Internet of things: Architecture, technology and key problems in implementation

Yue, Zhijia; Sun, Wanrong; Li, Peijia; Rehman, Masood Ur; Yang, Xiaodong

doi:10.1109/cisp.2015.7408082

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…An oftenfaced problem faced by the data analysts is that a vast quantity of data is available, but the format, quality, and Complexity 5 precise location are often not known, making retrieval and use difficult [48][49][50]. According to Yue et al [51] the core of the IoT lies with the sharing of information between things and things or between people and things. Yue et al [51] summarize the basic characteristics of things as comprehensive perception, reliable transmission, and intelligent processing.…”

Section: Cas Elements Of Data Infrastructuresmentioning

confidence: 99%

“…According to Yue et al [51] the core of the IoT lies with the sharing of information between things and things or between people and things. Yue et al [51] summarize the basic characteristics of things as comprehensive perception, reliable transmission, and intelligent processing. Comprehensive perception includes the acquisition of observations or measurements by using perception, acquisition, and measurement technology such as RFID [52] and twodimensional code and sensors.…”

Section: Cas Elements Of Data Infrastructuresmentioning

confidence: 99%

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Next Generation Data Infrastructures: Towards an Extendable Model of the Asset Management Data Infrastructure as Complex Adaptive System

2019

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Organizations are increasingly looking to adopt the Internet of Things (IoT) to collect the data required for data-driven decision-making. IoT might yield many benefits for asset management organizations engaged in infrastructure asset management, yet not all organizations are equipped to handle this data. IoT data is collected, stored, and analyzed within data infrastructures and there are many changes over time, resulting in the evolution of the data infrastructure and the need to view data infrastructures as complex adaptive systems (CAS). Such data infrastructures represent information about physical reality, in this case about the underlying physical infrastructure. Physical infrastructures are often described and analyzed in literature as CASs, but their underlying data infrastructures are not yet systematically analyzed, whereas they can also be viewed as CAS. Current asset management data models tend to view the system from a static perspective, posing constraints on the extensibility of the system, and making it difficult to adopt new data sources such as IoT. The objective of the research is therefore to develop an extensible model of asset management data infrastructures which helps organizations implement data infrastructures which are capable of evolution and aids the successful adoption of IoT. Systematic literature review and an IoT case study in the infrastructure management domain are used as research methods. By adopting a CAS lens in the design, the resulting data infrastructure is extendable to deal with evolution of asset management data infrastructures in the face of new technologies and new requirements and to steadily exhibit new forms of emergent behavior. This paper concludes that asset management data infrastructures are inherently multilevel, consisting of subsystems, links, and nodes, all of which are interdependent in several ways.

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Section: Cas Elements Of Data Infrastructuresmentioning

confidence: 99%

Section: Cas Elements Of Data Infrastructuresmentioning

confidence: 99%

Next Generation Data Infrastructures: Towards an Extendable Model of the Asset Management Data Infrastructure as Complex Adaptive System

2019

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“…The IoT paradigm evolves as a result of the huge availability of the internet access, communication protocols and sensors. It utilizes current technologies, such as: Cloud and mobile technologies (Yue et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

ElMenshawy¹,

Helmy²,

El-Tazi³

2019

Journal of Computer Science

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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.

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“…Every object has a distinctive identity and can access the network to integrate between both the physical and digital worlds to provide enhanced services to people. IoT can provide device to device, device to people and device to environment information transfer through the integration of information space and physical space [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…The prediction accuracy of detecting both events and noise was computed. The accuracy was measured as in(3) and(4…”

mentioning

confidence: 99%

A Correlation based Approach to Differentiate between an Event and Noise in Internet of Things

ElMenshawy¹,

Helmy²

2018

ijacsa

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Internet of Things (IoT) is considered a huge enhancement in the field of information technology. IoT is the integration of physical devices which are embedded with electronics, software, sensors, and connectivity that allow them to interact and exchange data. IoT is still in its beginning so it faces a lot of obstacles ranging from data management to security concerns. Regarding data management, sensors generate huge amounts of data that need to be handled efficiently to have successful employment of IoT applications. Detection of data anomalies is a great challenge that faces the IoT environment because, the notion of anomaly in IoT is domain dependent. Also, the IoT environment is susceptible to a high noise rate. Actually, there are two main sources of anomalies, namely: an event and noise. An event refers to a certain incident which occurred at a specific time, whereas noise denotes an error. Both event and noise are considered anomalies as they deviate from the remaining data points, but actually they have two different interpretations. To the best of our knowledge, no research exists addressing the question of how to differentiate between an event and noise in IoT. As a result, in this paper, an algorithm is proposed to differentiate between an event and noise in the IoT environment. At first, anomalies are detected using exponential moving average technique, then the proposed algorithm is applied to differentiate between an event and noise. The algorithm uses the sensors' values and correlation existence between sensors to detect whether the anomaly is an event or noise. Moreover, the algorithm was applied on a real traffic dataset of size 5000 records to evaluate its effectiveness and the experiments showed promising results.

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Internet of things: Architecture, technology and key problems in implementation

Cited by 15 publications

References 9 publications

Next Generation Data Infrastructures: Towards an Extendable Model of the Asset Management Data Infrastructure as Complex Adaptive System

Next Generation Data Infrastructures: Towards an Extendable Model of the Asset Management Data Infrastructure as Complex Adaptive System

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

A Correlation based Approach to Differentiate between an Event and Noise in Internet of Things

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