Mobility management and resource utilization are the two most important research issues in wireless mobile multimedia networks. In order to provide the guarantee of handoff probability; we propose a mobility based call admission control using artificial neural network to predict the accurate future position of mobile user based on the mobility history of the user. For mobility prediction we have used very common and popular error back propagation neural network model. The concept of a error back propagation neural networks is completely distributed that shows current position and predict next movement of a mobile user, with this mobility information resources are estimated and reserved to improve the overall quality of service (QoS) in terms of continuous service availability and handoff dropping probability. For better resource utilization, the proposed scheme will predict when and where the next handoff takes place and in which cell the handoffs will occur. In addition, a call admission control scheme is used to decide whether a call is accepted or rejected in the current traffic which is based on the amount of resources after a handoff. The allocation of radio resources are made by the queuing scheme in which the handoff calls and new calls are queued to get the services. The proposed scheme increases the ratio of mobility prediction and decreases the call dropping probability and handoff dropping probability up to a maximum extent.

In mobile multimedia communication systems, the limited bandwidth is an issue of serious concern. However for the better utilization of available resources in a network, channel allocation scheme plays a very important role to manage the available resources in each cell. Hence this issue should be managed to reduce the call blocking or dropping probabilities. This paper gives the new dynamic channel allocation scheme which is based on handoff calls and traffic mobility using hopfield neural network. It will improve the capacity of existing system. Hopfield method develops the new energy function that allocates channel not only for new call but also for handoff calls on the basis of traffic mobility information. Moreover, we have also examined the performance of traffic mobility with the help of error back propagation neural network model to enhance the overall Quality of Services (QoS) in terms of continuous service availability and intercell handoff calls. Our scheme decreases the call handoff dropping and blocking probability up to a better extent as compared to the other existing systems of static and dynamic channel allocation schemes

Quality of Services (QoS), network performance, link connectivity and consistent mobility with Call Admission Control (CAC) are the key concerns to ensure best services in mobile or e-healthcare domain. With highly connected environment diverse traffic conditions in healthcare where immediate service is the foremost priority. CAC can limit the call connections viewing emergency calls based on the location in the time-critical services or remote health monitoring. In this research paper our focus is on the selection of potential positions to deploy base stations with significant channel allocation. As part of prior work on CAC based on the Grey Wolf optimizer (GWO) scheme, proposed a channel allocation method to provide assured QoS for mobile or electronic healthcare applications. The proposed method presented with two different scenarios by the grid cross position and uniform mobility and experimental results show how efficiently CAC managed to control the call failure or dropping probability.

Both Microsoft's Windows 7 and Apple's Snow Leopard operating systems claim to provide users with a safer and more reliable environment, but no work has evaluated and compared their resilience against common DDoS attack traffic. The authors compare the effect of this type of attack traffic on both systems installed on the same iMac hardware platform under the same network attack conditions. In particular, they consider common DDoS traffic at different layers of the TCP/IP protocol stack-namely, ARP flood, ping flood, and TCP-SYN flood attacks, which correspond to layers 2, 3, and 4, respectively. Interestingly, the iMac computer with its native Snow Leopard system was found to exhaust more computing resources when compared to that of Windows 7 installed on the same platform and under the same attack conditions.