The Internet of Things (IoT) is transforming everyday objects. However, the limited memory, processing power, and network capabilities of its devices make them susceptible to security breaches. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a promising IoT protocol but faces significant security challenges. Existing research often focuses on individual attacks, utilizing various mitigation strategies, including machine learning and deep learning for detection. This paper proposes an Intrusion Detection System (IDS) using the ROUT-4-2023 dataset, which encompasses Black Hole, Flooding, DODAG Version Number, and Decreased Rank attacks. The study investigates network traffic features encompassing all four attacks, utilizing statistical information graphs. Additionally, it experiments with various machine learning models and deep learning architectures for comparative analysis, focusing on confusion matrix outcomes and computational efficiency. Results indicate that Random Forest classifier achieves 99% accuracy, while Transformers reach 97% F1-Score with training time of only 16.8 minutes over 5 epochs.

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.

A computer network can be defined as many computing devices connected via a communication medium like the internet. Computer network development has proposed how humans and devices communicate today. These networks have improved, facilitated, and made conventional forms of communication easier. However, it has also led to uptick in-network threats and assaults. In 2022, the global market for information technology is expected to reach $170.4 billion. However, in contrast, 95% of cyber security threats globally are caused by human action. These networks may be utilized in several control systems, such as home-automation, chemical and physical assault detection, intrusion detection, and environmental monitoring. The proposed literature review presents a wide range of information on Wireless Social Networks (WSNs) and Internet of Things (IoT) frameworks. The aim is first to be aware of the existing issues (issues with traditional methods) and network attacks on WSN and IoT systems and how to defend them. The second is to review the novel work in the domain and find its limitations. The goal is to identify the area’s primary gray field or current research divide to enable others to address the range. Finally, we concluded that configuration. Message Rapid Spanning Tree Protocol (RSTP) messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol (BPDU) forms. The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component. In this research, we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.

In the realm of Wireless Sensor Networks (WSNs), the detection and mitigation of sinkhole attacks remain pivotal for ensuring network integrity and efficiency. This paper introduces SFlexCrypt, an innovative approach tailored to address these security challenges while optimizing energy consumption in WSNs. SFlexCrypt stands out by seamlessly integrating advanced machine learning algorithms to achieve high-precision detection and effective mitigation of sinkhole attacks. Employing a dataset from Contiki-Cooja, SFlexCrypt has been rigorously tested, demonstrating a detection accuracy of 100% and a mitigation rate of 97.31%. This remarkable performance not only bolsters network security but also significantly extends network longevity and reduces energy expenditure, crucial factors in the sustainability of WSNs. The study contributes substantially to the field of IoT security, offering a comprehensive and efficient framework for implementing Internet-based security strategies. The results affirm that SFlexCrypt is a robust solution, capable of enhancing the resilience of WSNs against sinkhole attacks while maintaining optimal energy efficiency.

In the modern fast-developing business environment, logistics efficiency of supply chains is crucial. This study investigates human capital as an essential aspect of improving supply chain management. Recent research shows increasing awareness of the critical role skilled workers play in developing a successful supply chain. This entails the coordination of various supply chain dimensions to achieve operational efficiency. However, there is no direct relationship between factors such as hours of training and supply chain performance. Thus, a more sophisticated approach to understanding these dynamics is required. Effective supply chain management involves cooperation and coordination, with theories such as Resource-Based View or Social Capital Theory offering useful knowledge. Furthermore, it reveals leadership and talent management as core factors that can be used to optimize human capital in supply chains implying the importance of having strategic recruitment, training, and development processes. The research indicates that these comprehensive programs calling on continuous growth and empowerment of supply chain people are crucial to any organization. Such programs should aim to develop a proactive approach toward problem-solving and adaptability of market changes. Understanding the importance of competent people and investing in their growth can result in a team that not only adapts but also predicts changes to market trends. <!----> <!----> <!----> <!---->

Parkinson’s disease is a progressive and debilitating neurodegenerative disorder affecting millions of people worldwide. The disease is characterized by motor symptoms such as tremors, rigidity and postural instability, as well as non-motor symptoms such as depression and cognitive impairment. While there is no cure for Parkinson’s disease, there are various treatments available to manage symptoms and improve quality of life for patients. This case study examines a 65-year-old retired accountant, Mr. John Smith, who was diagnosed with Parkinson’s disease five years ago. Mr. Smith has been treated with a combination of medications, including levodopa and carbidopa and physical therapy to manage his symptoms. However, his symptoms have not significantly improved. This literature review explores the current research on Parkinson’s disease, including its pathophysiology, diagnosis and treatment. Parkinson’s disease is caused by the degeneration of dopamine-producing neurons in the brain, leading to a depletion of dopamine and the accumulation of alpha-synuclein protein, oxidative stress and inflammation. Diagnosis is based on clinical symptoms, neurological examination and response to dopaminergic therapy. Treatment focuses on managing symptoms, with medications and non-pharmacological interventions such as exercise and physical therapy. Deep brain stimulation is a surgical treatment option that has been shown to be effective in managing motor symptoms. While there is currently no cure for Parkinson’s disease, ongoing research into its pathophysiology and treatment holds promise for improving outcomes for patients. This case study highlights the importance of early diagnosis and personalized treatment plans for patients with Parkinson’s disease.