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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 temperature readings of the No.1 node from March 1st to 3rd.
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sensors-15-12273-f012: The temperature readings of the No.1 node from March 1st to 3rd.

Mentions: In our simulation, 500 homogeneous sensor nodes are randomly scattered in a 200 m × 200 m region. For each simulation, to reduce the randomness of the simulation result, we do the same experiment for 10 times and present the average result. The temperatures contained in Intel Berkeley dataset [19] is used to simulate the readings of the sensor nodes. Millions of pieces of recordings, including temperature, humidity, light and voltage, comprise the dataset generated by 54 sensor nodes deployed in the Intel Berkeley Research lab. Figure 12 presents the temperature readings of the No. 1 node from March 1st to 3rd. For each day, we find that the temperatures increase from about 7 o’clock to 14 o’clock, fluctuate from about 14 o’clock to 18 o’clock and decrease from about 18 o’clock to 7 o’clock in the next day. As discussed previously in Section 5.1, the decrease of the readings has a strong effect on the performance of the approaches. Therefore, we can perform an overall evaluation on the top- query approaches using the dynamics of the readings.


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

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

The temperature readings of the No.1 node from March 1st to 3rd.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-12273-f012: The temperature readings of the No.1 node from March 1st to 3rd.
Mentions: In our simulation, 500 homogeneous sensor nodes are randomly scattered in a 200 m × 200 m region. For each simulation, to reduce the randomness of the simulation result, we do the same experiment for 10 times and present the average result. The temperatures contained in Intel Berkeley dataset [19] is used to simulate the readings of the sensor nodes. Millions of pieces of recordings, including temperature, humidity, light and voltage, comprise the dataset generated by 54 sensor nodes deployed in the Intel Berkeley Research lab. Figure 12 presents the temperature readings of the No. 1 node from March 1st to 3rd. For each day, we find that the temperatures increase from about 7 o’clock to 14 o’clock, fluctuate from about 14 o’clock to 18 o’clock and decrease from about 18 o’clock to 7 o’clock in the next day. As discussed previously in Section 5.1, the decrease of the readings has a strong effect on the performance of the approaches. Therefore, we can perform an overall evaluation on the top- query approaches using the dynamics of the readings.

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