Method and device for providing edge-optimized embedded system

Abstract

The present invention relates to a method and apparatus for providing an edge-optimized embedded system, comprising: a network registration step in which each node or device is registered with the nearest gateway during a network initialization step; a subgraph generation step in which, when each registered node or device is identified, an edge node evaluates a weighted cost based on network information received and generates a subgraph using the weighted cost; and a monitoring step in which, to enhance network security, the edge node provides the network information to an SDN controller, and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation step further comprises: a spanning tree generation step in which an optimized spanning tree is generated to improve response time and latency performance; and a path selection step in which a gateway selects a Minimum Spanning Tree (MST), which is the shortest path for routing IoT data among the spanning trees.

Description

Technology Field

[0001] The present invention relates to a method and apparatus for providing an edge-optimized embedded system that applies graph theory to increase energy efficiency in the data transmission process of an Internet of Things wireless sensor network. Background Technology

[0002] The number of users and applications is increasing exponentially due to the new networking environment. As the number of users grows, so does the traffic of data passing through base stations. In 5G, Device-to-Device (D2D) communication is one of the best ways to reduce the possibility of service loss for cellular users. In particular, IoT technology plays a crucial role in the development of smart systems. Using IoT systems can address common system-level design issues such as energy efficiency, robustness, scalability, interoperability, and security. However, a high level of physical security is essential for IoT wireless communication to complement existing security measures.

[0003] Meanwhile, IoT networks are interconnected with wireless systems and can cooperate with other devices using multi-hop communication. Multi-hop communication refers to the process of transmitting data between devices or nodes that are not directly connected to each other but relay data through intermediate nodes; in multi-hop communication, data can reach its destination through a series of intermediate nodes, or 'hops'. This contrasts with single-hop communication, where data is transmitted directly between devices within each other's communication range.

[0004] Edge computing can be used to reduce latency, enhance security, and strengthen privacy protection. Edge computing refers to a distributed computing paradigm where data processing and analysis are performed closer to the data source, rather than sending all data to a centralized cloud or data center for processing. Instead of sending large amounts of data to centralized servers for processing, using edge computing enables localized processing, resulting in faster response times and improved network resource utilization. Specifically, edge equipment refers to devices that provide edge computing and is classified into edge nodes (also called edge servers) and edge devices. Edge nodes are network equipment located near the point of data generation that partially performs the role of a server; examples include gateways, routers, and micro data centers. Edge devices are devices that generate data and include smart devices such as Internet of Things (IoT) sensors that create or collect data, video / surveillance cameras, internet-connected home appliances, and smartphones.

[0005] Intelligent edge computing utilizes artificial intelligence technology on edge devices or edge nodes to provide real-time analysis and processing of data generated from edge devices, as well as functions such as situation awareness, reasoning, prediction, response, and control. It also manages the resources of edge devices and provides optimal computing offloading in edge computing. However, while data processing can be offloaded from cloud data centers to edge nodes, problems may arise due to the complex relationships between edge devices, edge servers, and cloud data centers. Prior art literature

[0006] (Patent Document 0001) KR 10-2015-0126293 B1 The problem to be solved

[0007] To solve such problems, the present invention can present an edge-optimized embedded system that increases energy efficiency using graph theory.

[0008] Various solutions for protecting IoT platforms have the problem that more messages are transmitted through communication links, negatively impacting the sustainability of the platform, but edge networks can deliver data to mobile sinks in a timely manner while reducing overhead for IoT devices.

[0009] The present invention utilizes artificial intelligence techniques to extract a minimum spanning tree to improve the response time and latency performance of an embedded system, and by using the path extracted through a two-layer system, it is also possible to provide complete protection against anonymous access. The present invention proposes an intelligent method for extracting a minimum spanning tree, which optimizes the cost function using real-time parameters and thereby identifies the end-to-end path. In other words, it can provide security at two major entry points: from the IoT system to the edge device, and from the edge device to the mobile sink. This layer can protect against network threats and enhance the stability of the nodes.

