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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

The network model for 100 sensor nodes in flat topology.
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f6-sensors-12-11307: The network model for 100 sensor nodes in flat topology.

Mentions: The proposed SensorAnt scheme is simulated by deploying different sensor nodes from 10 to 100 based on five different fields to reflect different sensor nodes density—200 × 200 m2 for 10 sensor nodes, 300 × 300 m2 for 20 sensor nodes, 400 × 400 m2 for 30 sensor nodes, 500 × 500 m2 for 40 sensor nodes and 600 × 600 m2 for 50, 60, 70, 80, 90 and 100 respectively. Figure 6 describes the network model which is flat topology based for one scenario used in this paper. The figure represents the snapshots from the QualNet it contains 100 sensor nodes deployed randomly in the field area 600 × 600 m2. All the sensor nodes have the same characteristics and operated with identical battery power. The blue links in the figure indicate to the wireless link that connected all the nodes to subnet (network field). While the green links indicate to the messages exchange during the simulation run time inside the sensor network, and the green numbers indicate to the sensor identification (sensor-ID).


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)

The network model for 100 sensor nodes in flat topology.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-12-11307: The network model for 100 sensor nodes in flat topology.
Mentions: The proposed SensorAnt scheme is simulated by deploying different sensor nodes from 10 to 100 based on five different fields to reflect different sensor nodes density—200 × 200 m2 for 10 sensor nodes, 300 × 300 m2 for 20 sensor nodes, 400 × 400 m2 for 30 sensor nodes, 500 × 500 m2 for 40 sensor nodes and 600 × 600 m2 for 50, 60, 70, 80, 90 and 100 respectively. Figure 6 describes the network model which is flat topology based for one scenario used in this paper. The figure represents the snapshots from the QualNet it contains 100 sensor nodes deployed randomly in the field area 600 × 600 m2. All the sensor nodes have the same characteristics and operated with identical battery power. The blue links in the figure indicate to the wireless link that connected all the nodes to subnet (network field). While the green links indicate to the messages exchange during the simulation run time inside the sensor network, and the green numbers indicate to the sensor identification (sensor-ID).

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