scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Energy-Efficient Air Pollution Monitoring with Optimum Duty-Cycling on a Sensor Hub

TL;DR: It is demonstrated that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information.
Abstract: Air pollution monitoring systems with energy-intensive sensors cannot afford to sample frequently in order to maximize time between successive recharges. In this paper, we propose an energy-efficient machine learning based sensor duty-cycling method for a sensor hub receiving data from the air-pollution sensors. In particular, we demonstrate that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information. Support Vector Regression is used to predict the missing samples during the period sensor is turned off.
Citations
More filters
01 Feb 2015
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

136 citations

Posted Content
TL;DR: The rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques is surveyed and open research challenges are identified and present directions for future research.
Abstract: The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

54 citations


Cites background from "Energy-Efficient Air Pollution Moni..."

  • ...The sensors are mostly very low-power [97]....

    [...]

  • ...LSPs are small, low-cost sensing units widely used to detect particulate matter [63, 88, 96, 97, 98]....

    [...]

Journal ArticleDOI
TL;DR: This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications and presents a few case studies that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality.
Abstract: With the advent of Internet of Things (IoT) devices, their reconfigurability, networking, task automation, and control ability have been a boost to the evolution of traditional industries such as health-care, agriculture, power, education, and transport. However, the quantum of data produced by the IoT devices poses serious challenges on its storage, communication, computation, security, scalability, and system’s energy sustainability. To address these challenges, the concept of green sensing and communication has gained importance. This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications. Further, a few case studies are presented that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality. Challenges associated with these green techniques, various open issues, and future research directions for improving the energy efficiency of the IoT systems are also discussed.

27 citations


Cites background from "Energy-Efficient Air Pollution Moni..."

  • ...based duty cycling scheme for air pollution sensing is proposed in the work [14]....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters is analyzed by using extensive experiments carried out in the Helsinki metropolitan region, and they demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors.
Abstract: Transit activities are a significant contributor to a person’s daily exposure to pollutants. Currently obtaining accurate information about the personal exposure of a commuter is challenging as existing solutions either have a coarse monitoring resolution that omits subtle variations in pollutant concentrations or are laborious and costly to use. We contribute by systematically analysing the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters. Through extensive experiments carried out in the Helsinki metropolitan region, we demonstrate that low-cost sensors can capture the overall exposure with sufficient accuracy, while at the same time providing insights into variations within transport modalities. We also demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors. For example, we demonstrate that location within the vehicle carriage can result in up to 25 % increase in daily pollution exposure – a significant difference that existing solutions are unable to capture. Finally, we highlight the practical benefits of low-cost sensors as a pollution monitoring solution by introducing applications that are enabled by low-cost wearable sensors.

15 citations

Proceedings ArticleDOI
09 Nov 2020
TL;DR: In this paper, the authors present an air quality sensing process needed for low-cost sensors which are planned for long-term use, including design and production, laboratory tests, field tests, deployment, and maintenance.
Abstract: Air pollution is a main challenge in societies with particulate matter PM 2.5 as the major air pollutant causing serious health implications. Due to health and economic impacts of air pollution, low-cost and portable air quality sensors can be vastly deployed to gain personal air pollutant exposure. In this paper, we present an air quality sensing process needed for low-cost sensors which are planned for long-term use. The steps of this process include design and production, laboratory tests, field tests, deployment, and maintenance. As a case study we focus on the field test, where we use two generations of a portable air quality sensor (capable of measuring meteorological variables and PM 2.5 ) to perform an indoor-outdoor measurement. The study found that all of the measurements shown to be consistent through validation among themselves. The sensors accuracy also demonstrate to be adequate by showing similar readings compared to the nearest air quality reference station.

13 citations


Cites background from "Energy-Efficient Air Pollution Moni..."

  • ...One of the main features of LSP relates to their low power consumption [14], [19]....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: An exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions shows that their performance varies spatially and temporally.

607 citations


"Energy-Efficient Air Pollution Moni..." refers background in this paper

  • ...Authors in [13], [15] built a case for using low cost pollution sensors despite the values sensed being indicative only....

    [...]

Journal ArticleDOI
TL;DR: The drivers behind current rises in the use of low-cost sensors for air pollution management in cities are illustrated, while addressing the major challenges for their effective implementation.

591 citations


"Energy-Efficient Air Pollution Moni..." refers background in this paper

  • ...Authors in [13], [15] built a case for using low cost pollution sensors despite the values sensed being indicative only....

    [...]

Proceedings ArticleDOI
11 Aug 2013
TL;DR: A vehicular-based mobile approach for measuring fine-grained air quality in real-time and two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device are proposed.
Abstract: Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.

332 citations


"Energy-Efficient Air Pollution Moni..." refers background in this paper

  • ...Different solutions proposed earlier for reducing energy cost of continuous context sensing with rechargable wireless sensor networks tried to find heuristics to adapt sampling rate of the energy-intensive sensors or reducing number of sensing reads for energy harvesting nodes [5], [6], [7], [10]....

    [...]

Journal ArticleDOI
TL;DR: This paper designs a data gathering optimization algorithm for dynamic sensing and routing (DoSR), and proposes a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness.
Abstract: In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.

237 citations

Journal ArticleDOI
TL;DR: The root causes of energy overhead in continuous sensing are examined and it is shown that energy-efficient continuous sensing can be achieved through proper system design.
Abstract: Today's mobile phones come with a rich set of built-in sensors such as accelerometers, ambient light sensors, compasses, and pressure sensors, which can measure various phenomena on and around the phone. Gathering user context such as user activity, geographic location, and location type requires continuous sampling of sensor data. However, such sampling shortens a phone's battery life because of the associated energy overhead. This article examines the root causes of this energy overhead and shows that energy-efficient continuous sensing can be achieved through proper system design.

179 citations


"Energy-Efficient Air Pollution Moni..." refers background in this paper

  • ...For Smartphone based mobile participatory-sensing, continuous sensing tasks were offloaded to a low-power sensing coprocessor called sensor hub so that energy hungry system-onchip (SoC) may sleep whenever possible [4]....

    [...]