WO2016156657A1

WO2016156657A1 - Arrangement for implementation of data decentralization for the internet of things platform

Info

Publication number: WO2016156657A1
Application number: PCT/FI2015/050219
Authority: WO
Inventors: Jarkko Vatjus-Anttila; Ville MICKELSSON
Original assignee: Cyberlightning Oy
Priority date: 2015-03-27
Filing date: 2015-03-27
Publication date: 2016-10-06

Abstract

The invention relates to an arrangement, a server, a method and a computer program product for implementation of data decentralization for the Internet of Things platform. The arrangement comprises at least one computing device connected directly to at least one sensor and the at least one computing device configured to collect data from a point of interest that the at least one sensor is configured to monitor and at least two servers configured to run independently and communicate with each other and with the at least one computing device connected directly to the at least one sensor wherein at least one of the servers is further configured to request data collected by the at least one sensor and distribute the received data for storing and analysis with at least one other server according to extent required to make use of available data storing capacity of the all the servers.

Description

Arrangement for implementation of data decentralization for the Internet of Things Platform

Technical field of the invention

The invention relates to an arrangement for implementation of data decentralization for the Internet of Things Platform. Also, the invention relates to a server for implementation of data decentralization for the Internet of Things Platform. Moreover, the invention relates to a method for implementation of data decentralization for the Internet of Things Platform. Finally, the invention relates to a computer program product for implementation of data decentralization for the Internet of Things Platform.

Background of the invention

The Internet of Things (" the loT") is a concept understood as the network of physical objects or "things" embedded with electronics, software, sensors and connectivity, such as routers, to enable it to achieve greater value and service by exchanging data with the manufacturer, operator and/or other connected devices. Each "thing" is uniquely identifiable through its embedded computing system and, at the same time, is able to interoperate within the existing Internet infrastructure.

The loT aims to offer advanced connectivity of devices, systems, and services. The advanced connectivity that the loT targets to goes beyond traditional machine-to- machine communications. It covers a variety of protocols, domains, and applications. The things in the loT can refer, for example, to a wide variety of devices such as heart monitoring implants, biochip transponders, monitoring sensors embedded to electricity heating systems, automobiles with built-in sensors, or field operation devices that assist in logistics or local monitoring purposes, for example. These devices collect da- ta with the help of various existing technologies and then autonomously flow the data between other devices.

The loT provides a plurality of new application areas for Internet connected automation to expand into and brings the Internet to industrial operation. The loT will also to generate large amounts of data from diverse locations that is aggregated at a very high-velocity. Hence, the loT increases the need to better index, store and process such data to avoid severe malfunctioning of the loT based systems.

An Artificial Neural Networks ("ANN", "Neural Networks") is generally understood as an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of neural networks is a structure of the information processing system that is composed of a large number of highly interconnected processing elements working in unison to solve specific problems. Neural networks, like people as well, learn by example. A neural network is configured for a specific application, such as pattern recognition or data classification. The configuration takes place through a learning process. Neural networks have an ability to derive meaning from complicated or imprecise data. Hence, they are applied to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Neural networks may be applied to identifying patterns or trends in data and they are suited for prediction or forecasting, for example.

In the loT networks an increasing number of cheap sensors are producing information at a pace which will become impossible to manage with any single centralized instal- lation for data management. That is why the information collection and processing needs to be distributed along the available network, not only in a cloud based installations but also to the edge of the network into processing power limited devices, such as routers. To be able to bring their full potential to industrial application, the loT networks will need the ability to effectively aggregate the data within the loT network and provide systems that learn according to the data available in them.

A practical implementation of data decentralization is needed to solve the problem of processing and storing the increasing amount of data in the loT networks. Also, the practical implementation of data decentralization is needed to solve the problem of ef- fective data aggregation to enable the loT networks to become fully learning systems. A platform for building the loT type of networks with capability to data decentralization is needed. List of Terms

Certain terms are used to describe the present invention and the following explanations of the terms are given for convenience in the context of the present invention:

A sensor ("sensor") means a measurement device, which is capable of measuring one dedicated value of environment and report the measured information to the device hosting the sensor. A sensor can be, for example, an NTC resistor measuring air temperature, a weather camera capturing environmental images, or database client downloading new data from dedicated open data set. In the context of the present invention the device hosting the sensor is called a computing device connected directly to the sensor.

A computing device ("computing device") in the context of the present invention means a device, which is capable to receive information from one or more sensors, is able to store data in persistent memory and is able to perform computations based on the received data.

Autoconfiguration ("autoconfiguration", "autoconfiguring") in the context of the pre- sent invention means a behavior model of a computing device or a network of computing devices. For a single computing device it means that the device software is able to detect the environment it is running in and adjust all necessary software run-time parameters according to the environment to gain the maximum performance out of the current situation. For a network of computing devices it means automatic negotia- tion of communication methods between different computation devices and automatic re-configuration of communication protocols even in the case of connection re- establishments. loT platform (the "loT platform") in the context of the present invention is to be un- derstood as an arrangement of software and hardware comprising a software package, which further comprises of a minimum of one computing device software, which can be installed as an autonomous installation to run on a varying kind of computing device hardware. Summary of the Invention

The object of the present invention is to provide an arrangement, a server, a method and a computer program product for implementation of data decentralization for the loT platform. The object of the present invention is to provide an arrangement, a server, a method and a computer program product for implementation of data decentralization for the loT platform acting as a building platform for an loT network.

The object of the present invention is to provide a platform for building an loT network that enables effective data decentralization and effectively uses full data processing and storing capacity of the loT network. Further, the present invention aims to provide a platform for building an loT network that enables effective data aggregation. The present invention also aims provide a platform for building an loT network that enables effective data decentralization and data aggregation that enable building fully learning loT networks.

