Cognitive IoT Research Topics

Cognitive IoT Research Topics is proposed in this research. It is defined by integrating IoT and Cognitive computing. Here we discuss about the applications, uses, previous issues, metrics and methods to be utilized for Cognitive IoT:

  1. Define Cognitive IoT

Initially we begin with the definition of Cognitive IoT, “Cognitive IoT” is also known as “Cognitive Internet of Things”, this define the blending of both IoT and cognitive computing techniques, like Machine Learning (ML) and Artificial Intelligence (AI). Through this blending, the IoT devices collects and transfer data as well as it analyze, process and learn from that data to create knowledgeable and content-applicable choices.

  1. What is Cognitive IoT?

Next to the definition we see the deep explanations of Cognitive IoT, it describes an IoT device or system’s ability to copy human-like cognitive functions like train from data, reasoning and decision –making. Machine Learning (ML), Natural Language Processing (NLP), predictive analysis and Artificial Intelligence (AI) are some of the advanced technologies blended in cognitive IoT systems with the data collected from different devices and sensors. It allows the IoT devices to examine and examine data in real-time, takeout precious patterns and understandings, moreover it gathers and transfer data. Creating knowledgeable choices and prediction by utilizing these data enhances the whole effectiveness and value of IoT applications.

  1. Where Cognitive IoT used?

After the deep explanations of cognitive IoT we discuss where to utilize this. While merging the knowledge of both IoT devices and Cognitive computing more different industries and applications are helpful. Some of the important industries that utilize Cognitive IoT are Agriculture, Manufacturing, Smart cities, Healthcare and Transportation. These are some of the various fields and uses where cognitive IoT has a large impact. A broad range of chances for creation and enhancement are made when the data-collection ability of the IoT are blended with analytical and decision-making influence of cognitive computing.

  1. Why Cognitive IoT technology proposed? , previous technology issues

Cognitive IoT technology is proposed in this work and it tackles existing technology issues. Some of them significantly get profit from cognitive IoT technologies are Identification in multi-cloud systems and improving safe task allocation. In multi-cloud services, configurations and resources from some cloud providers are blended to enhance scalability, redundancy and flexibility. To handle tasks and resources through many clouds while maintaining the efficacy and protection is difficult.


  • In this the traditional methods failed actual-time understandings into workload status and cloud resource.
  • The inefficiency to create knowledgeable task allocation decisions will cause inadequate performance restrictions and resource consumption.
  • Traditional methods have to optimize inadequate degree of resource allocation.
  • Performance decline and inadequate resource utilization are arising from less used or crowded cloud providers.
  • Multiple-cloud load balancing need complex methods and techniques.
  • Traditional methods failed in learning and adjustment.
  1. Algorithms / protocols

We proposed Cognitive IoT in this research to make decisions in a correct way and overcome several existing technology issues. Some of the algorithms that we utilized for Cognitive IoT are Deer Hunting Optimization Algorithm, Genetic Algorithm, Modified Cuckoo Search, Load-balanced amortized multi-scheduling algorithm, Bald Eagle Search Algorithm, Task Scheduling-Decision Tree Algorithm and Cognitive Dynamic Source Routing Protocol are some of the methods to be utilized in this research.

  1. Comparative study / Analysis

In comparative analysis we compared various methods to obtain the possible correct result. In this research for Cognitive IoT gateway we compared various methods like:

  • To identify the best weight of products to be supplied over the ideal set of supply paths with the assistance of Genetic algorithm.
  • Modified cuckoo search (MCS) algorithm has the ability to exceed with other parameters, and it displays an increased convergence rate to the true lowest point with the increased amount of dimensions.
  • We optimize the multi-objective task scheduling issue by decreasing makespan, improving resource utilization and sufficient load balancing among virtual machines with the help of decision Tree algorithm.
  1. Simulation results / Parameters

In this research, the metrics that suggested for evaluation be compared to some other existing techniques. Some of the methods that we compared are Accuracy, Convergence Speed, Resource utilization, efficiency and scalability are being compared with the Number of  users and some other parameters that utilized are Makespan, Best cost, Energy Consumption, Reliability and Availability.

