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The unmanned aerial vehicle (UAV) systems are a rapidly evolving and emerging technology with their widespread usage. This rapid growth of usage of UAVs has also augmented associated security issues and criminal activities. UAVs are rich sources of digital evidence and there is a growing need for acquisition and analysis of their stored and transferred data, including all the elements which form unmanned aerial system (UAS). This research paper presents a forensic investigation on an UAV, specifically the DJI Mavic Air, and associated smartphone app DJI GO 4 installed on Android device used as a part of the ground control station (GCS). Forensic analysis of flight logs, media files, and other important files of UAV and controlling application for identifying digital artifacts was done by a commercial forensic tool that does not require invasive or destructive methods for data acquisition. Through a five-step investigation methodology, it was found that the internal and external storage of DJI Mavic Air and associated Android device with installed DJI GO 4 application contain a significant and valuable amount of forensic data. Moreover, those data were classified into segments and provided details of some of the possible evidence in digital investigation.

End-user devices have the ability of accessing data networks and thereby generate data traffic. The amount of generated data traffic depends on many factors, including features of the device and the services it uses. Data access network is also one of the key determining factors of the amount of generated data traffic. In this paper, authors compared the amount of generated data traffic resulting from reproducing the same media content using a YouTube application during equal time frames. Three different applications were used to measure data traffic volume: GlassWire, My Data Manager, and 3G Watchdog. The measurements were performed via Wi-Fi and through mobile communication network. The results were compared in terms of how the amount of generated data traffic was displayed on each of the applications.

This paper presents a model for defining the behavioural patterns of smartphone users when offloading data from mobile to Wi-Fi networks. The model was generated through analysis of individual characteristics of 298 smartphone users, based on data collected via online survey as well as the amount of data offloaded from mobile to Wi-Fi networks as measured by an application integrated into the smartphone. Users were segmented into categories based on data volume offloaded from mobile to Wi-Fi networks, and numerous user characteristics were explored to develop a model capable of predicting the probability that a user with given characteristics will fall into a given category of data offloading. This model may prove useful for analysing smartphone user behaviour when offloading data.

The Industry 4.0 is experiencing significant challenges, including the need for an increased amount of data transmission with improved security, transparency and credibility. The 5th Generation Mobile Network (5G) and Blockchain are innovative emerging technologies that can respond to these needs. 5G will enable extremely large channel capacities and reduce data latency, while Blockchain's innovative data-sharing mode of operation delivers an improved high level of security, transparency, and credibility of stored data. Therefore, this paper presents a general survey of the potential application of 5G network and Blockchain technology in Industry 4.0. The results may be used by first-movers firms for gaining technological leadership on the market.

For the premium calculation and billing process in the motor insurance, current billing models are very general and not optimally tailored for the end users. Important parameters such as mileage, driving behavior and types of roads driven are not considered for the premium calculation. Development of advanced information and communication systems, significant drop in the technology cost of ownership, as well as the necessity for market differentiation in the insurance industry, facilitated the appearance of new billing models in the motor insurance industry. The purpose of this paper is to demonstrate underlying principles of the technology facilitating new billing models in the motor insurance industry. This paper gives an overview of the system architecture of one of the telematics systems offered and used on the market, as well as the data model used in the billing process. The potential of such a system is demonstrated through the real case project implemented in Eastern Europe.

Research has shown that using a mobile device while driving increases the risk of traffic accidents by up to five times. From 2010 to 2014, Croatia has doubled the number of offenses caused using mobile device while driving. Current methods of prevention have succeeded in reducing the number of offenses (reduction of 6% in 2016), which justifies the investment in ways to improve prevention. Except the financial penalties, activities in the form of prevention and awareness raising about the negative impact of the use of mobile devices on driver behavior and the safety of road traffic in Croatia are insufficiently pronounced. The aim of this research is to determine the intensity of the use of mobile devices and to find to what extent would certain technical, technological and regulatory measures trigger a reduction in use.

