M2M Communication Research Topics

M2M Communication Research Topics is widely used in many applications. It is a communication technology which transfers information among devices without the help of humans. In this we provide some details that are related to the M2M Communication technique.

  1. Define M2M communication

Initially we begin with a definition for M2M communication technology. It is an expansion of Machine-to-Machine communication, and it describes the straight interaction among machines or devices not with the help of human involvement. In this interaction the devices interchange data and information with everyone, allowing them to make decisions, work collaboratively and share details not with the help of human input.

  1. What is M2M Communication?

At the next stage we note down the detailed description of M2M communication. It is an extensive classification which is utilized to explain any technology which permits networked devices to interchange details and achieve actions not with the assistance of human instructions. Machine Learning (ML) and Artificial Intelligence (AI) provide the interaction among systems, permitting them to create their own autonomous choices.

  1. Where M2M communication used?

Afterwards the detailed description we discuss where to utilize M2M communication. It is utilized in different applications and industries to allow the devices to change the data and operate individually. Some fields where M2M communication is utilized are Smart Cities, Transportation and Logistics, Manufacturing and industry, Telecommunication and Environmental Monitoring.

  1. Why M2M technology proposed? , Previous technology issues

Our proposed technology provides direct data interchange among machines, allowing them to interact and work together not with the involvement of humans. This technology improves efficiency, streamlines processes and allows automations in different industries. We Link devices and allowing them to transfer information continuously, decreases human efforts, M2M communication enhances decision-making and makes the foundation for the extensive execution of the Internet of Things (IoT), providing a more connected and intelligent world. Some of the previous technology issues that it may handle are Power consumption, Interoperability, Scalability and Security and Privacy.

  1. Algorithms / Protocols

In this research we propose the following methods or algorithms to overcome the existing technology issues. The methods or algorithms that we utilize are Quality Diversity Optimization Algorithm (QDOA), Hybrid Attention Mechanism based Deep Learning Network (HAMDLN), Lloyd’s k-means algorithm.

  1. Comparative study / Analysis

We contrast various methods with existing techniques to obtain the best methods or algorithms for our research. The methods that we compared are as follows:

  • To enhance the security of data, the FL based safe clustering technique is utilized Lloyd’s k-means algorithm and dual CH is chosen to decrease power consumption.
  • For decreasing the complex data overcoming, that safe choice of channel is executed by the method QDOA on the basis of some important parameters, and the whole channel’s safety is segmented into three segments to improve the robust protection.
  • HAMDLN technique is utilized to improve monitoring and preservation, anomalies and the communication threats are identified and the link state is forecasted and then it also decreases packet loss by improving the accuracy on identification.
  1. Simulation results / Parameters

Now we compare our proposed technique parameters or performance metrics with the existing technology methods to obtain the best findings for this research when compared to others. The metrics that we compared are Latency, Packet Loss Rate, Energy Efficiency, Security Strengthen and Throughput.

  1. Dataset LINKS / Important URL

Here we offer some important links that are useful when we have any queries or doubts related to this proposed research, overview the following links to clarify the doubts.

  1. M2M Communication Applications

The applications that use the M2M communication system are as follows. It identifies the applications along different industries, automation, data-driven decision-making and providing enhanced efficiency. We provide some important applications like Building automation, Smart cities, Smart manufacturing, Healthcare monitoring and Environmental monitoring.

  1. Topology for M2M Communication

Let’s see the topology to be used for this research M2M communication. The term “Topology” defines the structure or design of the communication network, overviewing in what way the devices are connected and the data flows among them. The selection of topology will have the effect of the features like efficiency, reliability and scalability. We provide some general topologies utilized for M2M communication are Cellular Network Topology, Star Topology, Bus Topology, Ring Topology, Mesh Topology and Hybrid Topology.

  1. Environment in M2MCommunication

Environment for M2M communication comprises different features which are connected devices that interchange data continuously. In agriculture it helps forecasting farming by controlling the crop health and soil condition. In healthcare, for remote patients controlling and improve the healthcare delivery. The transportation industry gained advantage from M2M to confirm effective and monitored vehicle operations and fleet management. For home automation it depends on M2M to generate connected smart homes with automated frameworks and then the Environmental monitoring utilizes this for actual-time data gathering on water and air quality. These applications establish the accessibility of M2M communication along different environments, efficiency, fostering, enhanced decision-making and automation.

  1. Simulation Tools

For our proposed research we utilize the following software requirements. The simulation tool here we used to implement the research is NS-3.26. Then the processor that we used for this research is 2.5 GHZ and above. The operating system we employed to do the research is Ubuntu – 16.04 LTS (64 – bit).

