Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities

Fan, Lei; Qian, Cheng; Hatcher, William G.; Yu, Wei

doi:10.1109/access.2019.2931659

Cited by 33 publications

(12 citation statements)

References 94 publications

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“…For the purposes of this work, we adopt the IoT search engine framework as outlined in [36]. Under this framework, we consider a number of factors that are critical to searching and retrieving IoT devices and their data.…”

Section: A Iot Search Enginementioning

confidence: 99%

“…At the lowest level, we consider that IoT devices are reachable and addressable utilizing an IoT search address protocol, which features gateway addresses for geolocation services, as well as tags for the descriptions of device identifiers, types of services, etc. As demonstrated in [36], using a variety of network communication protocols, a system can be established that allows for open registration of IoT devices and their data for cross-platform sharing.…”

Section: A Iot Search Enginementioning

confidence: 99%

“…While the concept of IoT search and search engines may not be totally new [36]- [40], the increasing deployment and redundancy of IoT systems has increased the need for a fully-realized IoT search engine. This can be coupled with advancements in a variety of related technologies, such as big data and deep learning, which can aid in the achievement of this goal.…”

Section: Introductionmentioning

confidence: 99%

“…These include the heterogeneity of devices, networks, communication, data, and storage; device ownership, participation incentives, and security; and functional implementation aspects such as distributed architectures, communication protocols, query systems, and quality of service (QoS), among others. In this work, we thus first consider an IoT search engine framework, as laid out in [36], which considers the basic structures and heterogeneity of IoT. This framework layers Quality of Service (QoS), Query Engine, Indexing, and Devices, and allows for interchangeable software modules to optimize and facilitate the Query Engine.…”

Section: Introductionmentioning

confidence: 99%

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Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications.

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Section: A Iot Search Enginementioning

confidence: 99%

Section: A Iot Search Enginementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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“…The fundamental concept of IoT is the embedding of microchips and sensors into everyday objects, enabling computing and the interconnection of ''things'' across networks. With the increasing reliability and maturity of more advanced technologies, IoT has been widely adopted in a variety of smartworld systems, such as smart grids, smart transportation, smart agriculture, smart cities, smart healthcare, and smart public safety, among others [3]- [8], [8]- [15]. Moreover,…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments

et al. 2020

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With the immensity of distributed Internet of Things (IoT) devices and the exponential increase in data generated from a variety of IoT-driven smart-world applications, how to effectively provide data driven service supported by IoT has become a critical issue. While the state-of-the-art technologies have been developed and network infrastructures with high capabilities have been designed to deal with the data collection problem, there are still application scenarios, in which network infrastructure is not available or appropriate in large target areas (e.g., farmlands deployed with IoT sensors in operation, providing precise agriculture; emergency responder with IoT sensors, providing public safety service). To address the issue of efficiently collecting data from IoT devices deployed in large areas without predeployed network infrastructure, we formalize the problem space in a three-dimensional model that considers task, resource, and methodology. Based on the designed problem space, we propose a novel solution that deploys an unmanned aerial vehicle (UAV), as a critical next generation mobile network, to achieve intermittent IoT device connections and enable data collection based on delay tolerant network (DTN) protocol. The UAV flight path is determined using a Hilbert Curve-based path planning algorithm. Through a series of quantitative experiments, we validate the effectiveness of our approach in a network emulation environment, and confirm its advantages in comparison with several baseline approaches. The results of our research shows the capability of quality and cost control in IoT applications such as smart agriculture, public safety disaster recovery and rescue. INDEX TERMS Internet of Things, delay tolerant networks, next generation mobile networks, unmanned aerial vehicles, path planning, data collection.

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