Social Internet of Vehicles: Architecture and enabling technologies

Butt, Talal Ashraf; Iqbal, Razi; Shah, Sayed Chhattan; Umar, Tariq

doi:10.1016/j.compeleceng.2018.05.023

Cited by 93 publications

(43 citation statements)

References 19 publications

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“…Furthermore, IoV has rapid developments in recent years. Butt et al presented a scalable Social IoV (SIoV) architecture based on Restful web technology [14]. Chen et al proposed Cognitive IoV (CIoV) to enhance transportation safety and network security by communication technologies [15].…”

Section: Internet Of Vehicles Problemmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Gui

2020

Journal of Advanced Transportation

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Aggravating energy shortages and increasing labor costs have become global problems and have garnered special importance in recent years in the transportation sector, especially in taxi markets. Automatic vehicles have a bright future, however, there is an equal amount of skepticism and concern about safety for all the optimism. To unlock the potential of automatic vehicles in intelligent transportation systems, a lot more data and testing are required to promote safety level as far as possible and achieve the organizational aim of reducing accidents to zero tolerance. And it is vital to provide accurate models for vehicle movement analyses. In this study, Modified Digital Elevation (MDE) model and Spherical Bilinear Interpolation (SBI) model were proposed for vehicle movement analyses considering altitude. And the experimental data of 9,990 GPS-enabled taxis in Sanya, Zhengzhou, and Liaoyang were adopted to support comparisons. Measurement results showed that MDE model had over 99% less disparity with direct solution than original model and SBI model could further improve the effects. It indicated that the application of MDE model and SBI model could improve both accuracy and efficiency of vehicle movement analyses and it had a bright future in the field of automatic vehicles. Future directions could be improving models and expanding data.

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Section: Internet Of Vehicles Problemmentioning

confidence: 99%

“…Fourth, the distance between 1 and 2 was calculated approximately by Modi ed Digital Elevation Model. Based on Formula (12), the distance between points 1 + ℎ 1 , 1 , 1 and 2 + ℎ 2 , 2 , 2 can be calculated by Formula (14) when = + ℎ 1 .…”

Section: Original Modelmentioning

confidence: 99%

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Gui

2020

Journal of Advanced Transportation

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“…Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications as defined by Atzori et al [17]. In the IoT paradigm, many of the objects that surround us will be on the network [18][19][20]. According to Miorandi et al (2012) [21], it is predictable that the Internet will exist as a seamless fabric of classic networks and networked objects by 2022.…”

Section: Internet Of Vehicles Problem Intelligent Transportationmentioning

confidence: 99%

Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

Gui

2018

Journal of Advanced Transportation

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Urban traffic congestion has become a global problem and has garnered special importance in recent years in the transportation sector, especially in taxi markets. To unlock the potential of Internet of Vehicles (IoV) in Intelligent Transportation Systems (ITS), it was vital to make efficiency measurements. In this study, Distance Formula was built to calculate distances by GPS data based on mathematical equations, and Motorcade-Sharing (MS) Model was proposed to improve the efficiency of collaborative vehicles. The experimental data of 2191 GPS-equipped taxis in Sanya of China was adopted to make comparisons between original results and modelled results. Measurement results showed that MS Model had 10.54% more leisure taxis, reduced 5 overdriving taxis, and saved 33.73% running distance in total compared to the original. This indicated that the application of MS Model could not only alleviate urban traffic congestion but also optimize urban taxi markets, and it has a bright future in the field of taxi and other collaborative vehicles. Future directions could be improving MS Model and expanding data.

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“…SIoT is expected to enhance the features of existing distributed systems, such as service discovery and composition [1][2][3], information management [4][5][6][7], and service trustworthiness management [8][9][10]. Although SIoT has begun to be adopted in some domains, such as smart vehicles [11][12][13][14], smart homes [15], smart factories [16], and integrated transportation [17], current SIoT systems encounter numerous challenges that affect their usability and reliability in existing SIoT domains [18,19].…”

Section: Introductionmentioning

confidence: 99%

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Lye

Cheng

Tan

et al. 2020

Sensors

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Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

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Social Internet of Vehicles: Architecture and enabling technologies

Cited by 93 publications

References 19 publications

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

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