Our proposed system was compared over many performance pointers, and it is recognized that our research achieve them. The setting of the network metrics are given below as the table form:




Network parameter

Parameters Description
No. of primary users 10
No. of Cognitive IoT Gateway 4
No. of Secondary User 50
Distance 600

Packet parameters

No. of packets 100
Packet size 1024
Error rate 0.025
Port 8000

Energy parameters

Energy 7
Compressibility 15
Power 100
Communication range 50


Simulation time 300s


  1. Dataset LINKS / Important URL

Cognitive IoT is utilized in this research and we overcome many existing technology issues. Here we provide some links to overview the explanations or descriptions of Cognitive IoT related queries:

  1. Cognitive IoT Applications

Cognitive IoT is proposed in this work and it influences IoT devices and Cognitive computing to overcome more sectors and field of use. Several important applications of Cognitive IoT applications are:

  • Healthcare Monitoring and Personalization: Vital signs, sleep and activity are determined by the Cognitive IoT wearables and offer early disease detection and custom health advice.
  • Energy Management and Smart Grids: Building and home energy utilization are evaluated by Cognitive IoT system. On the basis of occupancy and surroundings they optimize heating, lighting and cooling to serve energy.
  • Industrial Equipment Predictive Maintenance: For predictive maintenance Cognitive IoT handles industrial machines and apparatus in actual-time and by utilizing Decreasing downtime and costs, sensor data and historical patterns to evaluate maintenance requirements.
  • Smart Agriculture: Cognitive IoT devices handles weather, crop and soil health and enhance fertilization, pest management to boost crop and irrigation.
  • Smart Cities and Traffic Management: The sensor and camera data in actual-time are examined by Cognitive IoT and it optimizing traffic flow, enhancing urban mobility and reducing congestion.
  1. Topology for Cognitive IoT

Here we see the topology for Cognitive IoT, the topology for Cognitive IoT system is the structure of its networks, components and devices. It explains in what way the different portions can do the work together to achieve things like processing, dissemination and collecting. To impact the optimal topology we utilize scalability restrictions, Application require and the design of the cognitive elements.

  1. Environment in Cognitive IoT

The environment for Cognitive IoT is as follows, these environments are those in which the Cognitive technologies are analyzed and used by IoT framework. The Cognitive IoTs effective process relies on the number of features and technologies that create this environment. The edge computing resources and cloud platforms offers the processing, analysis function and storage of Cognitive IoT. To make decisions in actual-time, the data processing is carried to the edge of the network. The entire environment creates the Cognitive IoT to be included in many different sections, but not inadequate to IoT devices, safety precautions, Communication networks, data analysis, cognitive technologies, user interfaces and more. Collectively the elements of this environment enable the examining of big amount of data, the creation of choices in actual-time, and the sending of enhanced user experiences in a broad variety of environments and sectors.

  1. Simulation tools

The Cognitive IoT is proposed in this research and we can simulate it by utilizing the following software requirements. The Cognitive IoT is developed by employing the tool NS 3.26 and is developed by utilizing the programming languages like C++ and python to obtain the possible correct outcome. It incorporates the operating system Ubuntu 16.06[LTS].

  1. Results

In this research we employ Cognitive IoT; this utilizes several uses, applications, methods and metrics to obtain the better outcome. It is proposed by overcome some existing technology issues and can be now utilized in many applications. It is developed by using the tool NS3.26 and the network environment here we proceed is  Ubuntu 16.06[LTS].

Cognitive IoT Research Ideas:

Below we offered are some of the Cognitive IoT based research topics that are helpful to us when we acquire any queries about this proposed system gothrough this:

