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An Iot Sensors and Scenarios Survey for Data Researchers

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An Iot Sensors and Scenarios Survey for Data Researchers Morais et al. Journal of the Brazilian Computer Journal of the Society (2019) 25:4 https://doi.org/10.1186/s13173-019-0085-7 Brazilian Computer Society RESEARCH Open Access An IoT sensor and scenario survey for data researchers Cleber M. de Morais1,2*† , Djamel Sadok1 and Judith Kelner1† Abstract This work surveys Internet of Things (IoT) experimental research published since 2015. We summarize the IoT state of the art during the last 2 years and extract important data that we apply to enhance the analysis of IoT solutions. The IoT scenario presents a promising universe to data analysis. This raises a number of questions: which are its most popular applications? What is the definition of scale in an IoT application? What sensors are more often used and for what IoT applications? How can a researcher compare IoT scenarios? To investigate these concerns, this survey analyzes 2 years’ worth of contributions made in three main scientific publishers. We focus on IoT experiments that were actually implemented using real equipment within one or more scenarios. Our first contribution is the classification of those IoT scenarios into seven main aspects. Each analyzed research study presents a specific configuration of a scenario’s variables and sensors. Our second main contribution takes place after the scenario mapping phase. We identify as many as 19 common categories of data types in use. The interrelation among the scenarios and the data types from sensors should assist data researchers in understanding current IoT dynamics. Keywords: Internet of things, Data analysis, Survey, Scenarios, Sensors Introduction - IoT as a paradigm paradigm allows applications to interact with machines, The Internet of Things (IoT) is a computationally sup- things, and/or humans through actuators to enforce new ported information system paradigm [1, 2]thatembraces control. a variety of applications. IoT services differ from other A case in point is a farm equipped with soil humid- IT-related techniques found in industrial or home office ity and temperature sensors. The farmer’s information context due to their ubiquitous and embedded character- dashboard may present sensors data in a geographical istics that permeate our daily lives [3]. While the main IoT and analytic way. Knowing the humidity levels, a farmer architecture(s) is still under definition, their underlying may decide whether or not to irrigate the soil. This also paradigm offers the means to gather one or more “things” depends on the type of crops planted. Then, the irrigation (sensors, interactive devices, or even complex ones) using actuators can be activated to increase field humidity. The well-defined communication interfaces. These can then IoT paradigm creates new opportunities for specialized share data and communicate with the outside world small devices (sensors and actuators) while it also intro- through some specially designed network gateways [4, 5]. duces new challenges for the storage and retrieval of large The final result is similar to service-oriented architectures amounts of data and its meaningful visualization [6]. that can be subject to service orchestration (e.g., through Such hyper-connected information sources often pro- their APIs). IoT events and information are processed vide a large-scale data offering. This is classified as a Big and presented as application output to a human’s interac- Data scale problem for storage, retrieval, and analysis [7]. tion or used for autonomic decision. Furthermore, the IoT The data analysis aspect is challenged by an intense data input, constant mutation, and complex scenario represen- *Correspondence: [email protected] tation. Other similar large data applications include those †Cleber M. de Morais and Judith Kelner contributed equally to this work. in a metropolitan scale [8] such as smart city services, 1 GPRT Lab, Federal University of Pernambuco, DINE Bulding, 1st floor Prof. domestic appliances in the context of the smart home [9], Moraes Rego avenue, Cidade Universitária, Recife/Pernambuco, Brazil 2Digital Media Department, Federal University of Paraíba, Demid building, and industrial applications [10]. Campus I, João Pessoa, João Pessoa/Paraíba 58051-900, Brazil © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Morais et al. Journal of the Brazilian Computer Society (2019) 25:4 Page 2 of 17 In those applications, the data intensity constraints may Exclusion criteria vary from within minutes to real time, for each sensor. The papers that did not deploy real sensor(s) in a given Accordingly, each scenario demands a different data strat- application domain were classified as out of the scope egy to deal with the context of each data. The scenario- of this analysis and removed from our study. We