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A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.

Shamsan Saleh AM, Ali BM, Rasid MF, Ismail A - Sensors (Basel) (2012)

Bottom Line: Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy.The routing protocol should be able to provide uniform power dissipation during transmission to the sink node.Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. ah_almshreqy@yahoo.com

ABSTRACT
Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

No MeSH data available.


Related in: MedlinePlus

Energy balanced for 50 sensors with different times for (a) SensorAnt and (b) EEABR.
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f15-sensors-12-11307: Energy balanced for 50 sensors with different times for (a) SensorAnt and (b) EEABR.

Mentions: Figure 15(a,b) illustrate the residual battery capacity of SensorAnt and EEABR respectively, after different periods of time. In Figure 15(a), it is observed that in the proposed SensorAnt, the energy residue is still balanced across the sensor nodes even if the battery depleted slowly and regularly with increasing run time. This is due to the self-adaptive scheme employed in the proposed SensorAnt which always monitors the sensors' remaining energies and only permits those sensor nodes with high residual energy to participate in the routing function. On the other hand, Figure 15(b) shows clearly that the sensor nodes in the EEABR scheme exhaust their residual energy very fast as the run time increases; which is due to the lack of an appropriate energy consumption distribution among the sensor nodes. Instead, the scheme only focuses on maintaining the efficient route at the expense of increased energy depletion.


A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.

Shamsan Saleh AM, Ali BM, Rasid MF, Ismail A - Sensors (Basel) (2012)

Energy balanced for 50 sensors with different times for (a) SensorAnt and (b) EEABR.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472886&req=5

f15-sensors-12-11307: Energy balanced for 50 sensors with different times for (a) SensorAnt and (b) EEABR.
Mentions: Figure 15(a,b) illustrate the residual battery capacity of SensorAnt and EEABR respectively, after different periods of time. In Figure 15(a), it is observed that in the proposed SensorAnt, the energy residue is still balanced across the sensor nodes even if the battery depleted slowly and regularly with increasing run time. This is due to the self-adaptive scheme employed in the proposed SensorAnt which always monitors the sensors' remaining energies and only permits those sensor nodes with high residual energy to participate in the routing function. On the other hand, Figure 15(b) shows clearly that the sensor nodes in the EEABR scheme exhaust their residual energy very fast as the run time increases; which is due to the lack of an appropriate energy consumption distribution among the sensor nodes. Instead, the scheme only focuses on maintaining the efficient route at the expense of increased energy depletion.

Bottom Line: Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy.The routing protocol should be able to provide uniform power dissipation during transmission to the sink node.Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. ah_almshreqy@yahoo.com

ABSTRACT
Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

No MeSH data available.


Related in: MedlinePlus