Shared orientation optimized iiot information perception method

Abstract

The application provides a shared orientation-optimized Internet of Things information sensing method, which comprises the following steps: receiving request information in each period, wherein the request information comprises request information sensed within a sensing range and / or request information shared by other nodes in the Internet of Things; sharing the received request information to other nodes of the Internet of Things in the optimal orientation; if the received request information does not reach a sleep condition, determining the optimal orientation of the next period after each period according to the received request information, and repeating the receiving of the request information until the received request information reaches the sleep condition, and entering a sleep state. The application can improve the information transmission efficiency of the Internet of Things and reduce energy consumption.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to an IoT information sensing method with shared orientation optimization. Background Technology

[0002] The Internet of Things (IoT) refers to a network that connects any object to the internet through information sensing devices and according to agreed-upon protocols, enabling information exchange and communication to achieve intelligent identification, location, tracking, monitoring, and management. In simple terms, the IoT is the "internet of interconnected things," encompassing two meanings: First, the IoT is an extension and expansion of the internet, with its core and foundation still being the internet; second, the user end of the IoT includes not only people but also objects, enabling the exchange and communication of information between people and objects, as well as between objects themselves. As a highly integrated and comprehensive application of next-generation information technology, the IoT is characterized by strong penetration, significant driving force, and good overall benefits. However, currently, the low information transmission efficiency and high energy consumption of the IoT greatly limit its development. Summary of the Invention

[0003] One object of this invention is to provide a shared orientation-optimized IoT information sensing method, improving the information transmission efficiency of the IoT and reducing energy consumption. Another object of this invention is to provide an IoT node. A further object of this invention is to provide an IoT. A still other object of this invention is to provide a computer device. A further object of this invention is to provide a readable medium.

[0004] To achieve the above objectives, this invention discloses a shared orientation-optimized Internet of Things (IoT) information sensing method, comprising:

[0005] Receive request information in each cycle, including request information perceived within the sensing range and / or request information shared by other nodes in the Internet of Things;

[0006] The received request information will be shared with other nodes in the Internet of Things at the optimal location;

[0007] If the received request information does not meet the sleep condition, the optimal orientation for the next cycle after each cycle is determined based on the received request information, and the request information is received repeatedly until the received request information meets the sleep condition, and then the system enters a sleep state.

[0008] Preferably, the request information includes request information perceived within a preset search area of ​​the perception range, wherein the perception range includes multiple search areas.

[0009] Preferably, sharing the received request information with other IoT nodes in the optimal location specifically includes:

[0010] If other IoT nodes in the optimal location are dormant, wake up the dormant nodes to make them active.

[0011] The request information is shared with the conscious node at the optimal location.

[0012] Preferably, determining the optimal orientation for the next cycle after each cycle based on the received request information specifically includes:

[0013] Determine the request information attributes of the received request information, the request information attributes including each request information received in each cycle, the location of the request information, and the frequency;

[0014] Determine the ratio of the frequency of each request message to the sum of the frequencies of all request messages;

[0015] The direction component is obtained by multiplying the ratio of each request information by the corresponding orientation.

[0016] The optimal orientation for the next cycle is obtained by summing the directional components of all requested information.

[0017] Preferably, it further includes:

[0018] The preset search area for the next period is determined based on the request information perceived by the search area in each cycle and the weight of each search area.

[0019] Preferably, determining the preset search area for the next period based on the request information perceived in each period's search area specifically includes:

[0020] Determine the regional information attributes of the request information received in all periods, wherein the regional information attributes include the frequency of the request information received in each search region in all periods;

[0021] The weight of each search region is determined based on the frequency of receiving request information, the duration of no search, node attributes, and whether it has been searched. The node attributes include node number, location information, communication range, sensing range, optimal orientation, status flag, sensing flag, maximum storage capacity, and number of search regions.

[0022] The preset search area for the next cycle is determined based on the regional information attributes and the weight of each search area.

[0023] The present invention also discloses an Internet of Things (IoT) node, comprising:

[0024] Receive request information in each cycle, including request information perceived within the sensing range and / or request information shared by other nodes in the Internet of Things;

[0025] The received request information will be shared with other nodes in the Internet of Things at the optimal location;

[0026] If the received request information does not meet the sleep condition, the optimal orientation for the next cycle after each cycle is determined based on the received request information, and the request information is received repeatedly until the received request information meets the sleep condition, and then the system enters a sleep state.

[0027] The present invention also discloses an Internet of Things (IoT) comprising multiple nodes as described above.

[0028] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0029] When the processor executes the program, it implements the method described above.

[0030] The present invention also discloses a computer-readable medium having a computer program stored thereon.

