Analyzing and evaluating the energy efficiency based on multi-5G small cells with a mm-waves in the next generation cellular networks

This paper evaluates the impact of multi-5G small cell systems on the energy efficiency (EE) in a Fifth Generation (5G) of cellular networks. Both the proposed model and the analysis of the EE in this study take into account (i) the path losses, fading, and shadowing that affect the received signal at the user equipment (UE) within the same cell, and (ii) the interference effects of adjacent cells. In addition, the concepts of new technologies such as large MIMO in millimeter range communication have also been considered. The simulation results show that the interference from adjacent cells can degrade the EE of a multi-cell cellular network. With the high interference the number of bits that will be transferred per joule of energy is 1.29 Mb/J with a 0.25 GHz bandwidth and 16 transmit antennas. While, with a 1 GHz bandwidth the transfer rate increases to 5.17 Mb/J. Whereas, with 64 transmit antennas the EE improved to 5.17 Mb/J with a 0.25 GHz BW and 20.70 Mb/J with a 1 GHz BW. These results provide insight into the impact of the number of antennas in millimeter range communication and the interference from adjacent cells on achieving real gains in the EE of multi-5G small cells cellular network

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

Determining the potential scalability of transport interventions for improving maternal, child, and newborn health in Pakistan

Background: Pakistan is far behind in achieving the Millennium Development Goals regarding the reduction of child and maternal mortality. Amongst other factors, transport barriers make the requisite obstetric care inaccessible for women during pregnancy and at birth, when complications may become life threatening for mother and child. The significance of efficient transport in maternal and neonatal health calls for identifying which currently implemented transport interventions have potential for scalability. Methods: A qualitative appraisal of data and information about selected transport interventions generated primarily by beneficiaries, coordinators, and heads of organizations working with maternal, child, and newborn health programs was conducted against the CORRECT criteria of Credibility, Observability, Relevance, Relative Advantage, Easy-Transferability, Compatibility and Testability. Qualitative comparative analysis (QCA) techniques were used to analyse seven interventions against operational indicators. Logical inference was drawn to assess the implications of each intervention. QCA was used to determine simplifying and complicating factors to measure potential for scaling up of the selected transport intervention. Results: Despite challenges like deficient in-journey care and need for greater community involvement, community-based ambulance services were managed with the support of the community and had a relatively simple model, and therefore had high scalability potential. Other interventions, including facility-based services, public-sector emergency services, and transport voucher schemes, had limitations of governance, long-term sustainability, large capital expenditures, and need for management agencies that adversely affected their scalability potential. Conclusion: To reduce maternal and child morbidity and mortality and increase accessibility of health facilities, it is important to build effective referral linkages through efficient transport systems. Effective linkages between community-based models, facility-based models, and public sector emergency services should be established to provide comprehensive coverage. Voucher scheme integrated with community-based services may bring improvements in service utilization

Sixth Generation (6G)Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research direction