[0010] Furthermore, edge computing and SDN controllers can be integrated to resolve retransmissions and network congestion, and security technology utilizing blockchain technology can be provided. means of solving the problem

[0011] A method for providing an edge-optimized embedded system according to an embodiment of the present invention for achieving the above objective comprises: a network registration step in which each node or device is registered with the nearest gateway during a network initialization step; a subgraph generation step in which, when each registered node or device is identified, an edge node evaluates a weighted cost based on network information received and generates a subgraph using the weighted cost; and a monitoring step in which, to enhance network security, the edge node provides the network information to an SDN controller and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation step may further include: a spanning tree generation step in which an optimized spanning tree is generated to improve response time and latency performance; and a path selection step in which a gateway selects a Minimum Spanning Tree (MST), which is the minimum path for routing IoT data among the spanning trees.

[0012] A method for providing an edge-optimized embedded system according to an embodiment of the present invention may further include the following steps in the network monitoring step: a step of registering information about network attributes in an SDN controller; a step of verifying data received from an edge node and then transmitting the verified data to the SDN controller; a step of the SDN controller sending a warning message to the edge node when performance is unsatisfactory during the communication process between the edge node and the SDN controller; a step of the SDN controller that sent the warning message reconfiguring the routing process; and a step of the SDN controller utilizing blockchain technology to enhance security.

[0013] A method for providing an edge-optimized embedded system according to an embodiment of the present invention, wherein the weight graph G(V, E), which is the input value in the minimum spanning tree, has the characteristics of an undirected graph, and the cumulative cost of the weights

[0014]

[0015] It can be calculated based on,

[0016] Here, u can represent an edge and v can represent a vertex.

[0017] A method for providing an edge-optimized embedded system according to one embodiment of the present invention may utilize the Krushkal algorithm to identify the subgraph or the minimum spanning tree, and the Krushkal algorithm may sort all edges within the graph or tree by weight and gradually construct the minimum spanning tree by adding edges in ascending order of weights as long as no cycle is generated even when edges are added.

[0018] A method for providing an edge-optimized embedded system according to one embodiment of the present invention, wherein the weight evaluation

[0019]

[0020] It can be calculated based on, where w_i is the i-th weight, e is the energy, and lnl is the link loss. can represent latency time.

[0021] A method for providing an edge-optimized embedded system according to an embodiment of the present invention, wherein the energy e is based on the power consumed during the transmission and reception of a data packet.

[0022]

[0023] It can be calculated based on, where Pr represents the received power, Pt represents the transmitted power, K represents the number of data packets, and N represents the node.

[0024] A method for providing an edge-optimized embedded system according to one embodiment of the present invention, wherein the link loss may occur due to link interference,

[0025] It can be calculated based on, where DL represents data lost and EC represents error computation.

[0026] A method for providing an edge-optimized embedded system according to an embodiment of the present invention, wherein the delay time

[0027] It can be calculated based on, where m represents the input beacon message and RTT (round-trip time) represents the round-trip time.

[0028] In the step of utilizing the blockchain technology, the SDN controller can store adjacent node IDs in a table, and upon receiving a request from a node, can use the table to verify the node ID, and if the node ID is authenticated by the SDN controller, can generate a secret key s_k for the ni node digitally signed with the master key MK.

[0029] A method for providing an edge-optimized embedded system according to one embodiment of the present invention, in the step of utilizing the blockchain technology, the blockchain is composed of various data blocks formed by combining hash values ​​in a chain form, and each block may be separately encrypted with a digital signature to ensure authentication, and the encryption of each block may be formed with a one-time pad and a data bit key using an XOR operation.

[0030] An edge-optimized embedded system providing device according to one embodiment of the present invention comprises: a network register unit in which each node or device is registered with the nearest gateway during a network initialization step; a subgraph generation unit that, when each registered node or device is identified, evaluates a weighted cost based on network information received by an edge node and generates a subgraph using the weighted cost; and a monitoring unit in which, to enhance network security, the edge node provides the network information to an SDN controller, and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation unit may further include: a spanning tree generation unit that generates an optimized spanning tree to improve response time and latency performance; and a path selection unit in which a gateway selects a Minimum Spanning Tree (MST), which is the minimum path for routing IoT data among the spanning trees. Effects of the invention

[0031] In the present invention, an energy-efficient edge optimization system using graph theory can be proposed to extend network lifespan with light overhead for constraint nodes.

[0032] In the present invention, an optimized subgraph can be explored based on link loss and latency factors using graph theory.

