CN117544657B - Intelligent community intelligent security method and system based on Internet of things - Google Patents

Intelligent community intelligent security method and system based on Internet of things Download PDF

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CN117544657B
CN117544657B CN202410030082.8A CN202410030082A CN117544657B CN 117544657 B CN117544657 B CN 117544657B CN 202410030082 A CN202410030082 A CN 202410030082A CN 117544657 B CN117544657 B CN 117544657B
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CN117544657A (en
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岳立军
岳卓一
赵少寒
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Hebei Wanxiang Internet Technology Co ltd
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
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    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
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    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/50—Safety; Security of things, users, data or systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/46—Cluster building
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3236—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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    • H04Q—SELECTING
    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an intelligent community intelligent security method and system based on the Internet of things, wherein the method comprises the following steps: equipment layout, community security data acquisition, time delay optimization, real-time monitoring of the Internet of things and data encryption transmission. The invention belongs to the technical field of data processing, and particularly relates to an intelligent community intelligent security method and system based on the Internet of things, which are used for calculating the minimum communication time delay between edge nodes and the calculation time delay of the edge nodes, establishing an objective function aiming at the total time delay of an edge calculation layer and optimizing the performance of the edge calculation layer; calculating the selected family number, realizing network seamless coverage, calculating the relation between new seamless network coverage and the number of effective nodes, defining an evaluation function, setting an energy threshold of a cluster head, and ensuring cluster stability; and generating parameters related to the node identity information through a hash function, calculating a detection result, sending the detection result and the authentication parameters to a receiver, calculating auxiliary parameters, and ensuring the safety in the data transmission process.

Description

Intelligent community intelligent security method and system based on Internet of things
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an intelligent community intelligent security method and system based on the Internet of things.
Background
The intelligent community security protection is a system for realizing community security management and prevention by utilizing an intelligent means, and aims to improve the safety and management efficiency of communities, reduce the occurrence of security events and provide safer and more convenient living environments for community residents. However, the existing intelligent community intelligent security has the technical problems that the response time of an intelligent security system is long, the risk of data loss is increased, and the scope of security incident is widened; the sensor node works uninterruptedly for a long time, the energy consumption is overlarge, the node fault is easy to be caused, and the maintenance cost is increased; the technical problem that the community security data is transmitted immediately and the security of the data is difficult to guarantee exists.
Disclosure of Invention
Aiming at the technical problems that the response time of an intelligent security system is long, the risk of data loss is increased, the scope of security events is widened, the community security data is transmitted to edge computing nodes by adopting a time delay optimization method, the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes are computed, an objective function is established aiming at the total time delay of an edge computing layer, the performance of the edge computing layer is optimized, and the response time of the intelligent security system is reduced; aiming at the technical problems that the existing sensor nodes continuously work for a long time, the energy consumption is too large, the node faults are easy to cause, and the maintenance cost is increased, the real-time monitoring of the Internet of things is adopted, the selected group number is calculated, the seamless network coverage is realized, the relation between the new seamless network coverage and the effective node number is calculated, an evaluation function is defined, the energy threshold of the cluster head is set, and the cluster stability is ensured; aiming at the technical problems that the community security data is transmitted immediately and the security of the data is difficult to guarantee, the data is transmitted in an encrypted mode, a public key and a private key are calculated, parameters related to node identity information are generated through a hash function, monitoring data are compiled into a file, a detection result is calculated, the encrypted detection result and authentication parameters are sent to a receiver, the receiver verifies the node identity of the sent information, auxiliary parameters are calculated, and the security of the transmission process is guaranteed.
