CN102026090A - Node positioning method in IOT (Internet of things) and node - Google Patents

Abstract

The invention discloses a node positioning method and a node in the Internet of Things, belonging to the technical field of communication. The method includes: receiving a request from a queryer's positioning node; obtaining the information of the queryer; and searching for information corresponding to the queryer's information in the user precision control table stored by the node in advance according to the confidence level of the queryer. location information; providing the location information to the queryer. The node includes: a receiving module, an acquiring module, a searching module and a sending module. The present invention predefines the user precision control table on the node, realizes returning position information of different precisions for different inquirers at the node level, and can provide multiple precision position services, thus enhancing the position privacy of the node.

Description

A node positioning method and node in the Internet of Things

technical field

The invention relates to the field of communication technologies, in particular to a node positioning method and nodes in the Internet of Things.

Background technique

The Internet connects resources distributed all over the world, forming a virtual network all over the world, making the interaction between people fast and efficient, and bringing great changes to human life. The predicted next-generation Internet will consist of trillions of connected nodes. These nodes are different from traditional servers and personal computers, but smart terminal devices with storage, processing and communication capabilities, such as smart phones and smart home appliances. and tag readers etc. The addition of smart terminals makes the Internet, in addition to the interaction between people, also forms the interaction between people and things, and even the interaction between things and things.

The Internet connected by smart objects is called the Internet of things (The Internet of things). It uses radio frequency identification, infrared sensors, global positioning systems, laser scanners and other information sensing devices to connect any item A network that connects the Internet for information exchange and communication to realize intelligent identification, positioning, tracking, monitoring and management.

The Internet of Things is a special network that combines the Internet in the information age with objects in the traditional sense, making objects become intelligent subjects, and the applications of daily life become more convenient through the computing and storage resources of the Internet. High efficiency has triggered major changes in the two fields of the Internet and real-world applications. As Internet technology is more and more integrated into traditional applications, the Internet of Things is getting more and more attention, and more and more Internet of Things technologies are appearing in various commercial applications, which greatly improves the efficiency and business profits of enterprises. .

Now, the Internet of Things has been used in some fields, such as logistics companies reading barcodes on objects during transportation to update the location of objects in real time; smartphones use GPS (Global Positioning System, Global Positioning System) or mobile base stations to obtain the current location, By accessing the Internet through a mobile network, users can use various location-based LBS (Location Based Service, location-based service) services in the network. In the future, IoT applications will gradually appear in many new scenarios, such as hospitals, homes, and shopping malls. Some hospitals have deployed WIFI (Wireless Fidelity, wireless fidelity) or RFID (Radio Frequency Identification, radio frequency identification) readers in all areas to track the whereabouts of patients at any time; more and more families will use intelligent home appliances, People can check and control their own air conditioners, heating and other equipment on the website anytime and anywhere; shopping malls will also realize fast payment by reading the electronic tags attached to the products and the electronic tags on the customer's mobile phone card at close range.

In most Internet of Things applications, the problem of object positioning is involved. For example, in logistics applications, it is necessary to query the logistics transfer station where the specified object is currently located, while in patient monitoring applications, it is necessary to determine the current specific location of the patient, such as latitude and longitude, or a specific floor. and other information. Most of the existing Internet of Things applications are limited applications for a specific organization, such as product logistics tracking. At present, RFID is often used in the Internet of Things as a way for objects to communicate with readers. There are usually two types of RFID tags carried by objects: passive tags and active tags. The former has no power and cannot implement complex interaction processes, while the latter has power. , can process information and data, and interact with the reader.

In the positioning application of passive tags, the position of the nearest reader that interacts with the tag is often recorded. With this position as the center of the circle, the circle with the communication distance of the reader as the radius can be considered as the area where the object is located. If there are multiple The reader interacts with the object at the same time, resulting in a more accurate position of the object. But in this kind of application, the object often has no privacy at all, it can only show its identity in front of the reader, and the reader can establish a mapping between its identity and location.

For the positioning application of active tags, the object also indicates its identity to the reader, but since the active tag can perform data processing, through its own privacy policy management, the privacy of the object can be controlled at the tag level of the object.

However, in different application scenarios, the accuracy of the location information provided by the object should be different. For example, under normal circumstances, ordinary people do not want to provide their own location information to others, but parents want to know their children’s location information from time to time. Whereabouts, nurses in hospitals also need to know the location of patients.

After analyzing the prior art, the inventor found that the prior art has at least the following disadvantages: the object itself cannot return location information with different accuracy to different visitors, and cannot realize positioning accuracy control.

Contents of the invention

The main purpose of the present invention is to realize the precision control of object positioning, and to provide a node positioning method and nodes in the Internet of Things. Described technical scheme is as follows:

A node location method in the Internet of Things, said method comprising:

receiving a request from a queryer's location node;

Obtain information about the inquirer;

Searching for location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence;

The location information is provided to the inquirer.

Receive requests from queriers for locating nodes, including:

The node receives the request from the queryer to locate the node sent by the data reader;

Correspondingly, providing the location information to the queryer includes:

The node returns the location information to the queryer through the data reader.

Receive the request from the location node of the queryer, and obtain the information of the queryer, including:

The client receives the request from the location node of the inquirer, and looks up the user group information to which the inquirer belongs in the user group table;

Correspondingly, searching the location information corresponding to the information of the queryer in the user precision control table stored by the node in advance according to the confidence of the queryer, including:

According to the user grouping information, the client searches for the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence.