[0033] In the present invention, a security strategy utilizing the intelligence of edge devices and SDN controllers can be provided. Layer 2 security not only supports confidential communication but can also eliminate security risks at the network registration stage. Brief explanation of the drawing

[0034] FIG. 1 is a flowchart of a method for providing an edge-optimized embedded system according to an embodiment of the present invention. Figure 2 is a flowchart for optimizing a subgraph by applying graph theory. Figure 3 is a flowchart of an SDN-based security system using blockchain. Figure 4(a) shows network throughput according to various data sizes. Figure 4(b) shows the network throughput according to the number of sensors. FIG. 5 is an edge-optimized embedded system providing device according to an embodiment of the present invention. Figure 6(a) shows propagation delay according to various data sizes. Figure 6(b) shows propagation delay according to the number of sensors. Figure 7(a) shows the packet loss rate according to various data sizes. Figure 7(b) shows the packet loss rate according to the number of various sensors. Figure 8(a) shows node overhead according to various data sizes. Figure 8(b) shows node overhead according to the number of sensors FIG. 9 is an edge-optimized embedded system providing device according to an embodiment of the present invention. Specific details for implementing the invention

[0035] The objects, specific advantages, and novel features of the present invention will become more apparent from the following detailed description and preferred embodiments in conjunction with the accompanying drawings. In this case, identical components in each drawing are denoted by the same reference numerals whenever possible. Additionally, detailed descriptions of already known functions and / or configurations are omitted.

[0036] The contents disclosed below focus on the parts necessary for understanding the operation according to various embodiments, and omit descriptions of elements that may obscure the gist of the explanation. Additionally, some components of the drawings may be exaggerated, omitted, or depicted schematically. The size of each component does not entirely reflect its actual size, and therefore, the contents described herein are not limited by the relative sizes or spacing of the components depicted in each drawing.

[0037] In describing the embodiments of the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such detailed descriptions may unnecessarily obscure the essence of the invention. Furthermore, the terms described below are defined in consideration of their functions within the present invention, and these may vary depending on the intentions or practices of the user or operator. Therefore, such definitions should be based on the content throughout this specification. Terms used in the detailed description are intended merely to describe the embodiments of the present invention and should not be limiting in any way. Unless explicitly stated otherwise, expressions in the singular form include the meaning of the plural form. In this description, expressions such as "include" or "comprise" are intended to refer to certain characteristics, numbers, steps, actions, elements, parts thereof, or combinations thereof, and should not be interpreted to exclude the existence or possibility of one or more other characteristics, numbers, steps, actions, elements, parts thereof, or combinations thereof other than those described.

[0038] Additionally, terms such as first, second, etc., may be used to describe various components, but said components are not limited by said terms, and said terms are used only for the purpose of distinguishing one component from another.

[0040] Hereinafter, with reference to the attached drawings, a method and apparatus for providing an edge-optimized embedded system according to an embodiment of the present invention will be described.

[0041] The flowchart for providing the edge-optimized embedded system of the present invention is shown in FIG. 1.

[0042] A method for providing an edge-optimized embedded system according to an embodiment of the present invention comprises the following key steps: a network registration step (step S110) in which each node or device is registered with the nearest gateway during a network initialization step; a subgraph generation step in which, when each registered node or device is verified (step S120), the edge node evaluates a weighted cost condition (step S130) based on network information received by the edge node and generates a subgraph using the weighted cost condition (step S140); and a monitoring step (step S160) in which, to enhance network security, the edge node provides the network information to an SDN controller, and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation step includes a spanning tree generation step (step S142) for generating an optimized spanning tree to improve response time and latency performance. The method further includes a path selection step (step S144) in which the gateway selects a Minimum Spanning Tree (MST), which is the minimum path for routing IoT data among the spanning trees.

[0044] Meanwhile, referring to FIG. 5, an edge-optimized embedded system providing device according to one embodiment of the present invention is characterized by including a device (500) capable of performing an edge-optimized embedded system, a processor (502), a memory (504), an input / output interface (506), and a network communication interface (508).

[0045] In addition, it is characterized by further including sensor nodes, source nodes, sink nodes, edge nodes, gateways, and SDN controllers.

[0046] A sensor node refers to a small, specialized device equipped with sensors designed to collect data from a physical environment. These sensor nodes are typically part of a wireless sensor network (WSN) or Internet of Things (IoT) deployment and play a crucial role in capturing real-world data and transmitting it to other nodes or a central system within the network.

