<p>The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context.</p>
With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-temporal resolution. These systems use inexpensive and low-cost sensors, with lower accuracy as compared to robust systems. This fact has raised some concern regarding the quality of the data gathered by the IoT systems, which may compromise the performance of the environmental models. Considering the relevance of the data quality in this scenario, this paper presents a study of the data quality associated with IoT-based air quality monitoring systems. Following a systematic mapping method, and based on existing guidelines to assess data quality in these systems, we have identified the main Data Quality (DQ) dimensions and the corresponding DQ enhancement techniques. After analyzing more than 70 papers, we found that the most common DQ dimensions targeted by the different works are accuracy and precision, which are enhanced by the use of different calibration techniques. Based on our findings, we present a discussion on the challenges that must be addressed in order to improve data quality in IoT-based air quality monitoring systems.
Recent developments in the field of Wireless Sensor Networks (WSN's) have rapidly positioned this technology as the premier form of variable monitoring. These types of networks maintain important advantages in their field of operation such as reliability, simplicity and ease of installation. However, regardless of these advantages, WSNs face challenges in power consumption. The RF transceiver, the component with the highest percentage of power consumption related to the dissipated power, is a glaring example of the challenges facing this technology.This work proposes an application of a signal processing technique to overcome the challenges of power consumption in a WSN network. The core of our solution is based on the application of digital filter to control the packet transmission through the network.
WSNs can be applied in several areas for the monitoring and control of variables. In the design process of a WSN, one of the most important design objectives is to minimize the energy required for sensing, signal processing and communication tasks to extend the lifetime of the network. This chapter discusses a broad variety of schemes used to reduce power consumption in WSNs. The design of sensors nodes involves several core aspects, such as supported sensors, the communication interface, applications, the control system and peripherals. Strategies to preserve the energy used by each of these components are discussed. A specific scheme using digital signal processing to reduce power consumption by decreasing the number of transmissions is proposed. The chapter also considers protocol architectures, focusing on link layer, network layer, and cross-layer approaches. Finally, a comparative analysis among the main techniques is presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.