Before searching the position information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence, it also includes:

The node specifies N inquirers, and sets the confidence level of each inquirer; wherein, N is a natural number;

The node acquires its current location information, determines the blurred area and calculates the location information of the blurred area;

The node determines a corresponding location information for each inquirer according to the confidence degree in the calculated location information, and stores the corresponding relationship between each inquirer and the location information, and obtains the user precision control table.

Identify areas of ambiguity, including:

The node determines a static location area based on a landmark reader, and calculates a location ambiguity for the static location area;

The node determines a dynamic location area according to historical location information, and calculates a location ambiguity of the dynamic location area;

The node composes the static location area and the dynamic location area into a fuzzy area.

The node determines a static location area based on a landmark reader, including:

The node determines the area where the landmark reader interacting with itself is located as a static location area; or,

The node determines the area where the landmark reader that interacts with itself is located and the area where the landmark reader that is not interacting with itself is located as a static location area.

The node determines a dynamic location area according to historical location information, including:

The node searches for the historical location information of the historical moment closest to the selected moment in the stored historical location information of itself;

A dynamic location area is determined according to the historical location information at the closest historical moment.

In the calculated location information, the node determines a corresponding location information for each queryer according to the confidence degree, including:

The node judges whether the confidence of each inquirer is higher than a specified value, and if so, establishes the corresponding relationship between the inquirer and the current location information; otherwise, establishes the position of the inquirer and the fuzzy area information correspondence.

Establishing the corresponding relationship between the inquirer and the location information of the blurred area, including:

If there are multiple fuzzy areas, compare the position ambiguity of each fuzzy area with the confidence of the inquirer, determine the fuzzy area with the smallest difference, and establish the fuzzy area with the smallest difference between the inquirer and the inquirer The corresponding relationship of the location information.

The node acquires its current location information, determines the blurred area and calculates the location information of the blurred area, including:

The node acquires its current location information periodically according to a preset period or when a preset condition is met, and determines a blurred area and calculates the location information of the blurred area.

A node in the Internet of Things, said node comprising:

A receiving module, configured to receive a request from a location node of an inquirer;

An acquisition module, configured to acquire the information of the inquirer;

A search module, configured to search for the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence;

A sending module, configured to provide the location information to the inquirer.

The receiving module is specifically configured to receive a request from the queryer for locating the node sent by the data reader; correspondingly, the sending module is specifically configured to return the location information to the query through the data reader By.

The nodes also include:

A setting module is used to specify N inquirers and set the confidence level of each inquirer; wherein, N is a natural number;

A calculation module, configured to obtain the current location information of the node itself, determine the blurred area and calculate the location information of the blurred area;

The establishment and storage module is used to determine a corresponding position information for each inquirer according to the confidence level in the calculated position information, and store the corresponding relationship between each inquirer and the position information, and obtain the user precision control surface.

The calculation module includes:

An ambiguous area determining unit, configured to determine a static location area according to a landmark reader, and calculate a position ambiguity of the static location area, determine a dynamic location area according to historical location information, and calculate a position ambiguity of the dynamic location area, The static location area and the dynamic location area form a fuzzy area.

The fuzzy area determination unit includes:

A static location area determining subunit, configured to determine the area where the landmark reader interacting with the node is located as a static location area; or, the landmark reader interacting with the node and the landmark not interacting with the node The area where the reader is located is determined as the static location area.

The fuzzy area determination unit includes:

The dynamic location area determination subunit is used to search the historical location information of the closest historical moment to the selected moment in the historical location information stored by the node itself, and according to the historical location information of the closest historical moment information to determine the dynamic location area.

The establishment and storage module includes:

A correspondence relationship establishing unit, configured to determine whether the confidence of each inquirer is higher than a specified value, and if so, establish a correspondence between the inquirer and the current location information; otherwise, establish a correspondence between the inquirer and the The corresponding relationship of the position information of the fuzzy area.

The correspondence establishing unit is used to compare the position ambiguity of each fuzzy region with the confidence of the inquirer if there are multiple fuzzy regions, determine the fuzzy region with the smallest difference, and establish the relationship between the inquirer and the inquirer. The corresponding relationship of the position information of the fuzzy area with the smallest difference.

The calculation module is used to obtain the current location information of itself periodically according to a preset period or when a preset condition is satisfied, determine a blurred area and calculate the location information of the blurred area.

The beneficial effect of the technical solution provided by the embodiment of the present invention is: by pre-defining the user precision control table on the node, the location information of different precisions can be returned to different inquirers at the node level, and multiple precision location services can be provided, thereby The location privacy of nodes is enhanced.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is a schematic structural diagram of a node positioning system based on the Internet of Things provided by an embodiment of the present invention;

FIG. 2 is a flowchart of a node positioning method in the Internet of Things provided by Embodiment 1 of the present invention;

FIG. 3 is a flowchart of a node positioning method in the Internet of Things provided by Embodiment 2 of the present invention;

FIG. 4 is a flow chart of establishing a user precision control table provided by Embodiment 2 of the present invention;

FIG. 5 is a flowchart of a node positioning method in the Internet of Things provided by Embodiment 3 of the present invention;

FIG. 6 is a flow chart of the determination process of the blurred area provided by the embodiment of the present invention;

FIG. 7 is a node structure diagram in the Internet of Things provided by Embodiment 4 of the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