[0047] Sensor nodes belong to specific groups, and each group can be assigned a unique ID.

[0048] A source node generally refers to a node within a network that generates or produces data, traffic, or information that is transmitted to other nodes through the network.

[0049] A sink node is a centrally or strategically located node within a network that serves as a data collector, processor, and data collector. It plays a role in efficiently managing data collected from various sources within the network and ensuring that it is processed and transmitted as needed for further analysis, storage, or decision-making.

[0050] A sink node is a node in a network graph that has no outgoing edges or connections; it only receives information or connections from other nodes and does not forward them to others. Sink nodes are typically connected to the endpoints or final destinations of some processes within the network. In a network, a sink node can represent a point where resources are consumed or where a node leaves the system.

[0051] Although sink nodes and edge nodes are both terms used in the context of network theory or graph theory, they can represent different concepts within a network graph.

[0052] Edge nodes, also known as boundary nodes, are network devices that reside at the edge of a network and are located near data generation points to partially perform the role of a server. Examples include gateways, routers, and micro data centers.

[0053] Edge nodes can perform tasks such as data routing, filtering, and transformation between internal and external networks, and play an important role in connecting local networks to wider networks such as the Internet.

[0054] Edge nodes can make local decisions, which refers to a decision-making process that occurs at a specific node or device within a network. These decisions are made based on information available at the node without necessarily involving or consulting other nodes in the network, and because local decisions are often made in real time, they can generally focus on optimizing the performance, efficiency, or behavior of individual nodes or a subset of nodes rather than considering the perspective of the entire network.

[0055] Edge nodes belong to specific groups, and each group can be assigned a unique ID.

[0056] A gateway can act as a bridge between a local network (e.g., a WSN or IoT network) and an external network such as the Internet, facilitating communication between devices within the local network and remote servers or services.

[0057] A gateway can refer to a device or software application that serves as an entry or exit point for data between different networks. It plays a crucial role in enabling heterogeneous networks to communicate effectively, even when operating with different technologies or protocols, by providing the necessary bridge for data to flow between incompatible networks, thereby ensuring efficient and seamless communication.

[0058] In a mobile gateway, each node can be static.

[0059] The gateway can perform data aggregation and maintain adjacent tables, and the tables are not static and can frequently change information when events occur.

[0060] Edge nodes can be interconnected to form a routing chain.

[0061] Edge nodes can provide necessary information about the network structure to the SDN controller.

[0062] The SDN controller is a critical component of SDN that provides centralized control, network programmability, and the ability to coordinate and optimize network operations in real time, which can simplify network management and enhance security.

[0063] An SDN controller can provide a single control point for configuring, managing, and monitoring network devices such as routers, dynamically adjust network traffic flow by configuring flow rules for network devices, and collect and analyze network telemetry data to monitor network performance, troubleshoot issues, and make informed decisions for network optimization.

[0064] Network traffic load can be distributed by utilizing SDN controller monitoring, and since the SDN controller possesses all network information, it can efficiently reduce additional overhead for nodes and links required to form routing paths.

[0065] The SDN controller can receive all metric results and thereby generate records for individual paths, where metric results generally refer to result values ​​obtained by measuring specific metrics or performance parameters, and mean quantitative measurements used to evaluate the status, efficiency, reliability, and overall performance of a network or its components.

[0066] The purpose of generating records for individual routes is to enable network administrators to optimize routing paths for better performance and to perform functions such as network troubleshooting and security analysis.

[0067] The SDN controller can reconstruct and send a new path with the help of edge nodes whenever the calculated metric value decreases to a specific threshold set in advance.

[0068] The intelligence of the SDN controller can be explored, where network intelligence refers to the capabilities of a network or its components that represent advanced functions beyond basic communication and data transmission, and this often includes the ability to process, analyze, and make decisions based on data and information aimed at optimizing network performance, enhancing security, and improving user experience.

[0069] It may further include an RSSI (received signal strength indicator) capable of determining the distance between nodes.

[0071] Hereinafter, a method for providing an edge-optimized embedded system according to an embodiment of the present invention will be described with reference to the attached drawings.

[0072] A flowchart of the method for providing an edge-optimized embedded system according to the present invention is shown in FIG. 1.

[0073] Referring to FIG. 1, in step S110, each node or device is registered with a nearby gateway during the network initialization step.

