A framework for orchestrating secure and dynamic access of IoT services in multi-cloud environments

IoT devices have complex requirements but their limitations in terms of storage, network, computing, data analytics, scalability and big data management require it to be used it with a technology like cloud computing. IoT backend with cloud computing can present new ways to offer services that are massively scalable, can be dynamically configured, and delivered on demand with largescale infrastructure resources. However, a single cloud infrastructure might be unable to deal with the increasing demand of cloud services in which hundreds of users might be accessing cloud resources, leading to a big data problem and the need for efficient frameworks to handle a large number of user requests for IoT services. These challenges require new functional elements and provisioning schemes. To this end, we propose the usage of multi-clouds with IoT which can optimize the user requirements by allowing them to choose best IoT services from many services hosted in various cloud platforms and provide them with more infrastructure and platform resources to meet their requirements. This paper presents a novel framework for dynamic and secure IoT services access across multi-clouds using cloud on-demand model. To facilitate multi-cloud collaboration, novel protocols are designed and implemented on cloud platforms. The various stages involved in the framework for allowing users access to IoT services in multi-clouds are service matchmaking (i.e. to choose the best service matching user requirements), authentication (i.e. a lightweight mechanism to authenticate users at runtime before granting them service access), and SLA management (including SLA negotiation, enforcement and monitoring). SLA management offers benefits like negotiating required service parameters, enforcing mechanisms to ensure that service execution in the external cloud is according to the agreed SLAs and monitoring to verify that the cloud provider complies with those SLAs. The detailed system design to establish secure multi-cloud collaboration has been presented. Moreover, the designed protocols are empirically implemented on two different clouds including OpenStack and Amazon AWS. Experiments indicate that proposed system is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, and any SLA violation by a cloud provider could be recorded and reported back to the user.N/

Temperature dependent elastic constants for crystals with arbitrary symmetry: combined first principles and continuum elasticity theory

To study temperature dependent elastic constants, a new computational method is proposed by combining continuum elasticity theory and first principles calculations. A Gibbs free energy function with one variable with respect to strain at given temperature and pressure was derived, hence the full minimization of the Gibbs free energy with respect to temperature and lattice parameters can be put into effective operation by using first principles. Therefore, with this new theory, anisotropic thermal expansion and temperature dependent elastic constants can be obtained for crystals with arbitrary symmetry. In addition, we apply our method to hexagonal beryllium, hexagonal diamond and cubic diamond to illustrate its general applicability.Comment: 22 pages, 3 figures, 2 table

Temperature dependent elastic constants and ultimate strength of graphene and graphyne

Based on the first principles calculation combined with quasi-harmonic approximation, in this work we focus on the analysis of temperature dependent lattice geometries, thermal expansion coefficients, elastic constants and ultimate strength of graphene and graphyne. For the linear thermal expansion coefficient, both graphene and graphyne show a negative region in the low temperature regime. This coefficient increases up to be positive at high temperatures. Graphene has superior mechanical properties, with Young modulus E11=371.0 N/m, E22=378.2 N/m and ultimate tensile strength of 119.2 GPa at room temperature. Based on our analysis, it is found that graphene's mechanical properties have strong resistance against temperature increase up to 1200 K. Graphyne also shows good mechanical properties, with Young modulus E11=224.7 N/m, E22=223.9 N/m and ultimate tensile strength of 81.2 GPa at room temperature, but graphyne's mechanical properties have a weaker resistance with respect to the increase of temperature than that of graphene

Economic analysis of wind power consumption promoted by regenerative electric heating with high proportion of renewable energy

With the rapid development of wind power, the randomness and volatility of wind power have also caused severe problems of wind power consumption. The phenomenon of wind abandonment is particularly prominent in the "three north" areas. The environmental protection and controllability of regenerative electric heating provide a way for wind absorption and abandonment. Therefore, this paper proposes a model to promote wind power consumption by using regenerative electric heating. Firstly, the principle and advantages of the heat storage electric heating equipment are described; secondly, the fine modeling of regenerative electric heating is carried out;then, the mode of using regenerative electric heating to promote wind power consumption is designed;finally, an example is given to analyze the wind power consumption effect and the revenue of load aggregators.Therefore, it is verified that this mode can effectively promote wind power consumption and reduce wind abandon generation, and provide reference for alleviating wind abandon and power limit problem

Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

Multivariate Time Series (MTS) widely exists in real-word complex systems, such as traffic and energy systems, making their forecasting crucial for understanding and influencing these systems. Recently, deep learning-based approaches have gained much popularity for effectively modeling temporal and spatial dependencies in MTS, specifically in Long-term Time Series Forecasting (LTSF) and Spatial-Temporal Forecasting (STF). However, the fair benchmarking issue and the choice of technical approaches have been hotly debated in related work. Such controversies significantly hinder our understanding of progress in this field. Thus, this paper aims to address these controversies to present insights into advancements achieved. To resolve benchmarking issues, we introduce BasicTS, a benchmark designed for fair comparisons in MTS forecasting. BasicTS establishes a unified training pipeline and reasonable evaluation settings, enabling an unbiased evaluation of over 30 popular MTS forecasting models on more than 18 datasets. Furthermore, we highlight the heterogeneity among MTS datasets and classify them based on temporal and spatial characteristics. We further prove that neglecting heterogeneity is the primary reason for generating controversies in technical approaches. Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models

Using vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings

A novel seam detection approach based on vector building maps is presented for low-attitude aerial orthoimage mosaicking. The approach tracks the centerlines between vector buildings to generate the candidate seams that avoid crossing buildings existing in maps. The candidate seams are then refined by considering their surrounding pixels to minimize the visual transition between the images to be mosaicked. After the refinement of the candidate seams, the final seams further bypass most of the buildings that are not updated into vector maps. Finally, three groups of aerial imagery from different urban densities are employed to test the proposed approach. The experimental results illustrate the advantages of the proposed approach in avoiding the crossing of buildings. The computational efficiency of the proposed approach is also significantly higher than that of Dijkstra’s algorithm