The technical scheme adopted by the invention is as follows: the invention provides an intelligent community intelligent security method based on the Internet of things, which comprises the following steps:
step S1: the method comprises the steps of (1) equipment layout, specifically, evaluating community environment and requirements and determining equipment installation positions;
step S2: collecting community security data;
step S3: the time delay optimization is specifically to transmit community security data to edge computing nodes, calculate the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes, establish an objective function aiming at the total time delay of an edge computing layer, optimize the performance of the edge computing layer and reduce the response time of an intelligent security system;
step S4: the method comprises the steps of monitoring the Internet of things in real time, specifically calculating a selected group number, realizing network seamless coverage, calculating the relation between new seamless network coverage and effective node numbers, defining an evaluation function, setting an energy threshold of a cluster head, carrying out data transmission by adopting a principle of forwarding the cluster head, and ensuring cluster stability;
step S5: the data encryption transmission, specifically, a public key and a private key of a computing system, generates parameters related to node identity information through a hash function, compiles monitoring data into a file, calculates a detection result, sends the encrypted detection result and authentication parameters to a receiver, verifies the node identity of the sent information by the receiver, calculates auxiliary parameters, and ensures the safety in the data transmission process.
Further, in step S1, the device layout includes the steps of:
step S11: evaluating community environment and requirements, knowing community scale, structure and safety requirements, and determining the types and the quantity of security equipment to be installed;
step S12: the installation position of the security equipment is determined, the security equipment is installed in a key area according to community characteristics, and the security equipment comprises a camera, an access control system, an infrared sensor, a smoke sensor and a door magnetic sensor.
Further, in step S2, the community security data is collected, specifically, collecting community security data, where the community security data includes video monitoring data, vehicle identification data, people flow data, access control data, security inspection data and alarm data.
Further, in step S3, the delay optimization includes the following steps:
step S31: the method comprises the steps of data preprocessing, namely transmitting community security data to an edge node by a sensor node, and carrying out data preprocessing on the received community security data by the edge node, wherein the data preprocessing comprises data cleaning, format conversion, denoising and data segmentation, so that the quality of the data is improved, and the method is suitable for subsequent analysis and processing;
step S32: calculating the minimum communication time delay between edge nodes, and generating a minimum spanning tree by using a Kruskal algorithm to obtain the minimum weight, wherein the minimum weight is the minimum communication time delay zeta between the edge nodes;
step S33: the calculation delay of the edge node is calculated by the following formula:
；
；
；
where E denotes the computation delay of the edge node, m is the total number of edge devices, t is the index of the edge devices,is the computing power of the edge device, a t Is the calculation task amount of the edge equipment, x t Is the processed data volume of the edge device, i.eData amount actually processed by the device, < >>The maximum data volume which can be processed by the edge equipment is shown as X, and the total data volume which needs to be processed by the edge computing layer;
step S34: and (3) optimizing performance, namely establishing the following objective function aiming at total time delay generated by the edge calculation layer, and optimizing the performance of the edge calculation layer, wherein the following formula is adopted:
；
wherein E is 1 Representing the total delay of the edge computation layer, which includes the communication delay between edge nodes and the computation delay of the edge nodes, ζ is the minimum weight, which is the minimum communication delay between edge nodes.
Further, in step S4, the real-time monitoring of the internet of things includes the following steps:
step S41: and calculating the selected families, which are used for determining how many families are divided into the monitoring area to realize seamless network coverage, wherein the families are non-overlapping monitoring subareas, and the formula is as follows:
；
wherein K is a selected family number, eta is an under-coverage rate, which represents the proportion of nodes which are not connected to the cluster, reflects the degree of no network coverage in the monitoring area, the value range of eta is [0,1], and M is the total node number of the monitoring area;
step S42: calculating the relation between the new seamless network coverage and the number of effective nodes, wherein the number of the effective nodes in the cluster gradually decreases along with the consumption of the node energy in the network, and calculating the relation between the new seamless network coverage and the number of the effective nodes by the following formula:
；
wherein eta is max The method is characterized in that the method is undercoverage of new seamless network coverage, S is the number of effective nodes in a cluster, the number of nodes which can normally work in the current state is represented, and N is the total number of nodes in a monitoring area;
step S43: the distance from the sample point to the center of the class cluster is calculated and used for adjusting the attribution degree of the sample point, and the formula is as follows:
；
where d is the distance from the sample point to the center of the class cluster, k is the index of the sample point, x k Is the eigenvector of the kth sample point, represents the position of the sample in the multidimensional space, v represents the center vector of the kth class cluster, represents the average value of the eigenvectors of all sample points in the cluster,is the Euclidean distance between the sample point and the center of the class cluster;
