A complete model for IOT application

Behura, Ashutosh; Narayan, Akash; Ray, Arun Kumar; Pani, Santosh Kumar

doi:10.1109/iss1.2017.8389334

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…e reusability metrics for each of these modules are defined as follows: In this section, we mapped the newly defined design quality metrics to the QoS factors taken from the ISO 9126 model [23] (defined for the quality assessment of computing systems and applications). e QoS factors selected for this purpose, from the works of [3,4,6,17,22], include complexity, functional suitability, performance, efficiency, compatibility, maintainability, portability, and usability. e metrics defined for design quality evaluation of IoT services are IoT design complexity metric, IoT transactional complexity metric, IoT transactional network load metric, IoT communication channel demand metric, IoT interoperability resolving network complexity metric, and IoTinteroperability resolving network load metric.…”

Section: Reusability Factor Of Iot Metricmentioning

confidence: 99%

“…e resource-constrained IoT devices, being arrayed in vastly dynamic settings and union of a range of technologies, cause conventional design standards and models to be insu cient to measure the quality of service (QoS) in IoT applications [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…At the second level, Tambotoh et al [14] identified the IoT characteristics as heterogeneity, resource-constrained devices, collaboration with hardware and resources, network mobility, embedded and adaptive devices, and design to monitoring IoT devices. At the next level, QoS factors for IoT systems and services are presented [3,4,6,17,22] based on ISO-9126 model [23]. It includes complexity, functional suitability, performance, efficiency, compatibility, maintainability, portability, and usability.…”

Section: Introductionmentioning

confidence: 99%

“…IoT Services[2], IoT characteristics[14], QoS factors[3,4,6,22], and design quality metrics (this work).…”

mentioning

confidence: 99%

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A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Waris

Jaleel

Shoaib

et al. 2022

Mathematical Problems in Engineering

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Internet of Things (IoT) infrastructure connects consumer electronics, household appliances, and other smart gadgets. For designing IoT systems and applications, different architectures and protocols are being used. The design quality of an IoT system and its services is assessed with quality of service (QoS) factors such as complexity, functional appropriateness, performance, efficiency, compatibility, maintainability, portability, and usability. The existing methods in the literature focus on measuring the quality of an IoT system during execution time; however, there is a need to define some standards that may be used to determine the quality of an IoT system from its design. This work models the IoT EcoSystem as a schema graph and presents a suite of metrics to determine the design quality of an IoT system and its services under the defined QoS factors. The presented metrics are mapped to the ISO-9126 QoS factors to ensure the standards and quality of IoT services during the design phase. The proposed metrics are validated with benchmarking on a case study smart healthcare IoT system. The results proved that the presented metrics are practical to quantify the quality of IoT design models under QoS factors, and the suite of metrics is able to compare differently designed IoT services.

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Section: Reusability Factor Of Iot Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…IoT Services[2], IoT characteristics[14], QoS factors[3,4,6,22], and design quality metrics (this work).…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Waris

Jaleel

Shoaib

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…However, for smart agriculture, the use of WSN to the IoT is the major driver, and the reason behind is that IoT combines various technology which exists already such as middleware system, WSN, RF identification, cloud computing and the end-user application [10][11][12]. The IoT helps in agriculture by empowering the farmers with automation technologies and the decision tools that integrates the services, knowledge and products to achieve the profit, better quality and the better productivity [13,14]. The below diagram Figure 1 shows the IoT system for agriculture, it has basically five blocks, in which the first block represents the data collection, here the various data such as temperature, humidity, moisture and the other data are collected [15].…”

mentioning

confidence: 99%

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

Vimala¹,

Ranjan²

2020

IJECE

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IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed.

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A Remote Program Loading Service Design and Implementation

Incereis

Akgun

2020

2020 Turkish National Software Engineering Symposium (UYMS)

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A complete model for IOT application

Cited by 3 publications

References 5 publications

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

A Remote Program Loading Service Design and Implementation

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