[0031] When the program is executed by the processor, it implements the method described above.

[0032] This invention provides an IoT information sensing method with optimized shared orientation. Within each cycle, the method receives request information, including requests perceived within the sensing range and / or requests shared by other nodes in the IoT. The received request information is shared with other IoT nodes at their optimal orientation, and the optimal orientation for the next cycle is determined based on the received request information. Therefore, this invention can sense request information in each cycle and also receive request information transmitted by other nodes in the IoT. Simultaneously, the current node has a preset optimal orientation, and the received request information is shared with the node at that optimal orientation in each cycle. Finally, based on the request information received in each cycle, it is determined whether a sleep condition has been met. If not, the optimal orientation for the next cycle is re-determined based on the received request information, and the next cycle begins. In the next cycle, all steps of each cycle are repeated until, in a certain cycle, the current node is determined to have met the sleep condition based on the received request information, at which point it enters a sleep state until it is awakened by other nodes. Therefore, this invention introduces an optimal orientation for information sharing. By re-determining the optimal orientation for the next cycle based on the request information received in each cycle, the direction of the shared request information is intelligently adjusted in real time, improving the efficiency of sensing and sharing request information in the Internet of Things (IoT). Furthermore, in each cycle, nodes have a probability of entering a dormant state. Nodes with low utilization rates can automatically enter a dormant state and can be awakened by other nodes when information transmission is needed again. This mechanism can improve the overall lifespan of the IoT and reduce its operating energy consumption. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A structural diagram showing a specific embodiment of the Internet of Things system of the present invention is shown;

[0035] Figure 2 A flowchart illustrating a specific embodiment of the IoT information sensing method with shared orientation optimization according to the present invention is shown;

[0036] Figure 3 A schematic diagram of the node search area is shown in a specific embodiment of the IoT information sensing method with shared orientation optimization according to the present invention.

[0037] Figure 4 A flowchart illustrating a specific embodiment S200 of the IoT information sensing method with shared orientation optimization according to the present invention is shown.

[0038] Figure 5 A flowchart illustrating a specific embodiment S300 of the IoT information sensing method with shared orientation optimization according to the present invention is shown.

[0039] Figure 6 A flowchart illustrating a specific embodiment S400 of the IoT information sensing method with shared orientation optimization according to the present invention is shown.

[0040] Figure 7 This diagram illustrates a specific embodiment of the IoT node of the present invention.

[0041] Figure 8 A schematic diagram of a computer device suitable for implementing embodiments of the present invention is shown. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] It should be noted that the IoT information sensing method with shared orientation optimization disclosed in this application can be used in the field of IoT technology, or in any field other than IoT technology. The application field of the IoT information sensing method with shared orientation optimization disclosed in this application is not limited.

[0044] To facilitate understanding of the technical solutions provided in this application, the relevant content of the technical solutions in this application will be described below. The IoT information sensing method with optimized shared orientation provided in this embodiment introduces the optimal orientation for information sharing. By re-determining the optimal orientation for the next cycle based on the request information received in each cycle, the direction of the shared request information is intelligently adjusted in real time, improving the efficiency of sensing and sharing request information in the IoT. Furthermore, in each cycle, nodes have a probability of entering a dormant state, allowing nodes with low utilization rates to automatically enter a dormant state. When information transmission is needed again, they can be awakened by other nodes to enter a waking state. This mechanism can improve the overall lifespan of the IoT and reduce the energy consumption of IoT operation.

[0045] The Internet of Things (IoT) provided in this embodiment of the invention includes multiple IoT nodes 1, such as... Figure 1 As shown in the diagram, each IoT node 1 receives request information in each cycle. The request information includes request information perceived within the sensing range and / or request information shared by other nodes in the IoT. The received request information is shared with other IoT nodes in the optimal location. If the received request information does not meet the sleep condition, the optimal location for the next cycle is determined based on the received request information, and the request information is received repeatedly until the received request information meets the sleep condition, at which point the node enters a sleep state.

[0046] The following uses IoT node 1 as an example to illustrate the implementation process of the IoT information sensing method with shared orientation optimization provided in this embodiment of the invention. It should be understood that the execution subject of the IoT information sensing method with shared orientation optimization provided in this embodiment of the invention includes, but is not limited to, IoT node 1.

[0047] According to one aspect of the present invention, this embodiment discloses an IoT information sensing method with shared orientation optimization. For example... Figure 2 As shown, in this embodiment, the method includes:

[0048] S100: Receive request information in each cycle, the request information including request information perceived within the sensing range and / or request information shared by other nodes in the Internet of Things;

[0049] S200: Share the received request information with other nodes of the Internet of Things in the optimal location.