A node positioning method in the Internet of Things provided by this embodiment is based on a node positioning system of the Internet of Things. The node positioning system of the Internet of Things includes: a landmark network for providing position information, and several readers with position self-awareness capabilities When the node enters its communication range, the landmark reader sends its location information to the node; the data transmission network is used to transmit the communication data between the node and the Internet, and several readers that provide data forwarding function When data is received, it uses a routing algorithm to forward the data to the next hop; the identity management server is used to store and manage the identities and identities of all nodes, devices, servers and users involved in related applications User precision information, which stores the identity information of all participating role entities. Realize the function of CA (Certificate Authority, e-commerce certification center), and assign public and private keys to each entity. If the identity-based encryption method is adopted, the public key is the entity ID (Identity, identity identification), and the identity management server only needs to set the algorithm parameters. And it is enough to calculate the private key corresponding to the entity ID; the location information server is used to store and query the information of the node, which can be a cloud-like distributed cluster, or different hosts scattered in the Internet, through p2p (peer -to-peer, a virtual storage network organized in the form of peer-to-peer networking). When the location information server is different hosts scattered in the Internet, the location information server and the identity management server can be independent of each other. As long as the user does not set a location information server as a user who can obtain his own location information, the location information server cannot obtain The plaintext information of the node's location protects the location privacy of the node.

The reader nodes in the landmark network and the data transmission network can simultaneously have the landmark function and the data transmission function, and the readers are connected to each other to form one or more ad-hoc (ad hoc network) networks. The node connected to the Internet is the gateway of the ad-hoc network, and the gateway undertakes the data transfer function between the node and the Internet. The gateway node opens a standard HTTP (HyperTextTransferProtocol, hypertext transfer protocol) proxy or socks (circuit-level underlying gateway) proxy service to accept data transmission requests and responses between the external network and the ad-hoc network.

Referring to FIG. 1 , this embodiment is a schematic structural diagram of a node positioning system based on the Internet of Things. The architecture is divided into two layers. The upper layer is the application in the Internet, including location-based applications and data servers of the Internet of Things. The applications of the Internet of Things are mainly location-based service applications, Web services, and portal sites, etc.; the data server mainly stores location-related information. data, and have corresponding calculation logic modules for underlying data processing. The lower layer of the architecture is the physical environment, which is mainly composed of an ad-hoc network of readers. The reader network is composed of readers deployed by different organizations. The readers mainly include landmark readers for indicating location functions and data readers for forwarding data. Some data readers are directly connected to the Internet, and these special data readers act as ad-hoc gateways.

The Internet application mainly interacts with the user of the logical subject upward, and mainly interacts with the reader network of the physical subject downward; the above-mentioned physical environment mainly interacts with the service of the application subject upward, and mainly interacts with the device smart label of the physical subject downward.

The node involved in the embodiment of the present invention refers to the target to be positioned, which can be any node in the Internet of Things, such as a terminal carrying a smart label, goods, patients, children, etc. The smart label can receive the request from the inquirer's positioning node, and after obtaining the inquirer's information, search for information corresponding to the inquirer's information in the user precision control table stored by the node in advance according to the inquirer's confidence. location information, and finally provide the corresponding location information to the queryer.

Example 1

Referring to Fig. 2, the present embodiment provides a node positioning method in the Internet of Things, including:

Step 1: Receive the request from the location node of the queryer;

Step 2: Obtain the information of the inquirer;

Step 3: Find the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence;

Step 4: Provide location information to the inquirer.

In this embodiment, receiving a request from a queryer's positioning node includes:

The node receives the request from the queryer to locate the node sent by the data reader;

Accordingly, location information is provided to the enquirer, including:

The node returns the location information to the queryer through the data reader.

In this embodiment, the request from the location node of the inquirer is received to obtain the information of the inquirer, including:

The client receives the request from the location node of the queryer, and searches the user grouping information of the queryer in the user grouping table;

Correspondingly, in the user precision control table stored by the node in advance according to the confidence of the inquirer, the location information corresponding to the information of the inquirer is searched, including:

According to the user group information, the client searches for the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence.

In this embodiment, before the node searches for the location information corresponding to the information of the inquirer in the user precision control table stored in advance according to the inquirer's confidence, it also includes:

The node specifies N inquirers, and sets the confidence level of each inquirer; where N is a natural number;

The node obtains its current location information, determines the blurred area and calculates the location information of the blurred area;

In the calculated location information, the node determines a corresponding location information for each inquirer according to the confidence degree, and stores the corresponding relationship between each inquirer and the location information, and obtains the user precision control table.

Among them, determine the blurred area, including:

The node determines a static location area based on the landmark reader, and calculates a location ambiguity for the static location area;

The node determines the dynamic location area according to the historical location information, and calculates the location ambiguity of the dynamic location area;

The node composes the static location area and the dynamic location area into a fuzzy area.

Among them, the node determines the static location area according to the landmark reader, including:

The node determines the area where the landmark reader it interacts with is located as the static location area; or,

The node determines the area where the landmark readers that interact with itself and the area where the landmark readers that do not interact with itself are located as static location areas.

Among them, the node determines the dynamic location area according to the historical location information, including:

The node searches the historical location information of the closest historical moment to the selected moment in the stored historical location information of itself;

The dynamic location area is determined according to the historical location information at the closest historical moment.

In this embodiment, the node determines a corresponding location information for each queryer according to the confidence degree in the calculated location information, including:

The node judges whether the confidence of each inquirer is higher than the specified value, if so, establishes the corresponding relationship between the inquirer and the current location information; otherwise, establishes the corresponding relationship between the inquirer and the location information of the fuzzy area .