Also, the object of the invention is through the decentralization of data allow saving operational costs compared to single installation of centralized server network. The present invention aims to split the incoming data into smaller streams, which are eas- ier to manage, and hence cheaper to develop on. Also, required bandwidth is lesser than in centralized solution which brings down costs operational wise.

Finally, the object of the present invention is to enable through data decentralization more fault tolerant solutions where no single entity of the network can cause severe collapse of the service in question.

The objects of the present invention are fulfilled by providing an arrangement for implementation of data decentralization for the Internet of Things, the loT, platform, the arrangement comprising

- at least one computing device connected directly to at least one sensor and the at least one computing device configured to collect data from a point of interest that the at least one sensor is configured to monitor; and

- at least two servers configured to run independently and communicate with each other and with the at least one computing device connected directly to the at least one sensor for executing data processing tasks on the loT platform; wherein at least one of the at least two servers is further configured to request data collected by the at least one sensor from the at least one computing device connected to the said sensor and distribute the received data for storing and analysis with at least one other of the at least two servers according to extent required to make use of available data storing capacity of the at least two servers.

Also, the objects of the present invention are fulfilled by providing a server for implementation of data decentralization for the Internet of Things, the loT, platform, the server comprising:

- at least one processor

- at least one data communication interface;

- at least one memory including a computer program code; and

- the at least one memory and the computer program code configured to, with the at least one processor, cause the server at least to

- run independently for executing data processing tasks of the loT platform; and

- communicate with at least one of the following: at least one similar server and at least one computing device connected directly to at least one sensor configured to collect data from a point of interest that the sensor is configured to monitor for the loT platform

wherein the server is further configured to request data collected by the at least one sensor from the at least one computing device connected directly to the at least one sensor and distribute the data received for storing and analysis with the at least one similar server according to extent required to make use of available data storing capacity of the said at least two servers.

Moreover, the objects of the present invention are fulfilled by providing a method for implementation of data decentralization for the Internet of Things, the loT, platform, the method comprising:

- collecting data from a point of interest by at least one computing device connected directly to at least one sensor configured to monitor the said point of interest; and

- executing data processing tasks for the loT platform by at least two servers configured to run independently and communicate with each other and with the at least one computing device connected directly to the at least one sensor

wherein the method further comprises requesting data collected by the at least one sensor from the at least one computing device connected directly to the at least one sensor on at least one of the at least two servers and distributing the data received for storing and analysis on the said at least two servers by at least one of the at least two servers according to extent required to make use of available data storing capacity of the said at least two servers.

Finally, the objects of the present invention are fulfilled by providing a computer program product on a non-transitory media for implementation of data decentralization for the Internet of Things, the loT, platform, the computer program product comprising:

- a computer readable code for collecting data from a point of interest by at least one computing device connected directly to at least one sensor configured to monitor the said point of interest; and

- a computer readable code for executing data processing tasks for the loT platform by at least two servers configured to run independently and communicate with each other and with the at least one computing device connected directly to the at least one sensor

wherein the computer program product further comprises a computer readable code for requesting data collected by the at least one sensor from the at least one computing device connected directly to the at least one sensor on at least one of the at least two servers and distributing the data received for storing and analysis on the said at least two servers by at least one of the at least two servers according to extent required to make use of available data storing capacity on the said at least two servers.

The basic idea of the invention is as follows: Data in the loT network is collected and processed in each node of the network separately. A node of the network is, for example, a server, a computing device connected to a sensor, a router or other such point of network through data may flow in the network. Processing the data separately in each of these nodes allows effective data aggregation, bandwidth savings and distribution of the computational load away from the centralized solution. At the same time, the data flow is not one direction only. Instead, the data may be sent also back in the communication chain within the network. This allows not only data aggregation and distribution solutions, but nodes which implement full-duplex communication method. Further, this enables building fully learning networks, and genetically enhanced algorithms for the computation process. Ultimately, a neural network, which is distributed across the cloud and heterogeneous group of processing power con- strained devices, may be built according to the present invention for plurality of practical applications in varying fields of industry.

Intelligent fleet management acts as a practical example of application to a field of in- dustry. A fleet consists of a plurality of vehicles, which reside in the field. These vehicles can be, for example, logistics trucks or forest harvesters. Each of the vehicles host a computing device connected to at least sensor, which collects data from the vehicle locally. The device connected to the sensor does data processing to the collected data locally. Each vehicle in the fleet reports their selected measured sensor data to the cloud installation, a network based installation, of the same software. The said installation is owned by the fleet manager. This allows the fleet manager to compute further data processing on the data gained from the complete fleet and also it allows submission of data processing results back to individual vehicles. There are several optimization tasks, which can be controlled this way by the fleet manager. Then, a group of fleet managers can make a decision to submit processed information further to the manufacturer of the fleet vehicles. The fleet vehicle manufacturer, who may receive this information from a plurality of the fleet managers, again may perform data processing computations on the received data. Also, the fleet vehicle manufacturer may allow feedback and communication back to the fleet manager lev- el. The example above presents a 3-tier chained intelligence with different stakeholders and different level of data aggregation on a setting of an loT network. Given that the needed level of privacy allows, this may be built to function as a self-learning neural network, which constantly optimizes the behavior of the fleet vehicles where-ever they are deployed, by utilizing the present invention.

The present invention provides an arrangement, a server, a method and a computer program product for implementation of data collection and processing distributed along the available a network of one or more servers and computing devices connected to the one or more servers and being directly connected to one or sensors providing data to the said network.