[0050] S300: If the received request information does not meet the sleep condition, determine the optimal orientation for the next cycle after each cycle based on the received request information and repeatedly receive the request information until the received request information meets the sleep condition, and enter the sleep state.

[0051] This invention provides an IoT information sensing method with optimized shared orientation. Within each cycle, the method receives request information, including requests perceived within the sensing range and / or requests shared by other nodes in the IoT. The received request information is shared with other IoT nodes at their optimal orientation, and the optimal orientation for the next cycle is determined based on the received request information. Therefore, this invention can sense request information in each cycle and also receive request information transmitted by other nodes in the IoT. Simultaneously, the current node has a preset optimal orientation, and the received request information is shared with the node at that optimal orientation in each cycle. Finally, based on the request information received in each cycle, it is determined whether a sleep condition has been met. If not, the optimal orientation for the next cycle is re-determined based on the received request information, and the next cycle begins. In the next cycle, all steps of each cycle are repeated until, in a certain cycle, the current node is determined to have met the sleep condition based on the received request information, at which point it enters a sleep state until it is awakened by other nodes. Therefore, this invention introduces an optimal orientation for information sharing. By re-determining the optimal orientation for the next cycle based on the request information received in each cycle, the direction of the shared request information is intelligently adjusted in real time, improving the efficiency of sensing and sharing request information in the Internet of Things (IoT). Furthermore, in each cycle, nodes have a probability of entering a dormant state. Nodes with low utilization rates can automatically enter a dormant state and can be awakened by other nodes when information transmission is needed again. This mechanism can improve the overall lifespan of the IoT and reduce its operating energy consumption.

[0052] In a preferred embodiment, the request information includes request information perceived within a preset search area of ​​the perception range, wherein the perception range includes multiple search areas.

[0053] Specifically, each node in the Internet of Things (IoT) has a sensing radius. The node can receive request information within this sensing radius, and the area within the node's sensing radius forms its sensing range. In this preferred embodiment, in each cycle, the node senses request information through only a preset search area. The small search range improves the efficiency of request information sensing and enhances the information sharing efficiency of the IoT. More preferably, the sensing range includes four search areas. For example, in a specific example... Figure 3 As shown, the node's sensing range is divided into four search regions: NE, SE, SW, and NW. Each search region is independent of the others, and within each cycle, the node only searches for and senses information in one of the four search regions: NE, SE, SW, and NW. More preferably, the search regions obtained by dividing the node's sensing range are all the same size.

[0054] It should be noted that, in this preferred embodiment, the node's sensing range includes four search areas. In other embodiments, the node's sensing range may also include other numbers of search areas. Those skilled in the art can set the search areas of the node's sensing range according to actual needs, and this invention does not limit this.

[0055] In a preferred embodiment, such as Figure 4 As shown, the step S200 of sharing the received request information with other IoT nodes in the optimal location specifically includes:

[0056] S210: If other IoT nodes at the optimal location are dormant, wake up the dormant nodes to make them awake.

[0057] S220: Share the request information with the node in the conscious state at the optimal location.

[0058] Specifically, in each cycle, when a node senses a request within a preset search area or receives a request shared by other nodes in the IoT, it can wake up a dormant node in the optimal location, bringing it to a waking state. This makes all nodes in the optimal location waking, allowing the node to send the received request to all waking nodes in its optimal location, thus achieving IoT information transmission. Simultaneously, at the end of each cycle, the optimal location of the node is re-determined based on the request information received in the current cycle. That is, the optimal location with the highest information transmission efficiency is determined at the end of each cycle to share request information in the next cycle. This allows for selective waking of some nodes and filtering out others in each cycle, enhancing the generalization capability of the IoT.

[0059] In a preferred embodiment, such as Figure 5 As shown, S300, determining the optimal orientation for the next cycle after each cycle based on the received request information, specifically includes:

[0060] S310: Determine the request information attributes of the received request information, wherein the request information attributes include each request information received in each cycle, the location of the request information, and the frequency.

[0061] S320: Determine the ratio of the frequency of each request message to the sum of the frequencies of all request messages.

[0062] S330: Multiply the ratio of each request information by the corresponding azimuth to obtain the directional component, and sum the directional components of all request information to obtain the optimal azimuth of the next cycle.

[0063] Specifically, for each node, probabilistically fluctuating its optimal location can further enhance the uncertainty of node perception and improve the generalization capability of the Internet of Things (IoT). Therefore, in this preferred embodiment, after a node shares its request information with other nodes, the request information attributes received in the current period are analyzed, and the optimal location for the next period is determined based on these attributes, thus achieving optimal probabilistic fluctuation of the optimal location.

