Energy efficient distributed power management algorithm with directional antenna for wireless sensor networks

2011 ◽  
Author(s):  
Nuraj Pradhan ◽  
Tarek Saadawi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Management ◽  
Energy Efficient ◽  
Directional Antenna ◽  
Wireless Sensor ◽  
Distributed Power ◽  
Management Algorithm

scholarly journals Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 301
Author(s):  
Rym Chéour ◽  
Mohamed Wassim Jmal ◽  
Sabrine Khriji ◽  
Dhouha El Houssaini ◽  
Carlo Trigona ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Management ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Hybrid Architecture ◽  
Optimization Strategy ◽  
Hybrid Energy ◽  
Power Manager ◽  
Efficient Power

Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%.

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