In recent years, in order to provide a better quality of service (QoS) to IoT devices, the cloud computing paradigm has shifted towards the edge. However, the resource capacity (e.g. bandwidth) in Fog network is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the Fog Network considering the evergrowing demand of QoS requirements imposed by the Ultra- Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the Fog Network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the Fog Network infrastructure (i.e., Fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game which occurs due to job delay and solve the network resource allocation problem by applying the best-fit resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.

The aim of this research is to explore factors influencing the management decisions to adopt human resource information system (HRIS) in the hospital industry of Bangladesh—an emerging developing country. To understand this issue, this paper integrates two prominent adoption theories—Human-Organization-Technology fit (HOT-fit) model and Technology-Organization-Environment (TOE) framework. Thirteen factors under four dimensions were investigated to explore their influence on HRIS adoption decisions in hospitals. Employing non-probability sampling method, a total of 550 copies of structured questionnaires were distributed among HR executives of 92 private hospitals in Bangladesh. Among the respondents, usable questionnaires were 383 that suggesting a valid response rate of 69.63%. We classify the sample into 3 core groups based on the HRIS initial implementation, namely adopters, prospectors, and laggards. The obtained results specify 5 most critical factors i.e. IT infrastructure, top management support, IT capabilities of staff, perceived cost, and competitive pressure. Moreover, the most significant dimension is technological dimension followed by organisational, human, and environmental among the proposed 4 dimensions. Lastly, the study found existence of significant differences in all factors across different adopting groups. The study results also expose constructive proposals to researchers, hospitals, and the government to enhance the likelihood of adopting HRIS. The present study has important implications in understanding HRIS implementation in developing countries.

Large-scale Internet applications, such as content distribution networks, are deployed in a geographically distributed manner and emit massive amounts of carbon footprint at the data center. To provide uniform low access latencies, Cisco has introduced Fog computing as a new paradigm which can transform the network edge into a distributed computing infrastructure for applications. Fog nodes are geographically distributed and the deployment size at each location reflects the regional demand for the application. Thus, we need to control the fraction of user traffic to data center to maximize the social welfare. In this paper, we consider the emerging problem of joint resource allocation and minimizing carbon footprint problem for video streaming service in Fog computing. To solve the largescale optimization, we develop a distributed algorithm based on the proximal algorithm and alternating direction method of multipliers (ADMM). The numerical results show that our algorithm converges to near optimum within fifteen iterations, and is insensitive to step sizes.

After the coronavirus disease 2019 (COVID-19) outbreak, the viral infection known as monkeypox gained significant attention, and the World Health Organization (WHO) classified it as a global public health emergency. Given the similarities between monkeypox and other pox viruses, conventional classification methods encounter difficulties in accurately identifying the disease. Furthermore, sharing sensitive medical data gives rise to concerns about security and privacy. Integrating deep neural networks with federated learning (FL) presents a promising avenue for addressing the challenges of medical data categorization. In light of this, we propose an FL-based framework using deep learning models to classify monkeypox and other pox viruses securely. The proposed framework has three major components: (a) a cycle-consistent generative adversarial network to augment data samples for training; (b) deep learning-based models such as MobileNetV2, Vision Transformer (ViT), and ResNet50 for the classification; and (c) a flower-federated learning environment for security. The experiments are performed using publicly available datasets. In the experiments, the ViT-B32 model yields an impressive accuracy rate of 97.90%, emphasizing the robustness of the proposed framework and its potential for secure and accurate categorization of monkeypox disease.