Mobile ad-hoc network detecting forest fire

Fighting Fire with Networks: How Mobile Ad-Hoc Networks Can Improve Forest Fire Detection

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Elliot Brynn in Climate & Environment December 2025 • 5 min read.

"A comparison of LAR and OLSR protocols shows promise for faster, more reliable detection using mobile technology."


Forest fires pose a significant threat to our environment and communities, demanding constant monitoring and rapid response. Traditional methods often fall short due to infrastructure limitations and communication breakdowns. Mobile Ad-Hoc Networks (MANETs) offer a promising solution, leveraging independent mobile nodes to create communication networks without relying on physical infrastructure.

MANETs can be rapidly deployed and adapted, making them ideal for emergency and rescue operations in disaster scenarios like forest fires. This technology utilizes portable devices such as laptops and smartphones to form a dynamic network, enabling real-time data collection and communication among firefighters and monitoring teams.

To optimize the use of MANETs in forest fire detection, it's crucial to understand the performance of different routing protocols. This article delves into a comparative analysis of two prominent protocols: Location-Aided Routing (LAR) and Optimized Link State Routing (OLSR). We'll explore how each protocol performs in simulated forest fire scenarios, assessing their strengths and weaknesses in terms of speed, reliability, and resource utilization.

LAR vs. OLSR: Which Protocol Prevails in a Fire Emergency?

Mobile ad-hoc network detecting forest fire

The research evaluates LAR (Location-Aided Routing) and OLSR (Optimized Link State Routing) using MATLAB software to simulate a forest fire scenario. Key performance metrics include energy consumption, packet delivery ratio (PDR), routing overhead, and end-to-end delay. Understanding these metrics is critical for determining which protocol is more effective in a real-world fire emergency.

LAR, a reactive protocol, focuses on discovering routes on demand, making it efficient in terms of resource usage. Conversely, OLSR, a proactive protocol, maintains continuous routing information, potentially reducing delays but increasing overhead. The simulation aims to identify which approach offers the best balance for forest fire detection.

  • Packet Delivery Ratio (PDR): LAR demonstrated a superior PDR of 0.6852 compared to OLSR's 0.2917, indicating a higher success rate in delivering data packets.
  • Energy Consumption: LAR consumed significantly less energy (682.6 joules) than OLSR (1833 joules), highlighting its efficiency in prolonging network lifespan.
  • End-to-End Delay: OLSR exhibited a lower end-to-end delay (0.9942 seconds) compared to LAR (2.215 seconds), suggesting faster initial route establishment.
  • Routing Overhead: LAR had a lower routing overhead (25.95) compared to OLSR (152.1), indicating less control traffic and better bandwidth utilization.
The results suggest that LAR offers a more efficient and reliable solution for forest fire detection due to its higher PDR, lower energy consumption, and reduced routing overhead. While OLSR excels in minimizing end-to-end delay, its higher resource demands make it less suitable for resource-constrained environments. These findings underscore the importance of selecting the appropriate routing protocol based on the specific needs and limitations of the deployment scenario.

MANETs: The Future of Early Wildfire Detection?

This study highlights the potential of MANETs to transform forest fire detection. The comparative analysis of LAR and OLSR protocols provides valuable insights for designing and deploying effective monitoring systems in fire-prone areas. The results clearly indicate that the LAR protocol is more reliable and effective for detecting forest fires.

While LAR shows promise, future research should explore hybrid approaches that combine the strengths of both reactive and proactive protocols. Investigating the performance of other network protocols and optimizing MANET configurations for specific environmental conditions will further enhance the effectiveness of these systems.

By leveraging the power of mobile technology and intelligent routing protocols, we can create faster, more reliable early warning systems that protect our forests and communities from the devastating impacts of wildfires. MANETs hold the key to proactive fire management, enabling rapid response and minimizing environmental and economic damage.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.17485/ijst/2016/v9i48/99556, Alternate LINK

Title: Performance Evaluation Of Lar And Olsr Routing Protocols In Forest Fire Detection Using Mobile Ad-Hoc Network

Subject: Multidisciplinary

Journal: Indian Journal of Science and Technology

Publisher: Indian Society for Education and Environment

Authors: Fahad Taha Al-Dhief, Ravie Chandren Muniyandi, Naseer Sabri

Published: 2016-12-28

Everything You Need To Know

1

What are Mobile Ad-Hoc Networks (MANETs), and how do they improve forest fire detection?

Mobile Ad-Hoc Networks (MANETs) are networks of independent mobile nodes, like laptops and smartphones, that can connect without relying on pre-existing infrastructure. This is crucial for forest fire detection because they can be quickly deployed in areas where traditional communication methods are unavailable or have failed. This allows real-time data collection and communication among firefighters and monitoring teams during an emergency.

2

What is the difference between Location-Aided Routing (LAR) and Optimized Link State Routing (OLSR), and which is better?

Location-Aided Routing (LAR) is a reactive protocol that discovers routes on demand, while Optimized Link State Routing (OLSR) is a proactive protocol that maintains continuous routing information. The evaluation in the study revealed that LAR demonstrated a superior Packet Delivery Ratio (PDR), lower energy consumption, and reduced routing overhead compared to OLSR. The implications are that LAR offers a more efficient and reliable solution for forest fire detection, especially in resource-constrained environments.

3

Why is the Packet Delivery Ratio (PDR) important for forest fire detection using MANETs?

The Packet Delivery Ratio (PDR) is a key metric representing the success rate of data packet delivery. In the context of fire detection, a higher PDR ensures that critical information reaches its destination reliably. In the comparison, LAR demonstrated a higher PDR (0.6852) than OLSR (0.2917), which means that LAR is more effective in ensuring that critical data, such as sensor readings and location updates, is successfully transmitted across the network.

4

How does energy consumption affect the performance of MANETs in forest fire detection?

Energy consumption is a critical factor for MANETs because the devices often rely on battery power. In the evaluation, LAR consumed significantly less energy (682.6 joules) than OLSR (1833 joules). This indicates that LAR is more efficient in prolonging the lifespan of the network nodes. This is particularly important in forest fire scenarios where continuous monitoring over extended periods is necessary, and resources may be limited.

5

What does end-to-end delay mean, and how does it compare for LAR and OLSR?

End-to-end delay refers to the time it takes for a data packet to travel from the source to the destination. OLSR exhibited a lower end-to-end delay (0.9942 seconds) compared to LAR (2.215 seconds). This means that OLSR can initially establish routes faster. However, the other metrics, such as Packet Delivery Ratio and Energy Consumption, are more important in this context. Therefore, LAR is more reliable overall.

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