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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

Comparison of standard deviation between Static and Mobile-SensorAnt with different Sensor Nodes.
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f21-sensors-12-11307: Comparison of standard deviation between Static and Mobile-SensorAnt with different Sensor Nodes.

Mentions: Figures 19 and 20 depict the energy efficiency and average energy of the deployed sensors for both the static and mobile SensorAnt. As it can be seen, the Mobile-SensorAnt incurs a little more energy cost compared to the Static-SensorAnt but is still acceptable. Increasing the network size under the same period of time doesn't make sense, since we applied the energy-balance scheme, in both situations, where the energy consumption is totally balanced between nodes. Figure 21 clarifies this further, showing the standard deviation of the energy cost for both scenarios. As in the previous plots, the deviations in energy levels between the static and mobile scenarios are very similar; thus validating the effectiveness of the SensorAnt algorithm.


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)

Comparison of standard deviation between Static and Mobile-SensorAnt with different Sensor Nodes.
© Copyright Policy
Related In: Results  -  Collection

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

f21-sensors-12-11307: Comparison of standard deviation between Static and Mobile-SensorAnt with different Sensor Nodes.
Mentions: Figures 19 and 20 depict the energy efficiency and average energy of the deployed sensors for both the static and mobile SensorAnt. As it can be seen, the Mobile-SensorAnt incurs a little more energy cost compared to the Static-SensorAnt but is still acceptable. Increasing the network size under the same period of time doesn't make sense, since we applied the energy-balance scheme, in both situations, where the energy consumption is totally balanced between nodes. Figure 21 clarifies this further, showing the standard deviation of the energy cost for both scenarios. As in the previous plots, the deviations in energy levels between the static and mobile scenarios are very similar; thus validating the effectiveness of the SensorAnt algorithm.

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