Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks

Abstract

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%.

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

“…Further, Roy et al [27] suggested a technique the enables privacy preservation with minimal verification latency during handover activity, based on experimental findings. Chéour et al [28] propose a power usage optimizer titled "Hybrid Energy-Efficient Power Manager Scheduling" (HEEPS) tailored to the needs of WSNs. The suggested power regulator is unique in that it combines many approaches, including time-out Dynamic Power Management, Dynamic Voltage and Frequency Scaling, and the Global Earliest Deadline First (GEDF) scheduler, to achieve greater scheduling efficiency.…”

“…In existing literature there are techniques that save energy optimizing various parameters of the WSN [28][29][30]. The current research targets to conserve energy through an optimized data storing approach to be used in WSN applications that require historical data related to monitor object activity.…”

“…■ Authors aim to employ existing power optimizer scheduling techniques including HEEPS and GEDF [28] to achieve greater scheduling efficiency. ■ Authors aim to adopt various precautionary measures [29] to reduce energy consumption including optimal management system, prolonging the elapsed time between two neighboring occurrences, which could save detecting and interacting energy, and preferentially conveying findings, potentially saving interacting energy.…”

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

“…Termination criteria are as follows: The AWOA algorithm is eliminated since the best CHs are obtained for the satisfaction of a termination criterion. Based on the CH, the nearest SN nodes are formed to obtain a cluster [25].…”

CSCRT Protocol with Energy Efficient Secured CH Clustering for Smart Dust Network Using Quantum Key Distribution

“…Not with standing, a significant number of their applications are comparative, for example, condition observing or reconnaissance. Normally, the nodes comprise of a radio handset and a microcontroller controlled by a battery, and in addition some sort of smart dust for identifying light, warm, moistness, temperature, and so forth [1,2].…”