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7Список литературы
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USING MOBILE TECHNOLOGIES AND THEIR SENSORS FOR WORKING WITH DATA
Serikov B.
Master of Natural Science teacher at Zhetysu University named after I. Zhansugurov DOI: 10.5281/zenodo.6532740
Abstract
The qualification to operate wandering sensors in workaday liveliness and the comprehension to chalk up applicable information victimization dapple technologies. The clause deliberate over the dominance of the cyberspace of article on top of the accustomed Internet. Sensors supported on item on the internet, the intelligence to chalk up considerable erudition and auspicate the inevitable wrapped up this enlightenment were furthermore considered. A description is acknowledged of putting together that feather and storehouse collection conventional from sensors. The possibleness of storing the controlled confidence in defile application is considered. The net of article (IoT) is a hypothesis of a engineering web of corporeal tangible ("things") accoutered with reinforced - in technologies for interplay with each over-the-counter or with the superficial environment, in the light of the collection of much above as a occurrence proficient of reconstructing pecuniary and community processes, eliminating the necessitate for humming experience from parting of activity and operations. nearly often, cyberspace of inanimate object (IoT) is related by oneself with family particulars and down home use. e.g., a" effective " icebox that buoy severally progression chop chop or control disciplines in the apartment, which is available from anywhere. on the other hand in fact, the approximation and capability of the contemporary technological progression is the application of the cyberspace of inanimate object in the environment of developed creation and business. by oneself with the highest commensurate of collections psychoanalysis that buoy accommodate a contemporary organization of effective software buoy you predispose the uttermost benediction from victimisationing the cyberspace of inanimate object in business. The cyberspace of inanimate object buoy transubstantiate indefinite mechanisation complexes.
Keywords: sensors, mobile devices, cloud technologies, Internet of Things, IoT.
Introduction. If earlier the computer was the subject of professional activity, then the distribution influenced the emergence of personal computers in families, now every person has several devices, for example, cameras, video cameras, computers, laptops, tablets, smartphones.With the development of technologies such as the Internet of things, smart home, the number of devices entering a person, family, apartment or home increases.According to a Strategy Analytics report, 10 digital devices per person are expected, and 100 digital devices per person are expected by 2030.
Problem analysis. Previously, the main functions of the phone were limited to voice calls and SMS sending, now the mobile phone offers a computer equipped with access to global (WAN), local (LAN) and private (PAN) networks, these devices have several sets of sensors (gyroscope, microphone, two cameras).
The corresponding trend is also typical for other devices (other technologies such as SmartTV, IP cameras and video surveillance systems, smart home). Internet of Things-IoT (Internet of Things) is a promising direction for the development of modern
technologies. Compared to the global Internet of things, which serves people's relationships, the Internet of things combines various household and professional devices.
The Internet of things allows you to create a network of many things, including the exchange of information, which allows you to improve the quality of everyday life, as well as use it in professional fields such as economics, medicine, scientific research.Popular technologies that are a global concept of the internet: RFID, cloud services, NFC and others.An example of the use of the Internet of things in everyday life is the collection, analysis and processing of data from sensors of mobile devices in the form of a mobile application, optimization of measurements of apartment meters through the information system and their analysis on the internet.
Problem solving. The system consists of server, client, and SQL databases. Figure 1 shows the architecture of the information system. This classic three-level architecture consists of: a client partition located on a smartphone, a server application, and a database server.
Parallel com puling hardware platform (SGI Origin 300C or 1 inux cluster)
Fig. 1 - Information System Architecture
The main purpose of the application is to send meter readings. This is done as follows: the user creates a photo and then sends it to the server, where the photo is recognized by IBM Watson, and the data is returned to the user form. After the user sends the data, they are sent over the Internet to the selected instance and stored in the database.
Due to the large number of sensors in the information system, images contain a large amount of data. These data are heterogeneous, which makes it difficult to process them. To solve these problems, it was decided to use cloud technologies to implement the information system.
Cloud technologies are an active and promising direction. When choosing a platform, the ability to recognize images is taken into account.a striking representative of this service is IBM Watson, which can connect to the selected IBM Bluemix.
The following services were used: visual recognition (IBM Watson), weather for Insights, IBM push notifications.
Anyone with a smartphone can use this app. It does not require high technique. Many features are available for regular smartphones. The special implementation process does not require additional functions, as it is designed for ordinary users.
Applications for smartphones
Smartphones are becoming a popular data collection tool. Many people carry a smartphone with them and use it to perform various tasks at least several times a day. Smartphones allow you to use various communication methods (phone, messaging, Bluetooth, Internet) and currently include more than 20 different sensors (for example, microphone, camera, temperature, light, gravity, motion, location sensors) that can be used to collect information about both the people using these smartphones and their environment. One of the most popular parts of smartphone software is apps. Applications are (usually small) pieces of software created for a specific task. Apps can be used to answer questions, but they interact with sensors on your phone and allow you to receive notifications. In the social sciences and
Health Sciences, smartphones are becoming increasingly popular as a research tool. An example of the application used in this project was the TABI application. This application was developed by researchers from the University of Utrecht and the Netherlands statistical office using open sources. The main purpose of the app being tested was to document travel behavior by passively recording GPS data without having a negative impact on battery life. In addition to passive data, the program allows you to ask questions in the program.