The present invention provides a platform for building an loT network that enables effective data decentralization. Further, the present invention provides a platform for building an loT network that enables effective data aggregation. Moreover, the pre- sent invention provides a platform for building an loT network that enables effective data decentralization and data aggregation that enable building fully learning loT networks.

The present invention enables communication between at least two servers in a two- directional way. The present invention enables communication between at a server and at least one computing device connected directly into at least one sensor in a two-directional way. The two-directional communication may comprise, for example, sending at least one of following: sensor data, aggregated sensor data, processing algorithms, configuration instruction to computing device connected directly into at least one sensor and parameters of the processing algorithms from one server to an- other server. Also, at the same time with the previously described sending type of communication, the communication may comprise, for example, receiving at least one of following: sensor data, aggregated sensor data, processing algorithms and parameters of the processing algorithms from one server to another server. The present invention allows at least one server in the arrangement to act as auto- configuring entity based on the received information at least one other server in the arrangement. The autoconfiguring also comprises ability capability of the at least one of the at least two servers of the arrangement to function temporarily without any connection to other servers of the arrangement.

Advantageously, the present invention may be implemented as an installation of software and hardware comprising at least two servers wherein at least one of the two servers communicates with at least one sensor and where the arrangement combine data collection and storage mechanisms for the loT platform to enable effective data decentralization for the loT platform. Also, the said installation of software and hardware may comprise remotely configurable methods and computer program product for implementing data processing algorithms for the loT platform that enable effective data decentralization for the loT platform. The implementation of the present invention may comprise a computer program product based solution for data collection and storage methods, programmable data processing environment capable in operating with local data and two-way communication interfaces, which allow sending information to other similar entities in the network and receive information from them are needed. For example, Python3 programming language, which offers basic tools in building such two-way communication interfaces may be utilized for practical implementation. Python3 tools, such as socket library, OpenSSL, Flask and Autobahn allow creation of a framework which is able to listen to UDP, TCP, TLS, HTTP and HTTPS traffic and similarly send to all those traffic types. Also, at the same time, Python3 runtime environment is available for a very wide range of different types of devices. This allows a single solution to be run in a wide variety of heterogeneous hardware. Smallest scale Python3 environment, called microPython, runs in microcontroller. The environment scales up and it can also run in a cloud based distributed computer network. Hence, by utilizing the standard communication methods, as mentioned above, and by implementing a proper two-directional communication method on top of them, a small footprint, scalable, data collection and processing platform, which implements genetically enhanced data processing algorithms and is therefore capable in acting eventually as a single entity in a larger loT neural network cluster, can be built.

In one advantageous embodiment of the invention there is at least one computing device connected directly to at least one sensor monitoring a point of interest and at least two servers that are configured to form a network on the loT platform wherein either of the at least two servers is further configured to communicate with each other to distribute the data they have or they have received from the at least one computing device connected directly to the at least one sensor on the network to an extent required to make use of available data storing capacity of the network.

In another advantageous embodiment of the invention the decentralization for an loT platform may be implemented to an extent required to make use of available data storing capacity of the network by distributing the data according real-time data storing capacity of the network.

In a third advantageous embodiment of the invention the data storing capacity of the network is monitored by at least one server provided according to invention and the available data storing capacity of the network and the extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available with all the servers of the network in real-time. The data stored on the network will be further distributed to the network if the data storing capacity of the network is extended and the said further distribution of the data is required to efficiently make use of available data storing capacity of the network.

In a fourth advantageous embodiment of the invention at least of one of the servers according to the present invention is configured request data from the at least one other server in the network and then combine the requested data with other data stored with the requesting server itself or on the network for analysis of the data and for further configuration of the data requests and data processing of the other servers of the network. At least one of the servers according to present invention is config- ured to analyze the data stored with itself or within the network according to at least one of the following: combining elements of data available with the at least one of the at least two servers itself or on the network, making calculations on the basis of data available with the at least one of the at least two servers itself or on the network and making calculations on the basis of combined elements of data available with the at least one of the at least two servers itself or on the network.

In a fifth advantageous embodiment of the invention at least one server according to the present invention is also configured to send commands to at least one similar server of the network to further configure the data requests and data processing of the at least one similar server.

Finally, in a sixth advantageous embodiment of the invention at least one of the servers according to the invention is configured to visualize the data available with the said server locally on the said server itself or for distributing the data within the net- work.

Further scope of applicability of the present invention will become apparent from the detailed description given hereafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications of the invention will become apparent to those skilled in the art from this detailed description. Brief description of the drawings

The present invention will become more fully understood from the detailed description given herein below and accompanying drawings which are given by way of illustration only, and thus are not limitative of the present invention and wherein

Fig. 1 shows a schematical representation of the main components of an arrangement according to the invention by way of an example; Fig. 2 shows an exemplary flow chart for main stages of a method according to the invention;

Fig. 3 shows another exemplary flow chart including a set of method stages according to the invention; and

Fig. 4 shows, by way of an example only, another set of method stages according to the invention.

Detailed description

In the following description, considered embodiments are merely exemplary, and one skilled in the art may find other ways to implement the invention. Although the specification may refer to "an", "one; or "some" embodiment(s) in several locations, this does not necessarily mean that each such reference is made to the same embodi- ment(s), or that the feature only applies to a single embodiment. Single feature of different embodiments may also be combined to provide other embodiments.

Figure 1 shows a schematical representation of the main components of an arrangement according to the invention by way of an example only. The arrangement according to the present invention has at least two servers 10 and is referred as "the server" later in the description regarding Figure 1. The arrangement according to the present invention also comprises at least one computing device 12 connected directly to at least one sensor 11. The arrangement according to the present invention may also comprise at least one other computing device 13 connected to the server 10 lo- cally. The at least one other computing device 13 connected to the server 10 locally may be part of another system connected to the loT network implemented on top of the loT platform according to present invention.

