Semantic Smart City: Context Aware Application Architecture

Sholla, Sahil; Naaz, Roohie; Chishti, Mohammad Ahsan

doi:10.1109/iceca.2018.8474777

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…These challenges are related to data heterogeneity, unreliable or redundant data or loss of data, local vs. centralized processing, efficient communication infrastructures, scalable context inference algorithms, and collaborative knowledge transfer of big data. Recent studies have set out to address some of the aforementioned issues [31,32] such as collecting and merging data from heterogeneous sources, using message queues and brokers to facilitate agent communication, and providing a context event processing of data to provide personalized context-aware services. However, proposals like the presented still fail to provide an architecture for collaborative scopes, where several systems can exchange relevant contextual information based on detected complex events, due to the lack of data standardization or harmonization process for exchanging the data.…”

Section: Implementations Of Context-aware Systems To Iot-based Smart Environmentsmentioning

confidence: 99%

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Muñoz-Arcentales

López-Pernas

Conde

et al. 2021

Sensors

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In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields.

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Section: Implementations Of Context-aware Systems To Iot-based Smart Environmentsmentioning

confidence: 99%

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Muñoz-Arcentales

López-Pernas

Conde

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The idea of a smart city architectural model that explores semantic computing to achieve city urbanization vision has been ongoing. 14 In such design, a conceptual design with group management procedures has been documented. 15 One such conceptualization is an open-source smart city implementation for smart transportation, smart home automation, smart unmanned aerial vehicles, and smart energy grid, among others.…”

Section: Related Workmentioning

confidence: 99%

Cyber‐physical network architecture for data stream provisioning in complex ecosystems

Okafor

Ndinechi

Misra

2021

Trans Emerging Tel Tech

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Intelligent fog cyber‐physical social systems (iFog CPSS) is a novel smart city project that uses intrinsic processes to automate microservices such as edge‐to‐fog or fog‐to‐cloud monitoring of complex real‐time activities. This article presents a dynamic cyber‐physical architecture that leverages iFog layers to map location‐based services (LBS) on a spine‐leaf datacenter clos topology. Individual edge clusters are connected to the edge‐fog layer, which communicates with iFog gateways for processing streams' requests. Use‐case application of artificial intelligence (AI) in vehicular ad‐hoc networks (VANETs) is introduced for data stream provisioning. In the validation study, a secure docker‐based iFog CPS experiment is carried out using traffic trace files from C++ modeller. iFog spine‐leaf architecture for fog‐computing and cloud‐computing are compared using two key metrics. For traffic workload utilization, the results show that 83.33% of the traffic workload is utilized at the Fog layer while 16.67% is consumed in the cloud layer. For latency profile, the results indicate that Fog and cloud streams had 20.31% and 77.69%, respectively. In terms of iFog VANET spine‐leaf congestion control, three distinct algorithms are studied, namely the proposed linear routing algorithm (LRA), LEACH, and collection tree protocol (CTP). In each case, the resource utilization for VANET gave 34.45%, 32.18%, and 33.37%, respectively. Latency response gave 11.76%, 82.35%, and 5.89%, respectively. Also, the throughput scenario offered 19.61%, 39.22%, and 41.17%, respectively. Consequently, the iFog scenario offers satisfactory LBS provisioning for VANETs clusters.

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“…In the context of IoT for Smart Cities environments, where smart applications are used to collect and to exchange different types of data, Sholl et al propose a Smart City architecture that harnesses the power of semantic technologies to allow machines and people to understand the relationships among data in a context-aware manner, and to extract knowledge [16]. Choi et al propose a software architecture to assist efficient middleware deployment in Smart Cities, by relying on semantic technologies [17].…”

Section: Related Workmentioning

confidence: 99%

A Discussion on Context-Awareness to Better Support the IoT Cloud/Edge Continuum

Silva

Sofia

2020

IEEE Access

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This paper debates on notions of context-awareness as a relevant asset of networking and computing architectures for an Internet of Things (IoT), in particular in regards to a smoother support of the the networking operation between Cloud and Edge. Specifically, the paper debates on notions of contextawareness and goes over different types of context-awareness indicators that are being applied to Edge selection algorithms, covering the approaches currently used, the role of the algorithms applied, their scope, and contemplated performance metrics. Lastly, the paper provides guidelines for future research in the context of Cloud-Edge and the application of context-awareness to assist in a higher degree of automation of the network and, as consequence, a better support of the Cloud to Edge continuum. INDEX TERMS Context-awareness, Internet of Things (IoT), Edge/Fog Computing.

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Semantic Smart City: Context Aware Application Architecture

Cited by 12 publications

References 6 publications

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

Cyber‐physical network architecture for data stream provisioning in complex ecosystems

A Discussion on Context-Awareness to Better Support the IoT Cloud/Edge Continuum

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