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Sensor-aware adaptive push-pull query processing in wireless sensor networks

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Sensor-aware adaptive push-pull query processing in wireless sensor networks. / Bose, R.; Helal, Sumi.
2010 6th International Conference on Intelligent Environments, IE 2010. IEEE, 2010. p. 243-248.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Bose, R & Helal, S 2010, Sensor-aware adaptive push-pull query processing in wireless sensor networks. in 2010 6th International Conference on Intelligent Environments, IE 2010. IEEE, pp. 243-248. https://doi.org/10.1109/IE.2010.51

APA

Bose, R., & Helal, S. (2010). Sensor-aware adaptive push-pull query processing in wireless sensor networks. In 2010 6th International Conference on Intelligent Environments, IE 2010 (pp. 243-248). IEEE. https://doi.org/10.1109/IE.2010.51

Vancouver

Bose R, Helal S. Sensor-aware adaptive push-pull query processing in wireless sensor networks. In 2010 6th International Conference on Intelligent Environments, IE 2010. IEEE. 2010. p. 243-248 doi: 10.1109/IE.2010.51

Author

Bose, R. ; Helal, Sumi. / Sensor-aware adaptive push-pull query processing in wireless sensor networks. 2010 6th International Conference on Intelligent Environments, IE 2010. IEEE, 2010. pp. 243-248

Bibtex

@inproceedings{9fa7456fe0d24f07807916f3d3ea653f,
title = "Sensor-aware adaptive push-pull query processing in wireless sensor networks",
abstract = "Till date, sensor network research has assumed that the cost of transmitting a sensor reading over the network is much higher than the cost of sampling a sensor. However, this assumption is no longer always valid, due to availability of new generation sensor platform hardware, which utilizes industry standard mesh-networking protocols such as ZigBee on top of relatively high-speed, yet low-power wireless radios. In fact, we have experimentally verified that the energy consumed for acquiring a sample from a sensor can be significantly higher than the energy consumed for transmitting its reading over the network. Hence, new querying strategies need to be formulated, which optimize the order of sampling sensors across the network in such a manner that sensors with expensive acquisition costs are not sampled unless absolutely required. We propose distributed pull-push querying mechanisms, which optimize the query plan by adapting to variable costs of acquiring readings from different sensors across the network. The goal of these mechanisms is to minimize the energy consumption of nodes executing a query while ensuring that the latency of query response does not exceed user-specified bounds. To validate our approach, we also describe experimental results, which analyze the performance of various plan options in terms of energy consumption and latency under the effect of various parameters such as selectivity of data and number of sensors participating in the query. {\textcopyright} 2010 IEEE.",
keywords = "Acquisitional query processing, Energy-aware information processing, Wireless sensor networks, Acquisition costs, Energy aware, Energy consumption, High-speed, Industry standards, Low Power, Networking protocols, Query response, Querying mechanisms, Sensor platform, Sensor readings, Variable costs, Wireless radios, Zig-Bee, Cost accounting, Costs, Data processing, Energy utilization, Intelligent agents, Mesh generation, MESH networking, Network protocols, Optimization, Query processing, Sensor networks",
author = "R. Bose and Sumi Helal",
year = "2010",
doi = "10.1109/IE.2010.51",
language = "English",
isbn = "9781424478361",
pages = "243--248",
booktitle = "2010 6th International Conference on Intelligent Environments, IE 2010",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Sensor-aware adaptive push-pull query processing in wireless sensor networks

AU - Bose, R.

AU - Helal, Sumi

PY - 2010

Y1 - 2010

N2 - Till date, sensor network research has assumed that the cost of transmitting a sensor reading over the network is much higher than the cost of sampling a sensor. However, this assumption is no longer always valid, due to availability of new generation sensor platform hardware, which utilizes industry standard mesh-networking protocols such as ZigBee on top of relatively high-speed, yet low-power wireless radios. In fact, we have experimentally verified that the energy consumed for acquiring a sample from a sensor can be significantly higher than the energy consumed for transmitting its reading over the network. Hence, new querying strategies need to be formulated, which optimize the order of sampling sensors across the network in such a manner that sensors with expensive acquisition costs are not sampled unless absolutely required. We propose distributed pull-push querying mechanisms, which optimize the query plan by adapting to variable costs of acquiring readings from different sensors across the network. The goal of these mechanisms is to minimize the energy consumption of nodes executing a query while ensuring that the latency of query response does not exceed user-specified bounds. To validate our approach, we also describe experimental results, which analyze the performance of various plan options in terms of energy consumption and latency under the effect of various parameters such as selectivity of data and number of sensors participating in the query. © 2010 IEEE.

AB - Till date, sensor network research has assumed that the cost of transmitting a sensor reading over the network is much higher than the cost of sampling a sensor. However, this assumption is no longer always valid, due to availability of new generation sensor platform hardware, which utilizes industry standard mesh-networking protocols such as ZigBee on top of relatively high-speed, yet low-power wireless radios. In fact, we have experimentally verified that the energy consumed for acquiring a sample from a sensor can be significantly higher than the energy consumed for transmitting its reading over the network. Hence, new querying strategies need to be formulated, which optimize the order of sampling sensors across the network in such a manner that sensors with expensive acquisition costs are not sampled unless absolutely required. We propose distributed pull-push querying mechanisms, which optimize the query plan by adapting to variable costs of acquiring readings from different sensors across the network. The goal of these mechanisms is to minimize the energy consumption of nodes executing a query while ensuring that the latency of query response does not exceed user-specified bounds. To validate our approach, we also describe experimental results, which analyze the performance of various plan options in terms of energy consumption and latency under the effect of various parameters such as selectivity of data and number of sensors participating in the query. © 2010 IEEE.

KW - Acquisitional query processing

KW - Energy-aware information processing

KW - Wireless sensor networks

KW - Acquisition costs

KW - Energy aware

KW - Energy consumption

KW - High-speed

KW - Industry standards

KW - Low Power

KW - Networking protocols

KW - Query response

KW - Querying mechanisms

KW - Sensor platform

KW - Sensor readings

KW - Variable costs

KW - Wireless radios

KW - Zig-Bee

KW - Cost accounting

KW - Costs

KW - Data processing

KW - Energy utilization

KW - Intelligent agents

KW - Mesh generation

KW - MESH networking

KW - Network protocols

KW - Optimization

KW - Query processing

KW - Sensor networks

U2 - 10.1109/IE.2010.51

DO - 10.1109/IE.2010.51

M3 - Conference contribution/Paper

SN - 9781424478361

SP - 243

EP - 248

BT - 2010 6th International Conference on Intelligent Environments, IE 2010

PB - IEEE

ER -