The server 10 essentially comprises of at least one processor 102, at least one memory unit 103 containing a computer program code 1031 for implementation of data decentralization for the loT platform, and at least one data communication interface 101 ("the data communication interface"). The server 10 implements the logic of the arrangement for implementation of data decentralization for the loT platform. The server 10 implements the logic of the arrangement for implementation of data decentralization for loT platform acting as a building platform for an loT network. Each of the at least two servers 10 run an instance of the loT platform. The server 10 executes data processing tasks for the loT platform. The server 10 is configured to run independently and communicate with other servers 10 of the loT platform and with the at least one computing device 12 connected directly to the at least one sensor 11. According to the present invention the at least two servers are required for implementation of data decentralization for the loT platform. However, the number of servers is not limited otherwise and the arrangement according to the present invention allows implementation of varying types of loT networks by utilizing the data decentralization. Advantageously, the server 10 may be is embedded to the at least one computing device 12 connected directly to the at least one sensor 11.

The computer program code 1031 for implementation of data decentralization for the loT platform is contained on the at least one memory unit 103 of the server 10. The computer program code 1031 comprises programmable logic for implementation of data decentralization for the loT platform. The computer program code 1031 comprises programmable logic for implementation of data decentralization for the loT platform on top of which an loT network with effective data decentralization can be built. The data communication interface 101 enables the server 10 to communicate over a network connection 14 with one or more servers 10 and one or more computing devices 12 connected directly to at least one sensor 11. The data communication interface 101 enables the server 10 to communicate over the Internet 14 with one or more servers 10 and one or more computing devices 12 connected directly to at least one sensor 11. The data communication interface 101 may also enable the server 10 to communicate with other computing devices 13 connected to the server 10 locally and with one or more sensors 11.

The at least one sensor 11 according to the present invention is a measurement de- vice, which is configured to monitor a point of interest. The at least one sensor 11 according to the present invention is configured to monitor a point of interest and gather data from the point of interest. A device hosting the sensor is called a computing device 12 connected directly to the at least one sensor 11. Advantageously, the at least one sensor 11 may also be embedded to the at least one computing device 12 con- nected directly to the at least one sensor 11. The at least one sensor 11 may also be connected directly to at least one server 10. According to the arrangement the at least one sensor 11 is configured to send data to at least one server 10 in its own pace and at least one server 10 is configured to receive the data. According to the arrangement the at least one sensor 11 is configured to send data it has gathered from the point of interest that it is configured to monitor to at least one server 10 in its own pace and at least one server 10 is configured to receive the data. Advantageously, according to the arrangement, the computing device 12 connected directly to the at least one sensor 11 is configured to send data to at least one of the at least two servers 10 of the arrangement in its own pace and the at least one of the at least two servers 10 of the arrangement is configured to receive the data. Also advantageously, according to the arrangement, the computing device 12 connected directly to the at least one sensor 11 is configured to send data it has gathered from the point of interest that it is configured to monitor to at least one of the at least two serv- ers 10 of the arrangement in its own pace and the at least one of the at least two servers 10 of the arrangement is configured to receive the data sent by the computing device 12 connected directly to the at least one sensor 11.

The at least one sensor 11 according to the arrangement is capable of measuring one dedicated value of environment and report the measured information to the computing device hosting the sensor 12. A sensor 11 can be, for example, an NTC resistor measuring air temperature, a weather camera capturing environmental images, or database client downloading new data from a dedicated open data set. The computing device 12 connected directly to the at least one sensor is a device, which is capable to receive information from one or more sensors, is able to store data in persistent memory and is able to perform computations based on the received data. Advantageously, the at least one computing device 12 connected directly to the at least one sensor 11 may have a server 10 embedded into it. Figure 2 shows an exemplary flow chart for main stages of a method according to the present invention and references are made to Figure 1 regarding the main components of the arrangement according to reference numbers. According to step 200 data decentralization for the loT platform is started by entering step 201. According to step 201 collecting data from a point of interest by at least one computing device 12 connected directly to at least one sensor 11 configured to monitor the point of interest takes place. The at least one sensor 11 is capable of measuring one dedicated value of the point of interest and report the measured information to the at least one computing device 12 connected directly to the said sensor 11. The at least one computing device 12 connected directly to the said sensor 11 stores the data gathered by the at least one sensor 11 according to step 202.

In step 203 the at least one server requests data collected by the at least one sensor

11 from the at least one computing device 12 connected directly to the at least one sensor 11.

According to step 204 the at least one server receives the data collected 202 by the at least one sensor 11 from the at least one computing device 12 connected directly to the at least one sensor 11 and distributes the data received for storing and analysis within at least two servers 10 according to extent required to make use of available data storing capacity of the said at least two servers. According to step 204 the at least one server distributes the data received from the at least one computing device

12 connected directly to the at least one sensor 11 for storing and analysis within itself and at least one other server according to extent required to make use of available data storing capacity of the said at least two servers. The data distributed main- tains its integrity when distributed by the at least one server 10.

In step 205 the data received by the at least one server from the at least one computing device 12 connected directly to the at least one sensor 11 has been distributed by the receiving server 10 with at least one other server of the loT platform. The serv- er 10 receiving the data and distributing it takes care of maintaining integrity of the da- ta when the data is distributed for storing and analysis within the network by the server 10 receiving it. The said at least one of the two servers communicate 10 with the at least one other server to manage distribution of the received data for storing and analysis within a network formed by the said at least two servers 10 and the at least one computing device 12 connected directly to the at least one sensor 11 to extent required to make use of available data storing capacity of the network. The extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available within all the servers 10 of the network in real-time. According to step 206 the data collected by the at least one sensor 11 and stored at least one computing device 12 connected directly to the said sensor 11 in step 202 is now made available within the loT platform in a decentralized form. Decentralization of the data makes effective use of full data storing capacity available within all the servers 10 of the network in real-time. Decentralization of the data according to the previous method steps prevents severe collapses of an loT network due to data overload on a single server 10. Further, data is split into smaller streams, which makes it easier to manage and saves the operating capacity of the network.

Figure 3 shows an exemplary flow chart for a set of method steps according to the present invention and references are made to Figure 1 regarding the main components of the arrangement according to reference numbers. According to step 300 data decentralization for the loT platform is started by entering step 301. According to step 301 collecting data from a point of interest by at least one computing device 12 connected directly to at least one sensor 11 configured to monitor the point of interest takes place. The at least one sensor 11 is capable of measuring one dedicated value of the point of interest and report the measured information to the at least one computing device 12 connected directly to the said sensor 11. The at least one computing device 12 connected directly to the said sensor 11 stores the data collected by the at least one sensor 11 according to step 302.

In step 303 the at least one server of the loT platform requests data collected by the at least one sensor 11 from the at least one computing device 12 connected directly to the at least one sensor 11. According to step 304 the at least one server receives the data collected 302 by the at least one sensor 11 from the at least one computing device 12 connected directly to the at least one sensor 11 and distributes the data re- ceived for storing and analysis within at least two servers 10 according to extent required to make use of available data storing capacity of the said at least two servers.

According to step 304 the at least one server 10 distributes the data received from the at least one computing device 12 connected directly to the at least one sensor 11 for storing and analysis within itself and at least one other server according to extent required to make use of available data storing capacity of the said at least two servers. Advantageously, the at least one computing device 12 connected directly to the at least one sensor 11 may also push the data collected by the at least one sensor 11 to at least one server 10 at its own pace to make use of available data storing and data processing capacity of one or more servers 10. The data maintains its integrity when distributed by the at least one server 10.

In step 305 the data received by the at least one server from the at least one compu- ting device 12 connected directly to the at least one sensor 11 has been distributed by the receiving server 10 with at least one other server of the loT platform. The server 10 receiving the data and distributing it takes care of maintaining integrity of the data when the data is distributed for storing and analysis within the network by the server 10 receiving it. The said at least one server 10 of the two servers 10 communicate with the at least one other server to manage distribution of the received data for storing and analysis within a network formed by the said at least two servers 10 and the at least one computing device 12 connected directly to the at least one sensor 11 to extent required to make use of available data storing capacity of the network. According to step 307 at least one server monitors the data storing capacity of the network in real-time. The extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available within all the servers 10 of the network in real-time. By monitoring the data storing capacity of the network, at least one server 10 of the network follows the status of data distribution and data storing capacity of the loT platform and the loT network build on top of it. Any of the servers storing data distributed according to step 305 and executing data processing tasks, may monitor the status of data distribution and data storing capacity of the loT platform. The one or more servers 10 monitoring the status of data distribution and data storing capacity of the loT platform is able to detect if the storing capaci- ty of the network is extended. In that case, step 308 may be taken. According to step 308 at least one of server 10 may further distribute the received and stored data within the network if the data storing capacity of the network is extended and the said further distribution of the data is required to efficiently make use of available data storing capacity of the network. The data storing capacity of the net- work is defined by the full data storing capacity available within all the servers 10 of the network in real-time. According to step 308 the data distributed further to the network maintains its integrity. Also, according to step 308, the data distributed further becomes available in accordance with step 309. According to step 309 the data received by the at least one server from the at least one computing device 12 connected directly to the at least one sensor 11 has been distributed by the receiving server 10 with at least one other server of the loT platform in accordance with steps 304 and 305 is further distributed in the network and available for processing for any server in the network.

According to step 310 the data first collected by the at least one sensor 11 and stored at least one computing device 12 connected directly to the said sensor 11 in step 302 is now made available within the loT platform in a decentralized form. Decentralization of the data makes effective use of full data storing capacity available within all the servers 10 of the network in real-time. Decentralization of the data according to the previous method steps prevents severe collapses of an loT network due to data overload and lack of data processing capacity on a single server 10 of the network. Further, the data is split into smaller streams, which makes it easier to manage and saves the operating capacity of the network.

Figure 4 shows an exemplary flow chart for another set of method steps according to the present invention and references are made to Figure 1 regarding the main components of the arrangement according to reference numbers. The process described according to steps 311 -315 may, advantageously, be run in parallel with other steps described in Figure 2 (steps 200-206) and Figure 3 (steps 300-310). According to step 310, as described in connection to Figure 3, the data first collected by the at least one sensor 11 and stored at least one computing device 12 connected directly to the said sensor 11 in step 302 is now made available within the loT platform in a decentralized form. Decentralization of the data makes effective use of full data stor- ing capacity available within all the servers 10 of the network in real-time. Step 311 comprises requesting data 316-317 by at least one of the at least two servers 10 from at least one other of the two servers 10 and combining the requested data 316-317 on the server 10 or within the network for analysis of the data and for further configuration of the data requests and data processing of the at least one similar server 10. At least one server 10 of the network formed by at least two servers 10 and at least one computing device 12 connected directly to at least one sensor 11 may, advantageously, request data that is being distributed and stored around the network 316-317 according to its needs regarding, for example, type data, source of data and purpose of data. The data may also be open data available via one of the servers in the network according to 316. After the requesting server 10 receives the data it has requested from 316-317, the requesting server 10 combines the data for analysis.

