Big Data Offloading using Smart Public Vehicles with Software Defined Connectivity

Munjal, Rashmi; Liu, William; Li, Xue Jun; Gutiérrez, Jairo

doi:10.1109/itsc.2019.8917322

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…They also used utility functions for multi-services for user satisfaction, network attributes, and service characteristics. In our previous work [3,34,35], we have introduced the use of a public transport network and offloaded data onto buses along with other networks for energy efficiency. We extended our work in the proposed manuscript with network selection and appropriate vehicle selection to offload data for energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Multi-Attribute Decision Making for Energy-Efficient Public Transport Network Selection in Smart Cities

Munjal

Liu

et al. 2022

Future Internet

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Smart cities use many smart devices to facilitate the well-being of society by different means. However, these smart devices create great challenges, such as energy consumption and carbon emissions. The proposed research lies in communication technologies to deal with big data-driven applications. Aiming at multiple sources of big data in a smart city, we propose a public transport-assisted data-dissemination system to utilize public transport as another communication medium, along with other networks, with the help of software-defined technology. Our main objective is to minimize energy consumption with the maximum delivery of data. A multi-attribute decision-making strategy is adopted for the selction of the best network among wired, wireless, and public transport networks, based upon users’ requirements and different services. Once public transport is selected as the best network, the Capacitated Vehicle Routing Problem (CVRP) will be implemented to offload data onto buses as per the maximum capacity of buses. For validation, the case of Auckland Transport is used to offload data onto buses for energy-efficient delay-tolerant data transmission. Experimental results show that buses can be utilized efficiently to deliver data as per their demands and consume 33% less energy in comparison to other networks.

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Section: Related Workmentioning

confidence: 99%

Multi-Attribute Decision Making for Energy-Efficient Public Transport Network Selection in Smart Cities

Munjal

Liu

et al. 2022

Future Internet

Self Cite

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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%

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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Multi-objective computation offloading based on Invasive Tumor Growth Optimization for collaborative edge-cloud computing

Wu,

Dong,

et al. 2023

Soft Comput

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Big Data Offloading using Smart Public Vehicles with Software Defined Connectivity

Cited by 7 publications

References 9 publications

Multi-Attribute Decision Making for Energy-Efficient Public Transport Network Selection in Smart Cities

Multi-Attribute Decision Making for Energy-Efficient Public Transport Network Selection in Smart Cities

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

Multi-objective computation offloading based on Invasive Tumor Growth Optimization for collaborative edge-cloud computing

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