Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

A simulation–optimization framework for post-disaster allocation of mental health resources

Extreme events, such as natural or human-caused disasters, cause mental health stress in affected communities. While the severity of these outcomes varies based on socioeconomic standing, age group, and degree of exposure, disaster planners can mitigate potential stress-induced mental health outcomes by assessing the capacity and scalability of early, intermediate, and long-term treatment interventions by social workers and psychologists. However, local and state authorities are typically underfunded, understaffed, and have ongoing health and social service obligations that constrain mitigation and response activities. In this research, a resource assignment framework is developed as a coupled-state transition and linear optimization model that assists planners in optimally allocating constrained resources and satisfying mental health recovery priorities post-disaster. The resource assignment framework integrates the impact of a simulated disaster on mental health, mental health provider capacities, and the Center for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) to identify vulnerable populations needing additional assistance post-disaster. In this study, we optimally distribute mental health clinicians to treat the affected population based upon rule sets that simulate decision-maker priorities, such as economic and social vulnerability criteria. Finally, the resource assignment framework maps the mental health recovery of the disaster-affected populations over time, providing agencies a means to prepare for and respond to future disasters given existing resource constraints. These capabilities hold the potential to support decision-makers in minimizing long-term mental health impacts of disasters on communities through improved preparation and response activities.

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

A Novel Swarm Urochordate Algorithm for Defensive Resource Assignment Problems

The weapon target assignment (WTA) problem is an important task to tactical arrangements in military commitment operations. It describes the optimal method to allocate defenses in opposition to threats in fighting situations. It is an NP-complete issue in which no accurate outcome for all conceivable situations is known. The time performance of created algorithms is a major challenge in modeling the WTA problem, which has only been lately considered in related papers. This article introduces a new algorithm called Swarm Urochordate Algorithm (SUA) which is inspired nature by tunicates to solve the WTA problem. The suggested method is compared to nine metaheuristic approaches recently established for 30 well-known testing benchmarks. Convergence and computer complexity are also examined. The experimental findings show that the method presented works better than previous competing metaheuristic approaches.

Design of Dynamic Resource Allocation Scheme for Real-Time and Non-Real-Time Traffics in The Advanced Mobile Communications Network

The 5G wireless technology is recently standardized for meeting intense demand. The Long Term Evolution (LTE) technology provides an easy, time-saving, and low-cost method for deploying a 4G/5G network infrastructure. To support multimedia service and higher bandwidth data delivery, an LTE MAC layer has QoS support with several QoS class indicator (QCI) levels. Based on LTE current QCI priority and QoS requirements in UEs, the original Max-Rate scheduler or Proportionally Fair (PF) algorithm could not achieve their goal owing to the UE’s dynamic physical capacity with a different channel quality indicator (CQI) at run time. For better QoS service than LTE networks, per UE’s CQI state for each resource block (RB) must be considered simultaneously in LTE MAC layer resource allocation with cross-layer support. As DL real estimated capacity is dynamic owing to a UE’s periodic CQI reporting, the CQI state in LTE scheduling must be considered. This study proposes a smart and flexible scheme for Enhanced Utilization Resource Allocation (EURA) including three novel mechanisms that can dynamically fit UEs’ CQI states. The simulation results in this study demonstrate that the proposed EURA scheme outperforms the contrast schemes, can save more rare radio capacity, and improve the utilization of radio resource assignment.