Further according to step 311 analyzing the data stored with at least one of the at least two servers 10 itself or within the network comprises at least one of the follow- ing: combining elements of data available with the at least one of the at least two servers itself or on the network, making calculations on the basis of data available with the at least one of the at least two servers itself or on the network and making calculations on the basis of combined elements of data available with the at least one of the at least two servers itself or on the network.

According to step 314, on the basis of the results of the analysis according to step 311 , the server 10 analyzing the data, may send commands to another of the at least two servers 10 of the loT platform in the network to further configure the data requests and data processing of the at least one of the said two servers 10. At least one of the servers 10 of the loT platform is configured to send commands to another of the at least two servers 10 of the loT platform to further configure the data requests and data processing of the at least one of the said two servers 10, 316-317. Sending commands to another of the at least two servers 10 of the loT platform in the network to further configure the data requests and data processing of the at least one of the said two servers 10 may also comprise sending back analyzed data to its source of origin to allow the source of data to configure its own data processing. The source data may, for example, be another server 10 or a computing apparatus 12 connected directly to at least one sensor 11. In parallel to step 314, step 313 may also be taken. According to step 313, at least one of the servers 10 in the network may visualize the data available with the server 10 itself locally or for distributing the data within the network. The visualized data may be accessible locally on the server 10 visualizing it. Also, the visualized data may be accessible locally on the server 10 visualizing it by at least one other computing device 13 connected to the server 10 locally. Moreover, the visualized data may be distributed to the network 316-317.

According to step 315, the data originally collected by at least sensor 11 is available and decentralized 316-317 in the network formed by at least two servers and at least one computing apparatus connected directly to the said sensor 11. The data available in the network 316-317 may be distributed to make use of full data storing and data processing capacity of the network. The data available in the network 316-317 may also be aggregated with other in the network 317 or with open data 316 available via one the servers in the network. However, the data available in the network 316-317 maintains its integrity throughout the aggregation of the data. Also, the data available in the network 316-317 maintains its integrity throughout the aggregation and visualization of the data. Any of the steps described or illustrated herein may be implemented using executable instructions in a general-purpose or special-purpose processor and stored on a computer-readable storage medium (e.g., disk, memory, or the like) to be executed by such a processor. References to 'computer-readable storage medium' and 'computer' should be understood to encompass specialized circuits such as field-programmable gate arrays, application-specific integrated circuits (ASICs), USB flash drives, signal processing devices, and other devices

Indeed, some significant detail regarding the computer must be provided, such as regarding: (i) a "memory" and a "processor" and regarding other elements of the com- puter and processor, (ii) what specific steps of the pertinent method the processor, memory, and these other elements can perform, and (iii) how the processor and memory and other components of the computer interrelate and work together. At a minimum, the description should also include a block diagram and flow charts illustrating this interrelationship. For a process invention where, for example, a "proces- sor" carries out all the steps of the process, it is insufficient to say merely that the pro- cess can be implemented by a "processor." The processor needs to be divided into at least a few functional modules, each such module implementing one step of the process. Further, some details regarding the processor modules should be provided in the description, such as regarding what specific steps of the process the modules can perform, and how the modules interrelate and work together to perform the process.

Some advantageous embodiments of the arrangement, the method, the server and the computer program product for data distribution in an IP management system ac- cording to the invention have been described above. The invention is not limited to the solutions described above, but the inventive idea can be applied in numerous ways within the scope of the claims.

Claims

1. An arrangement for implementation of data decentralization for the Internet of Things, the loT, platform, the arrangement comprising

- at least one computing device (12) connected directly to at least one sensor (1 1 ) and the at least one computing device (12) configured to collect data from a point of interest that the at least one sensor (1 1 ) is configured to monitor (200-202, 300-302); and

- at least two servers (10) configured to run independently and communicate with each other and with the at least one computing device (12) connected directly to the at least one sensor (1 1 ) for executing data processing tasks on the loT platform (203, 303)

characterized in that at least one of the at least two servers (10) is further configured to request data collected by the at least one sensor (1 1 ) from the at least one compu- ting device (12) connected to the said sensor (1 1 ) (202-203) and distribute the received data for storing and analysis with at least one other of the at least two servers (10) according to extent required to make use of available data storing capacity of the at least two servers (204-206, 304-305, 308-310, 315).

2. The arrangement according to claim 1 characterized in that the at least one computing device (12) connected directly to the at least one sensor (1 1 ) and the at least two servers (10) are configured to form a network on the loT platform and for the network the at least two servers (10) are further configured to communicate with each other to distribute the data received by either of the said at least two servers (10) on the network to an extent required to make use of available data storing capacity of the network (204-206, 304-305, 308-310, 315).

3. The arrangement according to claim 2 characterized in that at least one of the two servers (10) is embedded to the at least one computing device (12) connected di- rectly to the at least one sensor (1 1 ).

4. The arrangement according to claim 2 characterized in that the computing device (12) connected directly to the at least one sensor (1 1 ) is configured to send data to at least one of the at least two servers (10) in its own pace and the at least one of the at least two servers (10) is configured to receive the data (201 -202, 204, 301 -302, 304).

5. The arrangement according to claim 2 characterized in that the data collected by the at least one sensor (1 1 ) and received by at least one of the at least two servers

(10) maintains its integrity when distributed for storing and analysis to the network (204, 304).

6. The arrangement according to claim 2 characterized in that at least one of the at least two servers (10) of the network is further configured to monitor the available data storing capacity of the network and the extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available with all the servers (10) of the network in real-time (307).

