Machine Learning in Networking Projects

Machine Learning in Networking Projects

Machine learning is the process of learning data transmitted over the network. For instance, it is used in dynamically routing table updates. We guide research scholars to implement machine learning in networking projects. We can perform the following processes using the machine learning process.

  • Location prediction of network nodes 
  • Fault node separation and identification for performance optimization of network 
  • Energy harvesting for networks 
  • Predicting how much energy is being harvested from a network.

As machine learning algorithms increase knowledge, they keep humanizing incorrectness and effectiveness. This lets them construct superior decisions. As the quantity of data keeps increasing, Machine learning algorithms gain knowledge to make more exact predictions quicker.

If you are planning to do projects based on machine learning in networking and also looking for knowledgeable and experienced online guidance for your machine learning in networking-based projects, then you are totally at the exact one-stop solution to your search. Now let us look into the different aspects of projects in machine learning. Let us now start with the working of machine learning in networking.

Innovative Machine Learning in Networking Projects

Machine Learning Techniques for Networking 

The accuracy and novelty of the projects depend heavily on the techniques used. The techniques ensure the easy flexibility of the service. Now let us know about the techniques used for machine learning in networking projects. The techniques can be classified and listed as below:

  • Node Localization

–         K-NN, Reinforcement Learning (RL) – Estimate distance accurately, calculate the range

  • Node Coverage and Connectivity

–         Decision trees, ANN, Evolutionary Computation– Node classification in a sensor network (connected or failed nodes, identify nodes with poor or good connectivity)

  • Routing Layer Issues

–         Decision Tree, Random Forest – predict optimal routing paths depending on the lead data traffic.

  • MAC Layer Issues

–         SVM, Decision Tree, ANN– Channel assignment that is efficient

  • Sensor data aggregation

–         K-means, SVM, Reinforcement – determine optimal cluster heads in WSN nodes, set up the dynamic configuration for WSNs.

  • Event Monitoring and Target Detection

–         PCA, Deep Learning, Evolutionary Computing, Bayesian Learning – track multiple targets with efficient event monitoring

  • Energy Harvesting

–         SVM, Deep Learning, Evolutionary computing – Predicting how much battery power is required for optimum network lifetime, to predict the availability of energy harvest in the future.

  • Node Query Processing

–         kNN – Beacons on nodes, data transfer by handshake.

Since we have been working with research scholars and students from numerous research areas in machine learning in networking for the past ten years, we are highly experienced and have got a significant amount of research knowledge in the field. 

Research Issues of Networking 

The following are the issues associated with machine learning algorithms. There are two views for machine learning in the WSN application.

Network associated issue

  • Machine learning techniques have been employed for optimum node placement, localization, safety, energy-conscious route and clustered, QoS, allocation of resources, aggregation of data and fusing, and schedule in the network-related problem.

Application associated issue

  • Applications of machine learning to information processing, classification of events, and target identification.

These issues can be without difficulty dealt with when our experts are guides to you. The workforce in the world is equipped with the skills to deal with machine learning issues. So it is uniformly important for you to learn the technical skills to solve issues in machine learning. 

Our guidance pays individual concentration to teach you the technological details and mobilize resources to you so that you can evaluate yourself. Thus your skills are greatly improved with our expert guidance to implement machine learning in networking projects. Now let us look into the application of machine learning in networking.

Routing Protocol for Networking using Machine Learning 

Machine learning is important for you to know about algorithms for machine learning in networking projects. There are multiple ways to learn these algorithms online. However, we make your work easier. Our engineers have well trained in implementing the projects with these algorithms. Machine learning algorithms can be classified as below.

  • In Zone Routing Protocol, proactive components are handled by inter-zone steering Protocol.
  • IARP routes are created using Link State algorithms.
  • In MANET, there is a range of cluster algorithms, including portioning algorithms, hierarchical algorithms, density-based algorithms, and grid algorithms that are based on the detailed problem.
  • A new approach to detecting intrusion in MANETs was proposed through the use of effective K-Means.

Machine Learning for Multiple Misbehavior Detection in VANET

  • Using the geographic, behavioral, and concrete characteristics of the nodes that send safety packets, a machine learning algorithm is used to classify multiple misbehaviors in VANET.
  • A variety of behavior characteristics are considered when classifying the misbehaviors, including node speed deviation, received signal strength (RSS), packet number delivered, and packets dropped.
  • Classification accuracy is measured using two methods: Binary and Multi-Class.
  • Misbehaviors of all types are classified under one misbehavior class in binary classification.
  • Using a Multi-class classification, different misbehaviors can be classified according to the misbehaviors.
  • Based on the selected features, we can perform classification, for example, speed deviation, distance, RSS (received signal strength), number of generated packets, delivered packets, dropped packets, and collided packets.
  • Nodes in the vicinity of observers exchange their observations.
  • A variety of statistical methods are used in the evaluation of the experiments, including Naive Bayes, Ada Boost1, Random Forest, IBK, and J-48.

