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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 query latency in relation to various delay times.
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f11-sensors-15-11854: Success rate and query latency in relation to various delay times.

Mentions: We also evaluated the stability of DFS, RR and BR by means of the impact of the max_delay used to generate asynchronous transmission time to avoid collisions and the number of nodes in a sensor network. We varied the max_delay from 1 to 5 ms and compared the success rate and query latency of those methods in relation to various delay times. The number of sensor nodes was 121. The results are shown in Figure 11. We can clearly see that the methods are not affected by variation of the delay time, and complete data collection at the sink node is guaranteed.


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

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

Success rate and query latency in relation to various delay times.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-15-11854: Success rate and query latency in relation to various delay times.
Mentions: We also evaluated the stability of DFS, RR and BR by means of the impact of the max_delay used to generate asynchronous transmission time to avoid collisions and the number of nodes in a sensor network. We varied the max_delay from 1 to 5 ms and compared the success rate and query latency of those methods in relation to various delay times. The number of sensor nodes was 121. The results are shown in Figure 11. We can clearly see that the methods are not affected by variation of the delay time, and complete data collection at the sink node is guaranteed.

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