step S44: an evaluation function is defined using the formula:
；
wherein J (U, V) represents the value of the evaluation function for evaluating the performance of the fuzzy clustering algorithm, U is a membership matrix representing the membership of each data point to each cluster, V is a cluster center matrix representing the center point of each cluster, m is a fuzzy factor, and the optimal range of m is [1.5,2.5 ]]C is the number of clusters, n is the number of sample points, u ik Is the membership degree of the sample point belonging to the cluster, i is the index of the cluster, and meets the following conditionsWherein->；
Step S45: setting an energy threshold, wherein the cluster mainly processes a large amount of data, and consumes a large amount of energy when processing the data each time, and in order to prevent node faults caused by overlarge energy consumption of the cluster heads, setting an energy threshold beta, and when the energy of the cluster heads is reduced to beta, informing the cluster heads that the next node of the queue becomes a new cluster head and the previous cluster head becomes a common node; when the energy of all candidate cluster heads in the queue is smaller than the threshold value, the network convergence point reclusters the rest nodes, and after the clustering is completed, the data transmission is carried out by adopting the principle of cluster head forwarding, so that the data transmission distance of the nodes is shortened, the energy consumption of the cluster head nodes is reduced, and the stability of the clusters is ensured.
Further, in step S5, the encrypted data transmission includes the steps of:
step S51: a registration phase comprising the steps of:
step S511: each node in the network obtains the necessary information from the key generation center, selects a key for the system, and calculates the public key of the system using the following formula:
；
wherein P is pub Is the public key of the system, t 1 Is a private key of the system, and p is a generation parameter of the system;
step S512: parameters related to node identity information are generated through a hash function and are used for verifying the node identity and determining the authenticity of the identity, and the following formula is used:
；
wherein q is m Is a parameter related to node identity information generated by a hash function, m is an index of a node, H 1 Is a hash function, ID m Is the identity information of the node;
step S513: calculating a private key of the node, writing the private key t into a memory of the node m, wherein the formula is as follows:
；
wherein s is the private key of the node;
step S52: the detection stage comprises the following steps:
step S521: the sender selects a random number, the random number is a non-zero positive integer, a public key point and a detection value are calculated, and in order to generate a temporary public key for encryption and verification in encrypted transmission, data integrity detection is performed by using the following formula:
；
；
wherein w is a detection value, x is a random number, which is a non-zero positive integer selected by the sender, P is a base point on the elliptic curve, G is a calculated public key point, e (P, P) is a multiplier on the elliptic curve, q 1 The key generation center calculates parameters according to the identity information of the sender;
step S522: the sender compiles the monitored data into a file named m, calculates the encrypted detection result, encrypts the monitored data, and uses the following formula:
；
where M is the encrypted detection result, i.e. the encrypted data, and where up is a logical exclusive OR operation, H 2 Is a hash function;
step S523: the authentication parameters are calculated, so that the data is not tampered in the transmission process, and the true identity of the sender is verified, wherein the following formula is used:
；
wherein R is an authentication parameter, q 2 Is a parameter s calculated by the key generation center according to the identity information of the receiver 1 A key representing the sender generated by the key generation center;
step S524: the sender sends the encrypted detection result and the authentication parameter to the receiver;
step S53: in the implementation stage, the receiver needs to verify the identity of the node sending the information, calculates auxiliary parameters for decryption operation, and the formula is as follows:
；
wherein L is an auxiliary parameter, s 2 Is the receiver key generated by the key generation center.
The intelligent community security system based on the Internet of things comprises an equipment layout module, a community security data acquisition module, a time delay optimization module, an Internet of things real-time monitoring module and a data encryption transmission module;
the equipment layout module is used for evaluating community environment and requirements and determining equipment installation positions;
the community security data acquisition module is used for acquiring community security data, wherein the community security data comprises video monitoring data, vehicle identification data, traffic data, access control data, security inspection data and alarm data;
the time delay optimizing module is used for transmitting community security data to the edge computing nodes, computing the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes, establishing an objective function aiming at the total time delay of the edge computing layer, optimizing the performance of the edge computing layer and reducing the response time of the intelligent security system;
the real-time monitoring module of the Internet of things specifically calculates the selected group number, realizes network seamless coverage, calculates the relation between new seamless network coverage and the number of effective nodes, defines an evaluation function, sets the energy threshold of a cluster head, and adopts the principle of cluster head forwarding to perform data transmission so as to ensure the cluster stability;
the data encryption transmission module is used for calculating a public key and a private key, generating parameters related to node identity information through a hash function, compiling monitoring data into a file, calculating a detection result, sending the encrypted detection result and authentication parameters to a receiver, verifying the node identity of the sent information by the receiver, calculating auxiliary parameters, and guaranteeing the safety of the transmission process.
