Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -