A Sustainable Marriage of Telcos and Transp in the Era of Big Data: Are We Ready?

Naseer, Salman; Liu, William; Sarkar, Nurul I.; Chong, J. Michael; Lai, Eseng; Prasad, R. Venkatesha; Danh, Tran Cong; Chiaraviglio, Luca; Qadir, Junaid; Cao, Yue; Wu, Jinsong; Lutui, Raymond; Manzoor, Shahid

doi:10.1007/978-3-319-94965-9_21

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…In [11], authors exploit mobility prediction of data mules and use message farriers in sparse sensor networks for proactive data delivery. The controlling and planning of data mules mobility is testified in [12][13][14] and uses various optimization techniques in complex problems of data forwarding with optimal and sub-optimal techniques [15][16][17][18][19]. In the existing literature, authors focused on the data forwarding capacity of vehicular networks by considering the diverse type of vehicles having a specific type of wireless communication technology along with controlled movement and mobility prediction of the vehicles.…”

Section: Data Mulesmentioning

confidence: 99%

“…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]. In this paper, we explore the collection of big data produced by a massive number of smart devices using vehicular sensor networks as an alternate data dissemination channel.…”

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: Data Mulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…A network architecture is explained in [12] for smart city data offloading by using smart vehicles, and numerical analysis of this proposed architecture is demonstrated in [37]. In this architecture, the authors apply D2D communication, the daily vehicle count of Auckland roads and calculate the delay, throughput, and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 1 shows that mobile data demands will grow up to 38 PB per month by 2021 in New Zealand, which is 380% more than 2010 [10]. Broadband Internet and traditional core networks could also be possible candidates for SC data transmissions, but these networks are also now congested networks, traffic on the Internet has increased more rapidly than its existing capacity [11,12]. As a result, the problem of data dissemination between data sources and control units in SC ought to be solved by using some other types of hybrid networks instead of the using only Wi-Fi, 3G, LTE, Internet and so forth [13].…”

Section: Introductionmentioning

confidence: 99%

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

Naseer

Liu

Sarkar

2019

Sensors

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One of the main challenges of operating a smart city (SC) is collecting the massive data generated from multiple data sources (DSs) and transmitting them to the control units (CUs) for further data processing and analysis. These ever-increasing data demands require not only more and more capacity of the transmission channels but also results in resource over-provision to meet the resilience requirements, thus the unavoidable waste as a result of the data fluctuations throughout the day. In addition, the high energy consumption (EC) and carbon discharge from these data transmissions posing serious issues to the environment we live in. Therefore, to overcome the issues of intensive EC and carbon emission (CE) of massive data dissemination in SCs, we propose an energy-efficient and carbon reduction approach by using the daily mobility of the existing vehicles as an alternative communications channel to accommodate the data dissemination in SCs. To illustrate the effectiveness and efficiency of our approach, we take the Auckland City in New Zealand as an example, assuming massive data generated by various sources geographically scattered throughout the Auckland region, to the control centres located in the city. Results obtained show that our proposed approach can provide up to four times faster transferring the large volume of data by using the existing daily vehicles’ mobility, than the conventional transmission network. Moreover, our proposed approach offers about 32% less EC and CE than that of conventional network transmission approach.

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“…Similar work has been presented in [4], the author considered data center as a central point to pick up and deliver significant amount of data using public transport. In their next work, author [5] also proposed urban transport facilities and road infrastructure to complement traditional option for data transmission. UMass DieselNet [6] leverages opportunistic buses as a source and relay node contact to provide end-toend connectivity.…”

Section: Introductionmentioning

confidence: 99%

Big Data Offloading using Smart Public Vehicles with Software Defined Connectivity

Munjal

Liu

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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With the explosive increase in the number of mobile devices such as smartphones or laptops, the design of mobile applications becomes increasingly complex, power hungry and resource consuming. Therefore, conventional networks are facing serious problems such as traffic overload and energy consumption due to high traffic demands. As a result, network designers are looking for more options to accommodate numerous data requirements. Aiming to find a promising way to tackle this problem, we are investigating heterogeneous networking architectures, which utilize the existing public transport network as an alternative communication network along with infrastructure-based networks. We propose a heterogeneous network architecture called Software Defined Connectivity (SDC) that utilizes the flow of transport network such as buses, trains, and ferries to start the forwarding process from nearby parking/offloading spots to disseminate data along with conventional networks. Results show that the SDC architecture helps in data offloading over public transport vehicles as per the profiles of each user with significant savings of energy.

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A Sustainable Marriage of Telcos and Transp in the Era of Big Data: Are We Ready?

Cited by 4 publications

References 18 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

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

Big Data Offloading using Smart Public Vehicles with Software Defined Connectivity

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