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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 with 100 Sensor nodes at 2,000 s for (a) SensorAnt and (b) EEABR.
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f14-sensors-12-11307: Energy balanced with 100 Sensor nodes at 2,000 s for (a) SensorAnt and (b) EEABR.

Mentions: Figure 13 and Figure 14(a,b) show the energy balance plots of both algorithms. As can be seen, SensorAnt attains the more preferred energy balance in both the 50 and 100 sensor networks compared to the EEABR scheme over the 2,000 s period. Whereas the sensors in EEABR drained their battery energy rapidly, almost all sensor nodes in SensorAnt still have a high battery capacity which allows them to live longer and keep responding to the occurring events. This phenomenon is due to the fact that the packet traffic of EEABR scheme focuses on particular nodes that are close to the sink or located in the efficient route as outlined by its proactive method. In contrast, SensorAnt distributes the energy consumption over the whole WSN thereby achieving the required energy balance across the network. Accordingly, the sensor network life span is extended.


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 with 100 Sensor nodes at 2,000 s for (a) SensorAnt and (b) EEABR.
© Copyright Policy
Related In: Results  -  Collection

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

f14-sensors-12-11307: Energy balanced with 100 Sensor nodes at 2,000 s for (a) SensorAnt and (b) EEABR.
Mentions: Figure 13 and Figure 14(a,b) show the energy balance plots of both algorithms. As can be seen, SensorAnt attains the more preferred energy balance in both the 50 and 100 sensor networks compared to the EEABR scheme over the 2,000 s period. Whereas the sensors in EEABR drained their battery energy rapidly, almost all sensor nodes in SensorAnt still have a high battery capacity which allows them to live longer and keep responding to the occurring events. This phenomenon is due to the fact that the packet traffic of EEABR scheme focuses on particular nodes that are close to the sink or located in the efficient route as outlined by its proactive method. In contrast, SensorAnt distributes the energy consumption over the whole WSN thereby achieving the required energy balance across the network. Accordingly, the sensor network life span is extended.

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