2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 2016
DOI: 10.1109/ccece.2016.7726793
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Link duration estimation using neural networks based mobility prediction in vehicular networks

Abstract: The knowledge of Inter-vehicle link duration is an important parameter in Vehicular Ad hoc Networks (VANETs), as it is useful for vehicles to delay their information transmission if link breakage is anticipated before completing the transmission. In addition, it plays a pivotal role in routing, as it allows proactive construction of long-life paths, and optimizing nexthop selection in position-based routing (PBR). However, due to the high mobility of vehicles and the complicated vehicular mobility patterns in … Show more

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Cited by 15 publications
(7 citation statements)
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“…Works in [131][132][133][134] discuss IoT device's connectivity and wireless communication using ANN. Here, ANN plays a vital role in enhancing the driver behavior modeling [131], classifying the objectives [133,134], and predicting the speed of mobility [132].…”
Section: For Enhancing Connectivity In Iot Environmentsmentioning
confidence: 99%
See 1 more Smart Citation
“…Works in [131][132][133][134] discuss IoT device's connectivity and wireless communication using ANN. Here, ANN plays a vital role in enhancing the driver behavior modeling [131], classifying the objectives [133,134], and predicting the speed of mobility [132].…”
Section: For Enhancing Connectivity In Iot Environmentsmentioning
confidence: 99%
“…Works in [131][132][133][134] discuss IoT device's connectivity and wireless communication using ANN. Here, ANN plays a vital role in enhancing the driver behavior modeling [131], classifying the objectives [133,134], and predicting the speed of mobility [132]. Furthermore, IoT includes the increasing popularity of entities and objects that automatically transfer data over a network with unique identifiers.…”
Section: For Enhancing Connectivity In Iot Environmentsmentioning
confidence: 99%
“…The stability of a communication link can be expressed in terms of its lifetime, 153 its availability, 154 and also its duration. 155 Several factors are considered when predicting links stability between two connected vehicles, such as the movement direction, vehicle's speed and position, in addition to the link quality that is tightly related to the propagation channel characteristics such as shadowing and fading effects. 153 A link stability-based multihop clustering approach was proposed in Reference 156.…”
Section: Maintenance Phasementioning
confidence: 99%
“…Machine learning based techniques have recently emerged as an alternative to these model-based wireless link prediction techniques alleviating the need to resort to simplifying assumptions on vehicle mobility. The work in [12] relies on the use of alert messages sent by each vehicle periodically to convey information used by surrounding vehicles to feed a neuronal network in charge of predicting the expected average speed of the vehicle, from which it derives the V2V wireless link duration Similarly, the work in [13] relies on regular message exchanges between vehicles to collect various V2V wireless link metrics that are then transmitted to the infrastructure to feed a set of predictors that are combined using the adaboost algorithm [14] to build a more accurate prediction of V2V wireless link duration. This work is rather focused on V2I wireless links in an infrastructure based vehicular network where typically a vehicle get attached to multiple RSUs/BS during its trip.…”
Section: Related Workmentioning
confidence: 99%