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In the era of digitizing society, and connecting across the Internet to various devices, large amounts of data are stored that are stored in different bases. These data, by structure, can be structured and unstructured. Structured data are highly organized, operational for analysis and mostly integrated into databases.

One important source of information for us is the IoT devices, which can be a source of both types of data.On the other hand, unstructured data are unorganized, the so-called 'raw', require a lot of time to analyze meaning or to find a sense (books, post, medical records, videos ...). However, both data and data together form a heterogeneous whole as a data reservoir.

By analyzing these heterogeneous data obtained from various sources, along with publicly available data, it is possible to create a range of new products and services that are personalized for each user and in every way adapted to his needs.

What is important and should be underlined is the following.

First
• We are increasingly confronted by shifting from the Data-centric IoT environment to the Action- Centric IoT environment. Namely, Data-centric IoT devices (primarily sensors) serve primarily as a data source, therefore, as mentioned above for cameras, sensors, control valves, sensors that generate data according to the operating conditions of an industrial process, location data of equipment ... etc. Such IoT devices can generate large amounts of data, structured and unstructured, which are sent over a communication medium. However, these data will not necessarily serve to improve the real-time process based on them. We can say that the amount and type of data that is collected corresponds to Big Data, and the analytical tools and databases used then. More importantly, it is to understand how to collect the right set of data rather than just collecting. Because based on the right set of data it is possible to make the right decision based on the big data analytics, about the action that will be realized. For example, the system for predictive maintenance uses the collected data from the vibration sensor to determine which part of the machine needs to be recalibrated, and automatically shut off the equipment at the optimum moment to start the recalibration routine. So there will be no longer enough to find
cause-and-effect connections based on POST festum data analysis. Searching for these connections will have to be done with modern methods of predictive analytics. Consider an example of moving to a Action-centric environment where the data source does not only have to be an IoT device. So if we neglect the data source for a moment and pay attention to the current tools for analytics and business decision-making, which do not necessarily have to be a big-data type, we will see that they actually have great opportunities. Namely, based on these tools it is possible to see WHO, WHAT, HOW, WHEN, but what is missing is WHERE. This fifth dimension of the image gives a more complete insight into the analysis of existing data and is close to Action-Centric philosophy.

Second.
• An increasing impact of the analysis has unstructured data. Not only that, today, more than ever before, IT tools for AI (Deep Neural Network) artificial intelligence enable a very effective search of meaning in unstructured and structured data at the same time. In order to do this, we need the IT tools or the machine learning platform, ML, which allow the machine to learn to conclude on some topics based on a large amount of unstructured and structured data (Big Data Database). So far, we have applications that use the principle of machine learning (ML) to understand consumer taste by analyzing data from a large data database. In addition to this type of application, there are also applications that use the principle of machine learning (ML) to determine the identity of a person based on a video or image, and then applications that enable an advanced search process for a term, even if you do not know the keyword to look for by descriptively setting up your search filter.

Third.
• The fourth industrial revolution – IIoT Network Sensors is SENSOR INTELLIGENCE I strongly beleive that there is no sensor intelligence without IIoT network. We can speaking only of local part of analytical intelligence, but realy (artificial) intelligence lie on the Big Data analytics and machine learning (ML). So, IoT Network Sensors is SENSOR INTELLIGENCE.

TECHNOLOGY

Taking into consideration the proven results in solving the continual acquisition, processing and evaluation of data for gas emission analytics based on my software development and VPS IaaS Cloud solution (ANVISE™ - EPEMS210™ system), I came to the idea of a universal approach to solving the problem of acquisition, processing and analysis of data in real time, on any surface and anywhere in the world, via Cloud access, ie, a global information model and GPS/GSM/NB IoT technology. In fact, it is about using my second solution, already proven in GPS / GSM fleet monitoring of parameters of mobile assets, in the wider information solution ANVISX™- IoT integration platform.

