CN113825139B - Block chain-based fingerprint positioning method and system for position of Internet of things equipment - Google Patents

Abstract

The application discloses a method and a system for positioning position fingerprints of Internet of things equipment based on a blockchain, wherein the method comprises the steps of establishing an on-chain position fingerprint database based on the blockchain; uploading corresponding electromagnetic fingerprints by the Internet of things equipment with the positioned actual positions in the area, recording the electromagnetic fingerprints in a position fingerprint database, training a positioning model by solving nodes under an intelligent contract distribution chain, and generating an electromagnetic fingerprint positioning algorithm model after the solving nodes read the position fingerprint database; and receiving positioning requests of other nodes, generating positioning results on the chain by using an electromagnetic fingerprint positioning algorithm model according to the positioning requests, and returning the positioning results to the other nodes sending the positioning requests. The method solves the problems of lack of positioning capability and positioning tampering and forging of the Internet of things equipment, can be used for improving the positioning safety of the Internet of things equipment and provides positioning service for part of the Internet of things equipment without positioning capability.

Description

Blockchain-based location fingerprint positioning method and system for IoT devices

Technical Field

The present application relates to the field of information security technology, and in particular to a blockchain-based method and system for locating a fingerprint of an IoT device.

Background Art

The Internet of Things is a network of sensor and actuator nodes called "things". "Things" refer to devices or sensors that sense signals from the physical world and record them in digital form. Location-based services (LBS) are used in various fields including military, electronic medical, environmental monitoring, weather forecasting, early warning and rescue, and location information plays an important role in various wireless sensor network applications. The Global Positioning System (GPS) and Beidou positioning system can be used as LBS solutions, but not all IoT devices have GPS or Beidou positioning capabilities, and single GPS positioning is vulnerable to forgery and tampering attacks, so a single positioning technology is not suitable for all applications. As an auxiliary to Beidou and GPS positioning, electromagnetic fingerprint positioning technology uses the electromagnetic environment as a fingerprint, compares the electromagnetic fingerprint of the device with the fingerprint library formed in the early stage, to determine the location of the device, which can effectively solve the problem that IoT devices lack positioning modules and the positioning information is easily tampered.

In recent years, more and more Internet of Things (IoT) devices have been deployed, including mobile phones, computers, various sensors, etc. Location-based services (LBS) use location data to provide users with appropriate information, such as nearby businesses, nearby traffic conditions, etc. LBS is widely used in Internet of Vehicles, e-health, environmental monitoring, home office automation, etc. Location information plays an important role in various wireless sensor network applications. The Global Positioning System (GPS) can be used as a solution for location-based services. However, IoT vendors often do not prioritize security, which brings some security risks to positioning services in IoT. In addition, some IoT devices do not have the ability to actively locate themselves, such as cameras, sensors, etc., which do not have positioning modules.

Blockchain is a decentralized infrastructure that has gradually emerged with the increasing popularity of digital cryptocurrencies such as Bitcoin. The unique working mechanism of full network authentication enables it to have anti-fraud and anti-double payment characteristics in distributed systems and peer-to-peer (P2P) networks. After several years of development and improvement, blockchain has gradually separated from Bitcoin and become a new type of distributed and decentralized technical solution. It has independently become a hot spot for network technology innovation and has created a new data distributed storage technology. Its application has received more and more attention. As a new computing paradigm and collaboration model for establishing trust at a low cost, blockchain is changing the application scenarios and operating rules of many industries with its unique trust establishment mechanism. It is one of the indispensable technologies for building a new trust system in the future.

The application of blockchain technology provides a new way to solve the problems of complex offline data collection, privacy protection of centralized computing and storage, and difficulty in sharing positioning information in the process of electromagnetic fingerprint positioning. It combines smart contracts to realize the fingerprint positioning solution that combines on-chain computing with off-chain computing, forming an online updated distributed positioning solution. In summary, the research on IoT device positioning technology based on blockchain is of great significance.

Summary of the invention

The present application aims to solve one of the technical problems in the related art at least to some extent.

To this end, one purpose of the present application is to propose a blockchain-based IoT device location fingerprint positioning method, which can be used to improve the security of IoT device positioning and provide positioning services for some IoT devices that do not have positioning capabilities.

