Context computing for internet of things

Manaligod, Hector John T.; Diño, Michael Joseph S.; Ghose, Supratip; Han, Junghee

doi:10.1007/s12652-019-01560-3

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…The SCAMEU handles context-aware data exchanges in ILARF. In order to collect and exchange context-aware data in real-time, a variety of devices, such as smartphones, tablets, wearable devices, smart bands, smart sensors, cameras, smart speakers, and GPS devices, can be connected using different protocols [ 58 ]. This subsection discusses four protocols suitable for SCAMEU to exchange the data of devices and users: the MQTT protocol [ 59 , 60 ], the Hypertext Transport Protocol (HTTP) [ 61 , 62 ], the Constrained Application Protocol (CoAP) [ 63 , 64 ], and the Advanced Message Queuing Protocol (AMQP) [ 65 , 66 , 67 ].…”

Section: Ilarfmentioning

confidence: 99%

An Indoor Location-Based Augmented Reality Framework

Jiang

Subakti

2023

Sensors

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This paper proposes an indoor location-based augmented reality framework (ILARF) for the development of indoor augmented-reality (AR) systems. ILARF integrates an indoor localization unit (ILU), a secure context-aware message exchange unit (SCAMEU), and an AR visualization and interaction unit (ARVIU). The ILU runs on a mobile device such as a smartphone and utilizes visible markers (e.g., images and text), invisible markers (e.g., Wi-Fi, Bluetooth Low Energy, and NFC signals), and device sensors (e.g., accelerometers, gyroscopes, and magnetometers) to determine the device location and direction. The SCAMEU utilizes a message queuing telemetry transport (MQTT) server to exchange ambient sensor data (e.g., temperature, light, and humidity readings) and user data (e.g., user location and user speed) for context-awareness. The unit also employs a web server to manage user profiles and settings. The ARVIU uses AR creation tools to handle user interaction and display context-aware information in appropriate areas of the device’s screen. One prototype AR app for use in gyms, Gym Augmented Reality (GAR), was developed based on ILARF. Users can register their profiles and configure settings when using GAR to visit a gym. Then, GAR can help users locate appropriate gym equipment based on their workout programs or favorite exercise specified in their profiles. GAR provides instructions on how to properly use the gym equipment and also makes it possible for gym users to socialize with each other, which may motivate them to go to the gym regularly. GAR is compared with other related AR systems. The comparison shows that GAR is superior to others by virtue of its use of ILARF; specifically, it provides more information, such as user location and direction, and has more desirable properties, such as secure communication and a 3D graphical user interface.

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Section: Ilarfmentioning

confidence: 99%

An Indoor Location-Based Augmented Reality Framework

Jiang

Subakti

2023

Sensors

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“…However, the particularities of the context can not be ignored by researchers, at the risk of making the application of solutions in real scenarios unfeasible. It is necessary to realize the importance of the specificity of the context, limitations, and characteristics of each environment, to develop solutions that apply and with high potential for impact on environments [76]. Thus, the results obtained will be closer to generating economic gains in real scenarios.…”

Section: Solutions Contextmentioning

confidence: 99%

Time Series Compression for IoT: A Systematic Literature Review

de Oliveira,

da Rocha,

Puntel

et al. 2023

Wireless Communications and Mobile Computing

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Time series are widely used to record information in applications developed with Internet of Things (IoT) devices, where sensors are used to collect large amounts of data. These devices, even with the exponential technological evolution of the last years, present limitations for the handling of this data given the enormous volume of data collected. Low storage or processing capacity and environments with restricted connection resources make it difficult to manipulate and transmit information. In this context, compression of information collected in the format of time series can be an alternative to reduce the amount of data handled by devices. However, given the peculiarities of IoT devices and applications, the compression process cannot be more costly than handling raw data. To identify the existing solutions to compress time series for IoT applications. In this paper, we present a descriptive systematic literature review on this topic. Based on a well-defined protocol, 40 papers were selected for review to analyze the strategies used, as well as performance and limitations. In this review, we aim to identify different approaches to solving the problem of data compression in the context addressed, as well as identifying directions for future research. The use of performance metrics in the reviewed papers was also reported in detail, to better understand how the authors compare their solutions to others. Additionally, the relationship between time series compression and machine learning (ML) techniques was highlighted. Being aware of the state-of-the-art in time series compression solutions for IoT can help us project future trends and challenges regarding this process and also to identify which algorithms, methods, and techniques can best be used in combination with ML models, the purpose inherent to the IoT context.

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Intelligent Computational Edge: From Pervasive Computing and Internet of Things to Computing Continuum

Juric

2023

Handbook of Human‐Machine Systems

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Context computing for internet of things

Cited by 10 publications

References 11 publications

An Indoor Location-Based Augmented Reality Framework

An Indoor Location-Based Augmented Reality Framework

Time Series Compression for IoT: A Systematic Literature Review

Intelligent Computational Edge: From Pervasive Computing and Internet of Things to Computing Continuum

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