Yes, integrating two improved solutions, we design integration platform that enables quick solution for five aims of any IoT application: data acquisition, processing, storage, visualisation, and enterprise application integration.

Recognizing the increasing challenge in solving similar problems in the environment, agriculture, forestry and ecology such as: sustainable agriculture, water management, logging, air pollution, biodiversity, conservation of habitats, preventive maintenance of physical and technical resources, I have developed my own universal conceptual and an information and communication environment and an implementation & design eco system called ANVISX™ based on my XAMPCS proven development environment that meets all the levels and techniques needed to implement the IIoT / IPC and/or Wireless Sensor Network Cloud solution.

ANVISX is available both as a public cloud service and a software installable on- premise.
Its unique scalability allows ANVISXDL™ Gateway Loggers / Servers running on the edge devices, such as single-board computers, IIoT gateways or Linux PLCs.

ANVISX VPS Cloud for IIoT with elements of CISCO FOG / Azure EDGE / Network IoT concept for immediate response to the executable devices and local analytics on the edge

ANVISXDL™ Gateways : IIoT / Embedded Data Logger (Concentration unit)
• Provides the collection and storage of read data with an interval of at least 1 second called from PLC and / or wireless IIoT sensors (digital and / or analogue sizes) to a local RING (LIFO buffer) database with automatic shift cycle up to 365 days.
• Connects and automatically synchronizes collected data to Cloud over encrypted TCP / IP, HTTPS-MQTT or any other security SSL or higher-level shell (VPN, SSH ..)
• Provides phys. MODBUS RTU over SERIAL / TCPIP / GSM, as well as HTTPS, TCP and MQTT security APIs for locally connecting to IIoT sensors and other Data Loggers / PLCs via long gateway adapters and via wireless technology Wifi, LoRa, GSM, GSM LTE N1, BT ... Optionally, Data Logger is also a device that can analyze data on the spot and forms an immediate response to the executable devices, not waiting for an answer from the main server, i.e., the Cloud Data Center (which can often be delayed if the network is burdened or interrupted , which is not good for industrial IoT), and at the same time send all these changes to the field and, of course, to HOST, ie, ANVISX Cloud Data Center. With this capability we are approaching the network IIoT Cloud concept.

INDUSTRIES

• Agriculture
• Oil / Gas / Process Industries
• Power Engineering
• Smart Cities
• Transportation / Logistic
• Water / Wastewater

• OBJECTIVE FOR EVERY AREA
The goal is to process data, analyze them and offer them through the web services to interested users. The discovery of Big Data data that would be carried out through the methods of new technologies of machine learning (ML) and artificial intelligence (AI) would enable quality decision-making to a well- defined field of application. Web services would be merged with data derived from collaborative or publicly available services and databases.
The goal is therefore to design / generate / IT services for farmers, farms, advisory services, various companies and all those who make decisions. This is not about creating an integrated system for agricultural monitoring, or some other area of human life, it is about creating a ’decentralized integrated system’, the backbone of which is made up of web services and microservices, through which the ultimate information is formed - Big Service and service as required by the user :

1. Analytic&Predictive Services : based on important information from the ANVISX database

2. Logistic&PROXY Services : services based on information from the ANVISX database, other available IT services, and for the third interested parties

The final idea is to make integrated soluton from measurment of data values (pollutants, physical values temperature, wind level, air pressure...) to data analytics, communication systems and added value services including installation, commisioning,training, maintanance, engineering and data management.