[0064] In a specific example, an information sharing table can be designed for each node to record the request information attributes of the request information received in each cycle. This information sharing table can be designed as a four-tuple. In cycle t, the request information attributes of the request information j sensed by the i-th node Ni in the IoT are as follows:

[0065] (t, j, d, Freq)

[0066] Where d represents the angle of request j relative to Ni, the search area where the request information is located can be determined based on this angle. Freq is the frequency, representing the number of requests sensed by Ni at an angle of d degrees per cycle. When a node is in a dormant state and is awakened by other nodes, it will transmit request information to the awakened node at its optimal orientation. The initial optimal orientation can be randomly generated. Simultaneously, after completing a round of sensing operations and obtaining the attributes of the request information, the node will also modify its optimal orientation for the next cycle based on the information sharing table of the received request information.

[0067] Based on the information sharing table obtained by the node in each cycle, the optimal orientation of node Ni in the next cycle can be obtained by the following formula:

[0068]

[0069] in, Let represent the direction of the request information j sensed in period t, m represent the number of request information received in period t, and Freq(k) represent the frequency of the k-th request information.

[0070] In a preferred embodiment, the method further includes:

[0071] S400: Determine the preset search area for the next cycle based on the request information perceived by the search area in each cycle and the weight of each search area.

[0072] Specifically, since the request information perceived by a node in the preset search area is different in each cycle, it is necessary to determine the preset search area for the next cycle based on the request information perceived by the node in each cycle and the weight of each search area. By determining the preset search area for the next cycle based on the perceived request information and the weight of the search area, the area for perceiving request information in the next cycle can be adjusted in each cycle. That is, the search area most likely to perceive request information in the next cycle is determined as the preset search area, which can increase the probability of the node perceiving request information in each cycle, improve the efficiency and accuracy of IoT request information perception, and improve IoT information transmission capabilities.

[0073] It should be noted that, in practical applications, those skilled in the art can set the weight of each search region according to actual needs, and this invention does not limit this.

[0074] In a preferred embodiment, such as Figure 6 As shown, the step S400, which determines the preset search area for the next cycle based on the request information sensed in each cycle's search area, specifically includes:

[0075] S410: Determine the regional information attributes of the request information received in all periods, wherein the regional information attributes include the frequency at which each search region receives request information in all periods.

[0076] S420: Determine the weight of each search area based on the frequency of receiving request information, the duration of no search, node attributes, and whether it has been searched. The node attributes include node number, location information, communication range, sensing range, optimal orientation, status flag, sensing flag, maximum storage information, and number of search areas.

[0077] S430: Determine the preset search area for the next cycle based on the area information attributes and the weight of each search area.

[0078] Specifically, in this preferred embodiment, in order to measure the importance of each search region, the weight of each search region is determined in real time in each cycle, so as to combine the request information perceived in the current cycle and the weight of the search region to obtain the preset search region for the next cycle.

[0079] In a specific example, the sensed request information will be recorded in the node. Based on the sensed request information, the frequency of the sensed request information across all periods can be obtained. The sensed request information is recorded in the form of region information attributes, which are represented as a set, as shown below:

[0080] {RecNE, RecSE, RecSW, RecNW}

[0081] Here, RecNE, RecSE, RecSW, and RecNW represent the number of request messages received by the four search regions NE, SE, SW, and NW of Ni, respectively. Based on the region information attributes, the frequency of request messages received by each search region throughout all periods can be obtained, reflecting the real-time request message frequency of the four search regions of Ni for determining the optimal search region.

[0082] In this specific example, the node attributes of each node are as follows:

[0083] Ni(Nid, Node_X, Node_Y, Nc, Ns, Od, Sleep, Per, Save, Zones)

[0084] Where Nid represents the node number Ni, Node_X and Node_Y represent the coordinates of node Ni, Nc is the communication radius, Ns is the sensing radius, Od is the optimal orientation, Sleep is the sleep state bit, Per is the sensing flag bit, Save is the maximum amount of information stored in node Ni, and Zones represents the number of search areas divided by the node.