Wherein, establishing the corresponding relationship between the inquirer and the position information of the blurred area includes:

If there are multiple fuzzy areas, compare the position ambiguity of each fuzzy area with the confidence of the inquirer, determine the fuzzy area with the smallest difference, and establish the position of the inquirer and the fuzzy area with the smallest difference information correspondence.

Wherein, the node obtains its current location information, determines the blurred area and calculates the location information of the blurred area, including:

The node acquires its current location information periodically according to a preset cycle or when a preset condition is satisfied, and determines the blurred area and calculates the location information of the blurred area.

The method provided in this embodiment implements the return of location information with different accuracy for different queryers at the node level by pre-defining the user accuracy control table on the node, and can provide location services with multiple accuracy, thereby enhancing the location privacy of the node .

Example 2

Referring to Fig. 3, this embodiment provides a node location method in the Internet of Things, the location information of the node is cached in itself, the user directly submits a location query request to the node, and the node selectively returns different information through identity control according to the identity of the user. Accurate location information. Specifically include steps 101-104:

Step 101: Establish an ad-hoc network, the readers send Beacon information regularly, and become neighbors to each other, and establish an ad-hoc network; the landmark nodes are transferred through the data transmission node, and each other knows the position of itself and the other party in the ad-hoc network, In order to understand the location and radius of the entire ad-hoc network, as well as the ad-hoc gateways connected to the Internet.

For example, after reader A is deployed, it starts listening; when A receives the Beacon message from reader B, it parses B's message, and the required items include B's number, B's certificate, and the CA that issued B's certificate. There are also options such as B’s subordinate organization, etc.; node A verifies to the CA that issued the node B’s certificate. If the identity of node B is illegal, the CA returns failure, and node A continues to listen; otherwise, node A adds Node B's information, Node A and Node B become neighbors to each other. In this way, an ad-hoc network is established between the various nodes.

Step 102: The node pre-establishes a user accuracy control table, which stores the identity of the queryer and the corresponding location information. Referring to Fig. 4, this step 102 specifically includes steps 201-207:

Step 201: The owner sets the default location accuracy on the node.

Step 202: The owner judges whether a new control item needs to be created on the node, if yes, execute step 203, otherwise, end.

Step 203: The owner selects the queryer of the node.

Step 204: The owner determines the location accuracy of the inquirer.

Step 205: The owner stores the identity and location accuracy of the inquirer into the user accuracy control table as a 2-tuple, and then returns to step 202 to continue building the user accuracy control table.

The user precision control table has three columns, the first column is the serial number, the second column is the identity of the queryer, and the third column is the precision. The accuracy item is the location information corresponding to each queryer, and the node specifies N queryers, and sets the confidence level of each queryer, where N is a natural number. Set the specified value of the inquirer's confidence level to 1. If the inquirer's confidence level is greater than or equal to 1, the current location information of the node can be returned; if the inquirer's confidence level is less than 1, the specific inquirer's confidence level can , returns the location information of the fuzzy area corresponding to the queryer, such as returning the current ad-hoc network area, or returning a circle where the current node is located. Among them, the accuracy can be precise position information, or an action, such as switching to a line with a certain accuracy number. This can be used as a shortcut to facilitate multiple similar operations, just define the last line to jump to.

Table 1

* means match all, Table 1 is a specific user precision control table, return the precise location information of the current node to the queryer Bob, only return the current ad-hoc network location to Mike, and limit the node location to the ad-hoc network In the area, and to the Boss, it returns an area with a random offset position and radius to blur the position, and finally returns an unknown area to all other inquirers, and refuses to respond to its position information.

Further, in this step, the node may also obtain its current location information periodically according to a preset period or when a preset condition is met, and determine the blurred area and calculate the location information of the blurred area. Wherein, the preset period can be changed as required. For example, if the preset period is 2 minutes, the node obtains its own information every 2 minutes, determines the blurred area at this time, and calculates the location information of the blurred area. There are many preset conditions, such as when the node enters the reader monitoring area and so on.

Step 103: The node itself obtains the location information. When the node enters the communication range of the landmark reader node, the landmark reader node sends the node the location and communication range of the landmark node, the identification of the ad-hoc network, the central location and radius, and the ad-hoc network. -hoc network gateway and other information, the node records the information in the local cache.

For example, after the deployment of the landmark reader C, it starts to monitor; when it detects that a node D enters the communication range of C, C judges its own security policy, and if it is safe, it provides landmark data to D; when D receives After receiving the information of C, it decides whether to save the data sent by C according to its own strategy. If it is safe, save the landmark data to the local cache.

Step 104: The node autonomously responds to the location. When the node receives the user location query request, it obtains the corresponding location information from the user precision control table according to the identity of the queryer and returns it. Referring to Fig. 4, this step 104 specifically includes:

User U logs in to the client;

The client verifies the identity of user U;

The identity of user U has not been verified, and the query ends;

After verification, the private key d corresponding to U is obtained, and user U submits a query node request to the client;

The client queries the ad-hoc network information of the node and the data reader it belongs to from the database, and returns it to the user U;

User U forwards the query request in the ad-hoc network to which the node belongs;

The data reader to which the node belongs receives the node query request information;

The data reader sends a positioning request to the corresponding node through the ad-hoc network;

The node obtains the identity of the queryer through the ad-hoc network;

The node looks up the record of the queryer in its pre-stored user precision control table;

Reject if a record for that querier does not exist;

If the record of the queryer exists, find the accuracy item of the relevant record;

If the item is a jump item, jump to the corresponding precision item to find the corresponding position information;

If the item is not a jump item, the node finds the location L corresponding to the queryer, encrypts the information using an identity-based method, obtains the location ciphertext C (para, ID, L), and returns it to the data transmission reader;

The data transmission reader returns the location ciphertext to the querying user U;

User U uses the private key d to decrypt and obtain the location information Loc.

para is the parameter based on the identity encryption algorithm, and ID is the identity of the queryer. In order to protect location privacy, the default precision in the user precision control table is generally an unknown location.

