🌐Introduction
Last updated
Last updated
Zentamesh is a decentralized network technology that enables efficient and secure communication for Zentalk users, even when they are offline. In the Zentameshnet, nodes cooperate in the distribution of data and each device using a node with Zentalk acts as a node, connecting to each other in a distributed manner. Messages are relayed through the network using a flooding technique, also known as re-routing, and pass from node to node until they reach their designated recipients. Communication between the sender and recipient remains secret, as nobody knows which node is sending or receiving which message.
One of the key benefits of Zentamesh is its ability to operate even in areas without internet access, making it an ideal solution for users who need to stay connected in remote or underserved regions. The network uses advanced routing technologies, such as Q-learning and Artificial Neural Network (ANN) integration, to optimize communication and manage resources and reservations to meet real-time bandwidth and latency constraints.
This helps to ensure reliable and secure communication, even in dynamic environments. Q-learning is used to train an artificial intelligence (AI) agent to make decisions about routing messages through the network based on factors such as the availability of nodes, the distance between nodes, and the current load on the network. This allows the AI agent to learn and improve its decision-making over time, leading to more efficient and effective routing of messages through the network.
In addition to its advanced routing capabilities, Zentamesh also uses self-healing algorithms, such as Shortest Path Bridging, to guarantee all routes are available and reset corrupted or defective routers. When a Zentanode shuts down or a connection becomes unreliable, self-healing allows the network to still function. This helps to ensure that Zentamesh is a highly reliable and secure communication solution.
Overall, Zentamesh is an ideal solution for users who need efficient and secure communication, even when offline or in areas without internet access. Its decentralized nature, advanced routing capabilities, and self-healing algorithms make it a highly reliable and secure option for any application. The use of Q-learning in the routing of messages through the network helps to optimize communication and make the Zentameshnet even more effective and efficient.
Q-learning is a type of reinforcement learning algorithm that is used to train artificial intelligence (AI) agents to make decisions in complex environments. It is based on the idea of learning a value function that estimates the expected reward for taking a particular action in a particular state.
In Q-learning, the value function is known as the Q-function, and it is represented as a table or a function that maps states and actions to values. The Q-function is initialized with arbitrary values and is updated iteratively as the AI agent interacts with the environment.
At each time step, the AI agent observes the current state of the environment and chooses an action based on the current Q-function. After taking the action, the AI agent receives a reward from the environment and updates the Q-function to reflect the new information it has learned. This process continues until the AI agent reaches its goal or runs out of possible actions to take.
Q-learning is commonly used in control systems, robotics, and games, where the AI agent needs to make decisions based on incomplete or changing information. It is a powerful tool for training AI agents to act intelligently in complex and dynamic environments, and has been applied to a wide range of problems, including control of robots, traffic management, and game playing.
An artificial neural network (ANN) is a computational model that is inspired by the structure and function of biological neural networks. ANNs are composed of interconnected "neurons" that communicate with each other and process information. The neurons are organized into layers, with the input layer receiving data from the outside world, the hidden layers processing the data, and the output layer producing the result of the computation.
Each neuron in an ANN receives input from other neurons, processes it using an activation function, and sends the output to other neurons in the next layer. The activation function determines the output of a neuron based on its input, and can be a simple threshold function or a more complex non-linear function.
ANNs are trained using a large dataset and an optimization algorithm, such as stochastic gradient descent. The training process adjusts the weights of the connections between neurons to minimize the error between the predicted output and the desired output. The weights of the connections are typically initialized randomly and are adjusted iteratively as the network is trained.
ANNs are widely used in a variety of applications, including image and speech recognition, natural language processing, and decision-making. They are particularly useful for tasks that involve complex patterns or relationships that are difficult to represent using traditional algorithms. ANNs are also highly flexible and can be adapted to a wide range of problems by adjusting the number and size of the layers and the type of activation functions used.
In this expanded diagram:
The Zentamesh Network is the central node from which everything branches.
Zentachain incorporates an Artificial Neural Network (ANN) to improve efficiency and performance, including input-output training on nodes and detection of status and availability of nodes.
Zentamesh V2 uses Q-learning to optimize routing, improve offline packet transmission and reliability.
Zentamesh V2 also incorporates Neural Networks to analyze data patterns, optimize routing decisions, and increase network adaptability.
The ANN is used for Q-learning, estimating future rewards and improving performance. The ANN gets updated based on the difference in rewards and aids in enhancing decision making.
Zentagate runs the service that allows data and transactions to be routed on Zentamesh with Zentanodes, which allows the user to stay connected without internet access. It ensures extra encryption by AES and an anti-hack layer, to provide the user is engaged and secured safely. Zentagate connects the ecosystem to use Zentamesh such as the Internet. All nodes in Zentamesh can also act as their Gateway to expand the network. Since each node owner has its own ID, they can isolate their node with a pin code to connect to their nodes.
Zentagate runs the service that allows data and transactions to be routed on Zentamesh with Zentanodes, which allows the user to stay connected without internet access. It ensures extra encryption by AES and an anti-hack layer, to provide the user is engaged and secured safely. Zentagate connects the ecosystem to use Zentamesh such as the Internet. All nodes in Zentamesh can also act as their Gateway to expand the network. Since each node owner has its own ID, they can isolate their node with a pin code to connect to their nodes.
In the Zentachain network, security is of paramount importance. One of the main ways this is achieved is through the use of Advanced Encryption Standard 256-bit (AES-256) encryption. This is a symmetric encryption algorithm that is widely recognized for its high level of security and is used extensively for sensitive data protection.
Each piece of data transmitted through the Zentachain network is encrypted with AES-256 from the point of origin, whether that's a Zentanode or Zentagate. This encryption remains intact throughout the entire data transmission process.
When a data packet is to be sent, it's first encapsulated and encrypted at the source node. This process converts the raw data into an unreadable format, providing a robust shield against any potential interceptors. The data packet is then sent through the network, hopping from node to node or via the gateway, until it reaches its intended destination.
Each node or gateway involved in this data transmission process is only able to decrypt the metadata necessary for routing the data packet to the next point. They do not have the capability to decrypt the actual data content. This ensures that the data remains secure and unreadable to any unintended parties, even if they are part of the same network.
Upon reaching the destination node, the data packet is finally decrypted using the corresponding AES-256 key. This decryption process transforms the data back into its original, readable format. Since only the intended recipient node possesses the correct decryption key, the data remains secure from all other parties.
To sum it up, Zentachain employs AES-256 encryption to secure data transmission across the network. This sophisticated encryption mechanism ensures that all data, regardless of its journey through the network, remains secure and confidential until it reaches the intended recipient.