[0085] Each node has two states: awake and dormant. A node in the awake state can monitor surrounding IoT request information and transmit request information to nodes within its communication range. A node in the awake state consumes more energy than a node in the dormant state. A node in the awake state has a probability of transitioning to the dormant state in each cycle. A node in the dormant state stops all information transmission and cannot detect surrounding IoT request information. A node in the dormant state effectively saves energy, consuming only a small amount of energy. A node in the dormant state has a probability of transitioning to the awake state in each cycle and waiting for the next cycle. The Sleep state bit indicates whether node Ni is in the dormant state. The sensing flag bit of the i-th node Ni in cycle t can be obtained using the following formula:

[0086]

[0087] In this preferred embodiment, a weight W is introduced for each node search region. W reflects the search value of each search region, i.e., whether each search region is worth searching in each cycle. In a specific example, the influencing factors of the weight W of the search region include the frequency of receiving request information in each search region, the duration of no search, node attributes, and whether it has been searched before. Since the weight W of each search region should reflect the most intuitive search value, the weight W of each search region can be qualitatively determined according to the following three requirements:

[0088] 1) Search regions that have already been searched for the requested information will have a lower weight. Search regions that haven't been searched are more likely to be aware of the requested information compared to search regions that have already been searched.

[0089] 2) Search regions that haven't been searched for a long time will have their weight increased. To prevent new requests from appearing in search regions that haven't been searched for a long time, their weight needs to be continuously increased to avoid long waiting times for requests.

[0090] 3) For search regions that have already been searched, the weight of search regions with fewer perceived requests should be lower than the weight of search regions with more perceived requests.

[0091] Based on the above three requirements, the weights w of each search region of the node are... d It can be determined using the following formula:

[0092]

[0093]

[0094] d∈{NE,SE,SW,NW}

[0095] Among them, Freq d denoted by d, the perceived frequency of the search region is β, representing the frequency coefficient. In the experiment, the value of Save is used to eliminate cases where the frequency is 0. k is the flag bit coefficient, typically ranging from 1 to 5; a larger value results in a smaller influence of the perceived bit. t d This indicates the duration during which the search region d has not been searched. γ d δ represents the nearest distance from the centerline of the node search region d to the optimal orientation. Regions farther from the calculated optimal orientation have lower weights. d This is to eliminate the possibility of identical weights in special cases, δ d The four search regions correspond to very small fixed values: 0.0001, 0.0002, 0.0003, and 0.0004, respectively.

[0096] To further illustrate the present invention, this embodiment verifies its technical effectiveness through two experiments: an energy consumption comparison experiment and a sensing rate comparison experiment. In the experiments, a 400×400 (m) IoT area was set up, with 144 sensor nodes evenly distributed within it. The settings and performance of each node in the IoT were consistent throughout the experiment. Each node had a communication range Rc of 32 (m) and a sensing range Rs of 16 (m), with the initial optimal orientation being a random number from 0 to 360°. The sensing and communication ranges of the nodes were divided into four parts: 0–90°, 90°–180°, 180°–270°, and 270°–360°. The attributes of the requested information in the experiment are shown below:

[0097] (Rid, Request_X, Request_Y, Rq, Rt)

[0098] Where Rid represents the request number, Request_X and Request_Y represent the coordinates of the request, and the coordinates are generated according to a normal distribution. Rq represents the radius of the coverage area of ​​the request, which is 16 (m) in the experiment, and Rt represents the sensing angle of the request. Rt is initially set to 0, and is set to 1 if the request is sensed.

[0099] Each request information instance underwent 30 independent experiments. This paper compares the present invention with the RA algorithm (random activation), the ISOS scheme (intelligent self-organizing scheme), and the CAOP algorithm (Coverage Aware scheduling for Optimal Placement of Sensors).

[0100] 1. Energy consumption comparison experiment.

[0101] In this experiment, a wake-up probability p = 0.3 was set, and the number of request messages was 20 and 1000 for a unified experiment. The experimental conclusion is that when the number of requests is small, the energy maintenance of this invention is the highest. This is because the algorithm of this invention divides the entire sensing range into multiple modules, which can effectively sense sparse requests and reduce the energy consumption of the node's sending and receiving modules. When the number of requests is large, the algorithm of this invention needs to transmit information frequently, resulting in greater energy consumption during the sensing process. After 5 rounds of network iteration, the number of unsensitized requests in the network decreases, and this invention shows better energy maintenance performance, surpassing the other three methods after 25 rounds. This is due to the energy saving after dividing the nodes into regions and the node screening mechanism adopted. The ISOS algorithm is always in the working state of sending and receiving operators, resulting in the highest energy consumption. The RA algorithm and CAOP algorithm use omnidirectional sensing, and the cost of each sensing is higher than the directional sensing of this invention's algorithm, resulting in the highest energy maintenance of this invention's algorithm.

[0102] 2. Perception rate comparison experiment.