In this example, an encryption algorithm is used to protect the location information of the node, preventing it from being obtained by an unauthorized third party during transmission or in the database of the location management server. The specific encryption algorithm is an identity-based encryption method, and the public key used for encryption of this algorithm is the identity ID of the receiver. The recipient obtains the private key corresponding to its Id from the CA, and then decrypts the ciphertext. Using the identity-based encryption method can save the public key and certificate for querying users and groups in the node, which greatly saves the cache of the device.

The method provided in this embodiment realizes the return of location information with different accuracy for different inquirers at the node level by pre-defining the user accuracy control table on the node, and can provide location services with multiple accuracy, thereby enhancing the location privacy of the node .

Example 3

Referring to Fig. 5, this embodiment provides a node positioning method in the Internet of Things. The owner of the node defines the rules of users who can obtain the node location information on the identity management server in advance, and the node regularly sends different accuracy information to the location information server. For the ciphertext of location information, the inquiring user first obtains his identity attribute and key from the identity management server, then obtains the encrypted node location information from the query location server, and finally uses the key to decrypt to obtain the location of the node. The method specifically includes steps 301-305:

Step 301: Establish an ad-hoc network, the specific steps are the same as the method for establishing the ad-hoc network in Embodiment 2.

Step 302: Preset a user group table and a user accuracy control table. The node owner logs in to the identity management server in advance, sets user groups on the server, and classifies N inquirers with the same confidence into one query group, which means returning the same location result to the inquirers of the same query group; The defined query group sets a location precision, which is stored in the local cache of the node.

Table 2

serial number node Queryer query group 1 O1 bob T1 2 O1 Mike T1 3 O1 Boss T2 ... ... ... n O1 * T100

* means match all. Table 2 is a specific user grouping table on a server. For node O1, user Bob and Mike have the same relationship, both belong to group T1, and Boss belongs to group T2.

The accuracy item is the location information corresponding to each group of inquirers. The node specifies M query groups and sets the confidence level of each inquirer, where M is a natural number. Set the specified value of the confidence level of the query group to 1. If the confidence level of the query group is greater than or equal to 1, the current location information of the node can be returned; if the confidence level of the query group is less than 1, the specific query group’s confidence level can be , returns the location information of the fuzzy area corresponding to the query group, such as returning the current ad-hoc network area, or returning a circle where the current node is located.

table 3

Table 3 is a specific user group permission table in a node cache. Table 3 has three columns, the first column is the number, the second column is the identity group of the queryer, and the third column is the precision. Provide accurate location to user group T1, provide current ad-hoc area location information to user group T2, and return area information with the result of a function expression F to user T3.

Further, in this step, the node may also obtain its current location information periodically according to a preset period or when a preset condition is met, and determine the blurred area and calculate the location information of the blurred area. Wherein, the preset period can be changed as required. For example, if the preset period is 2 minutes, the node obtains its own information every 2 minutes, determines the blurred area at this time, and calculates the location information of the blurred area. There are many preset conditions, such as when the node enters the reader monitoring area and so on.

Step 303: The node itself obtains the location information, and the specific steps are the same as the method for the node itself to obtain the location information in Embodiment 1.

Step 304: Upload location information. After the node detects that there is a data transmission reader, it generates n pieces of location information (recorded as L1, L2, ..., Ln) according to the n user groups in its own cache, and encrypts them with the public key PubKey _Gi of n groups respectively, Form n location ciphertext messages C(L _O , PubKey _Gi ), and transmit them to the location information server through the data transmission ad-hoc network; the location information server saves the aforementioned n messages.

Step 305: Server location response. There are two specific application scenarios, which are described below.

The first application scenario is the scenario where the location information server and the identity management server are independent devices, and the location information server is used as a client. This step 205 specifically includes:

User U logs in to the application client;

The client verifies the identity of user U;

The identity of user U has not been verified, and the query ends;

After verification, user U submits a location request to query node O to the client;

The client queries the identity management server for the group T corresponding to the user, and obtains the group number ID _T and its private key d _T ;

The client queries the information of node O from its database, and finds the user precision control table of node O;

The client obtains O's location ciphertext C(L _O , ID _i ) according to ID _T and O information, and provides it to the user;

User U decrypts with his own private key d _T to obtain location information L _O .

In the second application scenario, the location information server and the identity management server are a scenario of a device, and this device is used as a client. This step 205 specifically includes:

User U logs in to the application client;

The client verifies the identity of user U;

The identity of user U has not been verified, and the query ends;

Through verification, user U obtains his own private key d _U , and user U submits a location request to query node O to the client;

The client queries the group T corresponding to the user from the database, and obtains the group ID _T and its private key d _T ;

The client queries the information of node O from the database and finds the user precision control table of node O;

The client obtains O's location ciphertext C(L _O , ID _i ) according to ID _T and O's user precision control table;

The client uses ID _U to encrypt the ciphertext C(L _O , ID _i ) and d _T again, obtains C(C(L _O , ID _i ), d _T ), and provides it to the user U;

User U decrypts with his own private key d _U to obtain C(L _O , ID _i ) and d _T , and then decrypts with d _T to obtain location information L _O .