The beneficial results obtained by adopting the scheme of the invention are as follows:
(1) Aiming at the technical problems that the response time of an intelligent security system is long, the risk of data loss is increased, the scope of security events is widened, time delay optimization is adopted, community security data are transmitted to edge computing nodes, the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes are computed, an objective function is built aiming at the total time delay of an edge computing layer, the performance of the edge computing layer is optimized, and the response time of the intelligent security system is reduced;
(2) Aiming at the technical problems that the existing sensor nodes continuously work for a long time, the energy consumption is too large, the node faults are easy to cause, and the maintenance cost is increased, the real-time monitoring of the Internet of things is adopted, the selected group number is calculated, the seamless network coverage is realized, the relation between the new seamless network coverage and the effective node number is calculated, an evaluation function is defined, the energy threshold of the cluster head is set, and the cluster stability is ensured;
(3) Aiming at the technical problems that the community security data is transmitted immediately and the security of the data is difficult to guarantee, the data is transmitted in an encrypted mode, a public key and a private key are calculated, parameters related to node identity information are generated through a hash function, monitoring data are compiled into a file, a detection result is calculated, the encrypted detection result and authentication parameters are sent to a receiver, the receiver verifies the node identity of the sent information, auxiliary parameters are calculated, and the security of the transmission process is guaranteed.
Drawings
FIG. 1 is a schematic flow chart of an intelligent community security method based on the Internet of things;
FIG. 2 is a schematic diagram of an intelligent community security system based on the Internet of things;
FIG. 3 is a flow chart of step S3;
fig. 4 is a flow chart of step S4;
fig. 5 is a flow chart of step S5.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate orientation or positional relationships based on those shown in the drawings, merely to facilitate description of the invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
First embodiment, referring to fig. 1, the intelligent community security method based on the internet of things provided by the invention comprises the following steps:
step S1: the method comprises the steps of (1) equipment layout, specifically, evaluating community environment and requirements and determining equipment installation positions;
step S2: collecting community security data;
step S3: the time delay optimization is specifically to transmit community security data to edge computing nodes, calculate the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes, establish an objective function aiming at the total time delay of an edge computing layer, optimize the performance of the edge computing layer and reduce the response time of an intelligent security system;
step S4: the method comprises the steps of monitoring the Internet of things in real time, specifically calculating a selected group number, realizing network seamless coverage, calculating the relation between new seamless network coverage and effective node numbers, defining an evaluation function, setting an energy threshold of a cluster head, carrying out data transmission by adopting a principle of forwarding the cluster head, and ensuring cluster stability;
step S5: the data encryption transmission, specifically, calculating a public key and a private key, generating parameters related to node identity information through a hash function, compiling monitoring data into a file, calculating a detection result, sending the encrypted detection result and authentication parameters to a receiver, verifying the node identity of the sent information by the receiver, calculating auxiliary parameters, and guaranteeing the safety of the transmission process.
Second embodiment, referring to fig. 1, the device layout in step S1, based on the above embodiment, includes the following steps:
step S11: evaluating community environment and requirements, knowing community scale, structure and safety requirements, and determining the types and the quantity of security equipment to be installed;
step S12: the installation position of the security equipment is determined, the security equipment is installed in a key area according to community characteristics, and the security equipment comprises a camera, an access control system, an infrared sensor, a smoke sensor and a door magnetic sensor.
Referring to fig. 1, in step S2, community security data is collected, specifically, community security data is collected, where the community security data includes video monitoring data, vehicle identification data, traffic data, access control data, security inspection data and alarm data.