Another object of this application is to propose a blockchain-based IoT device location fingerprint positioning system.

To achieve the above-mentioned purpose, an embodiment of the present application proposes a blockchain-based IoT device location fingerprint positioning method, comprising the following steps: establishing an on-chain location fingerprint database based on blockchain; uploading corresponding electromagnetic fingerprints by IoT devices whose actual locations have been located in the area, and recording the electromagnetic fingerprints in the location fingerprint database; training the positioning model by the solving node under the smart contract distribution chain, and generating an electromagnetic fingerprint positioning algorithm model after the solving node reads the location fingerprint database; receiving positioning requests from other nodes, generating positioning results on the chain using the electromagnetic fingerprint positioning algorithm model according to the positioning requests, and returning the positioning results to other nodes that sent the positioning requests.

To achieve the above-mentioned purpose, on the other hand, an embodiment of the present application proposes a blockchain-based IoT device location fingerprint positioning system, including: a database establishment module, used to establish an on-chain location fingerprint database based on blockchain; a model establishment module, used to upload corresponding electromagnetic fingerprints by IoT devices whose actual positions have been located in the area, and record the electromagnetic fingerprints in the location fingerprint database, and train the positioning model by the solving node under the smart contract distribution chain, and generate an electromagnetic fingerprint positioning algorithm model after the solving node reads the location fingerprint database; a positioning module, used to receive positioning requests from other nodes, generate positioning results on the chain using the electromagnetic fingerprint positioning algorithm model according to the positioning request, and return the positioning results to other nodes that sent the positioning request.

The blockchain-based IoT device location fingerprint positioning method and system of the embodiment of the present application can be used to improve the security of IoT device positioning and provide positioning services for some IoT devices that do not have positioning capabilities, and has the following beneficial effects:

1) Introducing location fingerprint solutions as a supplement to GPS to provide positioning services for devices that cannot be located and verify the authenticity of the location, increasing the cost of location forgery and tampering;

2) Using blockchain technology, by establishing a location fingerprint database on the chain, the credibility and security of the system can be enhanced, and the complexity of offline fingerprint collection can be reduced;

3) Through the combination of smart contracts and off-chain computing, it is possible to save computing time and resource consumption while ensuring the security of positioning.

Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

FIG1 is an overall framework of a blockchain-based IoT device location fingerprint positioning system according to an embodiment of the present application;

FIG2 is a flow chart of a method for locating a location fingerprint of an IoT device based on blockchain according to an embodiment of the present application;

FIG3 is a schematic diagram of a positioning calculation solution combining on-chain and off-chain according to an embodiment of the present application;

FIG4 is a design diagram of a computing task verification process according to an embodiment of the present application;

FIG5 is a schematic diagram of the structure of a blockchain-based IoT device location fingerprint positioning system according to an embodiment of the present application.

DETAILED DESCRIPTION

The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

This application is mainly to solve the problems of lack of positioning capability and positioning tampering and forgery of IoT devices. It adopts the position electromagnetic fingerprint algorithm to obtain auxiliary positioning information as a supplement and verification of the positioning signal. It uses the characteristics of blockchain data such as immutability and traceability to design an online updated distributed positioning solution under the blockchain framework. IoT devices with positioning capabilities upload their own positioning and electromagnetic fingerprints online to form an electromagnetic fingerprint library stored on the chain, which can be used by devices without positioning capabilities to perform electromagnetic fingerprint positioning and verify the positioning results.

Considering the booming development of 5G in the future, 5G base stations will be built on a large scale, which can be used to generate a location fingerprint database. Each mobile device receives a signal from one or more APs and uses the RSS of the mobile device as a location fingerprint, which is uploaded by the mobile device with a confirmed identity to the location fingerprint database as a reference for the location certificate of the remaining mobile devices. Location fingerprinting usually has two stages: offline and online. In the offline stage, in order to collect fingerprints at various locations and establish a database, a tedious survey is required in the designated area. The designated area is divided into grids, and the RSS is measured at the grid points, which is called a training set. In the online stage, the specific location of the mobile device is unknown, and it is even unlikely to be on the grid point. Assuming that the mobile device has measured the RSS of each AP, determining the location of the mobile device is to find the fingerprint in the fingerprint database that best matches the RSS measured by the mobile device. Once the best match is found, the location of the mobile device is estimated as the corresponding position of the best matching fingerprint. Although the location fingerprint algorithm can effectively solve the positioning problem of IoT devices that cannot be located, the establishment of a location fingerprint database requires a lot of tedious collection work. Faced with the problem of establishing a location fingerprint library, the nodes in the blockchain upload fingerprints by themselves, which can quickly and easily establish a location fingerprint library. The location fingerprint uploaded by the node is stored in the blockchain, and the location of a certain point can be obtained through the location fingerprint database and the RSS of the point.

