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On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model.

Bagula A, Castelli L, Zennaro M - Sensors (Basel) (2015)

Bottom Line: Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city.Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters.These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Systems and Advanced Telecommunication Laboratory Laboratory, Department of Computer Science, University of the Western Cape, Private Bag X17, Bellville 7535, Cape Town, South Africa. bbagula@uwc.ac.za.

ABSTRACT
Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called "anchor" nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.

No MeSH data available.


Related in: MedlinePlus

Average Playback Delay. (a) Randomly Generated Configuration; (b) Optimally Generated Configuration.
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f6-sensors-15-15443: Average Playback Delay. (a) Randomly Generated Configuration; (b) Optimally Generated Configuration.

Mentions: Figure 6 reveals the playback delay for both network configurations under different delay requirements. It reveals again the relative efficiency of the optimally-generated network configuration compared to the randomly generated one, as it leads to lower playback delays. Similarly to the energy performance, Figure 6 reveals a steep increase, where the playback delay increases with the delay requirements (loosening the delay requirements results in higher playback delay) followed by a plateau zone revealing a delay requirement threshold where further increases do not result in equivalent playback delay increases. This is in agreement with the results achieved in energy consumption, as in both cases, the loosening of the delay constraints leads to opening more and potentially longer paths for carrying the sensor readings; thus consuming more energy and potentially on longer paths with longer delays.


On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model.

Bagula A, Castelli L, Zennaro M - Sensors (Basel) (2015)

Average Playback Delay. (a) Randomly Generated Configuration; (b) Optimally Generated Configuration.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541838&req=5

f6-sensors-15-15443: Average Playback Delay. (a) Randomly Generated Configuration; (b) Optimally Generated Configuration.
Mentions: Figure 6 reveals the playback delay for both network configurations under different delay requirements. It reveals again the relative efficiency of the optimally-generated network configuration compared to the randomly generated one, as it leads to lower playback delays. Similarly to the energy performance, Figure 6 reveals a steep increase, where the playback delay increases with the delay requirements (loosening the delay requirements results in higher playback delay) followed by a plateau zone revealing a delay requirement threshold where further increases do not result in equivalent playback delay increases. This is in agreement with the results achieved in energy consumption, as in both cases, the loosening of the delay constraints leads to opening more and potentially longer paths for carrying the sensor readings; thus consuming more energy and potentially on longer paths with longer delays.

Bottom Line: Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city.Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters.These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Systems and Advanced Telecommunication Laboratory Laboratory, Department of Computer Science, University of the Western Cape, Private Bag X17, Bellville 7535, Cape Town, South Africa. bbagula@uwc.ac.za.

ABSTRACT
Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called "anchor" nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.

No MeSH data available.


Related in: MedlinePlus