Internet of Things as a Service (iTaaS): Challenges and solutions for management of sensor data on the cloud and the fog

Petrakis, Euripides G. M.; Sotiriadis, Stelios; Soultanopoulos, Theodoros; Renta, Pelagia Tsiachri; Buyya, Rajkumar; Bessis, Nik

doi:10.1016/j.iot.2018.09.009

Cited by 79 publications

(31 citation statements)

References 31 publications

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“…Furthermore, the task assignment and task offloading problems related to the healthcare applications are formulated in these studies [16,18,31,33]. The prior studies have focused on how to offload computation tasks to the cloud system in order to improve application performance on the user's devices and measure the delay optimal results of healthcare data without any risk.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to the sporadic nature of ECG signals, it is necessary to monitor patients continuously to have for accurate analysis of the heart problems [4]. Recently, advancements in Internet of Things (IoT) based medical sensors have grown progressively [5][6][7][8][9][10][11][12][13][14][15]; especially in heartbeat sensors that generate real-time delay-sensitive data that require immediate action for the results [16,17]. Generally, these sensors are integrated with limited constraint devices.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Mastoi

Wah

Raj

et al. 2020

Sensors

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Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Mastoi

Wah

Raj

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…To provide quantities, units, dimensions and values there are also some ontologies. Qu ontology 4 is one of the well-known ontologies in this field. Qu ontology has been developed to support different Systems Modelling Language (SysML) users [11].…”

Section: Related Workmentioning

confidence: 99%

“…There are some semantic approaches that model IoT devices and some aspects about them, such as service offered, kind of data, units, etc. ( [1], [2], [3], [4]). Recently some approaches have envisioned the need to provide a lightweight approach to the models, such as IoT-Lite [5], and the standard and popular Semantic Sensor Network model (SSN) [6], which recently has published a lightweight model with its core ontology, SOSA (Sensor, Observation, Sample, and Actuator) [7].…”

Section: Introductionmentioning

confidence: 99%

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams

Elsaleh

Bermúdez-Edo

Enshaeifar

et al. 2019

2019 Global IoT Summit (GIoTS)

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In recent years, the development and deployment of Internet of Things (IoT) devices has led to the generation of large volumes of real world data. Analytical models can be used to extract meaningful insights from this data. However, most of IoT data is not fully utilised, which is mainly due to interoperability issues and the difficulties to analyse data collected by heterogeneous resources. To overcome this heterogeneity, semantic technologies are used to create common models to share various data originated from heterogeneous sources. However, semantics add further overhead to data delivery, and the processing time to annotate the data with the model can increase the latency and complexity in publishing and querying the annotated data. In this paper, we present a lightweight semantic model to annotate IoT streams. The metadata descriptions that are provided in the models are used for search and discovery of the data using various attributes such as value and type. The proposed model extends commonly used ontologies such as W3C/OGC SSN ontology and its recent lightweight core, SOSA, and includes concepts to describe streaming IoT data. We also show use cases, tools and applications where the proposed model has been used.

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“…Semantics provide common information models with which the services using heterogeneous sources of information could interoperate using the same concepts and relationships between concepts. Examples of these solutions include information models describing IoT devices, services, types, and units of data, etc., such as the models in [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Elsaleh

Enshaeifar

Rezvani

et al. 2020

Sensors

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With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

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Internet of Things as a Service (iTaaS): Challenges and solutions for management of sensor data on the cloud and the fog

Cited by 79 publications

References 31 publications

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

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