Existing blockchain technology has bottlenecks in capacity, computing performance, and scalability under public chain and single chain structures. The data storage method of chained local storage makes it difficult to expand the business in parallel; the consensus model of the synchronous state machine makes it difficult to handle calculations with low latency requirements; and it is limited by the performance limits of nodes in the network and cannot meet the performance, capacity, and latency requirements in the IoT scenario. The positioning of IoT devices has the characteristics of large amount of positioning data, many nodes, small computing and storage capabilities, and high real-time positioning requirements. In order to solve these problems, this application proposes a blockchain-based location fingerprint positioning solution suitable for IoT scenarios.

The overall architecture of the system proposed in this application is shown in Figure 1. In order to adapt to the situation where there are many IoT nodes and the power of a single node is limited, a consortium chain structure is adopted. Full nodes are IoT nodes with strong computing and storage capabilities, such as computers or servers. They store all the information of the blockchain, participate in the consensus of the blockchain, and share the off-chain computing tasks. The light nodes acted by resource-constrained nodes mainly involve the uploading and positioning of location fingerprints. In addition, in the positioning scenario, nodes are divided into nodes with and without positioning capabilities. IoT devices with positioning functions upload their own location fingerprints, forming an online location fingerprint database on the chain for devices without active positioning capabilities to locate. Considering the large amount of IoT location data and high computing latency requirements, the system adopts a combination of main and sub-chains. The IoT nodes within the range form a sub-chain, which records the location fingerprint and positioning algorithm model of the area. The sub-chain is recorded on the main chain in the form of a hash, which reduces the storage and computing pressure of the main chain and improves the positioning efficiency.

As the initial node of the blockchain, the location fingerprints of all APs have been stored in the location fingerprint database without being updated. Mobile devices join the blockchain after being verified by existing nodes in the blockchain and broadcast their location fingerprints regularly. Nodes collect the received location fingerprint broadcasts, package them to generate new blocks, and add new blocks to the chain under the action of the consensus mechanism. Nodes determine whether a block is legal according to the following rules: the block must be generated by a legal node; the location fingerprint in the verification block is correct, that is, the Euclidean distance between the coordinates obtained by inputting RSS into the location fingerprint algorithm and the coordinates recorded in the block is acceptable; the hash value recorded in the block must be the hash value of the previous block, that is, the hash value of the last block in the current blockchain. The verified block will be added to the existing blockchain. The location fingerprints recorded in all blocks of the blockchain constitute the location fingerprint database, which is authentic and cannot be tampered with.

This application solves the problem of lack of positioning capability and positioning tampering and forgery of IoT devices. It adopts the position electromagnetic fingerprint algorithm to obtain auxiliary positioning information as a supplement and verification of the positioning signal. It uses the characteristics of blockchain data such as immutability and traceability to design an online updated distributed positioning solution under the blockchain framework. IoT devices with positioning capabilities upload their own positioning and electromagnetic fingerprints online to form an electromagnetic fingerprint library stored on the chain, which can be used by devices without positioning capabilities to perform electromagnetic fingerprint positioning and verify the positioning results.

The following describes a blockchain-based IoT device location fingerprint positioning method and system proposed according to an embodiment of the present application with reference to the accompanying drawings.

First, the blockchain-based IoT device location fingerprint positioning method proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

FIG2 is a flow chart of a blockchain-based method for locating an IoT device using fingerprints according to an embodiment of the present application.

As shown in FIG2 , the blockchain-based IoT device location fingerprint positioning method includes the following steps:

In step S101, a blockchain-based location fingerprint database is established.

