Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks
Wireless machine-to-machine (M2M) networks enable ubiquitous sensing and controlling via sensors, vehicles, and other types of wireless nodes. Capacity scaling law is one of the fundamental properties for high mobility M2M networks. As for high mobility M2M networks, vehicular ad hoc networks (VANET...
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| Format: | Article |
| Language: | English |
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Wiley
2013-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/234728 |
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| _version_ | 1849699305547366400 |
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| author | Xin Guan |
| author_facet | Xin Guan |
| author_sort | Xin Guan |
| collection | DOAJ |
| description | Wireless machine-to-machine (M2M) networks enable ubiquitous sensing and controlling via sensors, vehicles, and other types of wireless nodes. Capacity scaling law is one of the fundamental properties for high mobility M2M networks. As for high mobility M2M networks, vehicular ad hoc networks (VANETs) are a typical case. Since vehicles have social property, their moving trajectory is according to the fixed community. With the purpose of transmitting packets to different communities of VANETs and further improving the network capacity, we study the multicast capacity of bus-assisted VANETs in two scenarios: forwarding scenario and routing scenario. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the center spot of a restrict region of each vehicle. In forwarding scenario, all the buses deployed in all roads as intermediate nodes are used to forward packets for ordinary vehicles. In routing scenario, buses and ordinary cars construct a highway path supported by percolation theory to transmit urgent packets. Each ordinary vehicle randomly chooses k − 1 vehicles from the other ordinary vehicles as receivers. For the two kinds of scenarios, we derived the upper bound and lower bound, respectively. |
| format | Article |
| id | doaj-art-2c2723346ce74a5a88b9bee22ea8b66d |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2c2723346ce74a5a88b9bee22ea8b66d2025-08-20T03:18:38ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-12-01910.1155/2013/234728234728Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine NetworksXin GuanWireless machine-to-machine (M2M) networks enable ubiquitous sensing and controlling via sensors, vehicles, and other types of wireless nodes. Capacity scaling law is one of the fundamental properties for high mobility M2M networks. As for high mobility M2M networks, vehicular ad hoc networks (VANETs) are a typical case. Since vehicles have social property, their moving trajectory is according to the fixed community. With the purpose of transmitting packets to different communities of VANETs and further improving the network capacity, we study the multicast capacity of bus-assisted VANETs in two scenarios: forwarding scenario and routing scenario. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the center spot of a restrict region of each vehicle. In forwarding scenario, all the buses deployed in all roads as intermediate nodes are used to forward packets for ordinary vehicles. In routing scenario, buses and ordinary cars construct a highway path supported by percolation theory to transmit urgent packets. Each ordinary vehicle randomly chooses k − 1 vehicles from the other ordinary vehicles as receivers. For the two kinds of scenarios, we derived the upper bound and lower bound, respectively.https://doi.org/10.1155/2013/234728 |
| spellingShingle | Xin Guan Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks International Journal of Distributed Sensor Networks |
| title | Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks |
| title_full | Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks |
| title_fullStr | Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks |
| title_full_unstemmed | Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks |
| title_short | Multicast Capacity Analysis for High Mobility Social Proximity Machine-to-Machine Networks |
| title_sort | multicast capacity analysis for high mobility social proximity machine to machine networks |
| url | https://doi.org/10.1155/2013/234728 |
| work_keys_str_mv | AT xinguan multicastcapacityanalysisforhighmobilitysocialproximitymachinetomachinenetworks |