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Branch-based centralized data collection for smart grids using wireless sensor networks.

Kim K, Jin SI - Sensors (Basel) (2015)

Bottom Line: A smart grid is one of the most important applications in smart cities.To solve the problem, we divide a tree that a sensor network constructs into several branches.A conflict-free query schedule is generated based on the branches.

View Article: PubMed Central - PubMed

Affiliation: UGS Convergence Research Department, ETRI, 208 Gajeong-ro Yuseong-gu, Daejeon 305-700, Korea. enoch@etri.re.kr.

ABSTRACT
A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method.

No MeSH data available.


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Success rate and memory size in relation to various node counts.
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f12-sensors-15-11854: Success rate and memory size in relation to various node counts.

Mentions: We also varied the number of nodes in a sensor network and compared the success rate and the memory size of those methods according to various node counts. We set 5 ms as the max_delay. The results are shown in Figure 12. We can clearly see that the success rate is not affected by variation of the number of nodes in a sensor network. However, the memory size of DFS is affected by the number of sensor nodes, because an internal node executing the method stores all data received from its descendants. On the other hand, the proposed method is only slightly affected by the variation of the number of nodes. Therefore, we conclude that the proposed method is the most suitable to collect all data from nodes in a wireless sensor network, because it can guarantee complete data collection despite environment changes.


Branch-based centralized data collection for smart grids using wireless sensor networks.

Kim K, Jin SI - Sensors (Basel) (2015)

Success rate and memory size in relation to various node counts.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481998&req=5

f12-sensors-15-11854: Success rate and memory size in relation to various node counts.
Mentions: We also varied the number of nodes in a sensor network and compared the success rate and the memory size of those methods according to various node counts. We set 5 ms as the max_delay. The results are shown in Figure 12. We can clearly see that the success rate is not affected by variation of the number of nodes in a sensor network. However, the memory size of DFS is affected by the number of sensor nodes, because an internal node executing the method stores all data received from its descendants. On the other hand, the proposed method is only slightly affected by the variation of the number of nodes. Therefore, we conclude that the proposed method is the most suitable to collect all data from nodes in a wireless sensor network, because it can guarantee complete data collection despite environment changes.

Bottom Line: A smart grid is one of the most important applications in smart cities.To solve the problem, we divide a tree that a sensor network constructs into several branches.A conflict-free query schedule is generated based on the branches.

View Article: PubMed Central - PubMed

Affiliation: UGS Convergence Research Department, ETRI, 208 Gajeong-ro Yuseong-gu, Daejeon 305-700, Korea. enoch@etri.re.kr.

ABSTRACT
A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method.

No MeSH data available.


Related in: MedlinePlus