Interface and server infrastructure. The tourist application system consists of an interface and a back. The interface consists of the TOBI Travel app, which collects location data, identifies stops and routes, and provides it to the user for annotation. Both the source location data and the allowed data are stored locally in the SQLite database on your mobile device. The server part consists of an API written in GO that accepts and converts data, and consists of a Postgres database that eventually receives and stores data. A schematic overview of the internal infrastructure is shown in Figure 1. The app uses an API for processing raw sensor data and an API for transferring data from a mobile device to a database. Depending on the purpose of the program, it is easy to add other types of sensors (such as gravity, Light, Motion), make changes to the type of questions asked in the program, or change the way data is transmitted. In 2019, the infrastructure will be expanded by adding a module that can be used to forward data from the database to the device. In fact, it allows you to send dynamic messages (Push notifications based on the received data) or edit data in the database and forward the processed data for further comments or subsequent questions. Internet of Everything (IoE) is an information technological term that combines sensing, computation, information extraction, and communication functionalities together in a device. IoE allows different electronic devices with different capabilities to sense the environment and to communicate for data exchange [1]. IoE is the general form of wireless sensor networks. IoE nodes may have different classes, types, and capabilities. For example, smartphones, tablets, laptops, home appliances, and even cars are examples of nodes
in IoE. These nodes can sense the environment utilizing their different sensors and process data, retrieve useful information, communicate over the Internet, and control their behavior adaptively. IoE nodes' smartness and intelligence are not in their computational capacity, but in their ability to communicate and exchange information. Communication links allow these devices to learn from their sensed data. It trains these devices to leverage its information to perform new useful tasks. For example, a fridge with an embedded processor is not smart until it has the ability to communicate with people, other fridges, and supermarkets to order missing items. Moreover, it should select from different supermarkets to buy the items with price offers. This smartness came out from data communication over the Internet.
IoE is a complex approach with massive applications, dreams, and myths. It has uncountable applications in health, engineering, computer science, marketing, and even social sciences. However, it has many issues that require more investigation. Security and privacy dominated in the IoE research field. How to secure your data and applications is a hot research topic in IoE. However, people security as a drawback in the IoE paradigm should be studied. Many questions emerged in this field. What to sense from the environment and what to upload to the Internet? How to enhance privacy if sensors are everywhere in people's lives? How to teach people to deal with IoE in a responsible way? Can IoE be harmful?
Smart devices play a main role in IoE. They are equipped with multicommunication interfaces, such as Wi-Fi, Bluetooth, near-field communication (NFC), and cellular communication. In addition, they are equipped with a massive number of sensors. Moreover, they have embedded operating systems (OSs) that are referred to as IoT OSs. When smartphones are mentioned in this survey, we are referring to smartphones, tablets, and smartwatches since they have the same characteristics with few industrial differences. According to the statistics reported by Statista (https://www.statista.com/statistics/330695/number-of-smartphone-usersworldwide), the number of smartphones worldwide exceeded 2.8 billion with an estimation of 5 billion in 2019. Smartphones have been employed heavily in controlling and monitoring the process of hundreds of smart home products. For example, WeMo product allows the users to control multiple features in their houses, such as power usage of different appliances. This product is controlled by smartphones. Another example is Apple HomeKit for security and surveillance systems. A third example is Reemo that converts houses into smart homes. Smartphones play a monitoring and controlling role in these applications. However, smartphone capabilities and sensors allow them to play a greater role in health, identification, localization, and tracking.
Sensors are used to enhance the smartphones' usability. However, researchers and developers attempted to leverage these sensors in much more complex applications, such as user identification, subscriber tracking, and even personality traits. These applications require
mining of hidden information of the smartphones' sensor data. In other words, sensor data are leveraged in new indirect ways to predict and estimate new features not directly designed to be assessed by these sensors. This new usage paradigm of smartphone sensors reveals privacy and security issues since smartphone users are willing to upload their harvested data without any awareness of the information that can be mined from them [2]. This issue was referred to as big data accident. The author in proposed a system based on normal accident theory to show drawbacks of big data accident. He has shown that big data may be converted to "evil" in mining free uploaded information. In, the author has shown that users have limited control over the uploaded data which is one of the main privacy concerns in IoE. In, the authors proposed ten rules to guide the privacy and security issues in big data and the ethics that should be emerged. The main motivation in this work is to gain more insights into the privacy issues of smartphones as devices in IoE.