[0103] In this experiment, the perception performance of four algorithms was tested in 30 independent runs with 20 requests and an initial node wake-up probability of 0.3. Experimental conclusion: In a small-scale experimental scenario, the algorithm of this invention can quickly perceive requests within 5 iterations, achieving an accuracy of 98.83%. The performance of this algorithm is significantly better than the other comparison algorithms. This is because, when faced with sparse requests, the algorithm can continuously change its optimal location through information transmission while acquiring request information. Simultaneously, the location fluctuation and node selection mechanism also increase the network's generalization ability, demonstrating its powerful multi-region search advantage.

[0104] In this invention, the node state is stable within a self-organizing Internet of Things (IoT), eliminating the need for all nodes to interact. This increases the uncertainty of the node's search area, allowing for a higher probability of sensing randomly appearing request information. Furthermore, for each node, probabilistically fluctuating its optimal location further enhances the uncertainty of node perception, improving the IoT's generalization capability. However, the fluctuation probability cannot be too large, otherwise it becomes random searching, losing the accuracy of perception. This invention achieves reasonable probability fluctuation while maintaining the accuracy of request information perception, increasing the accuracy and efficiency of request information perception and transmission.

[0105] Based on the same principle, this embodiment also discloses an Internet of Things (IoT) node. For example... Figure 7 As shown, in this embodiment, the IoT node includes an information receiving module 11, an information sharing module 12, and a setting information module 13.

[0106] The information receiving module 11 is used to receive request information in each cycle. The request information includes request information perceived within the sensing range and / or request information shared by other nodes in the Internet of Things.

[0107] The information sharing module 12 is used to share the received request information with other nodes of the Internet of Things in the optimal location.

[0108] The setting information module 13 is used to determine the optimal orientation for the next cycle after each cycle based on the received request information if the received request information does not meet the sleep condition, and to repeatedly receive request information until the received request information meets the sleep condition and enters the sleep state.

[0109] In this invention, an IoT node receives request information in each cycle. This request information includes requests perceived within the sensing range and / or requests shared by other nodes in the IoT. The node shares the received request information with other IoT nodes in the optimal location and determines the optimal location for the next cycle based on the received request information. Therefore, this invention can sense request information in each cycle and also receive request information transmitted by other nodes in the IoT. Simultaneously, the current node has a preset optimal location and shares the received request information with the node in the optimal location in each cycle. Finally, based on the request information received in each cycle, it determines whether a sleep condition has been met. If not, the optimal location for the next cycle is re-determined based on the received request information, and the next cycle begins. In the next cycle, all steps of each cycle are repeated until, in a certain cycle, the current node is determined to have met the sleep condition based on the received request information, then enters a sleep state until it is awakened by other nodes. Thus, this invention introduces an optimal location for information sharing, re-determining the optimal location for the next cycle based on the request information received in each cycle, thereby intelligently adjusting the direction of shared request information in real time and improving the efficiency of sensing and sharing request information in the IoT. In addition, nodes have a chance to enter a dormant state during each cycle, so nodes with low utilization can enter a dormant state on their own. When information transmission is needed again, they can be woken up by other nodes and enter a waking state. This mechanism can improve the lifespan of the entire Internet of Things and reduce the energy consumption of Internet of Things operation.

[0110] In a preferred embodiment, the request information includes request information perceived within a preset search area of ​​the perception range, wherein the perception range includes multiple search areas.

[0111] Specifically, each node in the Internet of Things (IoT) has a sensing radius. The node can receive request information within this sensing radius, and the area within the node's sensing radius forms its sensing range. In this preferred embodiment, in each cycle, the node senses request information through only a preset search area. The small search range improves the efficiency of request information sensing and enhances the information sharing efficiency of the IoT. More preferably, the sensing range includes four search areas. For example, in a specific example... Figure 2 As shown, the node's sensing range is divided into four search regions: NE, SE, SW, and NW. Each search region is independent of the others, and within each cycle, the node only searches for and senses information in one of the four search regions: NE, SE, SW, and NW. More preferably, the search regions obtained by dividing the node's sensing range are all the same size.

[0112] It should be noted that, in this preferred embodiment, the node's sensing range includes four search areas. In other embodiments, the node's sensing range may also include other numbers of search areas. Those skilled in the art can set the search areas of the node's sensing range according to actual needs, and this invention does not limit this.

[0113] In a preferred embodiment, the information sharing module 12 is specifically used to wake up dormant nodes if other IoT nodes at the optimal location are dormant, thus bringing the dormant nodes to an active state. The request information is then shared with the active nodes at the optimal location.