7. The arrangement according to claim 6 characterized in that the at least two servers (10) are configured to further distribute received and stored data to the network if the data storing capacity of the network is extended and the said further distribution of the data is required to efficiently make use of available data storing capacity of the network (308-309).

8. The arrangement according to claim 1 characterized in that at least one of the at least two servers (10) is configured request data from the at least one other server (10) and combine the requested data with other data stored with itself or on the network for analysis of the data and for further configuration of the data requests and da- ta processing of the at least one similar server (31 1 -312, 316-317).

9. The arrangement according to claim 8 characterized in that at least one of the at least two servers (10) is further configured to send commands to the at least one similar server (10) of the network to further configure the data requests and data pro- cessing of the at least one similar server (314, 316-317).

10. The arrangement according to claim 8 characterized in that at least one of the two servers (10) is further configured to analyze the data stored with itself or on the network by at least one of the following: combining elements of data available with it- self or on the network, making calculations on the basis of data available with itself or on the network and making calculations on the basis of combined elements of data available with itself or on the network (304, 312).

1 1. The arrangement according to claim 10 characterized in that at least one of the two servers (10) is further configured to visualize the data available with the said server locally on the said server itself or for distributing the data on the network (313, 316-317).

12. A server (10) for implementation of data decentralization for the Internet of Things, the loT, platform, the server comprising:

- at least one processor (102)

- at least one data communication interface (101 );

- at least one memory (103) including a computer program code (1031 ); and

- the at least one memory (103) and the computer program code (1031 ) configured to, with the at least one processor (102), cause the server (10) at least to

- run independently for executing data processing tasks of the loT platform and communicate with at least one of the following: at least one similar server (10) and at least one computing device (12) connected directly to at least one sensor (1 1 ) configured to collect data from a point of interest that the sensor (1 1 ) is con- figured to monitor for the loT platform (200-203, 300-303)

characterized in that, the server (10) is further configured to request data collected by the at least one sensor (1 1 ) from the at least one computing device (12) connected directly to the at least one sensor (1 1 ) and distribute the data received for storing and analysis with the at least one similar server (10) according to extent required to make use of available data storing capacity of the said at least two servers (204-206, 304- 305, 308-310, 315).

13. The server (10) according to claim 12 characterized in that, the server (10) is further configured to communicate with the at least one similar server (10) to distrib- ute the data received for storing and analysis on a network on the loT platform formed by the said at least two servers (10) and the at least one computing device (12) connected directly to the at least one sensor (1 1 ) to extent required to make use of available data storing capacity of the network (204-206, 304-305, 308-310, 315).

14. The server (10) according to claim 12 characterized the server (10) further configured to receive the data from the at least one computing device (12) connected directly to the at least one sensor (1 1 ) and configured to send data to the server (10) in its own pace (201 -202, 204, 301 -302, 304).

15. The server (10) according to claim 13 characterized in that the server (10) is further configured to maintain integrity of the data collected by the at least one sensor (1 1 ) and received by the server (10) when the data is distributed for storing and analysis to the network (204, 304).

16. The server (10) according to claim 13 characterized in that the server (10) is further configured to monitor the available data storing capacity of the network and the extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available with all the servers (10) of the net- work in real-time (307).

17. The server (10) according to claim 16 characterized in that the server (10) is further configured to further distribute the received and stored data to the network if the data storing capacity of the network is extended and the said further distribution of the data is required to efficiently make use of available data storing capacity of the network (308-309).

18. The server (10) according to claim 12 characterized in that the server (10) is further configured to request data from the at least one other server (10) and combine the requested data with other data stored with itself or on the network for analysis of the data and for further configuration of the data requests and data processing of the at least one similar server (31 1 -312, 316, 317).

19. The server (10) according to claim 18 characterized in that the server (10) is further configured to send commands to the at least one similar server (10) of the network to further configure the data requests and data processing of the at least one similar server (315, 316-317).

20. The server (10) according to claim 18 characterized in that the server (10) is further configured to analyze the data stored with itself or within the network by at least one of the following: combining elements of data available with itself or on the network, making calculations on the basis of data available with itself or on the network and making calculations on the basis of combined elements of data available with itself or on the network (304, 312).

21. The server (10) according to claim 20 characterized in that the server (10) is further configured to visualize the data available locally for access on the said server (10) itself or for distributing the data to the network (313, 316-317).

22. A method for implementation of data decentralization for the Internet of Things, the loT, platform, the method comprising:

- collecting data from a point of interest by at least one computing device (12) connected directly to at least one sensor (1 1 ) configured to monitor the said point of interest (200-202, 300-302); and

- executing data processing tasks for the loT platform by at least two servers (10) configured to run independently and communicate with each other and with the at least one computing device (12) connected directly to the at least one sensor (1 1 ) (203, 303)

characterized in that the method further comprises requesting data collected by the at least one sensor (1 1 ) from the at least one computing device (12) connected directly to the at least one sensor (1 1 ) on at least one of the at least two servers (10) and distributing the data received for storing and analysis on the said at least two servers (10) by at least one of the at least two servers (10) according to extent required to make use of available data storing capacity of the said at least two servers (204-206, 304-305, 308-310, 315).

23. The method according to claim 22 characterized in that, the method further comprises at least one of the two servers (10) communicating with the at least one other server (10) to distribute the received data for storing and analysis within a network on the loT platform formed by the said at least two servers (10) and the at least one computing device (12) connected directly to the at least one sensor (1 1 ) to extent required to make use of available data storing capacity of the network (204-206, 304- 305, 308-310, 315).

