Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for intelligent tasks in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum system performance. We focus on classification intelligence tasks and formulate an optimization problem that considers both the accuracy requirements of tasks and the parallel computing capabilities of MEC systems. To solve the optimization problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation, and compression offloading. We use convex optimization and successive convex approximation to derive closed-form expressions for the subcarrier allocation, offloading decisions, computing capacity, and compressed ratio. Based on our solutions, we design an efficient computing offloading and resource allocation algorithm for intelligent tasks in MEC systems. Our simulation results demonstrate that our proposed algorithm significantly improves the performance of intelligent tasks in MEC systems and achieves a flexible trade-off between system revenue and cost considering intelligent tasks compared with the benchmarks.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0274

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization

In this paper, the multi-target tracking (MTT) with an unmanned aerial vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in the swarm communicate with each other to exchange information of targets during tracking. The communication between UAVs suffers from severe interference, including inter-UAV interference and jamming, thus leading to a deteriorated quality of MTT. To mitigate the interference and achieve MTT, we formulate a interference minimization problem by jointly optimizing UAV's sub-swarm division, trajectory, and power, subject to the constraint of MTT, collision prevention, flying ability, and UAV energy consumption. Due to the multiple coupling of sub-swarm division, trajectory, and power, the proposed optimization problem is NP-hard. To solve this challenging problem, it is decomposed into three subproblems, i.e., target association, path plan, and power control. First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets. Second, a jamming-sensitive and singular case tolerance (JSSCT)-artificial potential field (APF) algorithm is proposed to plan trajectory for tracking the targets. Third, we develop a jamming-aware mean field game (JA-MFG) power control scheme, where a novel cost function is established considering the total interference. Finally, to minimize the total interference, a dynamic collaboration approach is designed. Simulation results validate that the proposed dynamic collaboration approach reduces average total interference, tracking steps, and target switching times by 28%, 33%, and 48%, respectively, comparing to existing baselines.Comment: 14 pages, 17 figure

Geographies of disconnection : Poyang Lake National Nature Reserve

published_or_final_versionArchitectureMasterMaster of Landscape Architectur

Online Bayesian Data Fusion in Environment Monitoring Sensor Networks

Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K -ary input and K -ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms

Deep Reinforcement Learning-Based Resource Allocation for UAV-Enabled Federated Edge Learning

The resource allocation of the federated learning (FL) for unmanned aerial vehicle (UAV) swarm systems are investigated. The UAV swarms based on FL realize the artificial intelligence (AI) applications by means of distributed training on the basis of ensuring the security of private data. However, the direct application of the FL in UAV swarms will incur high overhead. Therefore, in this article, we consider the resource allocation problem in FL for UAV swarms. To avoid the high communication overhead between UAVs and the central server, we proposed an FL framework for UAV swarms based on mobile edge computing (MEC) in which model aggregation is migrated to edge servers. In the proposed framework, the total cost of the FL is defined as the weighted sum of the total delay of UAV swarms to complete the FL and system energy consumption. In order to minimize the total cost of FL, we propose a resource allocation algorithm for joint optimization of computing resources and multi-UAV association based on deep reinforcement learning (DRL). The simulation result shows that: 1) compared with the benchmark algorithm, the proposed algorithm can effectively reduce the total cost of FL; 2) the proposed algorithm can realize the trade-off between task completion delay and system energy consumption through weight changes

Numerical Simulation of Hydraulic Fracture Propagation Guided by Single Radial Boreholes

Conventional hydraulic fracturing is not effective in target oil development zones with available wellbores located in the azimuth of the non-maximum horizontal in-situ stress. To some extent, we think that the radial hydraulic jet drilling has the function of guiding hydraulic fracture propagation direction and promoting deep penetration, but this notion currently lacks an effective theoretical support for fracture propagation. In order to verify the technology, a 3D extended finite element numerical model of hydraulic fracturing promoted by the single radial borehole was established, and the influences of nine factors on propagation of hydraulic fracture guided by the single radial borehole were comprehensively analyzed. Moreover, the term ‘Guidance factor (Gf)’ was introduced for the first time to effectively quantify the radial borehole guidance. The guidance of nine factors was evaluated through gray correlation analysis. The experimental results were consistent with the numerical simulation results to a certain extent. The study provides theoretical evidence for the artificial control technology of directional propagation of hydraulic fracture promoted by the single radial borehole, and it predicts the guidance effect of a single radial borehole on hydraulic fracture to a certain extent, which is helpful for planning well-completion and fracturing operation parameters in radial borehole-promoted hydraulic fracturing technology