In this embodiment, an encryption algorithm is used to protect the location information of the node, preventing it from being obtained by an unauthorized third party during transmission or in the database of the location management server. The specific encryption algorithm is an identity-based encryption method, and the public key used for encryption of this algorithm is the identity ID of the receiver. The recipient obtains the private key corresponding to its Id from the CA, and then decrypts the ciphertext. Using the identity-based encryption method can save the public key and certificate for querying users and groups in the node, which greatly saves the cache of the device.

The method provided in this embodiment realizes the return of location information with different accuracy for different inquirers at the node level by pre-defining the user accuracy control table on the node, and can provide location services with multiple accuracy, thereby enhancing the location privacy of the node ; Predefine the user group user table on the identity management server, and classify the inquirers with the same confidence into a query group, which saves storage space and shortens the positioning time.

The determination process of the fuzzy area involved in the above-mentioned embodiments 1, 2, and 3 is specifically as follows:

The location information in the embodiment of the present invention can be a quaternion (location L, radius R, time stamp T, confidence B), indicating that the center of the possible area where the node is located at T time is L, the radius is R, and the location area The reliability is B. L and R determine the spatial uncertainty of the node position, T determines the time uncertainty of the node, and B determines the uncertainty of the node position information composed of the above three elements.

When the position of the node is accurate, L is the current position of the node, R is 0, the timestamp is the current time, and the confidence level is 1.

Specifically, the node's location ambiguity includes the uncertainty of the node's location trajectory and the uncertainty of the node's current location area. The uncertainty of the node's position trajectory means that the position of the node has time continuity. Only the historical position of the node is provided to the queryer, not the current position. For the queryer, the node position is accurate, but not real-time, just At a certain moment in the past, the location information quadruple can be (L, 0, T', 1); the uncertainty of the current location area of the node refers to the location of the node at the current moment provided by the queryer, but the location is not accurate, But for one area, the location information quadruple can be (L, r, T, b). In the latter case, if the position of the node must indeed fall in the circle area with L as the center and r as the radius, then the confidence b=1, otherwise the node needs to determine the set of areas {Mi} it may be in, and then These regions are assigned a certain probability bi.

A node defines multiple fuzzy areas, and indicates that it is in these areas with a certain probability, thereby hiding its position. Therefore, it is first necessary to define the aforementioned n regions, and the centers of the regions are some reader nodes. See Figure 6, the specific steps are as follows:

The node pre-sets the time uncertainty parameter a and the position uncertainty parameter c of the queryer Bob between 0 and 1 (including 0 and 1), because the confidence b can be determined by the time uncertainty parameter a and position uncertainty The degree parameter c is calculated according to a preset algorithm. For example, the algorithm is b=a×c. According to the calculation, the confidence degree b of the queryer Bob can be preset. The present invention does not limit the specific algorithm.

Step 401: Determine the static location area of the node according to the landmark reader. The node takes the area where the landmark reader that interacts with itself is located as a static location area, where the landmark reader that can interact with it can be one landmark reader or multiple landmark readers; further, the node will not interact with itself The area where the interactive landmark reader is located is also used as a static location area, wherein the landmark reader that does not directly interact with it may be one landmark reader or multiple landmark readers. Interaction means that two parties communicate with each other, including receiving signals and sending signals. However, since a node can only know the nearby landmark readers that directly interact with it, the landmark reader needs to maintain the information of neighbor landmark readers, and send its neighbor landmark readers to the node when interacting with the node. Therefore, a node can know a list of landmark nodes in its vicinity {Mi}={(Di, ri)}, where ri is the communication radius of the node at Di.

Step 402: Calculate the location ambiguity of the static location area. A node can hide itself by combining the conditions of other nodes around it. If the node density in the nearby area is high, it can be considered that the node has a high chance of appearing in this area, so the node needs to obtain the node density in the nearby area. This information is also obtained from the landmark reader when the node interacts with the landmark reader {Gi}, Then the number of nodes in n regions is Ni=Gi*ri ² .

其中N_o为节点O所在区域Do的节点数量，N_j为节点O附近区域的节点数量，其他区域的位置模糊度P_i为

b是位置置信度，δ是位置置信度对节点真实位置所在区域模糊度的影响因子，当b为0时，δ也为0，该区域的模糊度为0；反之，当b＝1时，δ为无穷大，该区域的模糊度为1。具体的实现可以根据需要构造算法，例如δ＝-log_w(1-b)就是满足上述要求的一个函数，w为微调系数，不同的节点有不同的w，防止被攻击者猜出。According to the existing knowledge, the position ambiguity P _o of the area Do where the node O is located can be calculated as

Among them, N _o is the number of nodes in the area Do where node O is located, N _j is the number of nodes in the area near node O, and the position ambiguity P _i of other areas is

b is the position confidence, and δ is the influence factor of the position confidence on the ambiguity of the area where the node's true position is located. When b is 0, δ is also 0, and the ambiguity of this area is 0; otherwise, when b=1, δ is infinite, and the ambiguity of this region is 1. The specific implementation can construct an algorithm according to the needs, for example, δ=-log _w (1-b) is a function that meets the above requirements, w is the fine-tuning coefficient, and different nodes have different w to prevent the attacker from guessing.

Step 403: The node searches for the historical location information of the historical moment closest to the selected moment in the stored historical location information of itself. Obtain the precise position D of the node some time ago. D=U(a) obtains the position of the node at time t, and U is an uncertain function of time.

