Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities

Naseer, Salman; Liu, William; Sarkar, Nurul I.

doi:10.3390/s19214735

Cited by 9 publications

(7 citation statements)

References 29 publications

(28 reference statements)

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“…In [55], authors define the data rate of IEEE 802.11ay up to 100 Gbps. In [5], authors define the data rate as 20 Gbps in an outdoor wireless range of 100 m 2 with frequency band 8000 MHz at OFDM modulation. In IEEE 802.…”

Section: Network Capacitymentioning

confidence: 99%

“…Hence, supported by the literature, we select IEEE 802.11p for 1000 m 2 cluster for smart devices to the vehicle and the vehicle to the RSUs, IEEE 802.16 for the 500 m 2 cluster, IEEE 802.11n for 250 m 2 cluster, and IEEE 802.11ay for 100 m 2 cluster. In this case, the multiplying factors are, 54 Mbps for IEEE 802.11p, 1Gbps for IEEE 802.16, 600 Mbps for IEEE 802.11n and 20 Gbps for IEEE 802.11ay, as seen in [5,[53][54][55][56].…”

Section: Network Capacitymentioning

confidence: 99%

“…We can use the VTDN architecture in the taxicab fleet of a smart city as an alternative channel of communication to reduce the load on conventional networks or cellular networks [3]. In [4,5], the authors use the annual average daily traffic of Auckland city and perform theoretical and mathematical analysis to calculate data transmission delays in smart cities. We can find a detailed framework of this system in [6].…”

Section: Introductionmentioning

confidence: 99%

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Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

et al. 2021

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In a smart city, a large number of smart sensors are operating and creating a large amount of data for a large number of applications. Collecting data from these sensors poses some challenges, such as the connectivity of the sensors to the data center through the communication network, which in turn requires expensive infrastructure. The delay-tolerant networks are of interest to connect smart sensors at a large scale with their data centers through the smart vehicles (e.g., transport fleets or taxi cabs) due to a number of virtues such as data offloading, operations, and communication on asymmetric links. In this article, we analyze the coverage and capacity of vehicular sensor networks for data dissemination between smart sensors and their data centers using delay-tolerant networks. Therein, we observed the temporal and spatial movement of vehicles in a very large coverage area (25 × 25 km2) in Beijing. Our algorithm sorts the entire city into different rectangular grids of various sizes and calculates the possible chances of contact between smart sensors and taxis. We further calculate the vehicle density, coverage, and capacity of each grid through a real-time taxi trajectory. In our proposed study, numerical and spatial mining show that even with a relatively small subset of vehicles (100 to 400) in a smart city, the potential for data dissemination is as high as several petabytes. Our proposed network can use different cell sizes and various wireless technologies to achieve significant network area coverage. When the cell size is greater than 500 m2, we observe a coverage rate of 90% every day. Our findings prove that the proposed network model is suitable for those systems that can tolerate delays and have large data dissemination networks since the performance is insensitive to the delay with high data offloading capacity.

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Section: Network Capacitymentioning

confidence: 99%

Section: Network Capacitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

et al. 2021

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“…Data are downloaded from devices in vehicles (built-in or portable). They send cyclical information about their location to a dedicated cloud service [ 37 ]. The location of the vehicle is determined by the location of the Base Transceiver Station (BTS) [ 38 ] usually in combination with the GPS system.…”

Section: Smart City Traffic Solutionsmentioning

confidence: 99%

Dynamic RFID Identification in Urban Traffic Management Systems

Pawłowicz

Trybus

Salach

et al. 2020

Sensors

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The paper covers the application of Radio Frequency IDentification (RFID) technology in road traffic management with regard to vehicle identification. Various infrastructure configurations for Automated Vehicle Identification (AVI) have been presented, including configurations that can be used in urban traffic as part of the Smart City concept. In order to describe the behavior of multiple identifications of moving vehicles, an operation model of the dynamic identification using RFID is described. While it extends the definition of the correct work zone, this paper introduces the concept of dividing the zone into sections corresponding to so-called inventory rounds. The system state is described using a set of matrices in which unread, read, and lost transponders are recorded in subsequent rounds and sections. A simplified algorithm of the dynamic object identification system was also proposed. The results of the simulations and lab experiments show that the efficiency of mobile object identification is conditioned by the parameters of the communication protocol, the speed of movement, and the number of objects.

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“…The reason behind is this it is never intended for large-scale data transfer but when used it generates a significant amount of carbon emissions while disrupting other Internet services. 2 Even though satellites and roadside units (RSUs) can alleviate the wired network while executing such large-scale data transfers but with considerably high communication maintenance costs. Especially, satellite communication with its higher data transmission delay and weather-related disruptions.…”

Section: Introductionmentioning

confidence: 99%

On-demand multi-hop forwarding for sustainable vehicular data transfer network

Bendali

Rahman

Malik

et al. 2021

International Journal of Distributed Sensor Networks

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Smart cities play a vital role to develop a sustainable infrastructure with efficient management of the Internet of things devices. The infrastructure is used to support various applications for smart hospitals, smart factories, and intelligent transportation systems. With the extensive deployment of Internet of things devices, unprecedented growth in data has lead to capacity and transfer issues. In this article, we proposed an efficient data transfer mechanism based on self-sustainable networks over the vehicular environment. Depending on whether the network is connected with vehicles available to support direct connection from the source to destination, we propose end-to-end and hop-by-hop forwarding for vehicular networks that are inherently disconnected. The evaluation results demonstrate that the lifetime of the discovered paths depends on the coverage area, vehicle mobility, and vehicle speed. Therefore, at times redundant disjoint paths are selected for communication. In the proposed work, selected vehicles are used to reach the destination.

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Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities

Cited by 9 publications

References 29 publications

Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

Dynamic RFID Identification in Urban Traffic Management Systems

On-demand multi-hop forwarding for sustainable vehicular data transfer network

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