Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment

Area of Science:

• Environmental Science
• Computer Science
• Network Engineering

Background:

• Forest fire monitoring is crucial for disaster prevention, with the Internet of Things (IoT) offering potential solutions.
• Existing IoT research often overlooks specific forest fire management needs.
• Low Power Lossy Networks (LLNs) are common in IoT environments, presenting routing challenges.

Purpose of the Study:

• To address the limitations of the Energy-efficient Routing Protocol for Low Power Lossy Networks (E-RPL) in forest fire monitoring.
• To propose a novel routing protocol, Compressed Data Aggregation and Energy-based RPL Routing (CAA-ERPL), to enhance network performance.
• To evaluate the effectiveness of CAA-ERPL compared to E-RPL in terms of energy consumption, delay, and packet delivery.

Main Methods:

• Development of the Compressed Data Aggregation and Energy-based RPL Routing (CAA-ERPL) protocol.
• Utilizing residual power as an objective function in the E-RPL protocol to form destination-oriented directed acyclic graphs (DODAGs).
• Performance evaluation using the Contiki Cooja simulator with varying numbers of nodes (10-50).

Main Results:

• CAA-ERPL demonstrated reduced packet transfer delay compared to E-RPL.
• The proposed CAA-ERPL resulted in lower energy consumption across sensor nodes.
• CAA-ERPL achieved a higher packet delivery ratio, indicating improved network reliability.

Conclusions:

• CAA-ERPL effectively overcomes the scalability and performance issues of E-RPL in forest fire monitoring scenarios.
• The protocol enhances network efficiency, making it suitable for resource-constrained IoT environments.
• This research contributes to more robust and efficient IoT-based forest fire detection and management systems.