Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

Sezer, Ömer Berat; Doğdu, Erdoğan; Özbayoğlu, A. Murat

doi:10.1109/jiot.2017.2773600

Cited by 351 publications

(174 citation statements)

References 174 publications

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“…However, they commonly treated the data aggregator purely as a transmitter and thus do not apply to the case, where update packets would be preprocessed (e.g., data compression and aggregation) to reduce the redundancy or even extract the "intrinsic content" from the collected raw data, before any transmission procedure begins. Actually, the joint operation of data preprocessing and transmission has been regarded as a promising solution for providing better context-aware services to users and meanwhile, overcoming the resource limitations on transmission capacity inherent in traditional IoT networks [14]- [16]. As such, it calls for additional efforts to study and optimize the information freshness when the data preprocessing and transmission are successively conducted.…”

Section: Related Workmentioning

confidence: 99%

“…In conventional IoT networks, due to the limited communication resource, a significant delay may occur during the packet transmission phase, which largely deteriorates the information freshness at the receiver side. As discussed in a line of existing work [14]- [16], to lighten the transmission burden and provide the end users with better context-aware services in IoT networks, it is preferable to first process the collected raw data with the edge/fog computing technique [17]- [19], and then transmit the resultant packet, which has a large reduction in size, to the actuator or monitor. However, the effect of such a data preprocessing procedure on the information freshness has not been fully understood.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Information Freshness in Computing-Enabled IoT Networks

Yang

Wang

et al. 2020

IEEE Internet Things J.

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Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many realtime IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually needs to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this work, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue, and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a minmax optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and effectiveness of the proposed algorithm are verified with extensive simulation results.Index Terms-Internet of things, information freshness, peak age of information, data preprocessing, derivative-free optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Information Freshness in Computing-Enabled IoT Networks

Yang

Wang

et al. 2020

IEEE Internet Things J.

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“…Moreover, some preliminary analytical schemes such as descriptive analysis (e.g., statistic analysis and data visualization) and diagnostic analysis are also applied in big data technologies. Recent papers reviewed the usage of big data analytics and intelligent algorithms in IoT scenarios [42], [43], [47], [48]. For example, the authors in [43] reviewed big data in IoT field from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks.…”

Section: Internet Of Ioementioning

confidence: 99%

“…Recent papers reviewed the usage of big data analytics and intelligent algorithms in IoT scenarios [42], [43], [47], [48]. For example, the authors in [43] reviewed big data in IoT field from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks. Additionally, the authors in [42] reviewed the state-of-the-art studies toward big IoT data analytics.…”

Section: Internet Of Ioementioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

173

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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“…Our solution to seamlessness and personalization is through context-awareness. Significant work has been done in contextawareness over the past decade [14], [31], [36], supporting better collecting, processing, and reasoning about context. In the IoT, a user's context can include any information that describes the user's situation, from location and time to ambient conditions or the presence of others [11].…”

Section: Introductionmentioning

confidence: 99%

rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

Hua

Liu

Kalbarczyk

et al. 2019

2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two realworld data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.

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Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

Cited by 351 publications

References 174 publications

Optimizing Information Freshness in Computing-Enabled IoT Networks

Optimizing Information Freshness in Computing-Enabled IoT Networks

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

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