Increase the WSN-Lifespan Used in Monitoring Forest Fires by PSO

Al-Hilfi, Hayder Ibrahim Mutar; Al-Nayar, Muna Mohammed Jawad

doi:10.18280/mmep.090304

Cited by 4 publications

(2 citation statements)

References 24 publications

(36 reference statements)

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“…For instance, setting a threshold level of power consumption for each process to prevent any errors of draining the entire available power is one such careful step that should be taken. Al-Hilfi et al [14] provided both a Particle Swarm Optimization (PSO) method for selecting the Cluster Head (CH) and a field deployment plan for clusters of deployed nodes. The procedure chooses the best CHs for cluster nodes using a fitness function.…”

Section: Figure 1 a General Schematic Of Wsnmentioning

confidence: 99%

Optimized Event-Based PID Control for Energy-Efficient Wireless Sensor Networks

Ramadhan,

Raafat,

Mahmood

2024

MMEP

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Wireless Sensor Networks (WSNs) face the critical challenge of reducing node energy consumption to extend network lifetimes in the presence of uncertainties and external disturbances. This paper proposes an event-based PID controller to minimize power consumption while maintaining required data rates in WSNs. Initially, the system is stabilized using a simple controller. Two structures are considered for this purpose: one based on state feedback control and the other utilizing a PID controller. The event-based PID controller is then developed to improve the WSN system's performance and reduce power consumption. The performance of the event-based PID-controlled system is evaluated for both structures. Results with the first structure show moderate improvements in settling time, overshoot, and error, with values of 9.78 sec, 0, and 9.715, respectively. Subsequently, significant enhancements are observed with the second structure of the event-based PID controller, incorporating a stabilizing PID controller. The results demonstrate a settling time of 0.0924 sec, overshoot of 3.58, and error of 0.01486. To further enhance the controlled system's performance, the Gray Wolf Optimization (GWO) method is applied to fine-tune the parameters (P, I, and D) of the implemented controllers. The application of the GWO algorithm maintains desirable characteristics, with settling time, overshoot, and error values of 7.7527 sec, 0.0358, and 6.4217, respectively. This research offers valuable insights for reducing power consumption and improving the performance of WSNs using an event-based PID controller and GWO optimization.

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Section: Figure 1 a General Schematic Of Wsnmentioning

confidence: 99%

Optimized Event-Based PID Control for Energy-Efficient Wireless Sensor Networks

Ramadhan,

Raafat,

Mahmood

2024

MMEP

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“…In the end, this results in better energy distribution throughout the network and better resource utilization. Other studies were related to our research that can be used for more information [59][60][61][62][63].…”

Section: Energy Optimization-based Clustering Algorithmsmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

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In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

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