optimal resource
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Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

Sub-Saharan Africa has recently witnessed rising growth rates, but the continent is still largely not industrialised. Mainstream empirical diagnosis has identified the paucity of physical and human capital as the main culprit. However, with the increasing inflow of capital into the continent, such arguments have become hackneyed. A possible culprit identified in the evolving development literature is the quality of institutions. How much has the quality of institutions, structured largely by the prevailing political economy of individual states, influenced Africa’s industrial performance? This study deploys descriptive and analytical methodologies to proffer answers to these questions. The estimates obtained from the Pool Mean Group Panel Autoregressive Distributed Lag (PMG-ARDL) as well as the Augmented Mean Group (AMG) panel estimators point strongly to the fact that institutions are bane of industrialization in Sub-Saharan Africa (SSA). Specifically, we find evidence that in the long run, regulatory quality, rule of law and control of corruption all impact the manufacturing subsector negatively and significantly. The panacea is not only within the matrix of optimal resource allocation, but must integrate the entire political and sociological process, involving governments at all levels, non-governmental organisations (NGOs) and faith-based groups.

Abstract Resource allocation to reproduction can change depending on size, as predicted by the size-dependent sex allocation. This theory is based on the fact that small individuals will invest in the allocation of sex with lower cost of production, usually male gender. In plants, there are some andromonoecy species, presence of hermaphrodite and male flowers in the same individual. Andromonoecy provides a strategy to optimally allocate resources to male and female function, evolving a reproductive energy-saving strategy. Thus, our objective was to investigate the size-dependent sex allocation in Solanum lycocarpum St. Hil. We tested the hypothesis that plants with larger size will invest in the production of hermaphrodite flowers, because higher individuals have greater availability of resources to invest in more complex structures involving greater energy expenditure. The studied species was S. lycocarpum, an andromonoecious species. From June 2016 to March 2017 the data were collected in 38 individuals, divided in two groups: the larger plant group (n=18; height=3-5 m) and the smaller plant group (n=20; height=1-2 m).Our data show that there was effect of plant size on the flower production and the sexual gender allocation. The larger plants showed more flowers and higher production of hermaphrodite flowers. Furthermore, in the flower scale, we observed allometric relationship among the flower’s traits with proportional investments in biomass, anther size and gynoecium size. Our results are in agreement with size-dependent sex allocation theory and andromonoecy hypothesis related to mechanisms for optimal resource allocation to male and female function.