A Quality‐of‐Things model for assessing the Internet‐of‐Things' nonfunctional properties

Qamar, Ayesha; Asim, Muhammad; Maamar, Zakaria; Saeed, Saad; Baker, Thar

doi:10.1002/ett.3668

Cited by 15 publications

(10 citation statements)

References 31 publications

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“…For achieving the best use of IoT, we will follow the approach in other works. [42][43][44] A cloud-based system will be built to ensure the success of the big data processing and hosting. The cloud system also allows other stakeholders mainly electric companies to participate in the system and utilize the smart services as in other works.…”

Section: Discussionmentioning

confidence: 99%

Accurate and time‐efficient negative binomial linear model for electric load forecasting in IoE

Daraghmi

Alsaadi

et al. 2019

Trans Emerging Tel Tech

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Accurate and efficient model predictive control (MPC) is essential for Internet of energy (IoE) to enable active real-time control, decentralized demand-supply balance, and dynamic energy management. The MPC consists of short-term electric load forecasting, whose accuracy is affected by the load characteristics, such as overdispersion, autocorrelation, and seasonal patterns. The forecasting efficiency depends on the computational time that is required to produce accurate results and is affected by the IoE data volume. Although several fundamental short-term forecasting models have been proposed, more accurate and efficient models are needed for IoE. Therefore, we propose a novel forecasting temporal negative binomial linear model (NBLM) that handles overdispersion and captures nonlinearity of electric load. We also classify the load into low, moderate, and high intraday seasons to increase the forecast accuracy by modeling the autocorrelation in each season, separately. The temporal NBLM was evaluated using real-world data from Jericho city, and its results were compared to other forecasting models. The temporal NBLM is found more accurate than the other models as the mean absolute percentage error (MAPE) is reduced by 29% compared to the ARMA model. In addition, the proposed model is more efficient as its running time is reduced by 63% in the training phase and by 87% in the forecast phase compared to the Holt-Winter model. This increase in accuracy and efficiency makes the proposed model applicable for load forecasting in IoE contexts where data volume is large and load is highly fluctuated, is overdispersed, is autocorrelated, and follows seasonal patterns.

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Section: Discussionmentioning

confidence: 99%

Accurate and time‐efficient negative binomial linear model for electric load forecasting in IoE

Daraghmi

Alsaadi

et al. 2019

Trans Emerging Tel Tech

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“…Then the thing share with the cyber-physical environment the outcomes that resulted from both sensing and actuating (Fig. 1) [22]. Data in IoT is collected, analyzed, organized, and communicated through hardware, software, and software systems.…”

Section: B Iot Devicesmentioning

confidence: 99%

“…The QoS service can be referred to as a quality assurance service of network connectivity, prioritizing applications across the network [22]. QoS is a crucial enabler of IoT networking because it handles network functionality, resources and offers secure connectivity.…”

Section: F Qos In Iotmentioning

confidence: 99%

IoT Provisioning QoS based on Cloud and Fog Computing

Samann

Zeebaree

Askar

2021

JASTT

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The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

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“…In a previous work, we identified three single duties that would capture a thing's capabilities in terms of sensing (collecting/capturing data), actuating (processing/acting upon data), and communicating (sharing/distributing data). A duty is either enabled or disabled ((0,1) in Figure ) according to the requirements and needs of the underdevelopment IoT applications.…”

Section: Viot‐compliant Thingsmentioning

confidence: 99%

Open challenges in vetting the internet‐of‐things

Maamar

Kajan

Asim

et al. 2019

Internet Technology Letters

Self Cite

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Internet‐of‐Thing (IoT) is a rapid‐emerging technology that exploits the concept of internetwork to connect things such as physical devices and objects together. A huge number of things (6.4 billion are in use in 2016) are already acting without direct human control raising a lot of concerns about the readiness and appropriateness of existing security practices, techniques, and tools to secure the data collected and protect people's private lives. As a first step, this paper presses the importance of having a dedicated process for vetting IoT (by analogy to vetting mobile apps) with focus on exposing things' vulnerabilities that could be the primary source of attacks. These vulnerabilities are identified according to things' duties decomposed into sensing, actuating, and communicating. A set of questions shed light on things' vulnerabilities per type of duty.

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A Quality‐of‐Things model for assessing the Internet‐of‐Things' nonfunctional properties

Cited by 15 publications

References 31 publications

Accurate and time‐efficient negative binomial linear model for electric load forecasting in IoE

Accurate and time‐efficient negative binomial linear model for electric load forecasting in IoE

IoT Provisioning QoS based on Cloud and Fog Computing

Open challenges in vetting the internet‐of‐things

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