Decentralization of the non-capital functions of Beijing : Industrial relocation and its environmental effects

Relocating Beijing's manufacturing industry, a key measure in the strategy for decentralizing Beijing's non-capital functions, may inadvertently increase environmental pressures in the receiving cities. This study develops a quantitative method to comprehensively identify the environmental effects caused by regional industrial relocations. First, a set of relocated industries is selected by considering the relevant policy motivations and sector-specific economic and environmental performance. Second, a discrete choice model is developed to simulate the relocating process, taking economic geographic factors as well as local environmental quality and regulation into account. Third, the Monte Carlo method is used to quantify the potential effects by considering the sectoral efficiencies of resource use and pollutant emissions. Three scenarios are developed for different priorities of economic growth and environmental regulation. The results show that the decentralization strategy is likely to reduce Beijing's industrial output value by between 68 and 176 billion RMB yuan, or 3.9%–10.1% of its annual total. Meanwhile, the decrease in Beijing's industrial water and energy consumption could range from 2.7% to 7.8% and from 6.4% to 8.1%, respectively, while the decrease in industrial emissions from various pollutants could range from 6.7% to 36%. The industrial output from the receiving cities in the Beijing–Tianjin–Hebei (BTH) region could increase in the range of 0.3%–17% in the scenarios, larger than their change rate of water and energy consumption (0.1%–3.5%) and pollutant emissions (0.1%–7.3%). Environmental pressures may intensify in Tianjin, Langfang, and Shijiazhuang as they are estimated to be the targets for 65% of all relocated industries. Overall, the results imply that the decentralization strategy is a promising approach for promoting regional sustainable development. The study recommends implementing stricter environmental regulations in the receiving cities

A mode switching based transient ride‐through phase‐locked loop

Abstract In the case of grid voltage quality problems, the traditional phase‐locked loop (PLL) is hard to detect the accurate grid frequency and phase during the transient response, which will be detrimental to the transient synchronous stability of grid‐connected inverters. This paper proposes a mode switching based transient ride‐through PLL (TRT‐PLL), aiming to improve the transient phase‐locking performance through detection technique and mode switching. The TRT‐PLL incorporates a hybrid filter (HF), a dual‐speed detection module, and a state switch into the traditional synchronous reference frame PLL (SRF‐PLL) structure. The theoretical analysis and paradigm design of TRT‐PLL is presented in detail. Digital simulations and physical experiments are carried out for the comparisons with other existed PLL techniques. The results demonstrate that the satisfied transient performance of the TRT‐PLL has significant advantages, especially in the event of phase jumps

Luteolin sensitizes the antitumor effect of cisplatin in drug-resistant ovarian cancer via induction of apoptosis and inhibition of cell migration and invasion

Abstract Luteolin, a polyphenolic flavone, has been demonstrated to exert anti-tumor activity in various cancer types. Cisplatin drug resistance is a major obstacle in the management of ovarian cancer. In the present study, we investigated the chemo-sensitizing effect of luteolin in both cisplatin-resistant ovarian cancer cell line and a mice xenotransplant model. In vitro, CCK-8 assay showed that luteolin inhibited cell proliferation in a dose-dependent manner, and luteolin enhanced anti-proliferation effect of cisplatin on cisplatin-resistant ovarian cancer CAOV3/DDP cells. Flow cytometry revealed that luteolin enhanced cell apoptosis in combination with cisplatin. Western blotting and qRT-PCR assay revealed that luteolin increased cisplatin-induced downregulation of Bcl-2 expression. In addition, wound-healing assay and Matrigel invasion assay showed that luteolin and cisplatin synergistically inhibited migration and invasion of CAOV3/DDP cells. Moreover, in vivo, luteolin enhanced cisplatin-induced reduction of tumor growth as well as induction of apoptosis. We suggest that luteolin in combination with cisplatin could potentially be used as a new regimen for the treatment of ovarian cancer