Machine learning is deployed as the dynamic updating process based on the routing tables in networking. The machine learning process includes the performance based on the process such as amount of harvesting energy prediction in networks, node localization in networking, separation and recognition of faulty nodes in the process of network optimization.
Network Research Issue with ML Based Solution
Generally, each and every research field includes some issues while implementing and innovating ideas. Similarly, the field of network includes some issues such as
- Node query processing
- k-NN is the machine learning technique that is deployed to acquire the handshake for data transfer and node beacon sending
- Energy harvesting
- Evolutionary computing, deep learning and SVM are used to predict the amount of battery energy required to maximize the network lifetime and predict the availability of energy harvesting
- Event monitoring and target detection
- Bayesian learning and PCA are used to track multiple target and efficient event monitoring and monitoring the efficient event monitoring
- Sensor data aggregation
- Reinforcement is deployed to decide the optimal cluster head in wireless sensor network nodes and the dynamic configuration of the nodes
- MAC layer issues
- Artificial neural network and decision tree are used for the efficient channel assignment
- Routing layer issues
- Random forest leads for the optimal routing path prediction based on the data traffic
- Node coverage and connectivity
- Evolutionary computation is deployed to classify the connected and failed nodes in the sensor network, identification of nodes with both the poor and good connectivity
- Node localization
- It includes both the reinforcement learning and k-NN to estimate the range and efficient distance
EER Protocols for WSN Using Machine Learning Algorithms
In wireless sensor network applications, there are two significant views and they are named as the associated issue based on application and network.
- Application associated issue
- The algorithms based on machine learning are deployed for the target tracking, identification and event classification of target class and information processing
- Network associated issue
- Scheduling
- Data aggregation and fusion
- Resource allocation
- QoS
- Energy aware routing and clustering
- Security
- Localization
- Optimal node deployment
Zone Routing Protocol and K-Means Clustering by Using Machine Learning
The inter zone routing protocol is used to regulate the reactive part when the intra zone routing protocol with the proactive part. In IARP the link state algorithms are deployed to create the route. The various clustering algorithms are included in MANET and they are deployed for the process such as density, grid and hierarchical for positioning. The dynamic K-means process is created using various approaches for the detection of intrusion.
Machine Learning Approach for Multiple Misbehavior Detection in VANET
The main objective of machine learning approach is to classify the multiple misbehaviors in VANET for the utilization of behavioral and concrete features in all the nodes to send the safety packets. The classification of misbehaviors includes the multifarious features such as.
- Dropped packets
- Number of packets delivered
- Received signal strength (RSS)
- Speed deviation of node
In addition, the machine learning approach includes two types of classification accuracies for the measurement and that are enlisted in the following.
- Multi class classification
- It is capable to categorize the misbehaviors into the specific misbehaving classes
- Binary classification
- All the classes based on misbehaviors are included in a particular misbehaving class
Our research professionals have listed out the process of classification that is performed through the utilization some features such as.
- Collided
- Dropped
- Delivered
- Number of packets generated
- Received signal strength (RSS)
- Distance
- Speed deviation
The observers of nodes are used to exchange the observation with some other nodes in the vicinity with the experiments and evaluations through the usage of Ada boost1 classifiers, random forest, J-48, IBK and naive Bayes. In addition, these are denoted as the classification algorithms.
LTE Multiple User MIMO Scheduling Example
While implementing the machine learning process, the randomly scheduled users in networking are included. The characteristics of training data is there in the user group to the label with low, medium, high and MIMO layers to represent the massive MIMO LTE network performance. The process of LTE network operation includes the scheduler to present the users to schedule the accessible MIMO layers. It is functioning with the channel state information and that is reported through UE with some algorithmic parameters and scheduling combinations. For instance, the multi user MIMO system model includes some elements such as.
- Channel state information
- Mobile station
- Base station
- Pre coding power allocation
- Multi user scheduling
With the help of all these recent research approaches based on machine learning, the research scholars may precede your research based on machine learning in networking projects along with our research professionals. We have a lot of recent research techniques, tools and protocols to provide the finest machine learning in networking projects. In addition, here we provided the innovative processes in routing and they are deployed in machine learning for your reference.
Machine Learning for MANET Routing
- UniformAnts
- It is the represents the simple ant optimization based technique to regulate the routes in wireless network. Some forward ants with the updating probability for routing tables in nodes and that travel towards the sink
- AntHocNet and extensions
- It is cited with the routing protocols with the utilization of colony optimization in AntHocNet and that is particularly designed for the requirements of wireless ad hoc network with the exploration using the swarm intelligence in wireless networks
- MANSI
- It is denoted as the multicast routing protocol for MANETs and it is parallel to the traditional multicast protocols and the core nodes are initiated with the creation of multicast tree and both the backward join reply packet and forward join request packet are included in this process
Machine Learning for WSN Routing
- HCR
- Hierarchical cluster based routing is abbreviated as HCR and it is denoted as the extension of LEACH clustering algorithm and it includes the genetic algorithm to create the clustering process. It is anticipated with the base station and that includes the complete knowledge about the network topology along with the status of battery in all the nodes
- Q-Routing
- It is considered as the significant packet routing process with the utilization of machine learning techniques. It is utilized to know about the finest routes and that includes some least latency towards the destination
- SARA
- Statistically assisted routing protocols are called SARA and it includes the routing process from the source to destination. The energy based greedy process to forward the techniques and some learning based algorithms
Machine Learning and Blockchain Technology in Networking
The efficient network operation and management, intelligent, security and decentralizations are achieved through the joint consideration of blockchain and machine learning and that provides the notable benefits to attract the finest interests in the industrial platform and academic spectrum. While processing blockchain, it includes the functions such as trusted decision making, security, privacy, decentralized intelligence, machine learning model sharing and facilitates training data in machine learning. The process of machine learning includes the notable impacts for the enhancement of blockchain in communication and networking systems along with the process such as,
- Intelligent smart contracts
- Security and privacy
- Scalability
- Energy and resource efficiency
Outline of the Integration of Blockchain and Machine Learning for Communication and Networking Systems
The machine learning adoption to create the BT based smart applications for the resilient process against the attacks. It includes several traditional machine learning techniques such as
- Deep learning (DL) algorithms
- Long short term memory (LSTM)
- Convolutional neural network (CNN)
- Bagging
- Clustering
- Support vector machines (SVM)
In addition, all these techniques are deployed in the analysis process of attacks for the blockchain based network. Several technologies are applied in some smart applications including the smart cities, healthcare, smart grid and unmanned aerial vehicle.
- Machine learning can benefit blockchain
- Intelligent smart contract
- Scalability
- Security and privacy
- Energy and resource efficiency
- Overview of blockchain
- Blockchain applied in communications and networking systems
- Taxonomy of blockchain
- Architecture of blockchain
- Blockchain can benefit machine learning
- Trusted for decision making
- Decentralized intelligence
- Security and privacy
- Data and model sharing
Our research professionals have created this page for your reference to mention a whole thought about the machine learning in networking projects. We hope that you got a wide ranging idea about the research significance to precede you research based on machine learning in networking projects. Our research experts provide assistance for all your research needs. So, join hands with us to reach better heights in your research career.