24. The method according to claim 23 characterized in that, the method further comprises maintaining integrity of the data received when the data is distributed for storing and analysis with the network by at least one of the said two servers (10) (204, 304).

25. The method according to claim 23 characterized in that, the method further comprises monitoring on at least one server (10) the available data storing capacity of the network and the extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available on all the servers (10) of the network in real-time (307).

26. The method according to claim 25 characterized in that, the method further comprises distributing the received and stored data further on the network by at least one of the two servers (10) if the data storing capacity of the network is extended and the said further distribution of the data is required to efficiently make use of available data storing capacity of the network (308-309).

27. The method according to claim 22 characterized in that the method further comprises requesting data by at least one of the at least two servers (10) from at least one other of the two servers (10) and combining the requested data with data available on the requesting server (10) or on the network for analysis of the data and for further configuration of the data requests and data processing of the at least one similar server (31 1 -312, 316-317).

28. The method according to claim 27 characterized in that, the method further comprises sending commands from at least one of the two servers (10) to another of the at least two servers (10) of the loT platform to further configure the data requests and data processing of the at least one of the said two servers (314, 316-317).

29. The method according to claim 27 characterized in that the method further comprises analyzing the data stored with at least one of the at least two servers (10) itself or on the network for at least one of the following: combining elements of data available with the at least one of the at least two servers (10) itself or on the network, making calculations on the basis of data available with the at least one of the at least two servers (10) itself or on the network and making calculations on the basis of com- bined elements of data available with the at least one of the at least two servers(10) itself or on the network (304, 312).

30. The method according to claim 29 characterized in that the method further comprises visualizing the data available on the at least one of the at least two servers

(10) locally on the said server (10) itself or for distributing the data to the network.

31. The method according to claim 22 characterized in that the method further comprises receiving the data from the at least one computing device (12) connected directly to the at least one sensor (1 1 ) by at least one of the at least two servers (10) wherein the at least one computing device (12) connected directly to at least one sensor (1 1 ) is configured to send data to at least one of the at least two servers (10) in its own pace (201 -202, 204, 301 -302, 304).

32. A computer program product on a non-transitory media for implementation of data decentralization for the Internet of Things, the loT, platform, the computer program product comprising:

- a computer readable code (1031 ) for collecting data from a point of interest by at least one computing device (12) connected directly to at least one sensor (1 1 ) config- ured to monitor the said point of interest (200-202, 300-302); and

- a computer readable code (1031 ) for executing data processing tasks for the loT platform by at least two servers (10) configured to run independently and communicate with each other and with the at least one computing device (12) connected directly to the at least one sensor (1 1 ) (203, 303)

characterized in that the computer program product further comprises a computer readable code for requesting data collected by the at least one sensor (1 1 ) from the at least one computing device (12) connected directly to the at least one sensor (1 1 ) on at least one of the at least two servers (10) and distributing the data received for storing and analysis on the said at least two servers (10) by at least one of the at least two servers (10) according to extent required to make use of available data storing capacity on the said at least two servers (204-206, 304-305, 308-310, 315).

33. The computer program product according to claim 32 characterized in that, the computer program product further comprises a computer readable code (1031 ) for at least one of the two servers (10) communicating with the at least one other server (10) to distribute from the at least on sensor (1 1 ) for storing and analysis with a network on the loT platform formed by the said at least two servers (10) and the at least one computing device (12) connected directly to the at least one sensor (1 1 ) on the loT platform to extent required to make use of available data storing capacity of the network (204-206, 304-305, 308-310, 315).

34. The computer program product according to claim 33 characterized in that, the computer program product further comprises a computer readable code (1031 ) for maintaining integrity of the data received when the data is distributed for storing and analysis with the network by at least one of the said at least two servers (10) (204, 304).

35. The computer program product according to claim 33 characterized in that, the computer program product further comprises a computer readable code (1031 ) for monitoring available data storing capacity of the network by at least one of the two servers (10) in real-time to make use of the available data storing capacity and the extent required to make use of available data storing capacity of the network is defined by the full data storing capacity available with the at least two servers (10) of the network in real-time (307).

36. The computer program product according to claim 35 characterized in that the computer program product further comprises a computer readable code (1031 ) for distributing the received and stored data further to the network by at least one of the two servers (10) if the data storing capacity of the network is extended and the said further distribution of the data is required to efficiently make use of available data storing capacity of the network (308-309).

37. The computer program product according to claim 32 characterized in that the computer program product further comprises a computer readable code (1031 ) for sending commands by at least one of the two servers (10) to another of the at least two servers (10) of the loT platform to further configure the data requests and data processing of the at least one of the said two servers (314, 316-317).

38. The computer program product according to claim 37 characterized in that the computer program product further comprises a computer readable code (1031 ) for requesting data by at least one of the at least two servers (10) from at least one other of the two servers (10) and combining the requested data on the server (10) requesting it with other data stored with the server (10) requesting the data or on the network for analysis of the data and for further configuration of the data requests and data processing of the at least one similar server (31 1 -312, 316-317).

39. The computer program product according to claim 37 characterized in that the computer program product further comprises a computer readable code (1031 ) for analyzing the data stored with at least one of the at least two servers (10) itself or on the network for at least one of the following: combining elements of data available with the at least one of the at least two servers (10) itself or on the network, making calculations on the basis of data available with the at least one of the at least two servers (10) itself or on the network and making calculations on the basis of combined elements of data available with the at least one of the at least two servers (10) itself or on the network (304, 312).

40. The computer program product according to claim 39 characterized in that the computer program product further comprises a computer readable code (1031 ) for visualizing the data available with the at least one of the at least two servers (10) lo- cally on the said server (10) itself or for distributing the data on the network (313, 316-317).