TY - CHAP
T1 - Using predictable observer mobility for power efficient design of sensor networks
AU - Chakrabarti, Arnab
AU - Sabharwal, Ashutosh
AU - Aazhang, Behnaam
PY - 2003
Y1 - 2003
N2 - In this paper, we explore a novel avenue of saving power in sensor networks based on predictable mobility of the observer (or data sink). Predictable mobility is a good model for public transportation vehicles (buses, shuttles and trains), which can act as mobile observers in wide area sensor networks. To understand the gains due to predictable mobility, we model the data collection process as a queuing system, where random arrivals model randomness in the spatial distribution of sensors. Using the queuing model, we analyze the success in data collection, and quantify the power consumption of the network. Even though the modeling is performed for a network which uses only single hop communication, we show that the power savings over a static sensor network are significant. Finally, we present a simple observer-driven communication protocol, which follows naturally from the problem formulation and can be used to achieve the predicted power savings.
AB - In this paper, we explore a novel avenue of saving power in sensor networks based on predictable mobility of the observer (or data sink). Predictable mobility is a good model for public transportation vehicles (buses, shuttles and trains), which can act as mobile observers in wide area sensor networks. To understand the gains due to predictable mobility, we model the data collection process as a queuing system, where random arrivals model randomness in the spatial distribution of sensors. Using the queuing model, we analyze the success in data collection, and quantify the power consumption of the network. Even though the modeling is performed for a network which uses only single hop communication, we show that the power savings over a static sensor network are significant. Finally, we present a simple observer-driven communication protocol, which follows naturally from the problem formulation and can be used to achieve the predicted power savings.
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U2 - 10.1007/3-540-36978-3_9
DO - 10.1007/3-540-36978-3_9
M3 - Chapter
AN - SCOPUS:35248847889
SN - 9783540021117
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 129
EP - 145
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Zhao, Feng
A2 - Guibas, Leonidas
PB - Springer-Verlag
ER -