Among them, the relationship between the parameter a of the time uncertainty and the selected time t satisfies the time decay function:

其中tn为定位时刻，α和β为预先设定的值，α为大于1的数，β为大于0的数。由于节点要提供历史位置记录，所以节点在每次获得地标信息时，会存入一个历史位置列表{Li}，在历史位置信息列表{Li}中找到时刻t1，使得t1与t最接近，从而得到t1对应的位置信息L。例如，定位时刻为下午三点，则通过时间不确定度的参数得到选定时刻为下午两点二十，在历史时刻表中最接近下午两点二十的时间为两点半，则获得两点半对应的历史位置信息。a=α ^-βΔt = α ^β(t-tn) , then

Where tn is the positioning time, α and β are preset values, α is a number greater than 1, and β is a number greater than 0. Since the node needs to provide historical location records, each time the node obtains landmark information, it will store a historical location list {Li}, and find time t1 in the historical location information list {Li}, so that t1 is the closest to t, thus Get the location information L corresponding to t1. For example, if the positioning time is 3:00 p.m., the selected time is 2:20 p.m. according to the parameter of time uncertainty, and the time closest to 2:20 p.m. in the historical timetable is 2:30 p.m., then two The historical location information corresponding to half a point.

Step 404: Determine the dynamic location area and calculate the location ambiguity of the dynamic location area according to the historical location information at the closest historical moment. F=V(U(a), c) obtains the possible location area of the node at time t, and V is a location uncertainty function.

Step 405: Construct node fuzzy regions. The static location area and the dynamic location area are composed of the fuzzy area {Mi}, and {pi} is used to represent the position ambiguity of the fuzzy area, so that the fuzzy area and the corresponding position ambiguity can be expressed as {(Mi, pi)}, where Select the pi closest to the confidence b, and its corresponding Mi is the area selected by the node, and finally return to this area. For example, the confidence level is 0.4, and the position ambiguity closest to 0.4 is 0.42, then return the corresponding position area when the position ambiguity is 0.42 to Bob.

Example 4

Referring to Figure 7, this embodiment provides a node in the Internet of Things, including:

Step 11: a receiving module, configured to receive a request from a queryer's positioning node;

Step 12: Obtaining a module for obtaining the information of the inquirer;

Step 13: a search module, used to search for the position information corresponding to the information of the inquirer in the user precision control table stored in advance by the node according to the inquirer's confidence;

Step 14: a sending module, configured to provide the location information to the inquirer.

In this embodiment, the receiving module is specifically configured to receive the request from the queryer's positioning node sent by the data reader; correspondingly, the sending module is specifically configured to return the location information to the queryer through the data reader.

In this embodiment, the nodes also include:

A setting module is used to specify N inquirers and set the confidence level of each inquirer; wherein, N is a natural number;

A calculation module, configured to calculate the current position information of the node itself, determine the fuzzy area and calculate the position information of the fuzzy area;

The establishment and storage module is used to determine a corresponding position information for each inquirer according to the confidence degree in the calculated position information, and store the corresponding relationship between each inquirer and the position information to obtain the user precision control table.

Among them, the calculation module includes:

The fuzzy area determination unit is used to determine the static location area according to the landmark reader, and calculate the location ambiguity of the static location area, determine the dynamic location area according to the historical location information, and calculate the location ambiguity of the dynamic location area, and combine the static location area and Regions of dynamic locations make up the ambiguity region.

Wherein, the fuzzy area determination unit includes:

The static location area determination subunit is used to determine the area where the landmark reader interacting with the node is located as the static location area; or, the area where the landmark reader communicating with the node is located, and the area where the landmark reader that does not interact with the node is located The area identified as the static location area.

Wherein, the fuzzy area determination unit includes:

The dynamic location area determination subunit is used to find the historical location information of the closest historical moment to the selected time in the historical location information stored by the node itself, and determine the dynamic location according to the historical location information of the closest historical moment. location area.

In this embodiment, the establishment and storage module includes:

Correspondence establishment unit, used to judge whether the confidence of each inquirer is higher than a specified value, if so, establish the corresponding relationship between the inquirer and the current location information; otherwise, establish the position of the inquirer and the fuzzy area information correspondence.

Wherein, the corresponding relationship establishing unit is used to compare the position ambiguity of each fuzzy area with the confidence of the inquirer if there are multiple fuzzy areas, determine the fuzzy area with the smallest difference, and establish the relationship between the inquirer and the inquirer. The corresponding relationship of the position information of the fuzzy area with the smallest difference.

In this embodiment, the calculation module is used to obtain the current location information of itself periodically according to a preset period or when a preset condition is satisfied, determine the blurred area and calculate the location information of the blurred area.

The node provided in this embodiment realizes returning location information with different precisions for different inquirers at the node level by predefining the user precision control table, and can provide location services with multiple precisions, thereby enhancing the location privacy of the node.

Finally, it should be noted that those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer programs, and the programs can be stored in a computer-readable In the storage medium, when the program is executed, it may include the processes of the embodiments of the above-mentioned methods. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM) and the like.

Each functional unit in the embodiment of the present invention may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like. Each of the above devices or systems may execute the methods in the corresponding method embodiments.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.

