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Random and directed walk-based top-(k) queries in wireless sensor networks.

Fu JS, Liu Y - Sensors (Basel) (2015)

Bottom Line: A strategy of choosing the "right" way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible.When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered.Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China. 14111005@bjtu.edu.cn.

ABSTRACT
In wireless sensor networks, filter-based top-  query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors' readings and declines in the overall range of all the readings. In this work, a random walk-based top-  query approach called RWTQ and a directed walk-based top-  query approach called DWTQ are proposed. At the beginning of a top-  query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the "right" way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime.

No MeSH data available.


Related in: MedlinePlus

The “mountain” the data and DWTQ algorithm.
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sensors-15-12273-f003: The “mountain” the data and DWTQ algorithm.

Mentions: As shown in Figure 3, there is a “mountain” with an extreme point and DWTQ is comprised of four modes, i.e., Random-Walk (RW) Mode, Directed-Walk (DW) Mode, Extreme-Point (EP) Mode and Leave (L) mode, to search the extreme point efficiently. Initially, there is no information about which direction the token should walk and then get the top- readings with a high probability. Therefore, the token needs to collect and process the information of the readings by RW Mode which is slightly different to RWTQ. When a node finds that there is a clear target direction in which the values of the readings always increase, the mode of the token is changed to DW Mode until the value of the readings reach an extreme point where the mode of the token is changed to EP Mode. After EP Mode, the token’s mode becomes L Mode immediately, which can lead the token out of the “mountain” quickly and then becomes RW Mode when the node finds that the value of the readings stops decreasing. If the pedometer count is smaller than a threshold, the mode of the token can switch between these four modes; if the pedometer count is larger than a threshold and the mode of the token is not DW and EP Mode, the token is transmitted to the base station directly; if the pedometer count is larger than a threshold and the mode of the token is DW Mode, the token is transmitted to the base station after the mode of the token changes to L Mode.


Random and directed walk-based top-(k) queries in wireless sensor networks.

Fu JS, Liu Y - Sensors (Basel) (2015)

The “mountain” the data and DWTQ algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507629&req=5

sensors-15-12273-f003: The “mountain” the data and DWTQ algorithm.
Mentions: As shown in Figure 3, there is a “mountain” with an extreme point and DWTQ is comprised of four modes, i.e., Random-Walk (RW) Mode, Directed-Walk (DW) Mode, Extreme-Point (EP) Mode and Leave (L) mode, to search the extreme point efficiently. Initially, there is no information about which direction the token should walk and then get the top- readings with a high probability. Therefore, the token needs to collect and process the information of the readings by RW Mode which is slightly different to RWTQ. When a node finds that there is a clear target direction in which the values of the readings always increase, the mode of the token is changed to DW Mode until the value of the readings reach an extreme point where the mode of the token is changed to EP Mode. After EP Mode, the token’s mode becomes L Mode immediately, which can lead the token out of the “mountain” quickly and then becomes RW Mode when the node finds that the value of the readings stops decreasing. If the pedometer count is smaller than a threshold, the mode of the token can switch between these four modes; if the pedometer count is larger than a threshold and the mode of the token is not DW and EP Mode, the token is transmitted to the base station directly; if the pedometer count is larger than a threshold and the mode of the token is DW Mode, the token is transmitted to the base station after the mode of the token changes to L Mode.

Bottom Line: A strategy of choosing the "right" way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible.When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered.Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China. 14111005@bjtu.edu.cn.

ABSTRACT
In wireless sensor networks, filter-based top-  query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors' readings and declines in the overall range of all the readings. In this work, a random walk-based top-  query approach called RWTQ and a directed walk-based top-  query approach called DWTQ are proposed. At the beginning of a top-  query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the "right" way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime.

No MeSH data available.


Related in: MedlinePlus