Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/21/4735 |
| _version_ | 1818014891459477504 |
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| author | Salman Naseer William Liu Nurul I Sarkar |
| author_facet | Salman Naseer William Liu Nurul I Sarkar |
| author_sort | Salman Naseer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:50:54Z |
| format | Article |
| id | doaj.art-ee049c9661c240238ea3483b1972690c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:50:54Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ee049c9661c240238ea3483b1972690c2022-12-22T02:07:02ZengMDPI AGSensors1424-82202019-10-011921473510.3390/s19214735s19214735Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart CitiesSalman Naseer0William Liu1Nurul I Sarkar2Department of Information Technology and Software Engineering, Auckland University of Technology, Auckland 1010, New ZealandDepartment of Information Technology and Software Engineering, Auckland University of Technology, Auckland 1010, New ZealandDepartment of Information Technology and Software Engineering, Auckland University of Technology, Auckland 1010, New ZealandOne 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.https://www.mdpi.com/1424-8220/19/21/4735smart citydelay tolerant networkinfrastructure offloadingopportunistic networkvehicular mobilityenergy consumptioncarbon emission |
| spellingShingle | Salman Naseer William Liu Nurul I Sarkar Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities Sensors smart city delay tolerant network infrastructure offloading opportunistic network vehicular mobility energy consumption carbon emission |
| title | Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities |
| title_full | Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities |
| title_fullStr | Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities |
| title_full_unstemmed | Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities |
| title_short | Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities |
| title_sort | energy efficient massive data dissemination through vehicle mobility in smart cities |
| topic | smart city delay tolerant network infrastructure offloading opportunistic network vehicular mobility energy consumption carbon emission |
| url | https://www.mdpi.com/1424-8220/19/21/4735 |
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