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Academic Editor:Sabah Mohammed
1, Embedded Software Convergence Research Center, Korea Electronics Technology Institute, 68 Yatap-dong, Bundang-gu, Seongnam 463-816, Republic of Korea
Received 3 February 2013; Revised 15 August 2013; Accepted 15 August 2013
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
In the USA, the largest consumer of energy is buildings, with residential applications accounting for 22.5% and commercial applications accounting for 18.6% [1]. In particular, buildings represent a major fraction (72.9%) of electricity consumption in the USA, including lighting, heating, ventilation, and air-conditioning (HVAC) system, and home and office appliances [2]. Accordingly, energy conservation concerns require us to balance energy use against occupant comfort. Smart energy systems are driven by the clear needs of concerning energy conservation and balancing building energy usage against occupant comfort requirements. Smart energy systems would be able to advance building energy efficiency by monitoring, manipulating, and leveraging contextual information across the building environments [3].
Since smart energy systems have become a prime target for energy savings and occupant comfort, indoor climate monitoring based on wireless sensor networks (WSNs) have been widely employed in attempts to collect various parameters from buildings, including temperature, humidity, CO2 , light, and occupancy. These signals could be used to analyze the building environment condition and infer the occupant's comfort level and finally control electric outlets, HVAC system, and lighting in order to improve building energy efficiency while preserving the occupant's comfort level. Therefore, a WSN consisting of various sensor nodes is seen as one of the pivotal enablers of smart energy systems.
Research on optimal sensor placement in WSNs for indoor environment monitoring is decades old and many lessons have been learned in our community. The research mainly focuses on investigating an appropriate placement solution of sensor nodes in WSNs and, thus, improving wireless communication quality, minimizing the total energy consumption of networks, and maximizing informativeness of sensed data at the same time. One example is to place the minimum number of sensor nodes (i.e., minimizing communication cost) and then predict values at locations where no sensor nodes are placed, being able to achieve highly accurate temperature distribution (i.e.,...