Things (IoMTs) or sensor nodes which equipped with sensors. These networked sensors are used to gather enormous data from different smart healthcare applications, and this collected data process for making appropriate decisions. Edge computing is an efficient platform that provides computational resources to collect sensor data. In the meantime, intelligent and accurate resource management by Artificial Intelligence (AI) has become the center of attention, especially in healthcare systems. With the help of AI, IoMT based healthcare devices will remarkably enhance the computational speed and range. But the challenging issue in these energy-hungry, short battery life, and delay intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Thus, this paper proposes Computation Offloading using Reinforcement Learning (CORL) scheme to minimize latency and energy consumption. We first formulate the problem as a combined latency and energy cost minimization problem, satisfying the lack of limited battery capacity and service latency deadline constraints. Moreover, proposed algorithm search optimal available resources node to offload task towards the trade-off between energy and latency. The experimental results show the benefits of the proposed scheme in terms of saving energy, minimizing latency, and maximum utilization of node resources in edge-enabled sensor networks. We are using an iFogSim simulator to validate the proposed scheme under realistic assumptions. [ABSTRACT FROM AUTHOR]

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem’s constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme. [ABSTRACT FROM AUTHOR]

The energy Internet (EI)-based vehicle-to-grid (V2G) technology facilitates the electric vehicles not only to distribute additional electricity into grid systems, but also support receiving back from the power grid in the form of charging. The secure key establishment is quite significant to initiate the bidirectional electricity power delivery into and from the system. To effectively implement any EI-based V2G communication, the authentication protocol must be free from cyber attacks. In this article, we not only explore the drawbacks of several smart grid-based authentication protocols but also bring forth the limitations of a recently presented EI-based V2G scheme by Gope and Sikdar. The examined drawbacks in this protocol may disrupt its proper functioning, since it faces desynchronization problems while logging into the mobile device bearing registration parameters. The scheme is also vulnerable to replay attack and man-in-the-middle attack. The user is also unable to validate session key in the protocol. Considering these limitations, we propose a novel and efficient V2G protocol framework enabling the vehicles to communicate or recharge at desired recharging stations. The results of the proposed framework are compared with several contemporary schemes, and its security features are validated by random oracle model-based formal analysis. [ABSTRACT FROM AUTHOR]

Insulated power cables are becoming increasingly popular in today’s developing distribution and transportion networks. However, due to aging, deterioration, and various operational and environmental stresses, insulation defects may appear and so the cable needs to be monitored in a timely manner to avoid unexpected failures. Many of these defects are responsible for partial discharge (PD) activity. The localization of the sources of these discharges is a highly decisive facet in the condition-based monitoring of power cables. The techniques for the localization of single-PD defects in insulated power cables are well presented in the current bibliography. However, when several simultaneous PD sources are active, the localization of the sources becomes quite complex. This paper develops an efficient technique for the separation and localization of multiple PD sources in a medium voltage cable. The experimental results are obtained with single-end-based measurements using a high-frequency current transformer in a laboratory environment. The data processing based on the spectral characteristics of the signals is carried out by using the power ratios technique in order to determine the presence of different types of PD. Once the signals are separated, the PD sources can be localized with an individualized analysis of each source through time-domain reflectometry. The proposed methodology can be very valuable to improve the location diagnostic capability of the condition-based monitoring solutions, especially for underground cables. [ABSTRACT FROM AUTHOR]

Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this article, we first summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine-learning algorithms. [ABSTRACT FROM PUBLISHER]