The distributed cooperative offloading technique with wireless setting and power transmission provides a possible solution to meet the requirements of next-generation Multi-access Edge Computation (MEC). MEC is a model which avails cloud computing the aptitude to smoothly compute data at the edge of a largely dense network and in nearness to smart communicating devices (SCDs). This paper presents a cooperative offloading technique based on the Lagrangian Suboptimal Convergent Computation Offloading Algorithm (LSCCOA) for multi-access MEC in a distributed Internet of Things (IoT) network. A computational competition of the SCDs for limited resources which tends to obstructs smooth task offloading for MEC in an IoT high demand network is considered. The proposed suboptimal computational algorithm is implemented to perform task offloading which is optimized at the cloud edge server without relocating it to the centralized network. These resulted in a minimized weighted sum of transmit power consumption and outputs as a mixed-integer optimization problem. Also, the derived fast-convergent suboptimal algorithm is implemented to resolve the non-deterministic polynomial-time (NP)-hard problem. In conclusion, simulation results are performed to prove that the proposed algorithm substantially outperforms recent techniques with regards to energy efficiency, energy consumption reduction, throughput, and transmission delay performance.

No abstract

The Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Monsoons Model Intercomparison Project (GMMIP) Tier-1 and Tier-3 experiments are introduced in this paper, and the model descriptions, experimental design and model outputs are demonstrated. There are three simulations in Tier-1, with different initial states, and five simulations in Tier-3, with different topographies or surface thermal status. Specifically, Tier-3 contains four orographic perturbation experiments that remove the Tibetan-Iranian Plateau, East African and Arabian Peninsula highlands, Sierra Madre, and Andes, and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan-Iranian Plateau and surrounding regions at altitudes above 500 m. These datasets will contribute to CMIP6's value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation, and to a better understanding of the orographic impact on the global monsoon system over highlands.

No abstract

The variability of interior atmospheric potential vorticity (PV) is linked with PV generation at the Earth's surface. The present paper reveals the features of the surface PV and provides a stepping stone to investigate the surface PV budget. In this study, the formats of the PV and PV budget adopting a generalized vertical coordinate were theoretically examined to facilitate the calculation of the surface PV and its budget. Results show that the formats of the PV and PV budget equations are independent of the vertical coordinate. While the vertical component of the surface PV dominates over the platform of the Tibetan Plateau, the horizontal component plays an important role over the slopes of the Tibetan Plateau, especially the southern slope owing to the strong in-situ meridional gradient of the potential temperature. These results indicate that the employment of complete surface PV not only provides a finer PV structure but also more appropriately reveals its effect on atmospheric circulation. Diagnosis based on reanalysis and model output demonstrates that the surface PV budget equation is well balanced both in terms of the climate mean and synoptic process, and the surface PV budget in June has a prominent diurnal cycle. The diabatic heating with a minimum in the early morning and a maximum from evening to midnight contributes dominantly to this diurnal cycle. It is further indicated that positive PV generation due to diabatic heating is essential for the formation, development, and movement of the Tibetan Plateau vortex.

No abstract

No abstract

In recent times, security and privacy at the physical (PHY) layer has been a major issue of several communication technologies which comprise the internet of things (IoT) and mostly, the emerging fifth-generation (5G) cellular network. The most real-world PHY security challenge stems from the fact that the passive eavesdropper’s information is unavailable to the genuine source and destination (transmitter/receiver) nodes in the network. Without this information, it is difficult to optimize the broadcasting parameters. Therefore, in this research, we propose an efficient sequential convex estimation optimization (SCEO) algorithm to mitigate this challenge and improve the security of physical layer (PHY) in a three-node wireless communication network. The results of our experiments indicate that by using the SCEO algorithm, an optimal performance and enhanced convergence is achieved in the transmission. However, considering possible security challenges envisaged when a multiple eavesdropper is active in a network, we expanded our research to develop a swift privacy rate optimization algorithm for a multiple-input, multiple-output, multiple-eavesdropper (MIMOME) scenario as it is applicable to security in IoT and 5G technologies. The result of the investigation show that the algorithm executes significantly with minimal complexity when compared with nonoptimal parameters. We further employed the use of rate constraint together with self-interference of the full-duplex transmission at the receiving node, which makes the performance of our technique outstanding when compared with previous studies.