Data Management for the Internet of Things: Design Primitives and Solution

Abu-Elkheir, Mervat; Hayajneh, Mohammad; Ali, Najah Abu

doi:10.3390/s131115582

Cited by 176 publications

(93 citation statements)

References 54 publications

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“…However, indexing and ranking processes are not included in the process chain. [Abu-Elkheir et al 2013] also explain life-cycle for IoT data starting from data production to data querying and analysis. The life-cycle does not cover indexing and ranking.…”

Section: Design Requirementsmentioning

confidence: 99%

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Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated with di erent granularities and have diverse structures, types and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop, implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.

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Section: Design Requirementsmentioning

confidence: 99%

“…The life-cycle does not cover indexing and ranking. Similar to [Barnaghi et al 2013a] and [Abu-Elkheir et al 2013] and based on the literature review we conducted, we propose a model that includes indexing and ranking for IoT data/resources. Fig.…”

Section: Design Requirementsmentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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“…Subsequently, this collected data needs to be analyzed in order to extract knowledge. Even when this processing can be performed in a traditional computing environment (centralized servers), this operation mode is prone to capacity limitations regarding processing power and storage volume [16,17]. In counterpoint, there are emerging distributed environments with more adequate features, including scalability means and parallel processing, to overcome the cited limitations of centralized architectures.…”

Section: Big Data Management For Iot Middlewarementioning

confidence: 99%

Distributed Data Service for Data Management in Internet of Things Middleware

Huacarpuma

Sousa

Holanda

et al. 2017

Sensors

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The development of the Internet of Things (IoT) is closely related to a considerable increase in the number and variety of devices connected to the Internet. Sensors have become a regular component of our environment, as well as smart phones and other devices that continuously collect data about our lives even without our intervention. With such connected devices, a broad range of applications has been developed and deployed, including those dealing with massive volumes of data. In this paper, we introduce a Distributed Data Service (DDS) to collect and process data for IoT environments. One central goal of this DDS is to enable multiple and distinct IoT middleware systems to share common data services from a loosely-coupled provider. In this context, we propose a new specification of functionalities for a DDS and the conception of the corresponding techniques for collecting, filtering and storing data conveniently and efficiently in this environment. Another contribution is a data aggregation component that is proposed to support efficient real-time data querying. To validate its data collecting and querying functionalities and performance, the proposed DDS is evaluated in two case studies regarding a simulated smart home system, the first case devoted to evaluating data collection and aggregation when the DDS is interacting with the UIoT middleware, and the second aimed at comparing the DDS data collection with this same functionality implemented within the Kaa middleware.

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“…This actually goes hand in hand with the fact that IoT is large in scale by nature; IoT systems need to cater for a large number of devices spread over relatively large geographical spaces [5]. As a consequence, the IoT is expected to generate a tremendous amount of heterogeneous real-time data which wireless networks need to cater for [9], [10] and database management solutions have to satisfy its requirements [11].…”

Section: Introductionmentioning

confidence: 99%

Front-end intelligence for large-scale application-oriented internet-of-things

et al. 2016

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The Internet-of-things (IoT) refers to the massive integration of electronic devices, vehicles, buildings, and other objects to collect and exchange data. It is the enabling technology for a plethora of applications touching various aspects of our lives such as healthcare, wearables, surveillance, home automation, smart manufacturing, and intelligent automotive systems. Existing IoT architectures are highly centralized and heavily rely on a back-end core network for all decision-making processes. This may lead to inefficiencies in terms of latency, network traffic management, computational processing, and power consumption. In this paper, we advocate the empowerment of front-end IoT devices to support the back-end network in fulfilling end-user applications requirements mainly by means of improved connectivity and efficient network management. A novel conceptual framework is presented for a new generation of IoT devices that will enable multiple new features for both the IoT administrators as well as end users. Exploiting the recent emergence of software-defined architecture, these smart IoT devices will allow fast, reliable, and intelligent management of diverse IoT-based applications. After highlighting relevant shortcomings of the existing IoT architectures, we outline some key design perspectives to enable front-end intelligence while shedding light on promising future research directions.

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Data Management for the Internet of Things: Design Primitives and Solution

Cited by 176 publications

References 54 publications

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Distributed Data Service for Data Management in Internet of Things Middleware

Front-end intelligence for large-scale application-oriented internet-of-things

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