Mobile phones are recognized as a primary platform for mitigating the digital divide and increasing economic growth, and the same appears to be true for Nigeria, the largest economy in Africa. Since 2012, mobile phone penetration has shown nearly linear growth, reaching 83% in 2016. However, this statistic falls to only 46% after correcting for ownership of multiple SIM cards and sharing of mobile phones among multiple users. The determinants of mobile phone ownership in Nigeria are poorly understood, which hinders research that could inform policies capable of increasing mobile phone penetration and eliminating the digital divide. To begin to fill this research gap, we have analyzed socio-economic factors related to mobile phone ownership in the country. We used a logit model and the latest national-level Datafirst ICT dataset (2012) about mobile phone adoption from 1552 individuals. The sample was stratified, clustered, and probability-weighted to make it representative of the situation at the national level. The results suggest that factors such as geographic location and income may not strongly influence mobile phone ownership, in contrast to what was previously thought. Instead, the strongest factors appeared to be education level, informal work, social engagement, type of electricity supply and employment status. Our analysis suggests that to increase mobile phone ownership and close the digital divide, policy makers should target younger adults, provide training in digital literacy specifically for mobile phone use, invest in electricity supply infrastructure, and develop content and applications in non-English languages. These findings may contribute to understanding mobile phone distribution in Nigeria as well as inform implementation of the country's ICT Roadmap 2017–2020 and Vision 2020.

In 2012, as part of its response to mobile digital divide, the Nigerian government introduced mobile phone subsidies to rural farmers and did not seem to have achieved the expected results. It was argued that no necessary analysis of socioeconomic factors affecting mobile phone ownership had been conducted previously. The paper took advantage of this period to examine socio-economic factors affecting the probability of mobile phone ownership in Nigeria. In order to estimate a logit model we used national representative data from DataFirst on households and individuals ICT access and usage in 2011-2012. In contrast to what had previously been thought, we found out that poverty may not be correlated with the probability of owning a mobile phone. Type of electricity source, education, and activity has the greatest effect on the probability of owning a mobile phone. The findings may help to improve more coordinated digital divide policy, and serve as a complement to ICT Roadmap 2017 to 2020 in Nigeria and similar countries.

Smart mobile devices (smartphones) represent an important factor in access to information and communications networks. The wide population of users with different demands is using smart mobile devices in many contexts. The context of use of smart mobile devices represents a joint impact of smart mobile devices and mobile applications, used information and communications services and the user’s environment. The integration of smart mobile devices into user’s everyday life is the background for the development of the conceptual model that will make conclusions about their habits and patterns of their behavior based on the collected data. The data analysis can determine user's affiliation to a particular segment of users. This paper will analyze new system for measurement, data analysis and reporting of the use of smart mobile devices and related information and communication services, depending on the context of use.

This paper presents a mathematical model for estimating total cost of ownership (TCO) of residential post-paid cell phone plans for mobile services in order to predict TCO over time. Several cost drivers of TCO are quantified, including calls, messaging, data transfers, and the choice of time unit for billing calls. The model may help analysts and end users alike optimize the cost, understand its components and principal drivers and ultimately choose the most cost-effective option among numerous cell phone plans from various operators, thereby reducing the risk of costly decisions based on irrational choices or biases.

This paper provides an overview of the main characteristics and development of the new generation of mobile communication systems known as Next Generation Mobile Network (NGMN). As predicted, current exploitation of the fourth generation mobile communication systems (4G) will reach 50 billion connected devices by 2020. Expectations are that its successor, currently developing fifth generation (5G), is going to be operable by 2020. This new generation of mobile communication systems tends to become a technology platform that will enable the development of new applications, business models, industries, such as massive machine-type communications. This will be possible primarily through the creation of acceptable ecosystem that could provide a massive machine-type communication using a single platform based on the Internet of Things (IoT) concept. NGMN enables the integration of all so far known and used machine-type communications, creating an environment of smart cities and a fully networked society under the new concept of Internet of Everything (IoE). However, such network also poses specific performance requirements reflected through higher transmission speeds, higher data volumes, reduced energy consumption, higher quality of service, and growth in the number of services and users of currently deployed mobile generation.