[0114] Specifically, in each cycle, when a node senses a request within a preset search area or receives a request shared by other nodes in the IoT, it can wake up a dormant node in the optimal location, bringing it to a waking state. This makes all nodes in the optimal location waking, allowing the node to send the received request to all waking nodes in its optimal location, thus achieving IoT information transmission. Simultaneously, at the end of each cycle, the optimal location of the node is re-determined based on the request information received in the current cycle. That is, the optimal location with the highest information transmission efficiency is determined at the end of each cycle to share request information in the next cycle. This allows for selective waking of some nodes and filtering out others in each cycle, enhancing the generalization capability of the IoT.

[0115] In a preferred embodiment, the setting information module 13 is specifically used to determine the request information attributes of the received request information. The request information attributes include each request information received in each cycle, the azimuth of the request information, and its frequency. The frequency of each request information is determined as a ratio to the sum of the frequencies of all request information. The ratio of each request information is multiplied by its corresponding azimuth to obtain a direction component. The direction components of all request information are then summed to obtain the optimal azimuth for the next cycle.

[0116] Specifically, for each node, probabilistically fluctuating its optimal location can further enhance the uncertainty of node perception and improve the generalization capability of the Internet of Things (IoT). Therefore, in this preferred embodiment, after a node shares its request information with other nodes, the request information attributes received in the current period are analyzed, and the optimal location for the next period is determined based on these attributes, thus achieving optimal probabilistic fluctuation of the optimal location.

[0117] In a preferred embodiment, the setting information module 13 is further configured to determine the preset search area for the next period based on the request information perceived by the search area in each period and the weight of each search area.

[0118] Specifically, since the request information perceived by a node in the preset search area is different in each cycle, it is necessary to determine the preset search area for the next cycle based on the request information perceived by the node in each cycle and the weight of each search area. By determining the preset search area for the next cycle based on the perceived request information and the weight of the search area, the area for perceiving request information in the next cycle can be adjusted in each cycle. That is, the search area most likely to perceive request information in the next cycle is determined as the preset search area, which can increase the probability of the node perceiving request information in each cycle, improve the efficiency and accuracy of IoT request information perception, and improve IoT information transmission capabilities.

[0119] It should be noted that, in practical applications, those skilled in the art can set the weight of each search region according to actual needs, and this invention does not limit this.

[0120] In a preferred embodiment, the setting information module 13 is specifically used to determine the regional information attributes of the request information received in all cycles. The regional information attributes include the frequency at which each search region receives request information in all cycles. The weight of each search region is determined based on its frequency of receiving request information, duration of no search, node attributes, and whether it has been searched. The node attributes include node number, location information, communication range, sensing range, optimal orientation, status flag, sensing flag, maximum storage capacity, and number of search regions. The preset search region for the next cycle is determined based on the regional information attributes and the weight of each search region.

[0121] Specifically, in this preferred embodiment, in order to measure the importance of each search region, the weight of each search region is determined in real time in each cycle, so as to combine the request information perceived in the current cycle and the weight of the search region to obtain the preset search region for the next cycle.

[0122] In this preferred embodiment, a weight W is introduced for each node search region. W reflects the search value of each search region, i.e., whether each search region is worth searching in each cycle. In a specific example, the influencing factors of the weight W of the search region include the frequency of receiving request information in each search region, the duration of no search, node attributes, and whether it has been searched before. Since the weight W of each search region should reflect the most intuitive search value, the weight W of each search region can be qualitatively determined according to the following three requirements:

[0123] 1) Search regions that have already been searched for the requested information will have a lower weight. Search regions that haven't been searched are more likely to be aware of the requested information compared to search regions that have already been searched.

[0124] 2) Search regions that haven't been searched for a long time will have their weight increased. To prevent new requests from appearing in search regions that haven't been searched for a long time, their weight needs to be continuously increased to avoid long waiting times for requests.

[0125] 3) For search regions that have already been searched, the weight of search regions with fewer perceived requests should be lower than the weight of search regions with more perceived requests.

[0126] Based on the above three requirements, the search region weight of a node can be determined by the following formula:

[0127]

[0128]

[0129] d∈{NE,SE,SW,NW}

[0130] Among them, Freq d denoted by d, the perceived frequency of the search region is β, representing the frequency coefficient. In the experiment, the value of Save is used to eliminate cases where the frequency is 0. k is the flag bit coefficient, typically ranging from 1 to 5; a larger value results in a smaller influence of the perceived bit. t d This indicates the duration during which the search region d has not been searched. γ d δ represents the nearest distance from the centerline of the node search region d to the optimal orientation. Regions farther from the calculated optimal orientation have lower weights. d This is to eliminate the possibility of identical weights in special cases, δ d The four search regions correspond to very small fixed values: 0.0001, 0.0002, 0.0003, and 0.0004, respectively.

