We formulate a scheduling problem that takes into account different hardware delays experienced by the secondary users (SUs) in a centralized cognitive radio network (CRN) while switching to different frequency bands. We propose a polynomial-time suboptimal algorithm to address our formulated scheduling problem. We evaluate the impact of varying switching delay, number of frequencies, and number of SUs. Our simulation results indicate that our proposed algorithm is robust to changes in the hardware spectrum switching delay and its performance is very close to its upper bound. We also compare our proposed method with the corresponding constant switching delay-based algorithm and demonstrate that our suggestion of taking into account the different hardware delays while switching to different frequency bands is essential for scheduling in CRNs.

Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.

The smart grid is now being deployed in many countries to provide cleaner and greener energy to the consumers. To implement this efficiently and effectively appropriate data collecting, processing and transmitting infrastructures are needed to be in place. Smart meters are used to measure the energy consumption details and patterns and transmit to meter data management system (MDMS) with the help of data aggregation units (DAU). The sheer amount of data need to be collected from the consumers are very huge and they fall under the category of big data. Currently, all the data collected from smart meters are stored in a centralized place for processing to forecast the energy demand. This approach is becoming a bottleneck for efficient data collection due to limited bandwidth capacities of Power Line Communication (PLC). In this paper, we propose a framework for distributed data aggregation approach with the help of fog computing architecture. With this approach the amount of data sent to the centralized storage space is limited and, therefore, the capacity of PLC is virtually improved without compromising the functionality.

Virtual machine allocation problem is one of the challenges in cloud computing environments, especially for the private cloud design. In this environment, each virtual machine is mapped unto the physical host in accordance with the available resource on the host machine. Specifically, quantifying the performance of scheduling and allocation policy on a Cloud infrastructure for different application and service models under varying performance metrics and system requirement is an extremely challenging and difficult problem to resolve. In this paper, the authors present a Virtual Computing Laboratory framework model using the concept of private cloud by extending the open source IaaS solution Eucalyptus. A rule based mapping algorithm for Virtual Machines (VMs) which is formulated based on the principles of set theoretic is also presented. The algorithmic design is projected towards being able to automatically adapt the mapping between VMs and physical hosts’ resources. The paper, similarly presents a theoretical study and derivations of some performance evaluation metrics for the chosen mapping policies, these includes determining the context switching, waiting time, turnaround time, and response time for the proposed mapping algorithm.