Semantic Interoperable Traffic Management Framework for IoT Smart City Applications

Balakrishna, Sivadi; Thirumaran, M.

doi:10.4108/eai.11-9-2018.155482

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…Many wireless video streaming systems have been developed, but most do not provide real-time video streaming. IoVT based video streaming solutions suitable for real-time traffic flow characterization are fewer still [18], [23]. Raktrakulthum et al [18] used machine learning for vehicle classification under low, moderate, and high traffic densities.…”

Section: Related Workmentioning

confidence: 99%

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Khan¹,

Khattak²,

Khan³

et al. 2021

ICST Transactions on Scalable Information Systems

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Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management.

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Section: Related Workmentioning

confidence: 99%

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Khan¹,

Khattak²,

Khan³

et al. 2021

ICST Transactions on Scalable Information Systems

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“…Several works have used semantics and ontologies within smart cities for a variety of functions, e.g., to add value to data collected from sensor and social data streams [85], to combine sensor and social data streams with machine learning techniques and to facilitate interoperability and information exchange [83]. A range of projects has explored the use of the semantic web in specific domains, such as energy and transport [28], mobility and autonomous vehicles [86], traffic jams and delay [87], surveillance system [88], emergency management [89], environment [90], parking [91], energy [92,93], water [94] and so on. Several projects and frameworks have also emerged, aiming to exploit semantics to enable interoperability, e.g., OpenIoT [95], CityPulse [96] and VITAL [97].…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

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“…In our earlier work [1], we have focused on accuracy detection and presented an approach to address traffic challenges. In this paper, we improve our initial approach to make it generic for heterogeneous IoT sensor data acquisition and analysis, finally extend our experimental evaluation and implement it using open source components, which were optimized for large-scale IoT data streams.…”

Section: Deploying the Algorithm To The Hardwarementioning

confidence: 99%

“…The popularity of private cars getting urban traffic is more. Because of this, traffic is becoming one of the big problems in urban areas all over the world [1]. The heavy traffic in urban cities is leading to congestion, accidents that have affected by the property loss, waste of time, environmental pollution, and sometimes it goes to the next level of man's death.…”

Section: Introductionmentioning

confidence: 99%

A Framework for IoT Sensor Data Acquisition and Analysis

Balakrishna¹,

Thirumaran²,

Solanki³

2018

EAI Endorsed Transactions on Internet of Things

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In the current scenario, around 35 billion Internet of Things (IoT) devices is connected to the internet. By 2025, it is predicted that the number will grow between 80 and 120 billion devices connected to the internet, supporting to generate 180 trillion gigabytes of new sensor data that year. The IoT sensor data is generated from various heterogeneous devices, communication protocols, and data formats that are enormous in nature. This huge amount of sensor data is unable to acquire and analyze manually. This is a significant problem for IoT application developers to make the integration of IoT sensor data automatically. However, the large amount of data has led to the inadequacy of the manual data acquisition and stressed the urgency into the research of IoT based frameworks in automatic. In this paper, we have proposed a framework for IoT sensor data acquisition and analysis (FSDAA). The FSDAA has been implemented on the ThingSpeak IoT Cloud platform for data analysis and visualizations, and compared with the state of the art schemes. Finally, the results show that the proposed FSDAA is efficient in terms of Accuracy, Precision, Recall, and F-measure.

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Semantic Interoperable Traffic Management Framework for IoT Smart City Applications

Cited by 21 publications

References 9 publications

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Explainable Artificial Intelligence for Developing Smart Cities Solutions

A Framework for IoT Sensor Data Acquisition and Analysis

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