scholarly journals Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5261
Author(s):  
Deok-Won Yun ◽  
Won-Cheol Lee
Keyword(s):  
Resource Management ◽  
Dynamic Spectrum ◽  
Spectrum Efficiency ◽  
Transmission Parameters ◽  
Handoff Latency ◽  
Time Space ◽  
Iot Devices ◽  
Management Functions ◽  
Limited Spectrum ◽  
Spectrum Resource Management

Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.

scholarly journals Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7902
Author(s):  
Deok-Won Yun ◽  
Won-Cheol Lee
Keyword(s):  
Resource Allocation ◽  
Resource Management ◽  
Real Time ◽  
Time Spectrum ◽  
Dynamic Spectrum ◽  
Case Based Reasoning ◽  
Industrial Iot ◽  
Frequency Domains ◽  
Time Resource ◽  
Spectrum Resource Management

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.

scholarly journals Secrecy-Oriented Optimization of Sparse Code Multiple Access for Simultaneous Wireless Information and Power Transfer in 6G Aerial Access Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingmin Zhang ◽  
Xiaokui Yue ◽  
Xuan Li ◽  
Haofei Zhang ◽  
Tao Ni ◽  
...  
Keyword(s):  
Multiple Access ◽  
Optimization Methods ◽  
Access Networks ◽  
Spectrum Efficiency ◽  
Sparse Code ◽  
Power Splitting ◽  
Power Transfer ◽  
Sixth Generation ◽  
Successive Convex Approximation ◽  
Iot Devices

This article focuses on the simultaneous wireless information and power transfer (SWIPT) systems, which provide both the power supply and the communications for Internet-of-Things (IoT) devices in the sixth-generation (6G) network. Due to the extremely stringent requirements on reliability, speed, and security in the 6G network, aerial access networks (AANs) are deployed to extend the coverage of wireless communications and guarantee robustness. Moreover, sparse code multiple access (SCMA) is implemented on the SWIPT system to further promote the spectrum efficiency. To improve the speed and security of SWIPT systems in 6G AANs, we have developed an optimization algorithm of SCMA to maximize the secrecy sum rate (SSR). Specifically, a power-splitting (PS) strategy is applied by each user to coordinate its energy harvesting and information decoding. Hence, the SSR maximization problems in the SCMA system are formulated in terms of the PS and resource allocation, under the constraints on the minimum rates and minimum harvested energy of individual users. Then, a successive convex approximation method is introduced to transform the nonconvex problems to the convex ones, which are then solved by an iterative algorithm. In addition, we investigate the SSR performance of the SCMA system supported by our optimization methods, when the impacts from different perspectives are considered. Our studies and simulation results show that the SCMA system supported by our proposed optimization algorithms significantly outperforms the legacy system with uniform power allocation and fixed PS.

EFEKTIVITAS MANAJEMEN SUMBER DAYA MANUSIA DALAM MENINGKATKAN MUTU SLB MUARO JAMBI

2021 ◽  
Vol 5 (1) ◽  
pp. 1-6
Author(s):  
Dedy Alamsyah ◽  
A Khalik ◽  
Dian Nisa Istofa
Keyword(s):  
Resource Management ◽  
Human Resources ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resources Management ◽  
Resources Management ◽  
Qualitative Descriptive ◽  
Education Personnel ◽  
Management Functions

This study aims to analyze the efficiency of human resource management in improving the quality of Muaro Jambi Extraordinary School. This study uses qualitative descriptive methods. Data is collected through observations, interviews, and documentation. The results showed that SLB Muaro Jambi applies two management functions in managing its SDM, namely the planning and implementation functions. Human Resource Management consists of planning the needs and development of educators and education personnel; procurement of capacity building training for teachers and staff/employees. The implementation of human resources involves committees, teachers, and staff/ employees to realize the activities that have been planned. Human Resources Management can effectively improve the quality of education in SLB.

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

scholarly journals Spectrum Sharing with Dynamic Cournot Game in Vehicle-Enabled Cognitive Small-Cell Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Guilu Wu ◽  
Huilin Jiang
Keyword(s):  
Nash Equilibrium ◽  
Spectrum Sharing ◽  
Dynamic Game ◽  
Small Cell ◽  
Dynamic Spectrum ◽  
Spectrum Allocation ◽  
Spectrum Efficiency ◽  
Dynamic Spectrum Allocation ◽  
Small Cell Networks ◽  
Cell Networks

Cognitive radio technology can effectively improve spectrum efficiency in wireless networks and is also applicable to vehicle small-cell networks. In this paper, we consider the problem of spectrum sharing among a vehicle primary user (V-PU) and multiple vehicle secondary users (V-SUs). This problem is modeled as a competition market, and the solution for V-SUs is designed using a non-cooperative game. A utility function that measures the profit of the V-PU considering quality of service (QoS) is proposed, aiming at maximizing the profit of the V-PU. Nash equilibrium is obtained as the best solution in our game. Then, the realistic vehicle-enabled cognitive small-cell network is considered in building the dynamic spectrum allocation problem. The V-SUs adjust their current strategies gradually and iteratively based on the observations on the strategies of the previous moment. This adjustment parameter is controlled by the frequency of adjustment. The stability analysis of the dynamic game is given out subsequently for dynamic spectrum allocation. The numerical results show the effectiveness of the proposed dynamic spectrum scheme for vehicle-enabled cognitive small-cell networks and Nash equilibrium point’s existence.

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