Optionally, in one embodiment of the present application, a blockchain-based on-chain location fingerprint database is established, including: when each mobile device receives a signal from one or more APs, the received signal strength vector of each mobile device is used as an electromagnetic fingerprint, and the electromagnetic fingerprint and location information of the mobile device are uploaded to the location fingerprint database.

In order to alleviate the situation where some IoT devices cannot be located and reduce the risk of location tampering and forgery, the location fingerprint algorithm is introduced as a supplement to GPS. In order to enhance security and persuasiveness, blockchain is used to simulate the positioning network of IoT devices, and a blockchain-based location fingerprint positioning solution is proposed. By placing the location fingerprint library and positioning calculations on the chain, the decentralized characteristics of blockchain are used to better protect data security and user privacy.

First, electromagnetic fingerprint positioning is performed. Each mobile device receives signals from one or more APs and uses the RSS of the mobile device as the electromagnetic fingerprint, which is uploaded to the electromagnetic fingerprint database by the mobile device with confirmed identity as a reference for the location certificates of the remaining mobile devices.

The electromagnetic fingerprint consists of two parts: the received signal strength vector RSS and the location information P. The vector RSS consists of the received signal strength measured by the nodes around the AP, expressed as: RSS = [ρ ₁ ,ρ ₂ ,…,ρ _n ], where ρ _i is the average received signal strength from the i-th AP. The location information is a two-tuple with a label P = (x, y).

In step S102, the IoT device with the actual position located in the area uploads the corresponding electromagnetic fingerprint and records the electromagnetic fingerprint in the location fingerprint database. The positioning model is trained by the solving node under the smart contract distribution chain, and after the solving node reads the location fingerprint database, an electromagnetic fingerprint positioning algorithm model is generated.

Optionally, in one embodiment of the present application, the verified electromagnetic fingerprint is recorded in the location fingerprint database, and the verification process further includes: calculating the distance between the node to be tested and each received signal strength vector in the fingerprint database; selecting the values of the K electromagnetic fingerprint calculation labels (x, y) closest to each other, and obtaining the positioning result by averaging the K position coordinates of the fingerprint. The calculation formula of the positioning result is:

Where (x _i ,y _i ) refers to the positions of the K points with the closest Euclidean distance to the received signal strength vector. If the error between the calculated positioning result and the result uploaded by the device is within a certain threshold, the verification is considered to be successful.

The electromagnetic fingerprint uploaded by the node is stored in the blockchain. Through the electromagnetic fingerprint library and the RSS of a certain point, the location information of this point can be obtained. The electromagnetic fingerprint method can be regarded as a classification or regression problem (the feature is the RSS vector and the label is the position). Supervised machine learning methods can train a mapping model from data to features to labels. This application uses the K-nearest neighbor (KNN) classification algorithm to determine the node position, that is, select the K fingerprint positions closest to the current RSS to estimate the current position.

In order to measure the distance between the real-time RSS sampling value and the sampling value in the location database, assuming that the number of sampling points and APs are m and n respectively, the Euclidean distance is used as the distance between two RSS samples:

Among them, ρ _ij refers to the RSS sampling value of the i reference point of the j-th AP in the electromagnetic fingerprint database, and ρ _j refers to the RSS sampling value from the j-th AP in real time.

The distance between the node to be tested and each RSS vector in the fingerprint library is calculated respectively, and the nearest K electromagnetic fingerprints are selected for the numerical calculation of the label (x, y). The average value of the K position coordinates of the fingerprint is used as the positioning result.

Where ( _xi , _yi ) refers to the positions of the K points with the closest RSS Euclidean distance. The optimal value of parameter K is estimated by 10-fold cross validation. The cross validation process is as follows:

(1) Divide the data set into ten parts, and use nine of them as training data and one as test data in turn for experiments. Each experiment will yield a corresponding accuracy rate (or error rate).

(2) The average of the correctness (or error rate) of the 10 results is used as an estimate of the accuracy of the algorithm.

(3) Repeat steps (1) and (2) and calculate the mean as an estimate of the accuracy of the algorithm.

The electromagnetic fingerprint positioning algorithm is as follows:

In step S103, the positioning request of other nodes is received, the positioning result is generated on the chain using the electromagnetic fingerprint positioning algorithm model according to the positioning request, and the positioning result is returned to the other node that sent the positioning request.

