Enhancing the Stability of the Improved-LEACH Routing Protocol for WSNs

Recently, increasing battery lifetime in wireless sensor networks has turned out to be one of the major challenges faced by researchers. The sensor nodes in wireless sensor networks use a battery as their power source, which is hard to replace during deployment. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most prominent wireless sensor network routing protocols that have been proposed to improve network lifetime by utilizing energy-efficient clustering. However, LEACH has some issues related to cluster-head selection, where the selection is done randomly. This leads to rapid loss of energy in the network. Improved LEACH is a LEACH alternative that has the ability to increase network lifetime by using the nodes' residual energy and their distance to the base station to select cluster-head nodes. However, Improved LEACH causes reduced stability, where the stability period is the duration before the death of the first node. The network stability period is important for applications that require reliable feedback from the network. Thus, we were motivated to investigate the Improved LEACH algorithm and to try to solve the stability problem. A new protocol is proposed in this paper: Stable Improved Low Energy Adaptive Clustering Hierarchy (SILEACH), which was developed to overcome the flaws of the Improved LEACH protocol. SILEACH balances the load between the nodes by utilizing an optimized method that considers the nodes' distance to the base station and their residual energy to select the cluster-head nodes and considers the nodes' distance to the cluster head and the base station to form clusters. The simulation results revealed that SILEACH is significantly more efficient than Improved LEACH in terms of stability period and network lifetime

Two Stages Transfer Algorithm (TSTT) for independent tasks scheduling in heterogeneous computing systems

Task scheduling is critical in heterogeneous systems especially with the huge number of tasks transmitted over grid causing system delay. Since heuristics are proposing methods for solving heterogeneous computing systems, several techniques were proposed for the scheduling on grid computing systems to get better execution time. In this paper, a proposed new heuristic algorithm named Two Stages Tasks Transfer (TSTT) algorithm to enhance Tenacious Penalty Based scheduling (TPB) algorithm. Heterogeneous Computing Scheduling Problem (HCSP) mathematical model has been used, where the independent tasks assigned to heterogeneous machines with different characteristics. Twelve datasets with different heterogeneity level examined using different heuristic algorithms to compare the performance with our proposed algorithm. The proposed algorithm showed its efficiency in term of makespan, resource utilization metrics for set of tasks

Impact of the COVID-19 pandemic on patients with paediatric cancer in low-income, middle-income and high-income countries: a multicentre, international, observational cohort study

OBJECTIVES: Paediatric cancer is a leading cause of death for children. Children in low-income and middle-income countries (LMICs) were four times more likely to die than children in high-income countries (HICs). This study aimed to test the hypothesis that the COVID-19 pandemic had affected the delivery of healthcare services worldwide, and exacerbated the disparity in paediatric cancer outcomes between LMICs and HICs. DESIGN: A multicentre, international, collaborative cohort study. SETTING: 91 hospitals and cancer centres in 39 countries providing cancer treatment to paediatric patients between March and December 2020. PARTICIPANTS: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, Wilms' tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas or neuroblastomas, in keeping with the WHO Global Initiative for Childhood Cancer. MAIN OUTCOME MEASURE: All-cause mortality at 30 days and 90 days. RESULTS: 1660 patients were recruited. 219 children had changes to their treatment due to the pandemic. Patients in LMICs were primarily affected (n=182/219, 83.1%). Relative to patients with paediatric cancer in HICs, patients with paediatric cancer in LMICs had 12.1 (95% CI 2.93 to 50.3) and 7.9 (95% CI 3.2 to 19.7) times the odds of death at 30 days and 90 days, respectively, after presentation during the COVID-19 pandemic (p<0.001). After adjusting for confounders, patients with paediatric cancer in LMICs had 15.6 (95% CI 3.7 to 65.8) times the odds of death at 30 days (p<0.001). CONCLUSIONS: The COVID-19 pandemic has affected paediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, many patients with paediatric cancer continued to receive their normal standard of care. This speaks to the adaptability and resilience of healthcare systems and healthcare workers globally

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality