The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers. However, the large communication delay at a low transmission rate for nodes far from the access point (AP) makes this offloading less meaningful. This paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, and it uses relay selection to help nodes far from the AP. The computation time of all tasks is minimized by adaptive task division and resource allocation (bandwidth and computation resource), and it is solved with an evolutionary algorithm. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms the methods with neither or only one function.

this research work presents a new technique for brain tumor detection by the combination of Watershed algorithm with Fuzzy K-means and Fuzzy C-means (KIFCM) clustering. The MATLAB based proposed simulation model is used to improve the computational simplicity, noise sensitivities, and accuracy rate of segmentation, detection and extraction from MR images of brain tumor. The preprocessing stage consists of denoising, skull stripping and image enhancement, after which MR images are segmented specially by using watershed algorithm followed by Fuzzy K-means and Fuzzy C-means (KIFCM) clustering algorithm. The experimental results of the proposed idea are also compared to the fuzzy C-mean, K-means, Maximization Expectation, and Mean Shift. Superiority of the proposed technique is evaluated through qualitative and quantitative validation experiments in term of noise sensitivity, capture range, computational simplicity and segmentation accuracy.