&lt;italic&gt;FogFlow&lt;/italic&gt;: Easy Programming of IoT Services Over Cloud and Edges for Smart Cities

Cheng, Bin; Solmaz, Gürkan; Cirillo, Flavio; Kovács, Ernö; Terasawa, Kazuyuki; Kitazawa, Atsushi

doi:10.1109/jiot.2017.2747214

Cited by 196 publications

(126 citation statements)

References 7 publications

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“…Fog Function has been applied into the open source fog computing framework FogFlow as a new programming model to enhance its programmability. Originally, FogFlow can orchestrate dynamic data flows over cloud and edges using a service topology [13]. The service topology statically defines the logical data processing flow of an IoT service and is triggered on demand by the requests from the consumer side, but it does not support the composition of multiple service topologies and it is not flexible to handle use case requirements which may change over time.…”

Section: Implementation and Use Case Validationmentioning

confidence: 99%

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Fog Function: Serverless Fog Computing for Data Intensive IoT Services

Cheng

Fuerst

Solmaz

et al. 2019

2019 IEEE International Conference on Services Computing (SCC)

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Fog computing can support IoT services with fast response time and low bandwidth usage by moving computation from the cloud to edge devices. However, existing fog computing frameworks have limited flexibility to support dynamic service composition with a data-oriented approach. Functionas-a-Service (FaaS) is a promising programming model for fog computing to enhance flexibility, but the current event-or topic-based design of function triggering and the separation of data management and function execution result in inefficiency for data-intensive IoT services. To achieve both flexibility and efficiency, we propose a data-centric programming model called Fog Function and also introduce its underlying orchestration mechanism that leverages three types of contexts: data context, system context, and usage context. Moreover, we showcase a concrete use case for smart parking where Fog Function allows service developers to easily model their service logic with reduced learning efforts compared to a static service topology. Our performance evaluation results show that the Fog Function can be scaled to hundreds of fog nodes. Fog Function can improve system efficiency by saving 95% of the internal data traffic over cloud function and it can reduce service latency by 30% over edge function.

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Section: Implementation and Use Case Validationmentioning

confidence: 99%

“…To overcome this issue, many dataflow-based approaches are proposed. For example, the initial version of FogFlow [13] can program IoT services over cloud and edges based on service topology. AWS Step Function [18] is able to build distributed applications using visual workflows.…”

Section: A Fog/edge Computingmentioning

confidence: 99%

Fog Function: Serverless Fog Computing for Data Intensive IoT Services

Cheng

Fuerst

Solmaz

et al. 2019

2019 IEEE International Conference on Services Computing (SCC)

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“…However, its focus is not exclusively on the smart city domain, nor does it offer a set of tools similar to the one described here. The CPaaS project [25,26] is an example of another currently ongoing project that has similar goals to OrganiCity. Additionally, there are projects like IoT-Lab [27,28], investigating crowdsourcing and IoT services for supporting multidisciplinary research tasks.…”

Section: Related Workmentioning

confidence: 99%

Managing Pervasive Sensing Campaigns via an Experimentation-as-a-Service Platform for Smart Cities

Amaxilatis

Mylonas

Díez

et al. 2018

Sensors

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The adoption of technologies like the IoT in urban environments, together with the intensive use of smartphones, is driving transformation towards smart cities. Under this perspective, Experimentation-as-a-Service within OrganiCity aims to create an experimental facility with technologies, services, and applications that simplify innovation within urban ecosystems. We discuss here tools that facilitate experimentation, implementing ways to organize, execute, and administer experimentation campaigns in a smart city context. We discuss the benefits of our framework, presenting some preliminary results. This is the first time such tools are paired with large-scale smart city infrastructures, enabling both city-scale experimentation and cross-site experimentation.

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“…The crowd estimation results are published to the IoT brokers so that multiple stakeholders involved in the pilot studies and their IoT applications can access the real-time results. The implemented IoT broker is called thin broker [6], which efficiently handles the queries and subscriptions with with higher throughput. Meanwhile, the crowd estimation results are visualized in the dashboard.…”

Section: Lunch Time Midnightmentioning

confidence: 99%

“…Therefore, we build the crowd estimation service using our "in-house" FIWARE-based IoT platform for exposing the estimated crowd sizes. The IoT platform provides real-time service endpoint to external systems using the light-weight IoT broker with higher throughput, which we call Thin Broker [6]. Multiple applications are developed by different stakeholders to access the crowd estimation results in our two pilots through the Thin Broker.…”

Section: Introductionmentioning

confidence: 99%

CrowdEstimator

Solmaz²

2018

Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services

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Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fionly crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems;©ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM MobiSys'18, http://doi.

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<italic>FogFlow</italic>: Easy Programming of IoT Services Over Cloud and Edges for Smart Cities

Cited by 196 publications

References 7 publications

Fog Function: Serverless Fog Computing for Data Intensive IoT Services

Fog Function: Serverless Fog Computing for Data Intensive IoT Services

Managing Pervasive Sensing Campaigns via an Experimentation-as-a-Service Platform for Smart Cities

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