[0074] In one embodiment of the present invention, network attributes of each node or device may be registered, and whenever data detected by each sensor node or network attributes of each node and device need to be transmitted, n available paths may be followed as defined in Equation 1.

[0075] Here, network properties may refer to characteristics or attributes related to network components, nodes, links, or entities.

[0076]

[0077] In the present invention, the transmitted network attributes may include information about all nodes and may be subject to the condition that all nodes must be connected to each other.

[0078] Referring to FIG. 1, when each registered node or device is identified (step S120), a weighted cost condition is evaluated based on the received network information (step S130), and a subgraph is generated using the weighted cost condition (step S140).

[0079] Specifically, in the sub-graph generation step (step S120), an optimized spanning tree is generated (step S142) to improve response time and latency performance, and a minimum spanning tree (MST), which is the shortest path for the gateway to route IoT data from the spanning trees, is selected (step S144).

[0080] Here, the Minimum Spanning Tree (MST) is a concept in graph theory and network optimization that represents a subset of edges in an undirected connected graph that connects all vertices without forming cycles and has the minimum possible sum of edge weights.

[0081] A Minimum Spanning Tree (MST) of a graph is a tree that connects all vertices with minimum cost, and each node satisfies the properties of a tree by having no circuits or cycles.

[0082] The weight graph G(V, E), which is the input to the Minimum Spanning Tree (MST), has the characteristics of an undirected graph, and the weight graph G can be composed of a set of vertices V, a set of edges E, and edge weights w.

[0083] Weights can represent cumulative costs, which can mean the total sum of the weights of the edges existing on the path.

[0084] Referring to mathematical formula 2, the cumulative cost W(T) can be calculated as a combination of sub-roots.

[0085]

[0086] Here, u and v may represent thresholds or parameters used to control the construction of the minimum spanning tree based on edge weights; specifically, u may represent an edge and v may represent a vertex.

[0087] Each edge node can have each weight.

[0088] All identified minimum spanning trees (MSTs) can be considered as a group.

[0089] Referring to mathematical equation 3, minimum spanning trees (MSTs) can be identified from graph G, and each minimum spanning tree can be said to be a subset of graph G.

[0090]

[0091] Krushkal's algorithm can be utilized to identify subgraphs or minimum spanning trees.

[0092] Here, Kruskal's algorithm can gradually construct an MST by sorting all edges by weight and adding edges in ascending order of weight, as long as adding edges does not generate a period.

[0093] Referring to FIG. 1, in step S130, the edge node can evaluate weight conditions based on the network information received.

[0094] Referring to mathematical formula 4, the minimum weight of each link can be calculated, and the weight is not a single distance value but an optimized value, and the network can have dynamic properties.

[0095]

[0096] Here, w_i is the i-th weight, e is the energy, and lnl is the link loss. represents latency time.

[0097] Referring to mathematical formula 5, energy e can be calculated based on the power consumed during the transmission and reception of data packets.

[0098]

[0099] Here, Pr can represent the received power, Pt the transmitted power, K the number of data packets, and N the node.

[0100] Referring to mathematical formula 6, in the local table, entries for all nodes can be maintained until energy consumption is less than a preset threshold.

[0101]

[0102] Here, s_i represents the i-th consumption, N represents the node, R represents the data rate, and threshold represents the set threshold value.

[0103] In one embodiment of the present invention, whenever data loss or error rate increases, link performance has the lowest priority, and such links are avoided by the system.

[0104] If the residual energy of any node is lower than the threshold, the corresponding flag value may be 0.

[0105] Flags are often binary values ​​(0 or 1) used to indicate a specific state, condition, or condition, and can generally be used as a simple way to enable or disable specific functions, settings, or actions within a network or system.

[0106] Based on Equation 7, the link loss (lnl) due to link interference can be calculated. Link interference can determine the performance of a link connected between consecutive nodes.

[0107]

[0108] Here, DL represents data lost, and EC represents error computation.

[0109] Latency for a specific link based on mathematical formula 8 ( ) can be calculated.

[0110]

[0111] Here, m represents the input beacon message, and RTT (round-trip time) represents the round-trip time.

[0112] A beacon message refers to a short, regularly transmitted signal or message used as a method to indicate the presence or availability of a device, network, or service.

