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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 throughput between Static and Mobile-SensorAnt with different periods of time.
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f22-sensors-12-11307: Comparison of throughput between Static and Mobile-SensorAnt with different periods of time.

Mentions: The result presented here compares the energy cost incurred by the Static and Mobile-SensorAnt scenarios with time. Figure 22 shows that the Mobile-SensorAnt outperforms the Static-SensorAnt throughout the simulation time. In the static case, the congestion and packet drops occur more than in the mobile case. It can be noted that in both cases, the first 400 s of the simulation time depicts increasing throughput; however, beyond this point, Mobile-SensorAnt stabilizes whereas the Static-SensorAnt shows decreasing trends in throughput. This is so because as the path between the sender and sink nodes becomes congested with more packets, the occurrence of packet drops is likely to increase when the network is not dynamic. In the Mobile-SensorAnt however, the sensor nodes keep moving about in the WSN, thereby resulting in the re-distribution of the system bottlenecks throughput the whole WSN.


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 throughput between Static and Mobile-SensorAnt with different periods of time.
© Copyright Policy
Related In: Results  -  Collection

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

f22-sensors-12-11307: Comparison of throughput between Static and Mobile-SensorAnt with different periods of time.
Mentions: The result presented here compares the energy cost incurred by the Static and Mobile-SensorAnt scenarios with time. Figure 22 shows that the Mobile-SensorAnt outperforms the Static-SensorAnt throughout the simulation time. In the static case, the congestion and packet drops occur more than in the mobile case. It can be noted that in both cases, the first 400 s of the simulation time depicts increasing throughput; however, beyond this point, Mobile-SensorAnt stabilizes whereas the Static-SensorAnt shows decreasing trends in throughput. This is so because as the path between the sender and sink nodes becomes congested with more packets, the occurrence of packet drops is likely to increase when the network is not dynamic. In the Mobile-SensorAnt however, the sensor nodes keep moving about in the WSN, thereby resulting in the re-distribution of the system bottlenecks throughput the whole WSN.

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