Dingo Optimization Based Cluster Based Routing in Internet of Things.

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) are geographically distributed sensors for data processing and exchange.
• Internet of Things (IoT) devices face energy conservation challenges due to limited resources.
• Existing clustering algorithms in WSNs suffer from short network lifespan, load imbalance, and high delays.

Purpose of the Study:

• To propose a novel cluster-based approach for energy efficiency in IoT networks.
• To enhance network stability, reduce end-to-end delays, and improve Quality of Service (QoS).
• To address limitations of existing clustering algorithms in WSNs.

Main Methods:

• Utilizing a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) for optimal cluster head (CH) selection.
• Considering multiple constraints: energy, distance, delay, overhead, trust, QoS, and security levels.
• Implementing fault tolerance and energy hole mitigation techniques for network stabilization.

Main Results:

• The SDO-BM model effectively selects optimal cluster heads based on diverse network parameters.
• The proposed approach demonstrates improved network lifespan and reduced end-to-end delays.
• Fault tolerance and energy hole mitigation techniques enhance network stability.

Conclusions:

• The SDO-BM model offers a significant improvement over existing methods for energy conservation in IoT WSNs.
• The approach provides a robust solution for optimizing cluster head selection and network management.
• This research contributes to more efficient and reliable WSNs for IoT applications.