Abstract
As citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to the limited budget, we often need to optimize the sensor placement in order to maximize the overall information gain according to certain criteria. Existing work is primarily concerned with single-type sensor placement; however, the environment usually requires accurate measurements of multiple types of environmental characteristics. In this paper, we focus on the optimal multi-type sensor placement in Gaussian spatial field for environmental monitoring. We study two representative cases: the one-with-all case when each station is equipped with all types of sensors and the general case when each station is equipped with at least one type of sensor. We propose two greedy algorithms accordingly, each with a provable approximation guarantee. We evaluated the proposed approach via an application in air quality monitoring scenario in Hong Kong and experimental results demonstrate the effectiveness of the proposed approach.
Highlights
Environmental monitoring plays an essential role in the era of smart cities [1,2], providing sufficient information for citizens’ decision making, such as whether the air quality is suitable for exercise outdoors, and as the primary data source for many longitudinal environment and health studies in order to better understand and assess the environment dynamics and their impact on public health over time [3,4,5]
The general sensor placement problem has been studied in many environmental monitoring applications, for example, temperature monitoring [8], water contamination [9], wind monitoring [10], soil moisture [11], etc
We evaluated the proposed multi-type placement scheme on the hourly air quality monitoring data for 2017 provided by the Hong Kong Environment Protection Department (EPD) website [16]
Summary
Environmental monitoring plays an essential role in the era of smart cities [1,2], providing sufficient information for citizens’ decision making, such as whether the air quality is suitable for exercise outdoors, and as the primary data source for many longitudinal environment and health studies in order to better understand and assess the environment dynamics and their impact on public health over time [3,4,5]. When considering all the fields at the same time, selecting Location A for deploying a station may not be a good choice when the budget is limited and a careful design of placement scheme is required to balance the trade-off between information gain and cost. We formulate the optimal multi-type sensor placement problem for environmental monitoring under a general budget constraint. We perform a case study using the air quality measurements in year 2017 from the official government stations of Hong Kong to demonstrate the proposed approach. This formulation provides guidance for city planners to design the multi-type sensor network for environmental monitoring.
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