Embodiment four, referring to fig. 1 and 3, based on the above embodiment, in step S3, the delay optimization includes the following steps:
step S31: the method comprises the steps of data preprocessing, namely transmitting community security data to an edge node by a sensor node, and carrying out data preprocessing on the received community security data by the edge node, wherein the data preprocessing comprises data cleaning, format conversion, denoising and data segmentation, so that the quality of the data is improved, and the method is suitable for subsequent analysis and processing;
step S32: calculating the minimum communication time delay between edge nodes, and generating a minimum spanning tree by using a Kruskal algorithm to obtain the minimum weight, wherein the minimum weight is the minimum communication time delay zeta between the edge nodes;
step S33: the calculation delay of the edge node is calculated by the following formula:
；
；
；
where E denotes the computation delay of the edge node, m is the total number of edge devices, t is the index of the edge devices,is the computing power of the edge device, a t Is the calculation task amount of the edge equipment, x t Is the processed data volume of the edge device, i.e. the data volume actually processed by the device, < >>The maximum data volume which can be processed by the edge equipment is shown as X, and the total data volume which needs to be processed by the edge computing layer;
step S34: and (3) optimizing performance, namely establishing the following objective function aiming at total time delay generated by the edge calculation layer, and optimizing the performance of the edge calculation layer, wherein the following formula is adopted:
；
wherein E is 1 Representing a total delay of an edge computation layer, the total delay of the edge computation layer including between edge nodesCommunication latency and computation latency of edge nodes, ζ is the minimum weight, which is the minimum communication latency between edge nodes.
By executing the operation, adopting time delay optimization, community security data are transmitted to the edge computing nodes, the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes are computed, an objective function is built aiming at the total time delay of the edge computing layer, the performance of the edge computing layer is optimized, the response time of an intelligent security system is reduced, and the technical problems that the response time of the intelligent security system is long, the risk of data loss is increased, and the scope of security event spreading is enlarged are solved.
In a fifth embodiment, referring to fig. 1 and fig. 4, the real-time monitoring of the internet of things in step S4 includes the following steps:
step S41: and calculating the selected families, which are used for determining how many families are divided into the monitoring area to realize seamless network coverage, wherein the families are non-overlapping monitoring subareas, and the formula is as follows:
；
wherein K is a selected family number, eta is an under-coverage rate, which represents the proportion of nodes which are not connected to the cluster, reflects the degree of no network coverage in the monitoring area, the value range of eta is [0,1], and M is the total node number of the monitoring area;
step S42: calculating the relation between the new seamless network coverage and the number of effective nodes, wherein the number of the effective nodes in the cluster gradually decreases along with the consumption of the node energy in the network, and calculating the relation between the new seamless network coverage and the number of the effective nodes by the following formula:
；
wherein eta is max Is the undercoverage of the new seamless network coverage, S is the number of effective nodes in the cluster, the number of nodes which can normally work in the current state is represented, and N is the total of the monitoring areasNode number;
step S43: the distance from the sample point to the center of the class cluster is calculated and used for adjusting the attribution degree of the sample point, and the formula is as follows:
；
where d is the distance from the sample point to the center of the class cluster, k is the index of the sample point, x k Is the eigenvector of the kth sample point, represents the position of the sample in the multidimensional space, v represents the center vector of the kth class cluster, represents the average value of the eigenvectors of all sample points in the cluster,is the Euclidean distance between the sample point and the center of the class cluster;
step S44: an evaluation function is defined using the formula:
；
wherein J (U, V) represents the value of the evaluation function for evaluating the performance of the fuzzy clustering algorithm, U is a membership matrix representing the membership of each data point to each cluster, V is a cluster center matrix representing the center point of each cluster, m is a fuzzy factor, and the optimal range of m is [1.5,2.5 ]]C is the number of clusters, n is the number of sample points, u ik Is the membership degree of the sample point belonging to the cluster, i is the index of the cluster, and meets the following conditionsWherein->；
Step S45: setting an energy threshold, wherein the cluster mainly processes a large amount of data, and consumes a large amount of energy when processing the data each time, and in order to prevent node faults caused by overlarge energy consumption of the cluster heads, setting an energy threshold beta, and when the energy of the cluster heads is reduced to beta, informing the cluster heads that the next node of the queue becomes a new cluster head and the previous cluster head becomes a common node; when the energy of all candidate cluster heads in the queue is smaller than the threshold value, the network convergence point reclusters the rest nodes, and after the clustering is completed, the data transmission is carried out by adopting the principle of cluster head forwarding, so that the data transmission distance of the nodes is shortened, the energy consumption of the cluster head nodes is reduced, and the stability of the clusters is ensured.
