An Adaptive Replica Configuration Mechanism Based on Predictive File Popularity and Queue Balance in Mobile Edge Computing Environment

In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

A Study on Analysis of Satisfaction for Engineering Convergence Subject

Our world is quickly moving towards the fourth industrial revolution including mobile, big data, AI, IoT, cloud computing, VR, etc. Recently, South Korea has been emphasizing convergence education to university. Thus, university has begun doing convergence education on their own by linking major subjects and liberal arts courses or linking different departments. In this paper, we analyzed learners’ satisfaction for operating convergence education effectively to increase education satisfaction and developed convergence curriculum and convergence skills required by society. for this study, a satisfaction survey is conducted for students majoring in engineering colleges. And the students’ experiences are collected through interviews and questionnaires for suggesting improved the convergence curriculum operation. We also did interviews and asked students about the meaning convergence education had for them, the impressions they had after taking the classes, and any opinions for further improvements. As a result of the analysis of student's satisfaction and satisfaction of convergence curriculum, it was analyzed as “approximately satisfied” with 3.6. Additionally, the correlation between student satisfaction and convergence curriculum satisfaction was analyzed, and the correlation coefficient showed a significant correlation with 0.732. In other words, it can be seen that students with high-student satisfaction are also highly satisfied with the convergence curriculum. Based on the result of the research and the student’s opinions, we would like to suggest that there should be subject development that is connected to careers or job searching for senior students, and additional research of practical educational methods are also needed.

Does citizen coproduction lead to better urban services in smart cities projects? An empirical study on e-participation in a mobile big data platform

With contemporary development of digital technology and smart cities initiatives, citizen co-production has created a new government-citizen interface. However, it remains inconclusive whether such citizen-government collaboration has achieved the fundamental goal of improving service quality for citizens. In this research, we tested the relationship between e-participation as a form of co-production and service performance, using multiple large longitudinal datasets from a smart city mobile platform. The results of the analysis show that citizen e-participation, in providing service feedback, is positively associated with the clearance rate of urban service requests in subdistrict service units, after controlling for various factors. We also found that the effect size of e-participation on service performance varies between different types of city services. E-participation has a stronger relative influence on complex problems that may involve multiple agencies, than with simple routine services.

Does citizen coproduction lead to better urban services in smart cities projects? An empirical study on e-participation in a mobile big data platform

With contemporary development of digital technology and smart cities initiatives, citizen co-production has created a new government-citizen interface. However, it remains inconclusive whether such citizen-government collaboration has achieved the fundamental goal of improving service quality for citizens. In this research, we tested the relationship between e-participation as a form of co-production and service performance, using multiple large longitudinal datasets from a smart city mobile platform. The results of the analysis show that citizen e-participation, in providing service feedback, is positively associated with the clearance rate of urban service requests in subdistrict service units, after controlling for various factors. We also found that the effect size of e-participation on service performance varies between different types of city services. E-participation has a stronger relative influence on complex problems that may involve multiple agencies, than with simple routine services.

How Internet of Things Is Transforming Project Management

Organizations are experiencing a transformation as a consequence of digital technologies such as social, mobile, big data, cloud computing, and internet of things. The transformation presents challenges at several levels, and project management is not an exception. There are changes in the project environment, the power structures, capabilities, skills, and standard practices, just to name a few. Considering the eventual obsolescence of many project portfolio management practices, the aim of this chapter is to discuss the influence of internet of things in this discipline. The analysis departs from rethinking project management insights and describes the impact of smart and connected products considering many dimensions. Recommendations for each PPM stage are developed, followed by a brief discussion of future research directions.

Using mobile big data to support emergency preparedness and address economically vulnerable communities during the COVID-19 pandemic in Nigeria

With the declaration of the coronavirus disease 2019 (COVID-19) pandemic in Nigeria in 2020, the Nigeria Governors’ Forum (NGF) instigated a collaboration with MTN Nigeria to develop data-driven insights, using mobile big data (MBD) and other data sources, to shape the planning and response to the pandemic. First, a model was developed to predict the worst-case scenario for infections in each state. This was used to support state-level health committees to make local resource planning decisions. Next, as containment interventions resulted in subsistence/daily paid workers losing their income and ability to buy essential food supplies, NGF and MTN agreed a second phase of activity, to develop insights to understand the population clusters at greatest socioeconomic risk from the impact of the pandemic. This insight was used to promote available financial relief to the economically vulnerable population clusters in Lagos state via the HelpNow crowdfunding initiative. This article discusses how anonymized and aggregated mobile network data (MBD), combined with other data sources, were used to create valuable insights and inform the government, and private business, response to the pandemic in Nigeria. Finally, we discuss lessons learnt. Firstly, how a collaboration with, and support from, the regulator enabled MTN to deliver critical insights at a national scale. Secondly, how the Nigeria Data Protection Regulation and the GSMA COVID-19 Privacy Guidelines provided an initial framework to open the discussion and define the approach. Thirdly, why stakeholder management is critical to the understanding, and application, of insights. Fourthly, how existing relationships ease new project collaborations. Finally, how MTN is developing future preparedness by creating a team that is focused on developing data-driven insights for social good.

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.