  1. Results

We propose a M2M communication technology that is used to exchange the information directly among devices without the help of any human instructions. Here we compare some metrics with the previous technology to obtain the best outcome. This research is executed by using the tool NS 3.26.

M2M Communication Research Ideas:

The following are the research topics that are based on M2M communication technology. These topics give some information about M2M communications like concepts, uses, application and some other details related to our proposed research.

  1. Design of a Smart Gateway for Network/Device aware transmissions in M2M Communication
  2. A Lightweight Authentication Protocol for M2M Communication in IIoT Using Physical Unclonable Functions
  3. An IoT Based Smart Grid: Peer-to-peer Energy Trading for Electric Vehicles Using M2M Communication Technology
  4. Dynamic Beam-Based Random Access Scheme for M2M Communications in Massive MIMO Systems
  5. Hypergraph-Based Joint Channel and Power Resource Allocation for Cross-Cell M2M Communication in IIoT
  6. Maximization of Energy Utilization in Machine to Machine Communication via Pricing Based Matching Algorithm
  7. Evaluation of Image Quality Assessment Metrics for Semantic Segmentation in a Machine-to-Machine Communication Scenario
  8. A Novel Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication
  9. Access Delay Optimization of Double-Contention Random Access Scheme in Machine-to-Machine Communications
  10. Energy efficiency and delay determinacy tradeoff in energy harvesting-powered zero-touch deterministic industrial M2M communications
  11. A robust transmission with enhancement of 5G PHY using FBMC and AMC for machine-to-machine communication node
  12. QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach
  13. Reinforcement Learning-Based Resource Allocation for M2M Communications over Cellular Networks
  14. Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks
  15. Dynamic Tree-Splitting Algorithm for Massive Random Access of M2M Communications in IoT Networks
  16. Antenna for Machine-to-Machine (M2M) Communication
  17. Deep Reinforcement Learning Algorithms for Machine-to-Machine Communications: A Review
  18. A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems
  19. Deep Learning-based Secure Machine-to-Machine Communication in Edge-Enabled Industrial IoT
  20. A Novel Cost-Effective Data and Service Availability Approach in Machine-to-Machine Communication Network
  21. Improvement of the energy consumption of the IoT/M2M communication system embedded in pirogues on the West African coast
  22. Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks
  23. M2M-SCM: A Spatial Channel Model for Mobile-to-Mobile Communications in the VHF and UHF Band
  24. Grants and hurdles machine to machine Communication at Scale
  25. SIC-RSRA for Massive Machine-to-Machine Communications in 5G Cellular IoT
  26. AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles
  27. Scalable priority-based resource allocation scheme for M2M communication in LTE/LTE-A network
  28. Chapter 3 – HetNet/M2M/D2D communication in 5G technologies
  29. A proactive Medium Access Control (MAC) for finite-sized machine-to-machine (M2M) communication networks
  30. Extended group-based verification approach for secure M2M communications
  31. An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks
  32. Adaptive rate and modulation scheme for M2M communication using quantum optical and 5G communication
  33. Distributed ranking-based resource allocation for sporadic M2M communication
  34. A Multi-service Adaptive LTE Uplink Scheduler Based on Reservation for Machine -to- Machine Communication
  35. Reinforcement Learning Based Preamble Resource Allocation Scheme for Access Control in Machine-to-Machine Communication
  36. Industry 4.0 Machine-to-Machine Communication Protocols and Architectures on the Shop Floor
  37. A Strategic Approach to Model the Machine-to-Machine Communication of Industrial IoT System for MQTT Protocol with a Case Study
  38. Internet of Things (IoT) for Secure Data and M2M Communications—A Study
  39. Comparative Study of Power Optimization Technique for M2M Communication Node Under 5G (NR)
  40. SLAP: A Secure and Lightweight Authentication Protocol for machine-to-machine communication in industry 4.0
  41. Intelligent Recognition for Fast Access to Machine to Machine
  42. Distributed Ledgers for Enhanced Machine-to-Machine Trust in Smart Cities
  43. Designed to cooperate: a Kant-inspired ethic of machine-to-machine cooperation
  44. Identity Authentication with Association Behavior Sequence in Machine-to-Machine Mobile Terminals
  45. Machine to Machine (M2M), Radio-frequency Identification (RFID), and Software-Defined Networking (SDN): Facilitators of the Internet of Things
  46. Machine-to-machine variability of roughness and corrosion in additively manufactured 316L stainless steel
  47. IoT Forensics: Machine to Machine Embedded with SIM Card
  48. Privacy-Preserving Presence Tracing for Pandemics Via Machine-to-Machine Exposure Notifications
  49. Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence
  50. Stochastic Game for Resource Management in Cellular Zero-Touch Deterministic Industrial M2M Networks