The main problem of using smart contracts to execute positioning algorithms is that they consume a lot of resources and take a long time. Since the calculation results of smart contracts must be recorded on the chain after the consensus of the nodes, a lot of redundant calculations are generated. In order to improve the calculation efficiency, the training process in the electromagnetic fingerprint positioning algorithm is placed off-chain, and the positioning calculation is placed on-chain, which ensures the security of the positioning calculation while reducing resource consumption.

The overall process of the on-chain and off-chain positioning solution is shown in Figure 3, which can be divided into three stages: the establishment of the electromagnetic fingerprint library, the off-chain positioning model training, and the electromagnetic fingerprint positioning. First, the IoT devices with positioning capabilities in the area upload their own electromagnetic fingerprints, and the electromagnetic fingerprints that have been verified for authenticity are recorded in the on-chain electromagnetic fingerprint library; the smart contract assigns the off-chain solving node to train the positioning algorithm model. After the solving node reads the on-chain electromagnetic fingerprint library, it completes the training of the positioning algorithm locally and uploads the signed training model. After the blockchain verifies the signature and model of the training model, it records the updated electromagnetic fingerprint positioning model on the chain; the node with positioning requirements initiates a positioning request to the smart contract, and the positioning algorithm is executed on the chain and the positioning result is returned to the positioning request node.

The system sets LCoin as a reward for participating in on-chain calculations, and LCoin can be used to initiate positioning requests in the system. The traceability and non-tamperability of blockchain ensure the credibility of electromagnetic fingerprints stored on the chain, and when nodes upload electromagnetic fingerprints, they must verify that the error is within 3m before they can be successfully uploaded to the chain; otherwise, nodes that upload false electromagnetic fingerprints will be fined.

In order to prevent a single node from maliciously uploading a large number of electromagnetic fingerprints at the same location in a short period of time, causing the system positioning to shift, the upload interval for each user is set, requiring each user to upload electromagnetic fingerprints no more than 5 times within ten minutes. At the same time, in order to prevent nodes from forging a large number of identities and uploading a large number of forged electromagnetic fingerprints, each time the node uploads an electromagnetic fingerprint, it needs to pay a part of the deposit. If a forged electromagnetic fingerprint is uploaded, that is, it fails to pass the verification, the deposit will be withdrawn; if the uploaded electromagnetic fingerprint is verified, the deposit will be returned to the node.

Optionally, in one embodiment of the present application, after generating the electromagnetic fingerprint positioning algorithm model, it also includes: using the hash value of the previous block as a random number to generate a computing node, and publishing it on the chain, so that the node obtains the serial number of the computing node by accessing the smart contract; the selected computing node sends the ID and timestamp to the smart contract to determine its participation in off-chain calculations; if the selected computing node does not respond within a preset time, it is deemed to have waived the computing right, and the smart contract will wait to obtain the hash of the previous block as a random number and select a new solution node, wherein after the smart contract assigns the computing node, all nodes on the alliance chain vote to the verification node, and after the verification is passed, the solution node, verification signature and electromagnetic fingerprint positioning algorithm model are recorded on the blockchain.

Specifically, the allocation and verification of off-chain computing tasks are implemented on smart contracts. In order to ensure the randomness of the selection of computing nodes, the hash value of the previous block is used as a random number to generate computing nodes and is announced on the chain. The node obtains the serial number of the computing node by accessing the smart contract. The selected node sends the ID and timestamp to the smart contract to determine its participation in off-chain computing. If the selected node does not respond within a certain waiting time, it is deemed to have given up the computing right, and the smart contract will wait to obtain the hash of the previous block as a random number and select a new solution node. To ensure the credibility of the off-chain computing results, after the smart contract assigns the computing node, all full nodes on the alliance chain will vote for the verification node. The verification node verifies the calculation results of the solution node and returns the verification results to the smart contract. The smart contract collects the verification results, and if more than a certain proportion of nodes are consistent, the verification is considered to be passed. If the verification fails, the solution node that performs this calculation will face a fine and a penalty of losing the qualification to participate in off-chain computing for a period of time, while the verification node will receive a reward. The smart contract will then reallocate the solution node for calculation. If the verification passes, the solution node, verification signature, and fingerprint positioning model will be recorded together on the blockchain.