[0113] Round-trip Time (RTT) represents the time it takes for a data packet to travel from a source to a destination and back to the source, and is an important metric used to measure the latency or delay of data transmission between two points in a network.

[0114] The source node can select adjacent edges based on the minimum value of the weighted cost min(w_i), where an 'edge' of the minimum spanning tree refers to a specific connection between two nodes (vertices) selected as part of the minimum spanning tree based on low weight or cost, and can serve to connect all nodes of the network with the minimum total cost.

[0115] In the present invention, continuous monitoring can be performed to ensure that the selected edge of the minimum spanning tree does not form a loop, and the execution of the Krushkal algorithm can be stopped when the beacon message edge m-1 is included in the path.

[0117] Figure 2 shows a flowchart of a system that optimizes subgraphs using graph theory.

[0118] During the network initialization process, each node must be registered with the nearest gateway, and then, when calculating the weight cost based on the received information, if it corresponds to the minimum value, the edge is extracted, and the data of such an edge can be transmitted after confirming that it is an optimized minimum spanning tree.

[0119] Nodes can become components of an undirected weighted graph depending on optimal conditions, and based on this, various spanning trees can be formed.

[0120] The gateway can perform the function of selecting the minimum tree among the spanning trees for routing IoT data.

[0122] <SDN 아키텍처의 IoT 보안>

[0123] In one embodiment of the present invention, security for a two-layer architecture can be provided, in the first step, each node can establish a security zone together with a gateway, and in the second step, the gateway can communicate securely with an SDN controller through collaboration with sensor nodes and source nodes, wherein the gateway may be an edge node.

[0124] An SDN controller can perform security measurements and calculate computing costs, where security measurement refers to the practice of quantitatively or qualitatively evaluating the effectiveness, strength, and robustness of security measures, controls, and mechanisms established to protect information, systems, and resources from unauthorized access.

[0126] Figure 3 illustrates the operating principle of a system utilizing an SDN controller to enhance security as an embodiment of the present invention.

[0127] Referring to Fig. 3, in step S300, information about network attributes is registered with the SDN controller.

[0128] Referring to mathematical formula 9, the registration packet RQ can be composed of each node identity field Nid, energy Nenergy, and time stamp (TS).

[0129]

[0130] In one embodiment of the present invention, information transmitted by a sensor node after receiving a request from an edge node can be registered in an SDN controller.

[0131] The SDN controller can store adjacent node IDs in a table and use that table to determine the node's ID upon receiving a request from the node.

[0132] Referring to Fig. 3, in step S310, after verifying the received data at the edge node, information about the data is transmitted to the SDN controller.

[0133] The SDN controller acts as a Key Distribution Center (KDC) and can obtain overall information about the entire network.

[0134] Each node can create its own memory table using information received from adjacent nodes.

[0135] Each adjacent node can perform flooding, a form of uncontrolled port assignment, on other nodes within the same transmission range.

[0136] To reduce response time, the gateway can additionally forward data received from IoT to edge nodes.

[0137] The SDN controller can maintain updated information about the network.

[0138] Once the SDN controller is able to control each node and device within each network, input data can be verified to secure a security system with guaranteed integrity, and blockchain technology can be applied to the nodes constituting the system.

[0140] Referring to FIG. 3, in steps S320 to S330, when the SDN controller is communicating with the edge node, if the performance is unsatisfactory, the SDN controller sends a warning message to the edge node and reconfigures the routing process to proceed again from the registration step (S300).

[0141] In the present invention, blockchain technology can be explored after going through the routing step and the authentication step.

[0143] Referring to Fig. 3, in step S350, the SDN controller utilizes blockchain technology for security.

[0144] In the present invention, each node can register itself on the blockchain and guarantee the integrity of personal information protection.

[0145] The SDN controller can store adjacent node IDs in a table and use that table to determine the node's ID upon receiving a request from the node.

[0146] Referring to Equation 10, the SDN controller can generate a secret key s_k for a node ni digitally signed with a master key MK when the node ID is authenticated.

[0147]

[0148] A node receives a key and verifies it; if the value matches, the corresponding node can be registered on the blockchain.

[0149] A blockchain is a combination of hash values ​​in the form of a chain, and it can be executed until the entire path is completely protected from privacy and integrity attacks.

[0150] A blockchain consists of various data blocks, and each block can be separately encrypted with a digital signature to ensure authentication, and the encryption of each block can be performed using an XOR operation with a one-time pad and a data bit key.

