Abstract-In this paper we investigate the time delay propagation rates in a Vehicular Ad-Hoc Network, where vehicular connectivity is supported by both Vehicle-to-Vehicle (V2V) and Vehicleto-Infrastructure (V2I) protocols. In our vision, seamless connectivity issues in a VANET with nearby network infrastructure, can be fixed by an opportunistic choice of a vehicular protocol between V2V and V2I. Such a decision is taken by each vehicle whenever it needs to transmit messages. Our technique -called as Vehicle-to-X-represents a handoff procedure between V2V and V2I, and vice versa, in order to keep vehicles connected independent of mobility issues and traffic scenarios.We investigate the time delay as a performance metric for protocol switching, and present the time propagation rates which occur when vehicles are transmitting warning messages, via V2V or V2I. Simulation results show how the simultaneous usage of pre-existing network infrastructure, together with inter-vehicular communications, provides low delays; while traditional opportunistic vehicular communications increases the transmission time delays and does not guarantee seamless connectivity to vehicles.
The received signal strength indicator (RSSI) and the link quality indicator (LQI) are the metrics that are commonly available in commercial off-the-shelf (COTS) sensor hardware. The former has been widely regarded as the main source for distance estimation and node localization. However, experimentally RSSI has been shown to behave in an inconsistent manner, even in ideal scenarios, and serve at best as bounds for distances. The latter is effectively a measure of chip error rate, and can be used to identify higher quality transmissions, and the combination RSSI/LQI can be expected to make more precise estimates with the tradeoff of increased delay and estimation cost. In this paper, we describe our distance estimation system that uses these two metrics and test our hypothesis purely through experimental measurements using sensor nodes. Results indicate that such a combination of metrics can be used to provide a tighter bound on the range of estimated distances. We then quantify the improvement in distance estimation by relying on these two metrics. Through a unique classification using fuzzy logic and TBM, we developed an algorithm that is capable of precise distance estimation within the range of 100cm to 400cm, on at least 80% of the times while reaching accuracy as high as 100%.
The received signal strength indicator (RSSI) and the link quality indicator (LQI) are metrics that are commonly available in commercial off-the-shelf (COTS) sensor hardware. The former has been widely regarded as a cheap alternative for distance estimation and node localization. However, experimentally RSSI has been shown to behave in an inconsistent manner, even in ideal scenarios, and serve at best as bounds for distances. The latter is effectively a measure of chip error rate, and can be used to identify higher quality transmissions, and the combination RSSI/LQI can be expected to make more precise estimates with the tradeoff of increased delay and estimation cost. In this paper, we describe our distance estimation system that uses these two metrics and test our hypothesis purely through experimental measurements using sensor nodes. Results indicate that such a combination of metrics can be used to provide a tighter bound on the range of estimated distances.
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