Conference Paper

On the Application of Social Internet of Things with Fog Computing: A New Paradigm for Traffic Information Sharing System

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According to the International Association of Public Transport (UITP), the current high speed of urbanization will equally bring rise in the challenges related to urban mobility in future. Thus, updating real-time traffic and road information is quite essential for limiting road accidents, enhance public and traffic safety. Most of the past research works in Intelligent Transportation Systems (ITS) provided advanced solutions for monitoring road traffic automatically. However, most of these systems overall efficiency depends on deployment of equipment such as actuators and sensors which can be costly. Beside cost factor, it’s coverage and performance might also be limited on cities which lacks proper and infrastructures to adapt with them. The rise of the Internet of Things (IoT) paradigm offers the promise to support innovative applications and services for smart cities and can even help those lagging cities to smarter and street safer. With this core point, this paper proposes a novel Internet of Things (IoT) based “Traffic Information Sharing System (TISS) architecture. The proposed architecture utilizes Social Internet of Things (SIoT) alongside with Fog Computing (FC) to provide with efficient traffic information sharing and dissemination for minimizing road accidents, providing street and surrounding awareness.
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... In the modern world, traffic jam is a burning issue because of the large number of vehicles. The significant increase in the number of vehicles worldwide has statisticized that the vehicle numbers will increase to 2 billion within the next 10 to 20 years [1]. Sixty-four per cent of these vehicles will be used in metropolitan areas, and the average waiting time in traffic jams will be increased a lot. ...
... Require: Video data of the junction. 1: Perform surveillance and capture the video from junction to junction by the UAV. 2: Send video data to the nearby MEC server securely. ...
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In this paper, an automated real-time traffic management scheme is proposed by using unmanned aerial vehicles (UAV) in an effective and secured way. However, owing to the low computational capability and limited battery capacity of a UAV, multi-access edge computing (MEC) is applied to enhance the performance of an automated UAV-based traffic management scheme. Additionally, blockchain technology is introduced in the automated traffic management scheme to store the traffic record for providing network repudiation and avoiding any third-party interference with the network. An algorithm is developed based on the concept of a pairwise compatibility graph for the UAV-assisted automated traffic management scheme wherein a deep learning (DL) model is used for vehicle detection. Moreover, a two-phase authentication mechanism is proposed for a faster and secure verification process of the registered devices in the proposed scheme. Finally, a result analysis is conducted based on the security analysis and performance analysis to verify the effectiveness of the proposed scheme.
... The first major type of SPS involves information acquisition from reports obtained from social media platforms combined with sensing data from fixed physical sensors installed across various locations. A few examples of this form of SPS include: (i) anomaly detection using surveillance cameras and social media posts (Banerjee et al., 2018) as can be seen in Figure 3 (a); and (ii) traffic accident detection based on social media and roadside traffic measurement sensors (Tran et al., 2018). ...
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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for decisively interpreting the real world. However, various limitations hinder physical sensing’s effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the environment. Despite its virtues, social sensing also inherently suffers from a few drawbacks (e.g., inconsistent reliability and uncertain data provenance). Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines to reconstruct the “state of the world,” both physically and socially. While a good number of interesting SPS applications have been studied, several critical unsolved challenges still exist in SPS. In this paper, we provide a comprehensive survey of SPS, emphasizing its definition, key enablers, state-of-the-art applications, potential research challenges, and roadmap for future work. This paper intends to bridge the knowledge gap of existing sensing-focused survey papers by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.
... The first major type of SPS involves information acquisition from reports obtained from social media platforms combined with sensing data from fixed physical sensors installed across various locations. A few examples of this form of SPS include: i) anomaly detection using surveillance cameras and social media posts [47]; and ii) traffic accident detection based on social media and roadside traffic measurement sensors [48]. The second major variant of SPS is integrated social media and mobile physical sensor-based sensing systems where social media data are analyzed to locate probable event locations and mobile agents are dispatched to further scrutinize the event reports. ...
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Propelled by versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for spontaneously capturing and interpreting real-world phenomenon. Despite its virtues, various limitations (e.g., high application specificity, partial autonomy, and sparse coverage) hinder physical sensing's effectiveness in critical scenarios such as disaster response. Meanwhile, social sensing is contriving as a pervasive sensing paradigm that leverages the observations from human participants equipped with portable devices and ubiquitous Internet connectivity (i.e., through social media or crowdsensing apps) to perceive the environment. While social sensing possesses a plethora of benefits, it also inherently suffers from a few drawbacks (e.g., inconsistent reliability, uncertain data provenance, and limited sensing availability). Motivated by the complementary virtues of both physical and social sensing, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that tightly integrates social and physical sensors at an unprecedented scale. The vision of SPS centers on mitigating the individual weaknesses of physical and social sensing while exploiting their collective strengths in reconstructing the "state of the world", both physically and socially. While a good amount of interesting SPS applications has been explored, several important unsolved challenges and open research questions prevail in the way of developing dependable SPS systems, which require careful study to address. In this paper, we provide a comprehensive survey of SPS, with an emphasis on its definition and key enablers, state-of-the-art applications, potential research challenges, and road-map for future work. This paper intends to bridge the knowledge gap in current literature by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.
... As the related technologies of the Internet of Things are widely used in various fields, the immediacy of information perception and collection technology becomes possible, and the perception ability of the transportation system has been unprecedentedly improved [11][12][13]. Cloud computing is a super computing model, which is a pool of resources [14,15], but it is not only distributed processing, but also an intelligent processing function which can be managed and coordinated independently on the basis of distributed architecture. ...
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... Furthermore, large datasets from numerous domains could also be utilized. Tran et al., [14] explained the traffic information sharing system utilizing the SIoT with fog computing. It describes about imparting a productive traffic data sharing and dissemination system by giving road and surrounding awareness, to limit the road accidents. ...
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... According to Sandheep, S. Most of these transportation applications use similar technology using GIS (geographic information system) that uses a GPS receiver to track locations [5]. According to Tran, V. L. Explain 64% of urban travel activities by 2050 will triple with an average time of 106 hours per year, including due to congestion and He explained to use public transportation as the main transportation to suppress private transportation [6][7]. According to Lau, J. K. S. help users plan bus trips based on information provided by other users, it is also possible to provide spatial-temporal data access as a web service [8]. ...
... Indeed, each vehicle in the considered VSN has its virtual counterpart named Vehicular Virtual Object (VVO) in the Fog server. Friend relationships can be established among VVOs, and based on this they can exchange information and resources easily [2] [3]. The friendship creation between the two vehicles requires a minimum time of continuous interaction. ...
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... Further, this system has emergency traffic light support to pass the ambulance in emergency situations. Many researchers [92,[121][122][123][124][125][126][127][128][129] also presented their work on smart traffic lights system using fog computing technology. Figure 24 represents smart traffic lights system. ...
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