Enabling smart cities through a cognitive management framework for the internet of things

Vlacheas, Panagiotis; Giaffreda, Raffaele; Stavroulaki, Vera; Kelaidonis, Dimitrios; Foteinos, Vassilis; Poulios, George; Demestichas, Panagiotis; Somov, Andrey; Biswas, Abdur Rahim; Moessner, Klaus

doi:10.1109/mcom.2013.6525602

Cited by 368 publications

(149 citation statements)

References 9 publications

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“…Zanella et al [17] discussed implementation strategies for urban systems leveraging the inherent characteristics of IoT to connect and integrate "a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services". [18] defines a social layer, which can potentially horizontally connect several application domains, on top of the IoT to simplify the management of huge volumes of objects. [19] proposes a similar concept, the so-called "Social Internet of Things" paradigm, in which things borrow the concepts of cooperation and social relationships for the establishment and management of social relationships between smart objects or things.…”

Section: Trends In Technologies Architectures and Infrastructures Fmentioning

confidence: 99%

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Degbelo

Granell

Trilles

et al. 2016

IJGI

134

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Abstract:The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city's operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens' data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.

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Section: Trends In Technologies Architectures and Infrastructures Fmentioning

confidence: 99%

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Degbelo

Granell

Trilles

et al. 2016

IJGI

134

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“…Some of the well known examples that are focused only on providing a data platform, semantic modelling and/or semantic sensor discovery are Km4city [22], SmartSantander [23], Spitfire [24], OpenIoT [25], IoT.est [26], OpenCube [27], iCore [28]. Start-City [29] semantically annotates and aggregates traffic data with traditional aggregation methods (i.e.…”

Section: Related Workmentioning

confidence: 99%

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Kolozali

Puschmann

Bermúdez-Edo

et al. 2016

IEEE Internet Things J.

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Abstract-With the growing popularity of Information and Communications Technologies (ICT) and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMQP). We provide a comprehensive analysis on the effect of adaptive and non-adaptive window size in segmentation of time series using SensorSAX and SAX approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilised segmentation approach, due to the fact that each geographically different sensory environment has got different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

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“…The SENSEI project [8] designed a system that provides a service of network and information management that enables reliable and efficient context information to be used in the environment with heterogeneous Wireless Sensor and Actor Networks (WSANs) into a global framework [9] proposes virtual objects of cognitive management framework and associated functionalities for IoT.…”

Section: ⅱ Related Workmentioning

confidence: 99%

Knowledge Driven Architectural Model to Support Smart Emergency Service in Web of Objects Based Iot Environment

Fattah¹,

Kibria²,

Jeong³

et al. 2015

The Journal of Korean Institute of Communications and Informati

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Ⅰ. IntroductionRecent advancement in the IoT [1] accompanies a smart life where real world objects including sensing devices are interconnected with each other. The Web representation of smart objects empowers innovative applications and services for various domains. To accelerate this approach, Web of Objects (WoO) [2] focuses on the implementation aspects to bring the assorted real world objects with the web applications. In this paper, we present a smart emergency service for shopping mall in the WoO based IoT infrastructure.The general goal of the WoO is to simplify object and application deployment, maintenance and operation of IoT infrastructures. WoO also aims to provide knowledge based IoT service by enabling object virtualization, semantic ontology based service composition and collaboration. In its implementation WoO platform [2] supports semantic modeling of objects, and plays a distinguished role to achieve thinking intelligence with objects of sensors, devices, resources and information.The heterogeneity of objects in WSSM and its application leads to adapt itself with WoO platform.WoO incorporates the application development and flexibility of access from the Web and grips the highly interconnected objects facility.

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Enabling smart cities through a cognitive management framework for the internet of things

Cited by 368 publications

References 9 publications

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Knowledge Driven Architectural Model to Support Smart Emergency Service in Web of Objects Based Iot Environment

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