• CLASSES and TYPES of USES
The fact is that in both life and ITC we can make a difference in the significance of the speed of data collection as well as the quantity of measuring positions at the same time.
In line with the above, regarding the difference between IoT and the industrial IoT concept, the method and the place of data collection and analysis are also reflected.
Simply put, for the industrial use of IoT, we need to have a quick response, ie on-site analysis, near the measuring point, while for slowly changing data values, where the latency of the network is not crucial in any decision, we can do the POST FESTUM analysis on ANVISX Cloud, and here NB IoT technology is the real thing.
Very simple classes division. We have only two : MOBILE and STATIONARY assets

• FIELD OF PHYSICAL AND TECHNICAL ASSETS
ASSET CLASS : STATIONARY and MOBILE
TYPE of ACQUISITION and ANLYTICS : SLOW, MID SLOW, FAST
1. MID RESPONSE IIoT AREA OF USAGE
In accordance with the analysis described above for the need of Action Centric IoT ecosystems, in this area the possibilities of utilization are truly impressive. Because based on the right set of data it is possible to make the right decision on the basis of big data analytics, the action that will be realized and thus lead to savings in the holding of the asset. Thus, the IIoT system for preventive maintenance of a machine that is based on the predictions of the condition of the machine ie, the physical and technical means uses the collected data from the specific sensors to determine which part of the machine is required to recalibrate, and automatically switch off the equipment at the optimum moment to start the routine for recalibration. Therefore, it is not enough to find cause-and-effect connections based on POST festum data analysis. The search for these connections will have to be done by modern methods of predictive analytics, and in the architectural concept it corresponds to the Network Industrial IoT Cloud FOG concept (CISCO) in combination with the 'classic' Cloud IoT concept.
2. FAST RESPONSE IIoT AREA OF USAGE
As I said ANVISX Data Logger is also a device that can analyze data on the spot and forms an immediate response to the executable devices, not waiting for an answer from the main server, i.e., the Cloud Data Center (which can often be delayed if the network is burdened or interrupted , which is not good for industrial IoT), and at the same time send all these changes to the field and, of course, to HOST, ie, ANVISX Cloud Data Center. With this capability we are approaching the network IIoT Cloud concept. Here is dominant CISCO FOG concept and MS Azure EDGE

• ADJUSTING M2M to the Internet of Things Ecosystem
ANVISX™ tailors existing M2M, remote monitoring & service technologies to the new IoT world based on open standards, cloud application deployments, big data storage and processing facilites, rich HTML5 based browser Uis etc.

• THE FOURTH INDUSTRIAL REVLOUTION – IoT Network Sensors is SENSOR INTELLIGENCE
I strongly beleive that there is no sensor intelligence without Industrial IoT network. We can speaking only of local part of analytical intelligence, but realy (artificial) intelligence lie on the Big Data analytics and machine learning (ML).
So, IIoT Network Sensors is SENSOR INTELLIGENCE !

SOLUTIONS

While the generic and open-ended nature of ANVISX™ is often an advantage, sometimes you need a solution tailored for a specific vertical market. For such cases, we offer different products based on the ANVISX™ IoT Platform. ANVISX™ tailors existing M2M device data, remote service technologies to the new IoT world based on open standars, cloud application deployments, big data storage and processing facilites , and other trends.

• Industrial Analytics & Automation: Client/Server & Cloud VPS solution - ANVISE™
• GPS Fleet Management - Vehicle & stationary Assets Telemetry : ANVISgps™ , ANVISassets™
• Automatic Meter Reading : Proxy SERVER for AMR - ANVISamr™
• Remote Monitoring : 3G Telemetry Station with PLC for Highway - ANVIStarmu™
• Sensor Networks : ANVISorganic™
• Intelligent Electronic Devices ready for IIoT EDGE concept

Industrial Analytics & Automation

1. ANVISems : Gas Emission Monitoring and Evaluation Systems

1.1. ANVISE™ - EPEMS 210™ – (Gas) Emission Processing, Evaluation and Monitoring System
A DATA ACQUISITION and HANDLING SYSTEM from AMS systems

• Emission Processing, Evaluation and Monitoring System (EPEMS) is a software and hardware system for data acquisition and handling (DAHS) for environmental emission data, which corresponds to today’s legal requirements for quality assurance of data obtained from Automated Measuring Systems (AMS), being monitoring air pollution, emission from stationary source, and is conceptually prepared for future guidelines. The system enables the continuous data acquisition, processing, evaluation, automatic data restoring, long-term storage, visualization and reporting of environmental data of most diverse areas.

