The framework consists of a group of deployed nodes in an undeterred environment. After deployment, clusters are formed based on geographical segmentation. The proposed method employs a three-hierarchy cluster formation, dividing the spatial region into three equal divisions. Communication within a cluster occurs exclusively between sensor nodes and their respective cluster heads. Information such as cluster head details, path information, energy for each node, and distances between nodes are stored in the segment table. Dynamic learning of node, route, and packet information is assisted by the LD2FA. The route table contains the information that is updated on a regular basis for every node. When a packet has to be broadcast, LD2FA finds the pathways that are open from the source to the destination. In addition to validating these pathways and updating the legitimate ones in the path table, LD2FA works with the LD2FA-PSO algorithm to analyze nodes, packets, and routes in order to remove unsolicited assaults and intruders from the network, such as Sybil attacks and selective forwarding. LD2FA-PSO finds the best route, guaranteeing safe and effective packet transport from source to destination.
Network Model:
The network model consists of N nodes dispersed over an unimpeded environment. Table 1 lists the distinct attributes of each node, which include threshold, maximum packet size, message size, and starting energy. The network lifespan is used as the key performance parameter by the framework, which groups nodes into clusters.
Table 1: Node Properties
Parameter
|
Value
|
Sensing Region Length
|
200 meters
|
Sensing Region Width
|
200 meters
|
Cluster Radius
|
30 meters
|
Sensing Range
|
36 meters
|
Number of Nodes
|
200
|
Packet Size
|
512 Bytes
|
Initial Energy (E₀)
|
200
|
X Coordinates
|
-50:0.1:50-0.1
|
Y Coordinates
|
40sind(2pi7X) + 75cosd(2pi2X)
|
Transmission Power
|
0.02
|
Reception Power
|
0.01
|
Initialization:
- Dynamic sensor nodes (η), initial energy for each node (ε), packet size (Pkts), and acknowledgment size (Ar) are initialized.
- Nodes are randomly placed in a mesh connection-based network.
Path Identification:
- Nodes are dynamically chosen from the network.
- All possible paths from source (S) to destination (D) are identified.
Path Validation and Update:
- LD2FA examines the validity of identified paths based on the path table (DFApt).
- Valid paths are updated in the path table.
PSO Optimization:
- PSO inspects node, route, and packet information in the path table.
The optimal path is chosen from the table. Secure and Efficient Data Transfer: Packets are transferred through the chosen optimal path, ensuring secure and efficient data transmission.
The network creation model ensures nodes are organized into clusters, and the network's lifetime is a key evaluation metric. The segment table stores vital information about each node, facilitating efficient communication and data transmission within the network. The path table, containing available paths, plays a crucial role in determining the optimal route for data transmission from source to destination
Learning Dynamic Deterministic Finite Automata (LD2FA)
Automata Components
- S: Source node
- D: Destination node
- Path validity indicator
Route Exploration
Dynamic Route Exploration: When the communication start from the source (S) and destination (D) nodes, LD2FA dynamically discover the route between them.
Individual LD2FA Construction: For each route searching, an individual LD2FA is dynamically designed. This algorithm detain the states and transitions equivalent to the specific route.
Learning-Based Adjudicator
Collection of LD2FAs: A learning-based method all the LD2FAs generated during route explorations.
Minimum Hop Count Determination: this minimum hop analyzes the collected LD2FAs and determines the final path for transmission based on the minimum hop count. This aims to optimize the chosen path.
Accepted Path Commitment: The path with the minimum hop count is established and committed as the best path for transmission. This decision is crucial for achieving efficient and energy-saving data transmission.
Encoding for Performance Improvement
Encoding Process: Encoding is functional to the packet stream to enhance performance along the accepted path.
Packet Stream Enhancement: The encoding procedure involves adapt the packet data to improve its efficiency and effectiveness during transmission.
Data Packet Transmission
Transmission the length of explores Route: The data packets are transmit all along the explored route, following the determined and optimized path.
Cluster network
Sybil Attack Detection
A Sybil attack is a type of where a node impersonates other nodes by adopting multiple different identities. However, in reality, there exists only individual physical node with a variety of distinct IDs. The Sybil attack detection process is crucial for maintaining the integrity and security of the network. The following outlines the procedure for detecting Sybil attacks:[15]
Procedure: Sybil Attack Detection
![](data:image/png;base64,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)
Explanation:
- Iteration Over Nodes (i = S to D): The algorithm iterates over each node in the network from the source (S) to the destination (D).
- Check Node Information (Ni): For each node (Ni), the algorithm checks its current information.
- Next Hop Information Check: It examines the NextHopInfo of the node. If the NextHopInfo matches the information stored in the Segment_Table, it indicates a potential Sybil attack.
- Malicious Node Blocking: If a Sybil attack is detected, the node (Ni) is identified as malicious, and its status is updated in the Segment_Table by setting Ni(malicious_node) = 1.
- Normal Node Setting: If no Sybil attack is detected, the status of the node is set to 0, indicating that it is not identified as a malicious node.
An application of Particle Swarm Optimization (PSO) is used in the investigation of path tables, the examination of nodes, and the examination of data pertaining to packets and routes. Unwanted assaults, such as selective forwarding and Sybil attacks, are successfully eliminated inside a network using this method. The development of optimum routes is made possible with the incorporation of PSO into the Dynamic Distributed Framework Algorithm (DDFA). Initially, a network that is built on mesh connections is begun, and nodes are placed in a random order inside the network. DDFA makes both the selection of nodes and the determination of all possible routes from the source to the destination in a dynamic manner. In order to verify routes, the path table is used, and DDFA will update the path table if it is determined that the routes are real. PSO then checks the information of the node, route, and packets contained inside the path table in order to pick the most effective route for the transmission of secure packets from the source to the destination from the path table.