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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: 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.

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.


Success rate.
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f8-sensors-15-11854: Success rate.

Mentions: In this section, we evaluate the success rate of the five algorithms, and the results are shown in Figure 8. The success rate is related to Equation (3). The vertical axis expresses the success rate. We can clearly see that DFS, RR and BR achieve a 100% success rate. In the algorithms, individual sensing values from all nodes in the sensor network arrive at the sink node without collisions. However, BC has an 88% success rate and BFS has 98%, because collisions occur among transmissions. For BC, a collision occurs when a query disseminates to each node and a response is forwarded to the sink node. On the other hand, a collision for BFS occurs when a node and its grand-parent send their query responses at the same time because they do not know each other. Therefore, most of the missing responses are generated by nodes at low levels. Considering the success rate, DFS, RR and BR satisfy the requirement of our service scenario, in which the sink node collects all raw data within a given period, one from each of the nodes in a sensor network.


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

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

Success rate.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-15-11854: Success rate.
Mentions: In this section, we evaluate the success rate of the five algorithms, and the results are shown in Figure 8. The success rate is related to Equation (3). The vertical axis expresses the success rate. We can clearly see that DFS, RR and BR achieve a 100% success rate. In the algorithms, individual sensing values from all nodes in the sensor network arrive at the sink node without collisions. However, BC has an 88% success rate and BFS has 98%, because collisions occur among transmissions. For BC, a collision occurs when a query disseminates to each node and a response is forwarded to the sink node. On the other hand, a collision for BFS occurs when a node and its grand-parent send their query responses at the same time because they do not know each other. Therefore, most of the missing responses are generated by nodes at low levels. Considering the success rate, DFS, RR and BR satisfy the requirement of our service scenario, in which the sink node collects all raw data within a given period, one from each of the nodes in a sensor network.

Bottom Line: 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.

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.