In this work, some of the interesting applications that have been proposed and designed for exploiting smart devices' sensor data are shown as the big opportunities in the new era of IoE. Nevertheless, the accuracy of these applications is shown as one of the substantial issues that requires answers. On the other hand, security and privacy issues are introduced as the doubts of these devices. In this work, we seek to show that security, privacy, and big data accuracy of smart devices in the era of IoE are data content stored not only in the device but also in Internet servers. However, even the raw data extracted from smart device sensors can introduce more threats than the stored contents.
Smart devices in this work are defined as the handheld devices. These include smartphones, tablets, and smartwatches. These devices have approximately the same internal architecture with differences in the speed, size, number of sensors, and storage capacity. In addition, they adopt the same operating systems and software stacks. The apps designed for a smartphone work and operate in tablets. Figure 1 shows the block diagram of the internal architecture of a smart device. As shown in the figure, smart devices have two main parts: processors and sensors. There are also other interfacing parts that connect the sensors to the processors, such as analog-to-digital convertors (ADC), digital-to-analog converters (DAC), voice codecs, and the main memories to handle smart devices' app instructions. The following sections overview the main part of the figure with emphasis on sensors.
Data mining is the science of digging useful information from big data records and repositories. These repositories are created from user contents and machine sensors. The issue is not how to harvest these data. The issue is how to mine it. Smartphones are equipped with tens of sensors and electronic components that generate data in real time [3]. These electronic components and sensors have been embedded in smartphones to enhance usability of these devices. However, researchers have found massive methods to leverage these components and sensors to obtain different information. Many open-access datasets have been collected over the
years. They can be downloaded freely from the Internet. One of these datasets is the LiveLab dataset, which consists of mobile logs of 100 volunteers over the 14-month period. The dataset consists of fifteen different SQL tables. It has been studied in more than 278 scientific papers according to Google Scholar. Different hidden information has been extracted from it. Another online available dataset is in which has been cited in 342 papers. 30 volunteers participated to collect it. It focused on the accelerometer sensor. It has been extended in and obtained another 130 citations. They extended it by more instances. However, they did not add more sensors. Another example of a dataset that has been collected is in. This dataset focused on the Wi-Fi module in the smartphone, accelerometer, and gyroscope. Moreover, smartwatch data has been also recorded. A final example is the massive dataset, which consists of life-logged data of 35 users over two months. It recorded all smartphone activities of the users. This dataset obtained approximately 100 citations. A common feature of all of these datasets is that they did not record any of the users' content or any private data. In other words, the data collected are treated as normal data from smartphone users. With more than a thousand paper published with different extracted information from these datasets of nonprivate contents and data, it is obvious how this nonprivate data led to
the extraction of massive information that can track and identify users' activities.
Conclusion. Within the framework of this article, an information system for collecting and processing data from touch sensors of mobile devices is considered.The system allows you to implement the above-mentioned functions and management through mobile applications and cloud services.
The internet connection opens up new opportunities for companies, governments, and consumers with a wide range of everyday devices -from fitness bracelets to industrial equipment. In addition to this feature, there are also new issues such as performance, data privacy, and security issues - all of which should be kept in mind.
References
1. L. Da Xu, W. He, and S. Li, "Internet of things in industries: a survey," IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233-2243, 2014. View at: Publisher Site | Google Scholar
2. Linda Sui. Global Smartphone User Penetration Forecast by 88 Countries: 2007 - 2020 // TekInc, 2015.
3. The Internet of Things: Making sense of the next megatrend [Electronic resource] - Access mode: http://www.goldmansachs.com/our-thining/pages/in-ternet-of-things/iot-report.pdf/ - Data correspond to Novem ber 2016.
ANALYSIS OF THE EFFICIENCY OF THE WELL FUND AT THE WEST KAZAKHSTAN FIELD
Rakhmetova A.
Master's student of Atyrau University of Oil and Gas named after Saf Utebayev
DOI: 10.5281/zenodo.6532751
Abstract
The efficiency of the operation of the oil well fund is analyzed, compared with the design indicators and the reasons for the discrepancy are identified.
The field was put into development in 1980, based on trial operation projects. Drilling of the deposit was carried out according to the pilot operation project.
In 2005, LLP "CTI" "RD "Kazmunaygas" recalculated the reserves of oil and dissolved gas. Keywords: well, field, object, development, producing wells.
The newly calculated and approved initial balance sheet and recoverable oil reserves of the Republic of Kazakhstan are;
in the category B + C1 - 6287.9/ 2957 thousand tons, stocks increased by 18 and 49%.
Including:
- for the I- th object - 4587.9 thousand tons. geological, 2294 thousand tons. recoverable:
- for the II-th object - 1,700 thousand tons. geological, 663 thousand tons. recoverable:
Initial oil reserves for the field in category C2 -959.5 / 429.3 thousand tons. increased by 78 and 141%.
In 2006, the II - development option was adopted for implementation, which provides for drilling and commissioning of 12 production wells and the transfer of 2 wells from the first facility to the second facility. The maximum fund of producing wells is 38.
