Temperature in an urban area exhibits a complicated pattern due to complexity of infrastructure. Despite geographical proximity, structures of a group of buildings and streets affect changes in temperature. To investigate the pattern of fine-grained distribution of temperature, we installed a densely distributed sensor network called UScan. In this paper, we describe the system architecture of UScan as well as experience learned from installing 200 sensors in downtown Tokyo. The field experiment of UScan system operated for two months to collect long-term urban temperature data. To analyze the collected data in an efficient manner, we propose a lightweight clustering methodology to study the correlation between the pattern of temperature and various environmental factors including the amount of sunshine, the width of streets, and the existence of trees. The analysis reveals meaningful results and asserts the necessity of fine-grained deployment of sensors in an urban area.
A wireless sensor network can be an effective tool for gathering data in a variety of environments. The data gathering process must be designed to conserve the limited resources of the sensors. In this paper, we propose Efficient Data GathEring (EDGE) protocol which satisfies such requirement because it avoids both flooding and periodic updating of routing packets. The tree created by EDGE will be reconstructed upon node failures or adding of new nodes. The simulation results compared to Directed Diffusion, DSR, AODV, and OLSR demonstrated that EDGE achieves a higher delivery ratio and shorter delay on various scenarios.
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