The specific process of the program is shown in Figure 4, which mainly includes the following steps:

Step 1: Randomly generate a computing node number and publish it on the contract.

number _com =hash _pre mod num _candidate

Among them, number _com is the calculation node number, hash _pre is the hash of the previous block, and num _candidate is the number of candidate nodes.

Step 2: The computing node obtains the published result from the contract, sends its ID and signature to the smart contract and performs the calculation locally. If the solving node does not respond within a certain time _limit , it is deemed to have automatically waived the computing right. At this time, the computing node will not be fined.

Step 3: The solution node returns the hash and signature of the result to the smart contract. After the smart contract verifies the signature, the hash value of the calculation result is stored on the chain. If the calculation node times out at this step, it is also considered to have automatically abandoned the calculation, and the node will be fined.

Step 4: The smart contract organizes nodes to vote for the verification node and publishes the verification node. The verification node obtains the results published on the contract, sends its ID to the smart contract and performs calculations locally. If the verification node does not respond within a certain time _limit , it is deemed to have automatically waived the verification right.

Step 5: The verification node returns the hash and signature of the result to the smart contract. The smart contract determines whether the verification is passed based on the result returned by the node and the threshold Pass _percentage . If the verification is passed, the computing node re-uploads the result itself to the smart contract.

Step 6: The smart contract records the result after checking. Finally, the computing nodes and verification nodes receive rewards.

According to the blockchain-based IoT device location fingerprint positioning method proposed in the embodiment of the present application, the location fingerprint is used as a supplement to GPS to provide the location of devices that cannot be located, and the cost of location attacks is increased. In addition, the system uses blockchain to model the positioning environment of IoT devices. The on-chain location fingerprint library protects fingerprint security and provides channels for online updates and data sharing. By combining smart contracts and off-chain computing, the fingerprint positioning algorithm is deployed on the blockchain, and the credibility of positioning is guaranteed through distributed computing.

Next, the blockchain-based IoT device location fingerprint positioning system proposed in accordance with an embodiment of the present application is described with reference to the accompanying drawings.

FIG5 is a schematic diagram of the structure of a blockchain-based IoT device location fingerprint positioning system according to an embodiment of the present application.

As shown in FIG. 5 , the blockchain-based IoT device location fingerprint positioning system includes: a database establishment module 100 , a model establishment module 200 and a positioning module 300 .

Among them, the database establishment module 100 is used to establish an on-chain location fingerprint database based on blockchain. The model establishment module 200 is used for the Internet of Things devices that have located their actual locations in the area to upload the corresponding electromagnetic fingerprints, and record the electromagnetic fingerprints in the location fingerprint database. The smart contract allocates the solution node under the chain to train the positioning model, and after the solution node reads the location fingerprint database, it generates an electromagnetic fingerprint positioning algorithm model. The positioning module 300 is used to receive positioning requests from other nodes, generate positioning results on the chain using the electromagnetic fingerprint positioning algorithm model according to the positioning request, and return the positioning results to other nodes that sent the positioning request.

Optionally, in one embodiment of the present application, a blockchain-based on-chain location fingerprint database is established, including: when each mobile device receives a signal from one or more APs, the received signal strength vector of each mobile device is used as an electromagnetic fingerprint, and the electromagnetic fingerprint and location information of the mobile device are uploaded to the location fingerprint database.

Optionally, in one embodiment of the present application, the electromagnetic fingerprint is recorded in a location fingerprint database, further comprising: respectively calculating the distance between the node to be measured and each received signal strength vector in the fingerprint database; selecting the values of the K electromagnetic fingerprint calculation labels (x, y) closest to each other, and obtaining the positioning result by taking the average value of the K position coordinates of the fingerprint.

Optionally, in one embodiment of the present application, the calculation formula of the positioning result is:

Wherein, (x _i , y _i ) refers to the positions of the K points with the closest Euclidean distance to the received signal strength vector.

It should be noted that the aforementioned explanation of the embodiment of the blockchain-based IoT device location fingerprint positioning method is also applicable to the blockchain-based IoT device location fingerprint positioning system of this embodiment, and will not be repeated here.