  1. A Review of Cognitive Dynamic Systems and Cognitive IoT
  2. Outage and Throughput Performance of NOMA Inspired Cognitive IoT Network with Imperfect SIC
  3. Overlay Cognitive IoT-Based Full-Duplex Relaying NOMA Systems With Hardware Imperfections
  4. Adaptive Resource Allocation in SWIPT-Enabled Cognitive IoT Networks
  5. Imperfect CSI-Based Resource Management in Cognitive IoT Networks: A Deep Recurrent Reinforcement Learning Framework
  6. Collaborative and Incremental Learning for Modulation Classification With Heterogeneous Local Dataset in Cognitive IoT
  7. Differentiated Security in the Age of Cognitive Internet of Things (CIoT)
  8. Deep Reinforcement Learning Optimal Transmission Algorithm for Cognitive Internet of Things With RF Energy Harvesting
  9. Radio-in-the-Loop Simulation Modeling for Energy-Efficient and Cognitive IoT in Smart Cities: A Cross-Layer Optimization Case Study
  10. Three-Dimensional Resource Matching for Internet of Things Underlaying Cognitive Capacity Harvesting Networks
  11. Fuzzy Logic based Uplink Power Control for Cognitive Internet-of-Things
  12. Deep Learning Algorithms for RF Energy Harvesting Cognitive IoT Devices: Applications, Challenges and Opportunities
  13. Short Packet-Based Status Updates in Cognitive Internet of Things: Analysis and Optimization
  14. Age of Information for Short-Packet Relaying Communications in Cognitive Internet of Things
  15. Exploiting SWIPT-Enabled IoT-Based Cognitive Nonorthogonal Multiple Access With Coordinated Direct and Relay Transmission
  16. Detection of Alzheimer’s Disease Using Deep Learning, Blockchain, and IoT Cognitive Data
  17. Smart Packet Transmission Scheduling in Cognitive IoT Systems: DDQN Based Approach
  18. Energy-Efficient Scheduling and Resource Allocation for Power-limited Cognitive IoT Devices
  19. Resource Optimisation in Cognitive-Based Internet-of-Things with Edge Computing
  20. Semantic Modelling of Multivariate Time-Series Data in Cognitive IoT
  21. Can We Improve the Information Freshness With Prediction for Cognitive IoT?
  22. Optimization of Energy Efficiency in UAV-Enabled Cognitive IoT With Short Packet Communication
  23. Temporal-Structure-Aware Interference Cancellation for Asynchronous Cognitive IoT
  24. An Efficient Deep CNN Design for EH Short-Packet Communications in Multihop Cognitive IoT Networks
  25. Human Short Long-Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities
  26. Cognitive Framework of Food Quality Assessment in IoT-Inspired Smart Restaurants
  27. Feature-Based Spectrum Sensing of NOMA System for Cognitive IoT Networks
  28. A Cognitive Social IoT Approach for Smart Energy Management in a Real Environment
  29. Cognitive IoT‐Based Health Monitoring Scheme Using Non‐Orthogonal Multiple Access
  30. Cognitive Balance for Fog Computing Resource in Internet of Things: An Edge Learning Approach
  31. Physical Layer Security Enhancement in Energy Harvesting-based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach
  32. Spectrum Recommendation in Cognitive Internet of Things: A Knowledge-Graph-Based Framework
  33. A Smart CIoT With Secure Healthcare Framework Using Optimised Deep Recuperator Neural Network Long Short-Term Memory
  34. CIOT: Constraint-Enhanced Inertial-Odometric Tracking for Articulated Dump Trucks in GNSS-Denied Mining Environments
  35. Deep learning based relay selection and precoders design for IoT cognitive relay networks
  36. Towards containerized, reuse-oriented AI deployment platforms for cognitive IoT applications
  37. A comprehensive soft security model for Cognitive Internet of Things
  38. Impact on blockchain-based AI/ML-enabled big data analytics for Cognitive Internet of Things environment
  39. Directional-antenna-based spatial and energy-efficient semi-distributed spectrum sensing in cognitive internet-of-things networks
  40. Retraction notice to “Localization algorithm of energy efficient radio spectrum sensing in cognitive internet of things radio networks” [Cogn. Syst. Res. 52 (2018) 21–26]
  41. Design cognitive IoT architecture framework for immersive visual technologies of air quality monitoring systems
  42. An efficient security system based on cancelable face recognition with blockchain over cognitive IoT
  43. Decentralized knowledge discovery using massive heterogenous data in Cognitive IoT
  44. Improvement of energy-efficient resources for cognitive internet of things using learning automata
  45. Cognitive internet of things-based framework for efficient consumption of electrical energy in public higher learning institutions
  46. Scalable and Energy-Efficient Deep Learning for Distributed AIoT Applications Using Modular Cognitive IoT Hardware
  47. Covid Patient Monitoring System for Self-quarantine Using Cloud Server Based IoT Approach
  48. A Survey on Cognitive Internet of Things Based Prediction of Covid-19 Patient
  49. Autonomic IoT: Towards Smart System Components with Cognitive IoT
  50. Cognitive IoT for Future City: Architecture, Security and Challenges