[0151] Each device can individually utilize blockchain principles, thereby forming a highly stable system.

[0152] Digital hashes can provide the integrity of the device, and third parties or intermediate devices cannot access IoT data until permission is obtained.

[0153] The node that first transmitted the data can control its own data and how that data can be accessed by other devices.

[0155] < Simulation >

[0156] In one embodiment of the present invention, a network environment can be simulated based on various data sizes and the number of nodes.

[0157] Each node can be randomly distributed along with the gateway and edge nodes.

[0158] There are no restrictions on constraints for the SDN controller.

[0159] The results can be evaluated for throughput, packet drop rate, propagation delay, and node overhead.

[0160] The simulation is run with 75 to 375 nodes, the data size can vary from 10KB to 50KB, and the simulation can run for 5000 seconds.

[0161] Nodes can have a homogeneous structure in terms of resources.

[0162] The initial energy of the node can start from 3j to 6j.

[0163] The transmission power of the node can be fixed at 10m.

[0164] The network size can be set to 5000m × 5000m.

[0165] The number of gateways and edge devices can be set to 10 and 5, respectively.

[0166] To evaluate the link interruption and data accuracy performance of the proposed system, malicious nodes can also be distributed in the simulation environment.

[0167] Table 1 shows the simulation variables used in the test.

[0168]

[0170] < Evaluation Results >

[0171] Evaluation metrics can be viewed as throughput, packet drop rate, propagation delay, and node overhead.

[0172] Referring to Figures 4(a) and 4(b), better performance can be observed in terms of network throughput than before, with network throughput increasing significantly by an average of 19% and 22%, respectively. This is the result of extracting spanning trees based on minimum cost using graph theory.

[0173] In one embodiment of the present invention, a path is updated by searching for link loss rate and latency parameters, and actual data packets can be delivered in a timely manner through efficient bandwidth utilization.

[0174] In one embodiment of the present invention, the proposed security algorithm can increase the strength of the system using a private key and remove fake packets from actual data.

[0175] In one embodiment of the present invention, an optimal path can be determined through an artificial intelligence approach and cost function calculation, and in particular, the subgraph can provide the shortest path through link evaluation and reduce communication channel costs.

[0177] Referring to Figures 6(a) and 6(b), the performance of the related solution can be compared in terms of propagation delay. It can be observed that as the number of nodes and data size increase, the propagation delay also increases, and it can be confirmed that the present invention can improve the propagation delay by an average of 19% and 31%.

[0178] In addition, the proposed system can minimize the most harmful attacks at the IoT layer, and edge devices can intelligently protect channels to the SDN controller.

[0180] Referring to Figures 7(a) and 7(b), it can be shown how the SDS-GIoT protocol demonstrates superior performance compared to other solutions in terms of packet deletion rate. The performance results show that as the number of nodes increases and the data size increases, the packet loss rate also increases. The present invention can reduce the packet loss rate by an average of 39% and 41% compared to other related tasks.

[0181] The SDN controller can continuously monitor network flow and issue commands to reroute data to edge devices, such as sensor nodes, using various conditions whenever performance is unsatisfactory.

[0182] When the routing table is updated, the corresponding node can be removed by the SDN controller if the energy is below the threshold.

[0183] The communication link can regularly monitor latency and data loss, and whenever harmful activity is performed, a relevant warning message is recorded in the forwarding table and the node can be notified of the situation.

[0185] In FIGS. 8(a) and 8(b), the node overhead is compared with other tasks, and intelligent computing can be utilized to enhance the stability of the IoT network through collaboration with edge computing. Generally, it can be observed that node overhead increases as the number of nodes and data size increase, but the present invention can improve the node overhead by an average of 28% and 37%, respectively, by maintaining the energy load balance of the nodes and reducing excessive overhead to the network. Explanation of the symbols

[0187] 500: A device capable of executing an edge-optimized embedded system 502 : Processor 504 : Memory 506: Input / Output Interface 508: Network Communication Interface