According to the blockchain-based IoT device location fingerprint positioning system proposed in the embodiment of the present application, the location fingerprint is used as a supplement to GPS to provide the location of devices that cannot be located, and the cost of location attacks is increased. In addition, the system uses blockchain to model the positioning environment of IoT devices. The on-chain location fingerprint library protects fingerprint security and provides channels for online updates and data sharing. By combining smart contracts and off-chain computing, the fingerprint positioning algorithm is deployed on the blockchain, and the credibility of positioning is guaranteed through distributed computing.

In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims (4)

1. A blockchain-based method for locating the location fingerprint of an IoT device, comprising the following steps: Establish a blockchain-based on-chain location fingerprint database, including: When each mobile device receives a signal from one or more APs, the received signal strength vector of each mobile device is used as an electromagnetic fingerprint, and the electromagnetic fingerprint and location information of the mobile device are uploaded to the location fingerprint database; The IoT device with the actual location in the area uploads the corresponding electromagnetic fingerprint, and records the electromagnetic fingerprint in the location fingerprint database. The solution node under the smart contract distribution chain trains the positioning model, and after the solution node reads the location fingerprint database, an electromagnetic fingerprint positioning algorithm model is generated; The hash value of the previous block is used as a random number to generate a computing node, and is announced on the chain, so that the node obtains the serial number of the computing node by accessing the smart contract; the selected computing node sends the ID and timestamp to the smart contract to determine its participation in the off-chain computing; if the selected computing node does not respond within a preset time, it is deemed to have given up the computing right, and the smart contract will wait to obtain the hash of the previous block as a random number and select a new solution node, wherein after the smart contract assigns the computing node, all nodes on the alliance chain vote to the verification node, and after the verification is passed, the solution node, verification signature and electromagnetic fingerprint positioning algorithm model are recorded on the blockchain; and Receive positioning requests from other nodes, generate positioning results on the chain using the electromagnetic fingerprint positioning algorithm model according to the positioning requests, and return the positioning results to other nodes that sent the positioning requests. 2. A blockchain-based IoT device location fingerprint positioning system, characterized by comprising: The database establishment module is used to establish an on-chain location fingerprint database based on blockchain, specifically including: When each mobile device receives a signal from one or more APs, the received signal strength vector of each mobile device is used as an electromagnetic fingerprint, and the electromagnetic fingerprint and location information of the mobile device are uploaded to the location fingerprint database; A model building module is used for uploading corresponding electromagnetic fingerprints by IoT devices whose actual positions have been located in the area, and recording the electromagnetic fingerprints in the location fingerprint database. The location model is trained by the solution node under the smart contract distribution chain, and after the solution node reads the location fingerprint database, an electromagnetic fingerprint positioning algorithm model is generated; A recording module is used to generate a computing node using the hash value of the previous block as a random number after generating the electromagnetic fingerprint positioning algorithm model, and publish it on the chain so that the node obtains the serial number of the computing node by accessing the smart contract; the selected computing node sends the ID and timestamp to the smart contract to determine its participation in the off-chain calculation; if the selected computing node does not respond within a preset time, it is deemed to have waived the computing right, and the smart contract will wait to obtain the hash of the previous block as a random number and select a new solution node, wherein after the smart contract assigns the computing node, all nodes on the alliance chain vote to the verification node, and after the verification is passed, the solution node, verification signature and electromagnetic fingerprint positioning algorithm model are recorded on the blockchain; The positioning module is used to receive positioning requests from other nodes, generate positioning results on the chain using the electromagnetic fingerprint positioning algorithm model according to the positioning requests, and return the positioning results to other nodes that sent the positioning requests. 3. The system according to claim 2 is characterized in that recording the electromagnetic fingerprint in the location fingerprint database further includes: calculating the distance between the node to be tested and each received signal strength vector in the fingerprint database respectively; selecting the values of the K electromagnetic fingerprint calculation labels (x, y) closest to each other, and obtaining the positioning result by taking the average value of the K position coordinates of the fingerprint. 4. The system according to claim 3, characterized in that the calculation formula of the positioning result is: Wherein, (x _i , y _i ) refers to the positions of the K points with the closest Euclidean distance to the received signal strength vector.