Ontologies as a semantic model in IoT

Mishra, Sanju; Jain, Sarika

doi:10.1080/1206212x.2018.1504461

Cited by 48 publications

(26 citation statements)

References 13 publications

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“…The term evaluation comprises the terms validation and verification [26]. Validation guarantees about the ontology that it responds to the system while verification refers to the technical process that guarantees the completeness, correctness and consistency.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Lightweight and fast encryption plays a significant role in acquiring the adapted security and privacy for IoT. Ontologies [26][27][28] are considered as an interesting area for IS as a basis for detecting and analyzing security issues. Ontologies are generally applied as representation schema, knowledge bases, or an annotation vocabulary.…”

Section: Security Issues In Swotmentioning

confidence: 99%

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Secure Semantic Smart HealthCare (S3HC)

Tiwari

Jain

Abraham

et al. 2019

JWE

Self Cite

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Healthcare is a significant domain having a huge knowledge base, a significant part which comes from medical, diagnostic and imaging devices and sensors. The health status of patients may be monitored and managed remotely by performing reasoning over this knowledge base. Specialists in HealthCare facilities are required to handle large quantity of data generated and make decisions. However, the heterogeneous and complex nature and the huge amount of data generated; the way it is represented and presented; and the security challenges may overburden the core abilities of thinking and reasoning of even highly skilled and knowledgeable experts putting the lives of patients at risk. The situation may become even worse when data is coming from various healthcare devices and sensors which are themselves characterized by a number of representation and serialization formats. To address the various challenges in healthcare, this paper tries to represent and hence exchange the data collected by healthcare devices meaningfully and securely. This allows all healthcare devices to operate in conjunction with each other facilitating deeper insights and enabling generation of intelligent recommendations.To transfer the collected data from devices to the knowledge base and vice versa, a healthcare IoT ontology with sensors and actuators is developed. SPARQL queries and SWRL rules are composed to provide personalized services and alleviating the doctors' workload.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Security Issues In Swotmentioning

confidence: 99%

Secure Semantic Smart HealthCare (S3HC)

Tiwari

Jain

Abraham

et al. 2019

JWE

Self Cite

View full text Add to dashboard Cite

show abstract

“…The last interesting paper of IoT model is the paper mainly addresses the data content transferring between the IoT devices. Indeed, the authors in [4] present and test a new model to control and manage the semantic web of IoT devices in which to manage their knowledge sharing, information extraction, information integration. To do so, they apply verification and validations approaches and applied quantitative techniques like metric based and criteria based strategies as well as qualitative solutions like application based and competency questions strategies.…”

Section: Models/architecturesmentioning

confidence: 99%

Internet of everything, networks, applications, and computing systems (IoENACS)

Shojafar

Sookhak

2019

International Journal of Computers and Applications

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“…Efficient job scheduling can yield fast and timely response that is desirable in smart systems. For example, in a smart healthcare system, a patient's condition needs fast notification to save the patient's life . Therefore, there is need to develop some efficient job scheduling algorithm to maximize utilization of these heterogeneous and resource‐constrained Fog devices .…”

Section: Introductionmentioning

confidence: 99%

“…For example, in a smart healthcare system, a patient's condition needs fast notification to save the patient's life. 9 Therefore, there is need to develop some efficient job scheduling algorithm to maximize utilization of these heterogeneous and resource-constrained Fog devices. 10 The overall objective is to minimize the response time and network usage without increasing energy consumption.…”

mentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

103

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Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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Ontologies as a semantic model in IoT

Cited by 48 publications

References 13 publications

Secure Semantic Smart HealthCare (S3HC)

Secure Semantic Smart HealthCare (S3HC)

Internet of everything, networks, applications, and computing systems (IoENACS)

A job scheduling algorithm for delay and performance optimization in fog computing

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