Machine Learning-based Multiple-user MIMO Scheduling in LTE

  • Random scheduling of users will be implemented in a Machine Learning implementation.
  • This training dataset shows how well our massive MIMO LTE network does by categorizing user groups into MIMO layers and labeling them [low, medium, high].
  • Schedulers receive information about how many users are available and how many MIMO layers there are.
  • Scheduling positions may be assigned based on UE channel state of affairs information as well as extra algorithmic parameters.

Machine Learning for MANET Routing Protocols 


  • The MANET routing protocol is known as MANSI.
  • The build of the tree is initiated by a core node with an onward Join demand message and toward the back Join respond to message, similar to traditional multicast protocols.

AntHocNet and extensions 

  • AntHocNet used ant colony optimization to be one of the most cited routing protocols.
  • In the literature, this is the most explored and evaluated method for using swarm intelligence in wireless networks since it is specifically designed to meet the needs of an ad-hoc wireless network.

Uniform Ants

  • The paper proposes a simple theorem to maintain routes in a wireless system based on ant-optimization.
  • The forward-only approach uses probabilistic routing tables, updating them as the ant approaches the sink. 

Machine Learning for WSN Routing Protocols 


  • Q-Routing is among the most essential and early studies in packet forwarding utilizing ML algorithms. 
  • This is a Q-learning-based system that finds the best pathways to locations with the least latencies.


  • It’s a single-source, standard routing technique with statistical assistance. 
  • Aside from various packet forwarding strategies based on resources and having to learn algorithms.


  • HCR (Hierarchical Cluster-Based Routing) is a variation of a very well LEACH clustering method that incorporates an evolutionary algorithm into the cluster formation process. 
  • The base station is believed to have a complete awareness of the network, including the topology and charge state of all nodes.

Blockchain Technology and Machine Learning in networking projects

  • The combined study of blockchain and machine learning (ML) can provide substantial advantages to accomplish decentralized, secured, intelligence, and modern information systems management and operation, and has piqued the attention of both academic and industrial.
  • Blockchain technology facilitates sharing of training information and machine learning models and enables security, confidentiality, and trust in machine learning decisions.
  • Moreover, machine learning will directly affect how blockchains are developed, including issues that include energy and resource competence, scalability, safety, isolation, and the implementation of the clever smart contract.
Interesting Top 10 Machine Learning 
Networking projects

Machine Learning Applications in Networking 

  • Various traditional machine learning techniques are utilized, such as bagging and clustering, Support Vector Machines (SVM), as well as Deep Learning (DL) algorithms, Long Short-Term Memory (LSTM), and including Convolutional Neural Network (CNN) is available to analyze blockchain-based attacks.
  • Many smart applications can be built using these technologies, including Smart Grids (SG), Unmanned Aerial Vehicles (UAV), smart cities, and healthcare.
  • By implementing machine learning, smart applications based on BT can withstand cyber-attacks better.

With this enlarged view on machine learning in networking projects, you can now attempt to choose a topic of research on your own. Or if you have got doubts, then be quick to contact us. We are ready all the time to answer your problems with machine learning networking projects. Get in touch with our experts for all your research needs.

Live Tasks
Technology Ph.D MS M.Tech
NS2 75 117 95
NS3 98 119 206
OMNET++ 103 95 87
OPNET 36 64 89
QULANET 30 76 60
MININET 71 62 74
MATLAB 96 185 180
LTESIM 38 32 16
CONTIKI OS 42 36 29
GNS3 35 89 14
NETSIM 35 11 21
EVE-NG 4 8 9
TRANS 9 5 4
PEERSIM 8 8 12
RTOOL 13 15 8
VNX and VNUML 8 7 8
WISTAR 9 9 8
CNET 6 8 4
ESCAPE 8 7 9
VIRL 9 9 8
SWAN 9 19 5
JAVASIM 40 68 69
SSFNET 7 9 8
TOSSIM 5 7 4
PSIM 7 8 6
ONESIM 5 10 5
DIVERT 4 9 8
TINY OS 19 27 17
TRANS 7 8 6
CONSELF 7 19 6
ARENA 5 12 9
VENSIM 8 10 7
NETKIT 6 8 7
GEOIP 9 17 8
REAL 7 5 5
NEST 5 10 9

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