By executing the operation, the real-time monitoring of the Internet of things is adopted, the selected family number is calculated, the seamless network coverage is realized, the relation between the new seamless network coverage and the effective node number is calculated, an evaluation function is defined, the energy threshold of the cluster head is set, the cluster stability is ensured, and the technical problems that the sensor node continuously works for a long time, the energy consumption is overlarge, the node failure is easy to cause, and the maintenance cost is increased are solved.
Embodiment six, referring to fig. 1 and 5, the embodiment is based on the above embodiment, and in step S5, the encrypted data transmission includes the following steps:
step S51: a registration phase comprising the steps of:
step S511: each node in the network obtains the necessary information from the key generation center, selects a key for the system, and calculates the public key of the system using the following formula:
；
wherein P is pub Is the public key of the system, t 1 Is a private key of the system, and p is a generation parameter of the system;
step S512: parameters related to node identity information are generated through a hash function, and the following formula is used:
；
wherein q is m Is a parameter related to node identity information generated by a hash function, m is an index of a node, H 1 Is a hash function, ID m Is the identity information of the node;
step S513: calculating a private key of the node, writing the private key t into a memory of the node m, wherein the formula is as follows:
；
wherein s is the private key of the node;
step S52: the detection stage comprises the following steps:
step S521: the sender selects a random number, the random number is a non-zero positive integer, a public key point and a detection value are calculated, and in order to generate a temporary public key for encryption and verification in encrypted transmission, data integrity detection is performed by using the following formula:
；
；
wherein w is a detection value, x is a random number, which is a non-zero positive integer selected by the sender, P is a base point on the elliptic curve, G is a calculated public key point, e (P, P) is a multiplier on the elliptic curve, q 1 The key generation center calculates parameters according to the identity information of the sender;
step S522: the sender compiles the monitored data into a file named m, calculates the encrypted detection result, encrypts the monitored data, and uses the following formula:
；
where M is the encrypted detection result, i.e. the encrypted data, and where up is a logical exclusive OR operation, H 2 Is a hash function;
step S523: the authentication parameters are calculated, so that the data is not tampered in the transmission process, and the true identity of the sender is verified, wherein the following formula is used:
；
wherein R is an authentication parameter, q 2 Is a parameter s calculated by the key generation center according to the identity information of the receiver 1 A key representing the sender generated by the key generation center;
step S524: the sender sends the encrypted detection result and the authentication parameter to the receiver;
step S53: in the implementation stage, the receiver needs to verify the identity of the node sending the information, calculates auxiliary parameters for decryption operation, and the formula is as follows:
；
wherein L is an auxiliary parameter, s 2 Is the receiver key generated by the key generation center.
By executing the operations, the data encryption transmission is adopted, the public key and the private key are calculated, parameters related to the node identity information are generated through a hash function, monitoring data are compiled into a file, a detection result is calculated, the encrypted detection result and authentication parameters are sent to a receiver, the receiver verifies the node identity of the sent information, auxiliary parameters are calculated, the safety of the transmission process is guaranteed, and the technical problems that community security data are transmitted immediately and the safety of the data is difficult to guarantee are solved.
