Privacy policy. This article helps you resolve various errors that occurs when you try to install SQL Server R2. When you try to install Microsoft SQL Server R2, you receive one or more of the following error messages or experience one or more of the following symptoms.
Additionally, you cannot continue with the setup. If you installed SQL Server over a computer network, install it again from a local drive, and then rerun the Setup program. For simplicity, in this work we only simulate our protocol using a single data rate. To simulate our protocol, the broadcast interval has been set to 5 s and the broadcast zone has been considered to be a circle with a radius of m. We chose T to be 3. The beaconing interval of GPSR is set to the default value, which is 3 s.
Also, for GPSR, we assumed that nodes have previous knowledge of the gateway locations, since here we are just interested in the routing results of GPSR. The metrics that we used for evaluation are the packet delivery ratio PDR and the packet delivery delay, which is the average latency since originating a data packet till the packet is received by the destination. The normalized duplication ratio is the number of control messages that have been rebroadcasted by more than one node over the whole number of control messages.
Simulation results 4. This results in multiple rebroadcasting of the same message and increases the number of duplications in the network. Different values of a. Note that Fig. Indeed, as we put more weight on the stability, the selected routes become more stable, and this increases the number of successful deliveries.
The reason is that, by giving all the weight to the stability, the number of duplications increases, as we discussed earlier. As the number of duplications reaches a certain level, they flood the network, and this will overflow the interface queues after the MAC layer, as observed from our simulations. Putting more weight on stability make routes live longer, and when the node density is higher, the route travels through many hops close to each other.
In addition, since we have a larger number of relays, there will be more time spent in forwarding packets and contending for accessing the medium, which causes the delay increases. For this purpose, we evaluated three scenarios: low-density, medium-density and high-density scenarios, using , and vehicles, respectively.
However, in the medium-density and high-density scenarios Fig. As Fig. In low-density scenarios, since there are not many vehicles available, there is not much competition between vehicles for becoming relays.
Hence, changing the filtering point will not have much effect on the result. However, when the number of vehicles increases, more vehicles compete to become the next relay, so the filtering point should be selected in a way that the best vehicle be selected for this purpose. Selecting large or low values of a will reduce the effect of the stability in the network: larger values will cause more duplication in the network and lower values will increase the role of progress and decrease the effect of stability of the links in the network.
Impact of the driver behavior on the network performance In these simulations, we consider the effect of the driver behavior parameters CV on the waiting times. Recall that, for each vehicle, we take n speed samples V0 , V1 ,. Hence, according to Eq. First, we consider four speed samples and we vary the standard deviation from 10 to As expected, as the standard deviation increases, the packet delivery ratio decreases see Fig. This is due to the fact that, when the standard deviation increases, the coefficient of variation increases, and hence the waiting time for a node to become a relay increases.
Additionally, the changes of the driver behavior affect the stability of the paths and create frequent disconnections. Consequently, other nodes will be selected as relays, and as a result the packet delivery ratio decreases significantly. We observe the same behavior as before. The delay increases with the increase of the standard deviation and it takes more time for a relay to be selected. For both figures Fig.
Impact of the standard deviation. Impact of the period of sampling. High density improves the packet delivery ratio by overcoming the problem of network disconnection. We kept n speed samples constant and equal to 4, and fixed the standard deviation to 10, 12 and From Fig.
The more the waiting time is, the more disconnections the network experiences and thus the higher the losses are. The higher the standard deviation is, the lower the delay becomes. This is explained with the fact that selected paths are more stable with lower standard deviation and the relays are quickly selected since the waiting times are smaller. Evaluation To evaluate our protocol we performed some simulations with different node densities and speed.
To see the effect of changing the maximum speed, we fixed the number of vehicles on the road to By increasing the node density in the simulation, the delay slightly increases. This happens because the number of vehicles that may become a relay increases; therefore, there are more opportunities for building blocks of a route and routes may potentially become more stable. In addition, these routes can contain more hops.
Hence, a vehicle can use a route which is more stable for a longer period than the scenarios with less number of vehicles, and this results in higher delivery delays, as shown in Fig.
In addition, as the number of nodes increases, the probability that a vehicle has more than one route entry in its routing table increases.
This increase in the stability of routes and connectivity in the network cause an increase in the packet delivery ratio, as can be seen in Fig. Increasing the speed will result in having links with smaller lifetime and this lead to less stable routes. Indeed, these routes with small lifetimes cannot maintain a large number of hops. Also, the likelihood that a link fails unexpectedly increases; in this case, a packet might be dropped in the middle of the route since one of the relays may not be in the transmission range anymore.
This decrease in the number of hops results in smaller delivery delays, as shown in Fig. Varying the number of nodes. Varying the maximum speed. In AODV, the route discovery process for the nodes outside the advertisement zone which has been set to three hops here will add a large delay to the packet delivery.
Increasing the advertisement zone in this case will lead to higher overhead and probably network overflow. However, the packet delay for GPSR and our suggested protocol are close to each other. In our case, a vehicle already has a route to a gateway, and forwarding a packet is as simple as a routing table lookup. In general, our experiments show that our protocol performs better in terms of packet delivery ratio and delay than GPSR and AODV, under different network settings.
Conclusion and future work VANETs will play an important role in the future, and communicating with road infrastructure units is one aspect that should be covered in order to provide specific services such as Internet access. We proposed a predictive gateway selection scheme, which uses vehicle movement parameters to select the path with the longest lifetime by predicting the future location of neighbors of a vehicle.
This helps to have more stable routes to the gateways, and it helps to maintain better quality of the network. We plan to simulate our protocol under a more realistic environment while considering further simulation parameters such as increasing the amount of data, number of vehicles, speed of vehicles, and so on. We will implement some of the missing features in the current work, such as the coefficient of variation, and monitor the behavior of our protocol. We plan to improve our protocol by suggesting a better location estimation scheme Eq.
It is also possible to improve the relay selection scheme by considering the quality of the received signal.
We plan to come up with a new approach to control the number of requests a vehicle will handle. References [1] S. Barghi, A. Benslimane, C. Taleb, A. Benslimane, K. Letaif, Towards an effective risk-conscious and collaborative vehicular collision avoidance systems, IEEE Transactions on Vehicular Technology 59 3 — Kiess, J. Rybicki, M. Singh, N. Bambos, B. Srinivasan, D. Perkins Ed. Johnson, C. Perkins, J.
Soliman, et al. Koodli Ed. Hsieh, Z. Zhou, A. Ni, Y. Tseng, Y. Chen, J. Bechler, L. Alriksson, T. Larsson, P. Johansson, J.
Gerald, Q. Korner, A. Hamidian, A. Sommer, F. Chakeres, C. Wolf, O. Storz, W. Devarapalli, et al. Baldessari, A. Festag, J. Tian, L. Han, K. Lochert, M. Mauve, H. Fussler, H.
Naumov, T. Sun, H. Yamaguchi, K. Yukimasa, S. Korkmaz, E. Ekici, F. Menouar, M. Start with a baseline instance. You can only apply cumulative updates to existing installations of the initial release of SQL Server. On an internet connected device, go to the cumulative update list for your version of SQL Server:.
Run Setup. Accept the licensing terms, and on the Feature selection page, review the features for which cumulative updates are applied. You should see every feature installed for the current instance, including machine learning features.
Continue through the wizard, accepting the licensing terms for R and Python distributions. During installation, you are prompted to choose the folder location containing the updated CAB files. This step requires a server restart. If you are about to enable script execution, you can hold off on the restart until all of the configuration work is done. After installation is finished, restart the service and then configure the server to enable script execution:.
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