Context-aware stream processing for distributed IoT applications

Akbar, Adnan; Carrez, François; Moessner, Klaus; Sancho, J.; Rico, Juan

doi:10.1109/wf-iot.2015.7389133

Cited by 33 publications

(24 citation statements)

References 11 publications

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“…The application of Complex Event Processing (CEP) rules to detect events and congestions in traffic data streams is presented in [9]. The authors propose a dynamic adaptation and optimization of CEP rules and their thresholds by using clustering techniques and demonstrate the usefulness and applicability of their approach using a real-world use case of ITS to detect congestions in near real-time.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Instead of real IoT and in-vehicle devices, and dedicated TrafficSense IoT/mobile application for traffic data collection, we use SUMO 9 [13], an open source, microscopic and continuous traffic simulator, developed by the Institute of Transportation Systems at the German Aerospace Center to achieve realistic traffic and mobility data (FCD) generation (Fig. 2).…”

Section: Traffic Streaming Analytics For Traffic Lights Adaptationmentioning

confidence: 99%

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Big Mobility Data Analytics for Traffic Monitoring and Control

Stojanović

2020

FU Aut Cont Rob

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With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Traffic Streaming Analytics For Traffic Lights Adaptationmentioning

confidence: 99%

Big Mobility Data Analytics for Traffic Monitoring and Control

Stojanović

2020

FU Aut Cont Rob

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“…PLUG-N-HARVEST integrates a safety mechanism for fault detection and filtering of data traffic exchanged among building IoT networks and energy-management systems through the BMS system. This safety mechanism [26] was developed in the RERUM EU project ( ict-rerum.eu ) based on a Complex Event Processor (CEP) [27]. The Safety CEP consists of a state machine which computes the incoming events according to a set of rules defined in a declarative language (called DOLCE [28]) which is used to correlate the events that are received.…”

Section: Cloud Ecosystem Of Integrated Ict Modulesmentioning

confidence: 99%

PLUG-N-HARVEST Architecture for Secure and Intelligent Management of Near-Zero Energy Buildings

Marin-Perez¹,

Michailidis

García-Carrillo³

et al. 2019

Sensors

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Building Automation (BA) is key to encourage the growth of more sustainable cities and smart homes. However, current BA systems are not able to manage new constructions based on Adaptable/Dynamic Building Envelopes (ADBE) achieving near-zero energy-efficiency. The ADBE buildings integrate Renewable Energy Sources (RES) and Envelope Retrofitting (ER) that must be managed by new BA systems based on Artificial Intelligence (AI) and Internet of Things (IoT) through secure protocols. This paper presents the PLUG-N-HARVEST architecture based on cloud AI systems and security-by-design IoT networks to manage near-zero ADBE constructions in both residential and commercial buildings. To demonstrate the PLUG-N-HARVEST architecture, three different real-world pilots have been considered in Germany, Greece and Spain. The paper describes the Spain pilot of residential buildings including the deployment of IoT wireless networks (i.e., sensors and actuators) based on Zwave technology to enable plug-and-play installations. The real-world tests showed the high efficiency of security-by-design Internet communications between building equipment and cloud management systems. Moreover, the results of cloud intelligent management demonstrate the improvements in both energy consumption and comfort conditions.

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“…New rules are generated dynamically whenever our algorithm detects a change in the context. The idea of using machine learning to generate optimized thresholds for CEP rules was proposed in our initial work [30] where we demonstrated a context-aware solution for monitoring traffic automatically. In this paper, we improve our initial approach, extend our experimental evaluation and integrate it with the more general hut architecture and hut instance, optimized for large scale IoT applications.…”

Section: ) Event Processingmentioning

confidence: 99%

An Ingestion and Analytics Architecture for IoT Applied to Smart City Use Cases

Ta-Shma

Akbar

Gerson-Golan

et al. 2018

IEEE Internet Things J.

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Abstract-As sensors are adopted in almost all fields of life, the Internet of Things (IoT) is triggering a massive influx of data. We need efficient and scalable methods to process this data to gain valuable insight and take timely action. Existing approaches which support both batch processing (suitable for analysis of large historical data sets) and event processing (suitable for realtime analysis) are complex. We propose the hut architecture, a simple but scalable architecture for ingesting and analyzing IoT data, which uses historical data analysis to provide context for real-time analysis. We implement our architecture using open source components optimized for big data applications and extend them where needed. We demonstrate our solution on two real-world smart city use cases in transportation and energy management.

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Context-aware stream processing for distributed IoT applications

Cited by 33 publications

References 11 publications

Big Mobility Data Analytics for Traffic Monitoring and Control

Big Mobility Data Analytics for Traffic Monitoring and Control

PLUG-N-HARVEST Architecture for Secure and Intelligent Management of Near-Zero Energy Buildings

An Ingestion and Analytics Architecture for IoT Applied to Smart City Use Cases

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