A Diversity-based Selfish Node Detection Algorithm for Socially Aware Networking

Xiao, Nan; Xinyi, Rang; Xiong, Zenggang; Xu, Fang; Zhang, Xuemin; Xu, Qiong; Zhao, Xiangmo; Ye, Conghuan

doi:10.1007/s11265-021-01666-y

Cited by 43 publications

(14 citation statements)

References 24 publications

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“…Optimization aims to discover the best solution based on the issue variables (Xiao et al , 2021). A list of variable values is provided that will be optimized.…”

Section: Proposed Methodsmentioning

confidence: 99%

A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Al-Shourbaji

Zogaan

2021

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PurposeThe human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm.Design/methodology/approachCloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers’ energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature’s novel algorithms using a simulation.FindingsEmpirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques.Practical implicationsThe paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cause the high application of this method in practical projects.Originality/valueThe main novelty of this paper is using an imperialist competitive algorithm for finding the best solution to the HR allocation problem in cloud-based e-commerce.

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“…Optimization aims to discover the best solution based on the issue variables (Xiao et al , 2021). A list of variable values is provided that will be optimized.…”

Section: Proposed Methodsmentioning

confidence: 99%

A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Al-Shourbaji

Zogaan

2021

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“…The commodity market and financial market are the key channels through which the risks of the crude oil market affect the smooth operation of the economy (Fan et al, 2021;Xiao et al, 2021). Commodity market risks are directly related to macroeconomic operation such as domestic output and price level (Shi and Sun, 2017).…”

Section: Hypothesesmentioning

confidence: 99%

Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

Jia

Dong²,

Wang

2022

Front. Energy Res.

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The impact channel of crude oil market risk on the macroeconomy is highly related to oil attributes. This paper uses a stepwise test method with dummy variables to identify the channel effect of commodity market risk as well as financial market risk and explore the characteristics of the channel effect in different periods dominated by different oil attributes. Furthermore, this paper investigates the asymmetric characteristics of the channel effect under the condition of crude oil returns heterogeneity. The empirical results show that: First, commodity market risk, as well as financial market risk plays a channel role in the impact of crude oil market risk on the macroeconomic operation. Second, there is a significant difference in the ability of the commodity market and financial market to cope with shocks of crude oil market risk in periods dominated by different attributes. During the period dominated by the commodity attribute of oil, both commodity market and financial market play the role of “risk buffer”; during the period dominated by dual attributes of oil, the commodity market risk plays the role of “risk buffer”, while the financial market risk plays the role of “magnifier” of the crude oil market risk. Third, the channel effect pattern and degree of commodity market risk and financial market risk are significantly asymmetric.

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“…Business networks point to the organization's official connections with its business associations for the exploitation of business opportunities (Chen et al, 2021). Social networks reveal their casual associations depending on the focal organization's cultural contexts and social values that are useful to identify opportunities by the company (Lew et al, 2016;Xiao et al, 2021). Sub-indexes of business social networks include interacting with customers, sales increase, human skills, easy access, credit management and reducing marketing costs.…”

Section: Business Social Networkmentioning

confidence: 99%

Assessing the implementation feasibility of intelligent production systems based on cloud computing, industrial internet of things and business social networks

Sun

Yang

2021

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PurposeThe increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.Design/methodology/approachIntelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R2, T-Value and GOF.FindingsThe study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.Research limitations/implicationsStudy limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.Originality/valueThe research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

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A Diversity-based Selfish Node Detection Algorithm for Socially Aware Networking

Cited by 43 publications

References 24 publications

A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

Assessing the implementation feasibility of intelligent production systems based on cloud computing, industrial internet of things and business social networks

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