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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

Total energy consumption for different WSNs with different sensor nodes.
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f7-sensors-12-11307: Total energy consumption for different WSNs with different sensor nodes.

Mentions: Several experiments results have been conducted to verify the impact of the network size. Figure 7 shows the comparison of the proposed SensorAnt with the EEABR model in terms of total energy consumption. Here, our SensorAnt shows better performance in terms of energy conservation compared to the EEABR algorithm. As can be seen, the total energy consumption for SensorAnt are smaller than EEABR with different SNs. This is because SensorAnt can optimize the energy usage of SNs inside WSN and explore the better path based on both function qualities used in our model. The SNs in SensorAnt always monitor their energy level to avoid those SNs with least energy from being used for an extended period of time. Clearly, EEABR exhausted much more energy at large number of sensor nodes. These prove that SensorAnt is an efficient mechanism to decrease the energy usage.


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)

Total energy consumption for different WSNs with different sensor nodes.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-11307: Total energy consumption for different WSNs with different sensor nodes.
Mentions: Several experiments results have been conducted to verify the impact of the network size. Figure 7 shows the comparison of the proposed SensorAnt with the EEABR model in terms of total energy consumption. Here, our SensorAnt shows better performance in terms of energy conservation compared to the EEABR algorithm. As can be seen, the total energy consumption for SensorAnt are smaller than EEABR with different SNs. This is because SensorAnt can optimize the energy usage of SNs inside WSN and explore the better path based on both function qualities used in our model. The SNs in SensorAnt always monitor their energy level to avoid those SNs with least energy from being used for an extended period of time. Clearly, EEABR exhausted much more energy at large number of sensor nodes. These prove that SensorAnt is an efficient mechanism to decrease the energy usage.

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