The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model’s performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology’s experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

Retention of employees is especially challenging in the aggressively competitive IT industry, and organizational success is heavily influenced by this factor. This thorough exploration considers the several factors that influence retention of employees, focusing on aspects like appreciation, remuneration, workplace, and independence at work. Drawing on theories such as the Job Characteristics Model, Self-Determination Theory, and Herzberg's Two-Factor Theory, the study incorporates findings from earlier research and common industry practice to give responses to what really matters. The review considers how non-monetary rewards, financial incentives, company culture, flexibility, and employees' control over their work influence them remaining with an organization. It also calls into question how the new work patterns of remote and hybrid working impact businesses, and how HR analytics, facilitated by AI, can shape retention practices. By pointing out current issues, setting out examples of best practice, and exploring future research areas, this report aims to provide actionable advice for business leaders and HR practitioners. The aim is to allow them to maintain a stable and loyal workforce within the IT sector, especially amidst ongoing talent shortages and changing employee expectations. The study recognizes the need for a balanced and responsive retention strategy, emphasizing job autonomy as a significant factor for employee satisfaction and commitment. In addition, strategic application of reward schemes, competitive pay levels, and benevolent working environments has a critical role to play.