Adaptive live video streaming on low-cost wireless multihop networks for road traffic surveillance in smart cities

Felici-Castell, Santiago; García-Pineda, Miguel; Segura-García, Jaume; Fayos-Jordan, Rafael; Lopez-Ballester, Jesus

doi:10.1016/j.future.2020.10.010

Cited by 20 publications

(16 citation statements)

References 19 publications

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“…The explosion of IoT technologies makes traffic management systems a modern alternative that can assist in congested situations [2] [3] [4]. IoT technologies are used to collect data from the traffic nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Other new trends in traffic monitoring systems are based on visual information obtained from video cameras. These systems, also called incident detection systems (IDS), are based on video frame analysis to detect congestion or traffic jams in less than 5s [6] [7] [2]. Some commercial products are available but are proprietary and expensive [2].…”

Section: Introductionmentioning

confidence: 99%

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Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

et al. 2022

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Traffic congestion affects quality of life by inducing frustration and wasting time. The congestion is also critical to vehicles with high emergencies such as ambulances or police cars. This leads to additional CO2 emissions. Traffic management requires the accurate modeling of congestion levels. Two main observable parameters identify the congestion state of a city: vehicle speed and density. Congestion has an intuitive definition rather than a quantitative one, and is associated with the disorder and randomness occurring in traffic parameters. Therefore, statistical analysis offers an efficient and natural framework for modeling such disorders. In this study, a differential-entropy-based approach was proposed for labelling purposes. Subsequently, supervised congestion prediction from traffic meta-parameters based on a convolutional neural network was proposed. Traffic parameters includes node localization, date, day of the week, time of day, special road conditions, and holidays. The proposed model is validated on the CityPulse dataset, which is a set of vehicle traffic records, collected in Aarhus city in Denmark over a period of six months, for 449 observation nodes. Simulation results on the CityPulse dataset illustrate that the proposed approach yields accurate prediction rates for different nodes considered. The proposed system can prevent traffic congestion by reorienting the drivers to follow other itineraries.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

et al. 2022

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“…Various algorithms and techniques, including fuzzy technology, neural network, genetic algorithm and support vector machines have been used in recent years. All have been tested experimentally and achieved promising results [6,7]. Figure 1 shows how intelligent video surveillance can detect and recognize different objects of different sizes and types.…”

Section: Introductionmentioning

confidence: 99%

Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Hamad¹

2021

IJCDS

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Intelligent video surveillance systems based on Internet of Things (IoT) technology have proven to be major primary components for security in many areas, such as smart cities. These systems are important because they provide messages that transfer information about people on campus among camera nodes, thereby providing real-time video surveillance monitoring. The proposed system consists of several cameras and an intelligent processing system, represented by a raspberry pi. The cameras are distributed in different locations in the university campus. Each camera node is connected to the internet and can communicate and share information with other nodes, as well as communicate with a central monitoring (server) via Message Queuing Telemetry Transport (MQTT) IoT protocol. The cameras can extract information in real time from video, and identify everyone as either students, teachers and/or employees using computer vision algorithms. Two methods of face detection and recognition techniques are applied: a feature-based technique that uses the Haar cascade, and an image-based technique that uses k-nearest neighbour (kNN). Face detection and recognition based on the Haar cascade classifier is more suitable for resources with embedded limited systems since it requires less computation, while kNN is more accurate and shows better results in a dynamic environment. All programs were written using opensource Python under a Linux operating system and by using OpenCV library.

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“…To solve the above problems, some hierarchical [6] [7][8] [9][10] [11] routing protocols have been proposed under the Ad-Hoc wireless network environment. The primary concept is that some specific nodes play the role of gateways to create a virtual backbone for message communication and routing [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Relative Complement Set Based For Connected Dominating Set Algorithm For the Ad Hoc Network

Chiang¹,

Hsieh²,

Hsieh³

et al. 2021

Preprint

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As mobile devices flourish, users can conveniently use the network services through the mobility of mobile devices. The most popular environment is the Mobile Ad Hoc Networks (MANETs), which consists of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Data transmission for MANET is mainly achieved through peer-to-peer communication and forwarding technology without restrictions on the direction or extent of movement. This type of flexible architecture is suitable for emergency relief and military applications. As a result, a set of network nodes, called connected dominating set (CDS), is thus proposed as the virtual backbone of MANET. It mainly forms a CDS through a group of gateway nodes, and through this CDS for message delivery and routing management to reduce overall power consumption. Therefore, in the past, many scholars have proposed improved CDS construction algorithms to achieve a minimum number of CDS. In this paper, the Improved Relative Complement Sufferage CDS (IRCS-CDS) algorithm is proposed to effectively find out the stable and minimum CDS. Besides, the IRCS-CDS can adapt to the changeability of MANET even in a more loosely distributed network environment. The complexity of the proposed IRCS -CDS algorithm is O(2n2+3n), which is lower than other algorithms. Therefore, the proposed IRCS-CDS algorithm is more stable and efficient even in a network environments with loose nodes or small node coverage radius.

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Adaptive live video streaming on low-cost wireless multihop networks for road traffic surveillance in smart cities

Cited by 20 publications

References 19 publications

Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Relative Complement Set Based For Connected Dominating Set Algorithm For the Ad Hoc Network

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