A survey on Internet of Things: Applications and challenges

Nalbandian, Shayan

doi:10.1109/ictck.2015.7582664

Cited by 26 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in harmony with manufacturing standpoint, we would like to remark that, to achieve smartness/ cognition, suitable methodss need to be developed based on a learning subsystem to characterize existing states of manufacturing and industrial system, ML could be taking as an promising and suitable tools to tackle with existing and upcoming issues [44].…”

Section: A Design Methodsmentioning

confidence: 99%

The Application of Cognitive IoT for Smart Manufacturing

Abdurahman*,

Patel

2020

IJITEE

View full text Add to dashboard Cite

In recent times, novel paradigms based on cognitive manufacturing services are evolving. This paradigm shift is conveyed by integrating manufacturing assets with the latest and enhanced methods and technologies. However, today’s manufacturing systems are facing various challenges, that is the researcher believed that existing technologies and tools specifically, IoT, existing learning techniques and learning systems, lack enough cognitive based intelligence and cannot achieve the expected enhancements and smart manufacturing developments. In Light of this assessing the advances in manufacturing sectors was the major goal of this work and to identify the cuuent issues and challenches in manufacturing systems being Cognitive and or smart Afterward, we assessed the research challenges and open issues and facilitate knowledge accumulation in efficiently in the applications of Cognitive Internet of Things (CIoT) for smart manufacturing systems.

show abstract

Section: A Design Methodsmentioning

confidence: 99%

The Application of Cognitive IoT for Smart Manufacturing

Abdurahman*,

Patel

2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…Security threats are huge in the IoT environment because of the physical accessibility to the objects, which are embedded with the sensors. Also, the majority of the objects communicate wirelessly so the environment is highly susceptible to security attacks (Nalbandian, 2015). As a result, a lot of research and investigation are needed to deal with these issues to increase the IoT usage in various applications.…”

Section: Motivationmentioning

confidence: 99%

“…(1) The notion of anomaly in IoT is domain dependent (2) There is a high noise rate in the data, since sometimes sensors have low quality and power, so noisy data may be interpreted as anomalies and vice versa (Nalbandian, 2015) In IoT, anomaly detection has several challenges in general and in contextual anomaly detection in particular, because, the context has to be taken into account in the anomaly detection process.…”

Section: Network Layermentioning

confidence: 99%

A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

ElMenshawy¹,

Helmy²,

El-Tazi³

2019

Journal of Computer Science

View full text Add to dashboard Cite

Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and realtime information transfer and communication, achieving intelligent management. IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns. Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications. Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies. Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection. As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things. Attributes were separated into two different categories, namely contextual attributes and behavioral attributes. K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies. Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.

show abstract

“…Internet of Things (IoT) technology is emerging rapidly and will become part of our day-to-day lives in the future by connecting anything, anywhere, at any time [1], [2], [3], [4], [5]. IoT comprises nearly all industries and applications such as home automation, building automation, health care, education, environment maintenance, intelligent transportation, smart appliances, smart campuses, smart grids, and smart cities [6], [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Weighted Adaptive Opportunistic Scheduling Framework for Smartphone Sensor Data Collection in IoT

2019

KSII TIIS

View full text Add to dashboard Cite

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

show abstract

A survey on Internet of Things: Applications and challenges

Cited by 26 publications

References 12 publications

The Application of Cognitive IoT for Smart Manufacturing

The Application of Cognitive IoT for Smart Manufacturing

A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

Weighted Adaptive Opportunistic Scheduling Framework for Smartphone Sensor Data Collection in IoT

Contact Info

Product

Resources

About