[0131] In this invention, the node state is stable within a self-organizing Internet of Things (IoT), eliminating the need for all nodes to interact. This increases the uncertainty of the node's search area, allowing for a higher probability of sensing randomly appearing request information. Furthermore, for each node, probabilistically fluctuating its optimal location further enhances the uncertainty of node perception, improving the IoT's generalization capability. However, the fluctuation probability cannot be too large, otherwise it becomes random searching, losing the accuracy of perception. This invention achieves reasonable probability fluctuation while maintaining the accuracy of request information perception, increasing the accuracy and efficiency of request information perception and transmission.

[0132] Since the principle behind solving the problem at this node is similar to the methods described above, the implementation of this node can be found in the implementation of the methods, and will not be repeated here.

[0133] Based on the same principle, this embodiment also discloses an Internet of Things (IoT). The IoT includes multiple nodes as described in this embodiment.

[0134] Since the principle behind this Internet of Things (IoT) problem-solving is similar to the methods described above, the implementation of this IoT can be found in the implementation section of the methods, and will not be repeated here.

[0135] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, specifically, a computer device can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0136] In a typical example, the computer device specifically includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method executed by the client as described above, or the method executed by the server as described above.

[0137] The following is for reference. Figure 8 It shows a schematic diagram of the structure of a computer device 600 suitable for implementing the embodiments of this application.

[0138] like Figure 8 As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate tasks and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0139] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal feedback (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 606 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed in storage section 608 as needed.

[0140] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program including program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611.

[0141] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0142] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0143] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0146] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0149] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0150] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for IoT information sensing with shared orientation optimization, characterized in that, include: Receive request information in each cycle, including request information perceived within the sensing range and / or request information shared by other nodes in the Internet of Things; The received request information will be shared with other nodes in the Internet of Things at the optimal location; If the received request information does not meet the sleep condition, the optimal orientation for the next cycle after each cycle is determined based on the received request information, and the request information is received repeatedly until the received request information meets the sleep condition, and then the sleep state is entered. The step of determining the optimal azimuth for the next cycle after each cycle based on the received request information specifically includes: determining the request information attributes of the received request information, the request information attributes including each request information received in each cycle, the azimuth of the request information, and the frequency; determining the ratio of the frequency of each request information to the sum of the frequencies of all request information; multiplying the ratio of each request information by the corresponding azimuth to obtain a direction component; and summing the direction components of all request information to obtain the optimal azimuth for the next cycle. The step of summing the directional components of all requested information to obtain the optimal azimuth for the next cycle includes: ； in, Let represent the direction of request information j sensed in period t, m represent the number of request information received in period t, Freq(k) represent the frequency of the k-th request information, and j represent the request information. This indicates the optimal orientation for the next cycle.

2. The IoT information sensing method with shared orientation optimization according to claim 1, characterized in that, The request information includes request information perceived within a preset search area of ​​the perception range, wherein the perception range includes multiple search areas.

3. The IoT information sensing method with shared orientation optimization according to claim 1, characterized in that, The specific steps of sharing the received request information with other IoT nodes in the optimal location include: If other IoT nodes in the optimal location are dormant, wake up the dormant nodes to make them active. The request information is shared with the conscious node at the optimal location.

4. The IoT information sensing method with shared orientation optimization according to claim 2, characterized in that, Further includes: The preset search area for the next period is determined based on the request information perceived by the search area in each cycle and the weight of each search area.

5. An Internet of Things (IoT) node, characterized in that, include: The information receiving module is used to receive request information in each cycle, the request information including request information perceived within the sensing range and / or request information shared by other nodes in the Internet of Things; The information sharing module is used to share the received request information with other nodes in the Internet of Things at the optimal location; The information module is configured to determine the optimal orientation for the next cycle after each cycle based on the received request information if the received request information does not meet the sleep condition, and then repeatedly receive the request information until the received request information meets the sleep condition and enters the sleep state. In a preferred embodiment, the setting information module is specifically used to determine the request information attributes of the received request information, the request information attributes including each request information received in each cycle, the location of the request information, and the frequency; Determine the ratio of the frequency of each request message to the sum of the frequencies of all request messages; The direction component is obtained by multiplying the ratio of each request information by the corresponding orientation. The optimal orientation for the next cycle is obtained by summing the directional components of all requested information. The step of summing the directional components of all requested information to obtain the optimal azimuth for the next cycle includes: ； in, Let represent the direction of request information j sensed in period t, m represent the number of request information received in period t, Freq(k) represent the frequency of the k-th request information, and j represent the request information. This indicates the optimal orientation for the next cycle.

6. An Internet of Things (IoT) characterized in that, It includes multiple nodes as described in claim 5.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-4.

8. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-4.

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