Claims (19)

1. a node location method in the Internet of Things, is characterized in that, described method comprises: receiving a request from a queryer's location node; Obtain information about the inquirer; Searching for location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence; The location information is provided to the inquirer. 2. The method according to claim 1, wherein receiving the request from the location node of the inquirer comprises: The node receives the request from the queryer to locate the node sent by the data reader; Correspondingly, providing the location information to the queryer includes: The node returns the location information to the queryer through the data reader. 3. The method according to claim 1, characterized in that receiving the request from the location node of the inquirer to obtain the information of the inquirer comprises: The client receives the request from the location node of the inquirer, and looks up the user group information to which the inquirer belongs in the user group table; Correspondingly, searching the location information corresponding to the information of the queryer in the user precision control table stored by the node in advance according to the confidence of the queryer, including: According to the user grouping information, the client searches for the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence. 4. The method according to claim 1, wherein, before searching the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence, further comprising: The node specifies N inquirers, and sets the confidence level of each inquirer; wherein, N is a natural number; The node acquires its current location information, determines the blurred area and calculates the location information of the blurred area; The node determines a corresponding location information for each inquirer according to the confidence degree in the calculated location information, and stores the corresponding relationship between each inquirer and the location information, and obtains the user precision control table. 5. The method according to claim 4, wherein determining the blurred area comprises: The node determines a static location area based on a landmark reader, and calculates a location ambiguity for the static location area; The node determines a dynamic location area according to historical location information, and calculates a location ambiguity of the dynamic location area; The node composes the static location area and the dynamic location area into a fuzzy area. 6. The method according to claim 5, wherein said node determines a static location area according to a landmark reader, comprising: The node determines the area where the landmark reader interacting with itself is located as a static location area; or, The node determines the area where the landmark reader that interacts with itself is located and the area where the landmark reader that is not interacting with itself is located as a static location area. 7. The method according to claim 5, wherein the node determines a dynamic location area according to historical location information, comprising: The node searches for the historical location information of the historical moment closest to the selected moment in the stored historical location information of itself; A dynamic location area is determined according to the historical location information at the closest historical moment. 8. The method according to claim 5, wherein, in the calculated position information, the node determines a corresponding position information for each queryer according to the degree of confidence, including: The node judges whether the confidence of each inquirer is higher than a specified value, and if so, establishes the corresponding relationship between the inquirer and the current location information; otherwise, establishes the position of the inquirer and the fuzzy area information correspondence. 9. The method according to claim 8, wherein establishing the corresponding relationship between the inquirer and the location information of the blurred area comprises: If there are multiple fuzzy areas, compare the position ambiguity of each fuzzy area with the confidence of the inquirer, determine the fuzzy area with the smallest difference, and establish the fuzzy area with the smallest difference between the inquirer and the inquirer The corresponding relationship of the location information. 10. The method according to any one of claims 4 to 9, wherein the node obtains its current location information, determines the blurred area and calculates the location information of the blurred area, comprising: The node acquires its current location information periodically according to a preset period or when a preset condition is met, and determines a blurred area and calculates the location information of the blurred area. 11. A node in the Internet of Things, characterized in that the node comprises: A receiving module, configured to receive a request from a location node of an inquirer; An acquisition module, configured to acquire the information of the inquirer; A search module, configured to search for the location information corresponding to the information of the inquirer in the user precision control table stored by the node in advance according to the inquirer's confidence; A sending module, configured to provide the location information to the inquirer. 12. The node according to claim 11, wherein the receiving module is specifically configured to receive a request from the queryer for locating the node sent by the data reader; correspondingly, the sending module is specifically configured to The location information is returned to the inquirer by the data reader. 13. The node according to claim 11, wherein the node further comprises: A setting module is used to specify N inquirers and set the confidence level of each inquirer; wherein, N is a natural number; A calculation module, configured to obtain the current location information of the node itself, determine the blurred area and calculate the location information of the blurred area; The establishment and storage module is used to determine a corresponding position information for each inquirer according to the confidence level in the calculated position information, and store the corresponding relationship between each inquirer and the position information, and obtain the user precision control surface. 14. The node according to claim 13, wherein the computing module comprises: An ambiguous area determining unit, configured to determine a static location area according to a landmark reader, and calculate a position ambiguity of the static location area, determine a dynamic location area according to historical location information, and calculate a position ambiguity of the dynamic location area, The static location area and the dynamic location area form a fuzzy area. 15. The node according to claim 14, wherein the unit for determining the blurred area comprises: A static location area determining subunit, configured to determine the area where the landmark reader interacting with the node is located as a static location area; or, the landmark reader interacting with the node and the landmark not interacting with the node The area where the reader is located is determined as the static location area. 16. The node according to claim 14, wherein the unit for determining the blurred region comprises: The dynamic location area determination subunit is used to search the historical location information of the closest historical moment to the selected moment in the historical location information stored by the node itself, and according to the historical location information of the closest historical moment information to determine the dynamic location area. 17. The node according to claim 14, wherein the establishment and storage module comprises: A correspondence relationship establishing unit, configured to determine whether the confidence of each inquirer is higher than a specified value, and if so, establish a correspondence between the inquirer and the current location information; otherwise, establish a correspondence between the inquirer and the The corresponding relationship of the position information of the fuzzy area. 18. The node according to claim 17, wherein the correspondence relationship establishing unit is configured to use the position ambiguity of each fuzzy region and the confidence degree of the queryer as a function of if there are multiple fuzzy regions. By comparison, the fuzzy area with the smallest difference is determined, and the corresponding relationship between the queryer and the position information of the fuzzy area with the smallest difference is established. 19. The node according to any one of claims 13 to 18, wherein the calculation module is used to obtain the current location information of itself periodically according to a preset cycle or when a preset condition is established, and determining the blurred area and calculating the location information of the blurred area.

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