The Next Grand Challenges: Integrating the Internet of Things and Data Science

Ranjan, Rajiv; Rana, Omer F.; Nepal, Surya; Yousif, Mazin; James, Philip; Wen, Zhenya; Barr, SL; Watson, Paul; Jayaraman, Prem Prakash; Georgakopoulos, Dimitrios; Villari, Massimo; Fazio, Maria; Garg, Saurabh; Buyya, Rajkumar; Wang, Lizhe; Zomaya, Albert Y.; Dustdar, Schahram

doi:10.1109/mcc.2018.032591612

Cited by 89 publications

(57 citation statements)

References 13 publications

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“…The first subsection introduces the preprocessing of the raw data. The second subsection discusses the importance of spacial 8 fitting and shows the adopted method. Finally, the third subsection provides with the solution of short-term prediction of air quality.…”

Section: Data Processingmentioning

confidence: 99%

“…Therefore, it is preferred that the fine-grained and real-time air quality sensing is performed based on a large number of tiny sensors, which can be densely deployed and have faster sensing procedure. Such solution creates a special application of Internet-of-Things (IoT) in smart city [7], where massive data can be collected and analyzed [8]. In this way, an air quality distribution map with high spatial resolution and low latency can be established to provide valuable information for citizens, such as suggesting them to keep away from highly polluted area or helping decide the best ventilation system for a building [9].…”

Section: Introductionmentioning

confidence: 99%

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UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

Bai

Yang

et al. 2019

IEEE Network

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As air pollution is becoming the largest environmental health risk, the monitoring of air quality has drawn much attention in both theoretical studies and practical implementations. In this article, we present a real-time, fine-grained and power-efficient air quality monitoring system based on aerial and ground sensing. The architecture of this system consists of four layers: the sensing layer to collect data, the transmission layer to enable bidirectional communications, the processing layer to analyze and process the data, and the presentation layer to provide graphic interface for users. Three major techniques are investigated in our implementation, given by the data processing, the deployment strategy and the power control. For data processing, spacial fitting and short-term prediction are performed to eliminate the influences of the incomplete measurement and the latency of data uploading. The deployment strategies of ground sensing and aerial sensing are investigated to improve the quality of the collected data. The power control is further considered to balance between power consumption and data accuracy. Our implementation has been deployed in Peking University and Xidian University since February 2018, and has collected about 100 thousand effective data samples by June 2018.

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Section: Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

Bai

Yang

et al. 2019

IEEE Network

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“…The occurrence of S p i = 0 and S d i = ∆T represents the violation of the constraint Eqn. (13), indicating that state S i involves improper power control at previous steps, which will not be included in the optimal solution. Performing "off" action: If we perform a 0 on state S i , it means that we execute no sensing task at time S t i .…”

Section: A Power Control For Single Devicementioning

confidence: 99%

“…Since the deployment of tiny sensing devices can be dense and the data collection can be frequent, the air quality distribution can be updated with low latency and high resolution [10], [11]. Such a solution creates a promising application of Internet-of-Things (IoT) in smart city [12], where massive data can be collected and analyzed [13]. The citizens are able to benefit from the valuable information provided by the air quality sensing system, by following the suggestions like keeping away from the highly polluted area or deciding the best ventilation system for a building [14].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Bai

Bian

et al. 2019

IEEE Internet Things J.

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Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation and optimization of our own air quality sensing system, which provides realtime and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

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“…The architecture should be able to include the offloading of computing resource to the edge, merging the concepts of producer and consumer, which are progressively losing their distinction since generators of information are also used to receive and manipulate data . Typical osmotic computing challenges concern the following: Runtime microservice deployment , how to deploy microservices smoothly with a dedicated engine; Microservice configuration , solution for automatic configuration of the network with ML platforms; Microservices security , protect data and functions from illegitimate actions; Edge computing , how to distribute the load across the network with mobile offloading on the edge or IoT; Monitoring of the quality of service (QoS) , usage of monitoring platforms for keeping the efficiency and reliability of the infrastructure almost intact; Microservice orchestration and elasticity control , how to deploy devices and provision or deprovision resources when the load changes. …”

Section: Apollon Osmotic Middleware For Data Integrationmentioning

confidence: 99%

An osmotic computing infrastructure for urban pollution monitoring

2019

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Summary Urban pollution control systems suffer from the presence of fixed stations in a greater number than mobile monitoring devices. Data gathered from such stations provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Fortunately, the spread of technologies for mobility has fostered the development of new approaches like mobile crowdsensing (MCS), increasing the chances of using mobile devices, even personal ones, as suitable sensors for the urban monitoring scenario. Nevertheless, one of the open challenges is the management of integrated heterogeneous data flows, differing in terms of typology, technical specifications (eg, transmission protocols), and semantics. The osmotic computing paradigm aims at creating an abstract level between mobile devices/Internet‐of‐Things devices and a cloud platform, which enables opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and development of a middleware that integrates data coming from mobile and Internet‐of‐Things devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed for security management.

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The Next Grand Challenges: Integrating the Internet of Things and Data Science

Cited by 89 publications

References 13 publications

UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

An osmotic computing infrastructure for urban pollution monitoring

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