An embodiment seven, referring to fig. 2, based on the embodiment, the intelligent community security system based on the internet of things provided by the invention comprises an equipment layout module, a community security data acquisition module, a time delay optimization module, an internet of things real-time monitoring module and a data encryption transmission module;
the equipment layout module is used for evaluating community environment and requirements and determining equipment installation positions;
the community security data acquisition module is used for acquiring community security data, wherein the community security data comprises video monitoring data, vehicle identification data, traffic data, access control data, security inspection data and alarm data;
the time delay optimizing module is used for transmitting community security data to the edge computing nodes, computing the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes, establishing an objective function aiming at the total time delay of the edge computing layer, optimizing the performance of the edge computing layer and reducing the response time of the intelligent security system;
the real-time monitoring module of the Internet of things specifically calculates the selected group number, realizes network seamless coverage, calculates the relation between new seamless network coverage and the number of effective nodes, defines an evaluation function, sets the energy threshold of a cluster head, and adopts the principle of cluster head forwarding to perform data transmission so as to ensure the cluster stability;
the data encryption transmission module is used for calculating a public key and a private key, generating parameters related to node identity information through a hash function, compiling monitoring data into a file, calculating a detection result, sending the encrypted detection result and authentication parameters to a receiver, verifying the node identity of the sent information by the receiver, calculating auxiliary parameters, and guaranteeing the safety of the transmission process.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.

Claims (5)

1. An intelligent community intelligent security method based on the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
step S1: layout of equipment;
step S2: collecting community security data;
step S3: the time delay optimization is specifically to calculate the minimum communication time delay between edge nodes and the calculation time delay of the edge nodes, and an objective function is established aiming at the total time delay of an edge calculation layer;
step S4: the method comprises the steps of monitoring the Internet of things in real time, specifically calculating a selected group number, obtaining a relation between new seamless network coverage and effective node numbers, defining an evaluation function, and setting an energy threshold of a cluster head;
step S5: the data encryption transmission, specifically, generating parameters related to node identity information through a hash function, calculating a detection result, sending the detection result and authentication parameters to a receiver, and calculating auxiliary parameters;
in step S3, the delay optimization includes the following steps:
step S31: data preprocessing, namely performing data preprocessing on received community security data by an edge node;
step S32: calculating the minimum communication time delay between edge nodes, and generating a minimum spanning tree by using a Kruskal algorithm to obtain the minimum weight, wherein the minimum weight is the minimum communication time delay zeta between the edge nodes;
step S33: the calculation delay of the edge node is calculated by the following formula:
；
；
；
where E denotes the computation delay of the edge node, m is the total number of edge devices, t is the index of the edge devices,is the computing power of the edge device, a t Is the calculation task amount of the edge equipment, x t Is the amount of processing data of the edge device, +.>The maximum data volume which can be processed by the edge equipment is shown as X, and the total data volume which needs to be processed by the edge computing layer;
step S34: and (3) optimizing performance, namely establishing the following objective function aiming at total time delay generated by the edge calculation layer, and optimizing the performance of the edge calculation layer, wherein the following formula is adopted:
；
wherein E is 1 Representing the total delay of an edge computing layer, wherein the total delay of the edge computing layer comprises the communication delay between edge nodes and the computing delay of the edge nodes, and ζ is the minimum weight, and the minimum weight is the minimum communication delay between the edge nodes;
in step S4, the real-time monitoring of the internet of things includes the following steps:
step S41: the selected number of families is calculated using the following formula:
；
wherein K is a selected family number, eta is an under-coverage rate, which represents the proportion of nodes which are not connected to the cluster, and M is the total node number of the monitoring area;
step S42: the new seamless network coverage is calculated in relation to the number of active nodes using the following formula:
；
wherein eta is max The method is characterized in that the method is undercoverage of new seamless network coverage, S is the number of effective nodes in a cluster, the number of nodes which can normally work in the current state is represented, and N is the total number of nodes in a monitoring area;
step S43: the distance from the sample point to the center of the category cluster is calculated by the following formula:
；
where d is the distance from the sample point to the center of the class cluster, k is the index of the sample point, x k Is the eigenvector of the kth sample point, v denotes the v-th class cluster center vector,is the Euclidean distance between the sample point and the center of the class cluster;
step S44: an evaluation function is defined using the formula:
；
