A Reference Model for Internet of Things Middleware

Cruz, Mauro A. A. da; Rodrigues, Joel J. P. C.; Al‐Muhtadi, Jalal; Korotaev, Valery V.; Albuquerque, Victor Hugo C. de

doi:10.1109/jiot.2018.2796561

Cited by 238 publications

(121 citation statements)

References 32 publications

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“…Together with the development of a multi-gas sensor, the information collected by these devices should be easily available to the users and also to other devices that could use the provided data to contribute to the best experience to the users of this system as well as to decreasing avoidable costs, making possible investments on demanding areas [7,10,13,89].…”

Section: Internet Of Things (Iot) Verticals Opportunities and Economimentioning

confidence: 99%

“…With the paradigm of the IoT, it is expected the number of connected devices will reach 50 billion by 2025 with an estimated increase of $1 to 2.5 trillion USD on the GWP by the same year [11,13,89]. The sectors (IoT verticals) where the impact of the new technologies will be introduced and the use of a multi-gas smart sensor can be seen on Figure 1.…”

Section: Internet Of Things (Iot) Verticals Opportunities and Economimentioning

confidence: 99%

“…For instance, a gas sensor supporting IEEE 802.15.4 and MQTT should be able to communicate with a smart window with a GPRS module and operate with CoAP. This integration is possible through mediator software, called middleware, where data gathered by sensors are transferred and stored on this software, and it makes the necessary adaptations for these data being suitable for other devices under different communication protocols [13,89].…”

Section: Sensing Requirements For Iot-based Gas Sensorsmentioning

confidence: 99%

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IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities

Gomes

Rodrigues

Rabêlo

et al. 2019

JSAN

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Ambient gas detection and measurement had become essential in diverse fields and applications, from preventing accidents, avoiding equipment malfunction, to air pollution warnings and granting the correct gas mixture to patients in hospitals. Gas leakage can reach large proportions, affecting entire neighborhoods or even cities, causing enormous environmental impacts. This paper elaborates on a deep review of the state of the art on gas-sensing technologies, analyzing the opportunities and main characteristics of the transducers, as well as towards their integration through the Internet of Things (IoT) paradigm. This should ease the information collecting and sharing processes, granting better experiences to users, and avoiding major losses and expenses. The most promising wireless-based solutions for ambient gas monitoring are analyzed and discussed, open research topics are identified, and lessons learned are shared to conclude the study.Technologies to sense gases and wireless communications support are elaborated in Sections 5 and 6, respectively. The most promising solutions in terms of sensing methods and IoT-enabled solutions are discussed in Section 7 and open research topics are identified. Lessons learned are shared at Section 8 and, finally, Section 9 concludes the study. Background on Environmental GasesSome gases are the key to ensure the functionality of systems and entire industries, as well as the presence of other gases being a problem in other fields, causing the loss of entire production lines, as in the food industry, or even cause the loss of lives and explosions. In this section, the most important gases in terms of pollution monitoring and control, health issues, and accident prevention are listed with their main characteristics.Oxygen (O 2 ) is the most important gas for life, and is crucial in numerous fields. Patients under anesthesia, or recovering from surgery and from certain diseases need controlled O 2 doses to keep them alive and fully recovered. The decrease of oxygen levels in enclosed spaces can be related with other gases leakage, which would lead people inside these spaces to asphyxiate, leading to an unconscious state, or even to death [29]. Numerous industrial processes rely on the correct concentration of this gas to achieve the best results, mainly chemical and combustion, which without the correct percentages will not grant the best performance of systems. Engine control systems depend on the correct mixture to achieve the expected performances, whether it is lower fuel consumption or power and speed [29][30][31][32].Carbon dioxide (CO 2 ) is a colorless, odorless gas, generated by the oxidation and combustion of hydrocarbon, as well as by living beings in the respiration process. It is a key gas for the greenhouse effect, and the increase of its levels, in the presence of other gases, is related with atmospheric pollution. It is also the key gas on the oxygen production by the photosynthesis process. The accumulation of this gas in enclosed spaces can be responsible to s...

