Active Learning Based on Crowdsourced Data

The paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or not. The proposed solution reduces the amount of work needed to annotate large sets of data. Furthermore, it allows a perpetual increase in the trained network quality by the inclusion of new samples, gathered after network deployment. The paper also discusses means of limiting network training times, especially in the post-deployment stage, where the size of the training set can increase dramatically. This is done by the introduction of the fourth set composed of samples gather during network actual usage.

Energy Consumption Delay Evaluation of NB-IoT Terminal Based on Markov Model

The energy consumption of terminal of Internet of Things has attracted much attention in the study of smart Internet of Things. How to simulate the energy consumption process of the terminal from the theoretical level, so as to analyze the energy consumption and delay of the terminal are important issues. In this paper, taking the power monitoring terminal as an example, a Markov model is established for the Narrow-Band Internet of Things (NB-IoT) terminal with periodic automatic reporting. The working state of the terminal includes PSM (Power Saving Mode), random access (RACH), data transport and receive (Tx/Rx), short eDRX (Extended Discontinuous Reception), long eDRX and terminal disconnection (ERROR). According to the proposed model, the effects of network quality, maximum possible number of RACH request times (Rmax ) and data retransmission times (N1, N2) on terminal energy consumption and delay are analyzed. The numerical results show that network quality, maximum number of random access and maximum number of data retransmission directly affect the energy consumption and service quality of the terminal. Reasonable configuration of the above indicators can effectively improve the service life of the terminal and meet the customer’s requirements for the terminal service quality under the condition of maximum power saving. The model provides a reference for energy consumption and delay optimization of NB-IoT terminal.

Predictions from an Empirical Study in the Turkish Mobile Telecommunications Market on the Determinants of Mobile Customer Churn

Acquiring new customers compared to holding the existing ones is costlier and more troublesome for businesses, so customer retention is of great importance in today's intensely competitive environment. It is crucial in customer relations management to identify and analyse customers whose loyalty decreases and who tend to leave, and prevent churn through various methods under resource constraints. This issue is especially prominent in the mobile communication market. This paper uses a binomial logit model based on a survey with 637 mobile users in Turkey to determine the factors affecting customer churn and how they find their effect. Results indicate that, among various factors, network quality, billing, tariff level, tariff plan, and education level are the determinants affecting customer churn and associated with the intention to switch. Our findings demonstrate implications for both managers and rule-makers in the mobile telecommunications sector.

Rural Versus Non-Rural Residence and Psychosocial Outcomes Among Caregivers of Patients with Dementia and Related Disorders

We used data on 718 dementia caregivers and multivariable linear regression to test associations between residential locale and psychosocial outcomes (grief, wellbeing, burden, quality of life [QOL], self-efficacy/mastery, and social networks). Rural residence (versus urban or suburban) was not associated with the psychosocial outcomes. However, for rural caregivers, greater self-efficacy/mastery was associated with lower grief (versus urban/suburban) and burden (versus suburban), and greater social network quality was associated with lower burden (versus suburban) and higher QOL (versus urban). Interventions targeting self-efficacy/mastery and social networks may be particularly effective at improving rural caregivers’ mental health and QOL.

DOES SOCIAL NETWORK QUALITY DIFFER BETWEEN FACEBOOK AND INSTAGRAM? APPLICATION OF SNSQUAL MODEL

Although numerous studies investigated service quality in online environment, the social network quality has been inadequately captured by previous empirical research. Thus, the present study focuses on measuring social network quality. Specifically, it aims to examine potential differences in perceived social network quality between two popular social networks, namely Facebook and Instagram. The empirical data are based on gathering primary data using questionnaire based on SNSQUAL model, developed by Phillips et al. (2016). Descriptive and bivariate statistical analysis were conducted using data collected from undergraduate and graduate students who use social networks on regular bases. The study results show significant differences in 16 out of 27 social network quality items, revealing that Instagram’s social network quality was rated significantly higher than Facebook’s. These findings may contribute to the development of service excellence approach that aims to enhance social networks’ performance.

Guagingof key performance indicators for 2G mobile networks in Calabar, Nigeria

The increase in the number of mobile subscribers, coupled with the increase in mobile services is enough reason to monitor the QoS of mobile network operators frequently. This work looks into the QoS of network operators in Calabar, Nigeria, taking into consideration some KPIs ((CSSR, DCR, CST, HOSR, and network quality and network coverage). Analysis of data obtained after a benchmarking drive test shows that Globacom network was within NCC performance threshold for all network KPIs monitored. Also, MTN network performed poorly in HOSR but met the minimum benchmark in other network KPIs. Airtel network failed in the required DCR benchmark but was within the minimum benchmark for other KPIs while 9mobile failed in CSSR and DCR performance threshold but met the performance threshold for other KPIs. This result will be useful to the regulatory body, NCC, those in academic, RF engineers, network subscribers and especially, the network operators which we expect, will optimize their networks immediately.

A Boltzmann Machine Optimizing Dynamic Routing for FANETs

Routing optimization for FANETs is a kind of NP-hard in the field of combinatorial optimization that describes simple and difficult to handle. The quality of routing has a direct impact on the network quality of FANETs, and the design of routing protocols becomes a very challenging topic in FANETs. In this paper, we study the characteristics of dynamic routing, combine the characteristics of FANETs themselves, use the energy of nodes, bandwidth, link stability, etc. as the metric of routing, and use Boltzmann machine for routing search to form an optimized dynamic routing protocol. The NS3 simulation simulator is used to compare and study with traditional MANET dynamic routing AODV and DSR, and the simulation results show that the routes obtained by using Boltzmann machine search are better than AODV and DSR in many aspects such as end-to-end average delay, average route survival time and control overhead.

Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland

The main purpose of this study is to investigate the relationships between key sources of air pollutant emissions (sources of energy production, factories which are particularly harmful to the environment, the fleets of cars, environmental protection expenditure) and the main environmental air pollution (SO2, NOx, CO and PM) in Poland. Models based on MLP neural networks were used as predictive models. Global sensitivity analysis was used to demonstrate the significant impact of individual network input variables on the output variable. To verify the effectiveness of the models created, the actual data were compared with the data obtained through modelling. Projected courses of changes in the variables under study correspond with the real data, which confirms that the proposed models generalize acquired knowledge well. The high MLP network quality parameters of 0.99–0.85 indicate that the network generalizes the acquired knowledge accurately. The sensitivity analysis for NOx, CO and PM pollutants indicates the significance of all input variables. For SO2, it showed significance for four of the six variables analysed. The predictions made by the neural models are not very different from the experimental values.