also based architecture analysis (SAAM) is considered quite excluded papers that enhanced one aspect of IoT per- mature in the software engineering field [11]. Scenarios formance, but lacked an implementation into the consid- have been used during system design as a method for ered system. This was the case for example of research comparing design alternatives for development [12]. Some describing enhancements made to sensor network com- works classify the IoT objects (as in [13, 14]), but this munications protocols. A similar strong exclusion crite- strategy has not been applied to IoT scenarios so far. rion regarded works that exclusively used simulation and The present work aims to help in answering some simulated sensor operation. Note that IoT solutions that research questions that may occur to data analysis process data from a dataset or trace, as opposed to data researchers. We provide the reader with details of the being gathered by sensors, were also excluded from this most common emerging IoT scenarios currently found. sample. Finally, the sample did not include studies pub- We explain the way data structures and demand can lished in books and book chapters, concentrating only on change from one scenario to the next. A classification scientific articles. of existing sensors ranked in terms of popularity and its application is presented. More importantly, we qualify the Resulting sample different attributes according to usage contexts and pro- The final result consisted of 48 articles from the three data vide guidelines for their data analysis [15]. Finally, we sources that satisfied our selection criteria (see Table 1). provide a technique that binds sensors’ qualification with Although the resulting sample seems reduced, it repre- the scenario’s description in an analytic way. sents the most relevant papers that have real implemen- First, the procedures that were used is this survey are tation. This is a tricky point, because the majority of the described in the “Methodology” section. After that, the IoT systems are too complex to be implemented, even in “Analysis” section will present the classifications and cat- small scale. So, each deployed application should be val- egories used by this work. Then, the results are presented, ued, to further research studies. The actual state of an IoT showing the major contributions of this work. At last, the application is crucial to better understand the complexity conclusions from this research, some future works, threats of data analysis, visualization, and sensor utilization. to validity, and references are described. To help the keen reader further in exploiting our results, we also provide in Sample’s analysis the Appendix 1 section the main raw data of analysis and The analysis section is split into three parts: scenario, sen- the identified structures. sors, and application analysis. The first part classifies and qualifies the context of use of IoT solutions. The objec- Methodology tive is to understand how each IoT scenario is placed and This work falls into the category of analytic bibliographi- its idiosyncrasies. Then, the second part of the analysis cal research. Three main publishers (namely, ACM, IEEE, focuses on the sensors themselves. The main concern is and Springer) were considered as part of the main cor- the sensor classification and its application in each case. pus of analysis. The selected time frame is from 2015 to The last part of the analysis offers the classification of 2016, to show the newest applications and sensors in a the use of sensors, within a characterized scenario. As closed sample. The initial search query was “Internet of an example, a temperature sensor can be used for an Things” and “Experiment.” The objective was to sample agricultural or an e-health application. all papers that actually implement a concrete IoT solu- tion, with real sensors in a context or domain. The total Analysis number of examined papers reached a staggering 1087. Scenarios’ variables After this first selection, the inclusion and exclusion cri- A main aspect of the IoT scene is how the context of use teria(describedbelow)wereappliedtofilteroutsomeof and scale can change the data analysis. An IoT application the works. Table 1 Sample distribution by publisher and year Inclusion criteria 2015 2016 Publisher’s total The inclusion criterion was that any article must have as ACM549 its focus IoT technology while also presenting an exper- IEEE 4 14 18 imental setup or testbed running over real hardware. In addition, the publishing date must fall between the years Springer 7 14 21 2015 and 2016, inclusive. Year’s total 16 32 48 Morais et al. Journal of the Brazilian Computer Society (2019) 25:4 Page 3 of 17 can vary in many aspects. As an example, a smart city and of a smart city IoT system could show similarity to those a smart home are both categorized as IoT, but the smart from a smart industry.
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