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Section: Internet Of Things (Iot) Verticals Opportunities and Economimentioning

confidence: 99%

Section: Internet Of Things (Iot) Verticals Opportunities and Economimentioning

confidence: 99%

Section: Sensing Requirements For Iot-based Gas Sensorsmentioning

confidence: 99%

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IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities

Gomes

Rodrigues

Rabêlo

et al. 2019

JSAN

Self Cite

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“…There have been many research work on IoMT-based platforms, applications, and services, such as remote health monitoring, fitness programs, chronic diseases, and elderly care [15] [16] [17] [18] [19]. In [15], a novel joint IoMT and Product Lifecycle Management (PLM) based framework is proposed for medical healthcare applications to regulate the information transfer from one entity to another and between devices in an efficient and accurate way, while managing the battery lifecycle and energy of the resource-constrained tiny wearable devices.…”

Section: Related Workmentioning

confidence: 99%

Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

Guan

et al. 2019

Future Generation Computer Systems

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The emergence and rapid development of the Internet of Medical Things (IoMT), an application of the Internet of Things into the medical and healthcare systems, have brought many changes and challenges to modern medical and healthcare systems. Particularly, machine learning technology can be used to process the data involved in IoMT for medical analysis and disease diagnosis. However, in this process, the disclosure of personal privacy information must receive considerable attentions especially for sensitive medical data. Cluster analysis is an important technique for medical analysis and disease diagnosis. To enable privacy-preserving cluster analysis in IoMT, this paper proposed an Efficient Differentially Private Data Clustering scheme (EDPDCS) based on MapReduce framework. In EDPDCS, we optimize the allocation of privacy budgets and the selection of initial centroids to improve the accuracy of differentially private Kmeans clustering algorithm. Specifically, the number of iterations of the K-means algorithm is set to a fixed value according to the total privacy budget and the minimal privacy budget of each iteration. In addition, an improved initial centroids selection method is proposed to increase the accuracy and efficiency of the clustering algorithm. Finally, we prove that the proposed EDPDCS can improve the accuracy of the differentially private k-means algorithm by comparing the Normalized Intra-Cluster Variance (NICV) produced by our algorithm on two datasets with two other algorithms.

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“…Many initiatives introduced IoT architecture models to improve the composability and extensibility of various large scale systems. [52][53][54][55] The goal of all these architectures is to maintain the flow of information between interconnected business devices and cloud-based systems. The details of such architectures differ depending on the application domain.…”

Section: Pygrid Systemmentioning

confidence: 99%

PYGRID: A software development and assessment framework for grid‐aware software defined networking

et al. 2018

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The Internet of Things (IoT) envisions to connect Advanced Metering Infrastructure (AMI) to the Internet to facilitate efficient resource management in smart cities. Smart grid is the evolutionary approach towards a more dynamic, situation‐aware, and elastic power systems in smart cities. However, due to the huge scale, heterogeneity and complexity of the many interconnected cyber and physical devices, current network and security technologies are incapable of best serving these needs. Destructive attacks targeting these components such as Denial of Service (DoS) may fully use grid structure and disrupt its crucial service. Recent researches offer the use of modern Software Defined Networking (SDN) to solve synonymous Cyber Physical Systems (CPS) network and security challenges. These researches noted the lack of an adequate platform to simulate grid systems network infrastructure. Therefore, in this paper, we present PYGRID, a software development and assessment framework for grid‐aware SDN. Additionally, a top level SDN application is developed in SDN‐enabled smart grid network to demonstrate the effectiveness of such merger and to mitigate DoS attacks. PYGRID is developed using PYTHON, an OSI‐approved open source language considering standard client and server industrial communication protocols. IEEE 14‐bus power system is adopted to demonstrate the potent and effectiveness of the proposed solution. We conducted multiple experiments to validate the developed framework over 14‐bus system using PYPOWER. To the best of our knowledge, the resulted platform may be considered the first completely open source implementation of a secure SDN enabled smart grid simulator.

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A Reference Model for Internet of Things Middleware

Cited by 238 publications

References 32 publications

IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities

IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities

Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

PYGRID: A software development and assessment framework for grid‐aware software defined networking

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