As network resources are shared between many users, resource management must be a key part of any communication system as it is needed to provide seamless communication and to ensure that applications and servers receive their required Quality-of-Service. However, mobile environments also need to consider handover issues. Furthermore, in a highly mobile environment, traditional reactive approaches to handover are inadequate and thus proactive techniques have been investigated. Recent research in proactive handover techniques defined two key parameters: Time Before Handover (TBH) and Network Dwell Time (NDT) for a mobile node in any given networking topology. Using this approach, it is possible to enhance resource management in common networks using probabilistic mechanisms because it is possible to express contention for resources in terms of No Contention, Partial Contention, and Full Contention. This proactive approach is further enhanced by the use of a contention queue to detect contention between incoming requests and those waiting for service. This paper therefore presents a new methodology to support proactive resource allocation for future networks such as Vehicular Ad-Hoc Networks. The proposed approach has been applied to a vehicular testbed and results are presented that show that this approach can improve overall network performance in mobile heterogeneous environments.

Wireless and mobile communication systems have evolved considerably in recent years. Seamless mobility is one of the main challenges facing mobile users in wireless and mobile systems. However, highly mobile users lead to a high number of handover failures and unnecessary handovers due to the limited resources and coverage limitations with a high mobile speed. The traditional handover models are unable to cope with high mobile users in such environments. This paper proposes, an intelligent handover decision approach to minimize the probability of handover failures and unnecessary handovers whilst maximizing the usage of resources in highly mobile environments. The proposed approach is based on modelling the system using a Markov chain to enhance the system’s performance in terms of blocking probability, mean queue length and transmission delay. The results are compared with the traditional handover model. Simulation is also employed to validate the accuracy of the proposed model. Numerical results have shown that the proposed method outperforms the traditional algorithm over a wide range of handover failures and significantly reduced the number of such failures and unnecessary handovers. The results of this study show that quality if service (QoS) measures of such systems can be evaluated efficiently and accurately using the proposed analytical model. However, the performance results have also shown that it is still necessary to explore an effective model for operational spaces. In addition, the proposed model can also be adapted to various types of networks considering the high speed of the mobile user and the radius of the network.

Skin cancer is a common form of cancer that affects millions of people worldwide. Early detection and accurate diagnosis of skin cancer are crucial for effective treatment and management of the disease. There has been a growing interest in using deep learning techniques and computer vision algorithms to develop automated skin cancer detection systems in recent years. Among these techniques, convolutional neural networks (CNN) have shown remarkable performance in detecting and classifying skin lesions. This paper presents a comprehensive study using CNN and deep learning techniques for skin cancer detection using the International Skin Imaging Collaboration (ISIC) dataset. The proposed architecture is a compact deep CNN that is trained using a dataset of benign and malignant skin lesion images. The proposed architecture has achieved 84.8% accuracy, 83.8% TPR, 83.7% TNR, 81.6% F1-score and 80.5% precision for performance evaluation. The experimental results show promising results for the accurate and efficient detection of skin cancer, which has the potential to improve the diagnosis and treatment of this life-threatening disease.