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Data Processing in the Internet of Things
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Data Processing in the Internet of Things

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 12750

Special Issue Editor

Dr. Pascal Hirmer

E-Mail Website
Guest Editor
Institute of Parallel and Distributed Systems, University of Stuttgart, D-70569 Stuttgart, Germany
Interests: Internet of Things; edge and cloud computing; distributed systems; networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We gladly invite you to submit a paper to our Special Issue in the MDPI journal Information, titled “Data Processing in the Internet of Things.”

The Internet of Things (IoT) has become an important technology to make people’s lives easier and has established itself in many different domains, such as Smart Homes, Smart Cities, or Smart Factories. In the IoT, heterogeneous hardware devices, usually equipped with sensors and actuators, communicate through standardized network protocols to reach common goals.

In IoT applications, realizing efficient data processing is an important issue, since traditional data processing techniques (ETL, RDBMS, data warehousing, etc.) do not fit the specific requirements of the IoT. For example, storing IoT streaming data in a database or data warehouse and, in a further step, processing of the data, could only work in small IoT scenarios. In smart cities, for example, the amount of data being produced is too high to be first stored and then processed afterwards. Furthermore, for long-running data analysis, newly arriving data cannot be considered during data processing.

Hence, there is a high need to process IoT data as streams, using enhanced techniques such as Complex Event Processing or Data Stream Mining which process IoT streaming data directly, without the need to store them. Data that is not needed anymore can then be discarded or stored for long-term analysis. Using streaming approaches, newly arriving data can be considered as well.

In this Special Issue, we want to focus on data processing in the Internet of Things using streaming approaches. We are especially interested in new approaches in the areas of data stream processing, Complex Event Processing, data stream mining, time-window or length-window based data processing, machine learning, practical applications in IoT data processing, or best practices in this area. Survey papers may also be submitted to this Special Issue.

Dr. Pascal Hirmer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Data stream processing, e.g., using time or length windows
  • IoT data processing using data stream mining
  • Complex Event Processing
  • Machine learning on IoT data
  • Approaches to increase robustness in IoT data processing
  • Best practices for IoT data processing
  • Practical applications and use cases of data processing in the IoT
  • Smart Factories, Smart Cities, Smart Homes
  • Surveys of data processing in the IoT

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Published Papers (3 papers)

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21 pages, 7758 KiB  
Article
Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
by Najem Naji, Mohamed Riduan Abid, Driss Benhaddou and Nissrine Krami
Information 2020, 11(11), 530; https://doi.org/10.3390/info11110530 - 15 Nov 2020
Cited by 20 | Viewed by 4973
Abstract
Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology [...] Read more.
Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology to evaluate performance in terms of routing capabilities, data collection, and throughput. The proposed EMS uses the Context-Based Reasoning (CBR) Model to represent different types of buildings and offices. We implemented a new energy-efficient policy for electrical heaters control based on a Finite State Machine (FSM) leveraging on context-related events. This demonstrated significant effectiveness in minimizing the processing load, especially when adopting multithreading in data acquisition and control. To optimize sensors’ battery lifetime, we deployed a new Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send data under specific conditions and at particular times to avoid redundant data transmissions. EACRA increases the sensors’ battery lifetime by optimizing the number of samples, used modules, and transmissions. Our proposed EMS design can be used as a model to retrofit other kinds of buildings, such as residential and industrial, and thus converting them to SEEBs. Full article
(This article belongs to the Special Issue Data Processing in the Internet of Things)
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19 pages, 2957 KiB  
Article
Heracles: A Context-Based Multisensor Sensor Data Fusion Algorithm for the Internet of Things
by Flávia C. Delicato, Tayssa Vandelli, Mario Bonicea and Claudio M. de Farias
Information 2020, 11(11), 517; https://doi.org/10.3390/info11110517 - 4 Nov 2020
Cited by 1 | Viewed by 2676
Abstract
In the Internet of Things (IoT), extending the average battery duration of devices is of paramount importance, since it promotes uptime without intervention in the environment, which can be undesirable or costly. In the IoT, the system’s functionalities are distributed among devices that [...] Read more.
In the Internet of Things (IoT), extending the average battery duration of devices is of paramount importance, since it promotes uptime without intervention in the environment, which can be undesirable or costly. In the IoT, the system’s functionalities are distributed among devices that (i) collect, (ii) transmit and (iii) apply algorithms to process and analyze data. A widely adopted technique for increasing the lifetime of an IoT system is using data fusion on the devices that process and analyze data. There are already several works proposing data fusion algorithms for the context of wireless sensor networks and IoT. However, most of them consider that application requirements (such as the data sampling rate and the data range of the events of interest) are previously known, and the solutions are tailored for a single target application. In the context of a smart city, we envision that the IoT will provide a sensing and communication infrastructure to be shared by multiple applications, that will make use of this infrastructure in an opportunistic and dynamic way, with no previous knowledge about its requirements. In this work, we present Heracles, a new data fusion algorithm tailored to meet the demands of the IoT for smart cities. Heracles considers the context of the application, adapting to the features of the dataset to perform the data analysis. Heracles aims at minimizing data transmission to save energy while generating value-added information, which will serve as input for decision-making processes. Results of the performed evaluation show that Heracles is feasible, enhances the performance of decision methods and extends the system lifetime. Full article
(This article belongs to the Special Issue Data Processing in the Internet of Things)
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19 pages, 1778 KiB  
Article
Models for Internet of Things Environments—A Survey
by Ana Cristina Franco da Silva and Pascal Hirmer
Information 2020, 11(10), 487; https://doi.org/10.3390/info11100487 - 20 Oct 2020
Cited by 9 | Viewed by 4385
Abstract
Today, the Internet of Things (IoT) is an emerging topic in research and industry. Famous examples of IoT applications are smart homes, smart cities, and smart factories. Through highly interconnected devices, equipped with sensors and actuators, context-aware approaches can be developed to enable, [...] Read more.
Today, the Internet of Things (IoT) is an emerging topic in research and industry. Famous examples of IoT applications are smart homes, smart cities, and smart factories. Through highly interconnected devices, equipped with sensors and actuators, context-aware approaches can be developed to enable, e.g., monitoring and self-organization. To achieve context-awareness, a large amount of environment models have been developed for the IoT that contain information about the devices of an environment, their attached sensors and actuators, as well as their interconnection. However, these models highly differ in their content, the format being used, for example ontologies or relational models, and the domain to which they are applied. In this article, we present a comparative survey of models for IoT environments. By doing so, we describe and compare the selected models based on a deep literature research. The result is a comparative overview of existing state-of-the-art IoT environment models. Full article
(This article belongs to the Special Issue Data Processing in the Internet of Things)
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