QoS and Energy Aware Cooperative Routing Protocol for Wildfire Monitoring Wireless Sensor Networks

Author:

Maalej Mohamed1,Cherif Sofiane1ORCID,Besbes Hichem1

Affiliation:

1. Engineering School of Telecommunications of Tunis (Sup’Com), City of Communications Technologies, El Ghazala 2083, Ariana, Tunisia

Abstract

Wireless sensor networks (WSN) are presented as proper solution for wildfire monitoring. However, this application requires a design of WSN taking into account the network lifetime and the shadowing effect generated by the trees in the forest environment. Cooperative communication is a promising solution for WSN which uses, at each hop, the resources of multiple nodes to transmit its data. Thus, by sharing resources between nodes, the transmission quality is enhanced. In this paper, we use the technique of reinforcement learning by opponent modeling, optimizing a cooperative communication protocol based on RSSI and node energy consumption in a competitive context (RSSI/energy-CC), that is, an energy and quality-of-service aware-based cooperative communication routing protocol. Simulation results show that the proposed algorithm performs well in terms of network lifetime, packet delay, and energy consumption.

Funder

European Community’s Seventh Framework Programme

Publisher

Hindawi Limited

Subject

General Environmental Science,General Biochemistry, Genetics and Molecular Biology,General Medicine

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1. Self-Organizing Blimp Drones for Wildfire Detection;Lecture Notes in Networks and Systems;2024

2. A Self-Sustained CPS Design for Reliable Wildfire Monitoring;ACM Transactions on Embedded Computing Systems;2023-09-09

3. Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home;International Journal of Environmental Research and Public Health;2023-03-27

4. Traffic Aware Routing with Round Robin Technique for Equating the Load in Software Defined WSN;2023 Second International Conference on Electronics and Renewable Systems (ICEARS);2023-03-02

5. Reinforcement Learning to Improve QoS and Minimizing Delay in IoT;Intelligent Automation & Soft Computing;2023

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