Due to technological advancements, most devices are generating a significant amount of data which needs appropriate technology to handle the data generated by IoT devices. Fog computing addresses this challenges in a decentralised manner. This paper proposes an efficient resource allocation and management strategies with energy efficiency (ERAM-EE) to effectively allocate available resources in Fog-enabled networks. The ERAM-EE algorithm utilises the channel gain matrix of the interconnected network to assign IoT devices to Fog nodes (FNs) through resource blocks (RBs) with three stages. In the initial stage, one FN is assigned to each IoT device through a single RB by calculating the maximum value of the channel gain. In the subsequent stage, the remaining RBs are assigned to unassigned FNs for future task-offloading processes. Finally, the unassigned RBs are allocated to IoT devices by calculating the maximum channel gain of the Fog–IoT networks. Simulated results indicate that the ERAM-EE scheme confirms that each IoT device is mapped with minimum one FN and RB for effective task scheduling and resource management. Analysis reveals that the ERAM-EE method achieved an increase in EE of up to 7, 8, and 18 Mbit/J compared to existing schemes for varying IoT devices, FNs and RBs respectively.

Summary The advancements in wireless communication go in leaps and bounds ushering in due attention to spectrum sharing. Spectrum scarcity is one of the major limitations causing hardships in the existing wireless networks. Cognitive Radio Networks (CRNs) emerge as a solution to tide over such humps. It prompts the secondary user (SU) to look out for unused spectrum and utilize them. The CRN helps the SU by permitting it to switch over to unused portions of the spectrum. When a primary user (PU) claims back the spectrum, SU is obliged to perform a spectrum handoff. The SU decides the type of policy to be chosen for the handoff. Such a decision‐making step during the handoff of the spectrum is imperative only if a changing policy is required. In this research work, Artificial Neural Networks (ANNs), Logistic Regression and Support Vector Machine (SVM) are proposed and implemented for a seamless handoff in CRN. From the experimental verifications, it is observed that the training accuracy is 97.9% and 97.6% for ANN and SVM, respectively. But during the actual phase, SVM to a certain extent performed better. This is due to the convergence nature of SVM on global minima.

Summary In recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.