Claims

Claim 1 A network registration step in which each node or device is registered with the nearest gateway during a network initialization phase; a subgraph generation step in which, when each registered node or device is verified, an edge node evaluates a weighted cost based on received network information and generates a subgraph using the weighted cost; and a monitoring step in which, to enhance network security, the edge node provides the network information to an SDN controller, and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation step further includes a spanning tree generation step in which an optimized spanning tree is generated to improve response time and latency performance; and a path selection step in which a gateway selects a Minimum Spanning Tree (MST), which is the shortest path for routing IoT data among the spanning trees; wherein the weighted graph G(V, E), which is the input value in the minimum spanning tree, has the characteristics of an undirected graph, and the cumulative cost of the weights A method for providing an edge-optimized embedded system that can be calculated based on, where u represents an edge and v represents a vertex. Claim 2 A method for providing an edge-optimized embedded system, wherein the network monitoring step further comprises: a step of registering information about network attributes in an SDN controller; a step of verifying data received from the edge node and then transmitting the verified data to the SDN controller; a step in which, if performance is unsatisfactory during the communication process between the edge node and the SDN controller, the SDN controller sends a warning message to the edge node; a step in which the SDN controller that sent the warning message reconfigures the routing process; and a step in which the SDN controller utilizes blockchain technology to enhance security. Claim 3 delete Claim 4 A method for providing an edge-optimized embedded system according to claim 1, wherein the Krushkal algorithm can be utilized to identify the subgraph or the minimum spanning tree, and the Krushkal algorithm sorts all edges within the graph or tree by weight and can progressively build the minimum spanning tree by adding edges in ascending order of weights as long as no cycle is generated when edges are added. Claim 5 A network registration step in which each node or device is registered with the nearest gateway during a network initialization phase; a subgraph generation step in which, when each registered node or device is verified, an edge node evaluates a weighted cost based on received network information and generates a subgraph using the weighted cost; and a monitoring step in which, to enhance network security, the edge node provides the network information to an SDN controller, and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation step further includes a spanning tree generation step in which an optimized spanning tree is generated to improve response time and latency performance; and a path selection step in which the gateway selects a Minimum Spanning Tree (MST), which is the shortest path for routing IoT data among the spanning trees; and wherein the weight evaluation It can be calculated based on, where w_i is the i-th weight, e is the energy, and lnl is the link loss. A method for providing an edge-optimized embedded system that indicates latency time. Claim 6 In paragraph 5, the above energy e is based on the power consumed during the transmission and reception of data packets. A method for providing an edge-optimized embedded system that can be calculated based on, where Pr is the received power, Pt is the transmitted power, K is the number of data packets, and N represents a node. Claim 7 In paragraph 5, the above link loss may be caused by link interference, A method for providing an edge-optimized embedded system that can be calculated based on, where DL represents data lost and EC represents error computation. Claim 8 In paragraph 5, the above delay time A method for providing an edge-optimized embedded system that can be calculated based on, where m is an input beacon message and RTT (round-trip time) represents the round-trip time. Claim 9 A method for providing an edge-optimized embedded system, characterized in that, in the step of utilizing the blockchain technology of claim 2, the SDN controller can store adjacent node IDs in a table, and upon receiving a request from a node, can use the said table to verify the node ID, and if the node ID is authenticated by the SDN controller, can generate a secret key s_k for the ni node digitally signed with the master key MK. Claim 10 A method for providing an edge-optimized embedded system, wherein, in the step of utilizing the blockchain technology, the blockchain is composed of various data blocks formed by combining hash values ​​in a chain form, and each block may be separately encrypted with a digital signature to ensure authentication, and the encryption of each block is formed using an XOR operation with a one-time pad and a data bit key. Claim 11 A network register in which each node or device is registered with the nearest gateway during the network initialization phase; a subgraph generation unit that, when each registered node or device is verified, evaluates a weighted cost based on network information received by an edge node and generates a subgraph using the weighted cost; and a monitoring unit in which, to enhance network security, the edge node provides the network information to an SDN controller, and the SDN controller monitors the network using blockchain technology; wherein the subgraph generation unit further includes a spanning tree generation unit that generates an optimized spanning tree to improve response time and latency performance; and a path selection unit in which the gateway selects a Minimum Spanning Tree (MST), which is the shortest path for routing IoT data among the spanning trees; wherein the weighted graph G(V, E), which is the input value in the minimum spanning tree, has the characteristics of an undirected graph, and the cumulative cost of the weights It can be calculated based on an edge-optimized embedded system providing device, where u represents an edge and v represents a vertex.

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