• The hardware and software components fulfils the standards for stationary source emission based on documents for Data Acquisition And Handling Systems (DAHS), Technical Committee, CEN, as well as the EU directives, regulations for power combustion plants and waste incinerators for limit values, and other national requirements.

• The basic request that has been set for such a system is ability to continuously monitor, recording and reporting about quality processed emission data from measuring points in technological process or acquisition subsystems - external units (Embedded Data Loggers), for the defined sets of periodical or arbitrary intervals. Quality processed emission data means that processing is over different types, sorts and class of data with necessary calculations, evaluations and validation protocols (the time dependent rules) under fail- safe data and adapts them for storage into central database.

2. ANVISems-MGAS

2.1. ANVISE-M-GAS™ Emission Tracker
ANVISE™M-GAS Emission Tracker
Monitoring Emission from Mobile Gas Analyzer Station With GPS/GPRS/NB IoT module

• For process control and gas flow monitoring, our industrial facilities require equipment able to operate 24 hour a day in harsh environmental conditions. Reliability, low maintenance and cost are the major drivers, coupled with the never ending decrease of the required detection levels. The same demand we can ask for mobile gas analyzers platforms which we needs for dynamic monitorig emission or imission in specific urban/ extra urban area.

GPS Fleet Management : Vehicle & stationary Assets Telemetry

1. ANVISgps - Solution for Tracking & Control Mobile Assets
Vehicle Telemetry and Remote Control
A GPS/GPRS/NB IoT solution

1.1. ANVISgps™ - MOBTRACK

• Satellite tracking system works by using a vehicle mounted a special device containing a GPS receiver and a GSM transceiver. GPS of the device from the satellites collect information on the current location of vehicles, processes the data and sends them on to the GSM part that sends the GSM service (GPRS) using a mobile phone network, and on TCP / IP carrier to the target server from which the data is available to end user via the Internet network. The end user at any time, via the web or desktop applications can get information about the current vehicle location, speed and so on.

1.2. ANVISassets™ - Tracker, mobile and stationary assets monitoring

• The applications that allows this device are several and in different verticals (industry, asset tracking, agriculture, smart buildings, monitoring of hospital material…), but the main focus is in the logistics.
• It’s a perfect solution, inter alia, for monitoring the cold chain. Once installed the ANVISassets™Tracker to the product or products that want to be hold at a particular temperature, the device will send its location and temperature. That will quickly know if the cold chain has been interrupted, where it has happened and detect which has been the problem to solve it expeditiously.

Sensor Networks

ANVISX can be easily used to monitor multiple sensors. If You for example, operate huge organic greenhouse, Your greenhouse always needs to have certain temperature and humidity percentage.
These variables keep changing at various times of the day and as equipment and worker enter or leave the warehouse.

1. ANVISorganic

1.1. ANVISorganic™

•Using predictive analytics, we can accurately predict and analyze dry from the (mobile) data in agriculture and make decisions on irrigation and drainage engineering on any surface in the world.

• We are getting data from WSN (Wireless Sensor Network subsystem), and it is first condition the System must have. The rest of IT logistic is cloud data receiving service device on the edge (ANVISXDL™ Gateways or other devices by other vendors) and cloud Big Data Analytics.
• In this way, for example, we can accurately predict and analyze dry period in agriculture and make a decision on irrigation and drainage engineering on any surface in the world.
• Aﬅer implementing GPS/GLONASS vehicle tracking, dispatchers optimized the machinery performance by coordinating a pile forming machine and a pushdozer operation in the field.
• Historical tracking data visualized on maps gave ANVISorganic analysts a lot of information to reflect on.