wherein J (U, V) represents the value of the evaluation function, U is a membership matrix representing the membership of each data point to each cluster, V is a cluster center matrix representing the center point of each cluster, m is a fuzzy factor, c is the number of clusters, n is the number of sample points, U ik The membership degree of the sample points belonging to the cluster, i is the index of the cluster;
step S45: setting an energy threshold value and setting an energy threshold value beta, wherein when the energy of the cluster head is reduced to beta, the cluster head informs the next node of the queue to become a new cluster head, and the previous cluster head becomes a common node; when the energy of all candidate cluster heads in the queue is smaller than a threshold value, the network convergence point reclusters the rest nodes, and after the clustering is completed, the data transmission is carried out by adopting a principle of cluster head forwarding;
in step S5, the encrypted data transmission includes the steps of:
step S51: a registration phase comprising the steps of:
step S511: the public key of the computing system is calculated using the following formula:
；
wherein P is pub Is the public key of the system, t 1 Is a private key of the system, and p is a generation parameter of the system;
step S512: parameters related to node identity information are generated through a hash function, and the following formula is used:
；
wherein q is m Is a parameter related to node identity information generated by a hash function, m is an index of a node, H 1 Is a hash function, ID m Is the identity information of the node;
step S513: the private key of the node is calculated using the following formula:
；
wherein s is the private key of the node;
step S52: the detection stage comprises the following steps:
step S521: the sender selects a random number, calculates a public key point and a detection value, and the following formula is used:
；
；
wherein w is a detection value, x is a random number, which is a non-zero positive integer selected by the sender, P is a base point on the elliptic curve, G is a calculated public key point, e (P, P) is a multiplier on the elliptic curve, q 1 The key generation center calculates parameters according to the identity information of the sender;
step S522: the sender compiles the monitored data into a file named m, calculates the encrypted detection result, and uses the following formula:
；
where M is the encrypted detection result, i.e. the encrypted data, and where up is a logical exclusive OR operation, H 2 Is a hash function;
step S523: the authentication parameters are calculated using the following formula:
；
wherein R is an authentication parameter, q 2 Is a parameter s calculated by the key generation center according to the identity information of the receiver 1 A key representing the sender generated by the key generation center;
step S524: the sender sends the encrypted detection result and the authentication parameter to the receiver;
step S53: in the implementation stage, the receiver needs to verify the identity of the node sending the information, and calculates auxiliary parameters by the following formula:
；
wherein L is an auxiliary parameter, s 2 Is the receiver key generated by the key generation center.
2. The intelligent community security method based on the internet of things of claim 1, wherein the intelligent community security method based on the internet of things is characterized in that: in step S1, the device layout includes the steps of:
step S11: evaluating community environments and demands;
step S12: and determining the installation position of the security equipment, and installing the security equipment in a key area according to community characteristics.
3. The intelligent community security method based on the internet of things of claim 1, wherein the intelligent community security method based on the internet of things is characterized in that: in step S2, the community security data is collected, specifically, collecting community security data, where the community security data includes video monitoring data, vehicle identification data, people flow data, access control data, security inspection data and alarm data.
4. An intelligent community intelligent security system based on the internet of things, for implementing the intelligent community intelligent security method based on the internet of things as set forth in any one of claims 1-3, wherein: the system comprises a device layout module, a community security data acquisition module, a time delay optimization module, an Internet of things real-time monitoring module and a data encryption transmission module.
5. The intelligent community security system based on the internet of things of claim 4, wherein: the equipment layout module is used for evaluating community environment and requirements and determining equipment installation positions;
the community security data acquisition module is used for acquiring community security data;
the time delay optimization module is used for transmitting community security data to edge computing nodes, computing the minimum communication time delay between the edge nodes and the computing time delay of the edge nodes, and establishing an objective function aiming at the total time delay of an edge computing layer;
the real-time monitoring module of the Internet of things specifically calculates the selected family number, realizes network seamless coverage, calculates the relation between new seamless network coverage and the number of effective nodes, defines an evaluation function and sets the energy threshold of the cluster head;
the data encryption transmission module is used for calculating a public key and a private key, generating parameters related to node identity information through a hash function, compiling monitoring data into a file, calculating a detection result, sending the encrypted detection result and authentication parameters to a receiver, verifying the node identity of the sent information by the receiver, and calculating auxiliary parameters.
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