Bandwidth Estimation in Network Probing Techniques Utilizing Min-Plus Algebraic Methods
Diagnosis of Anemia using Non-Invasive Anemia Detector through Parametrical Analysis
The Effectiveness of Jaya Optimization for Energy Aware Cluster Based Routing in Wireless Sensor Networks
Stress Analysis and Detection from Wearable Devices
Intrusion-Tolerant Sink Configuration: A Key to Prolonged Lifetime in Wireless Sensor Networks
Channel Estimation and It’s Techniques: A Survey
Impact of Mobility on Power Consumption in RPL
Implementation of Traffic Engineering Technique in MPLS Network using RSVP
FER Performance Analysis of Adaptive MIMO with OSTBC for Wireless Communication by QPSK Modulation Technique
Performance Evaluation of Advanced Congestion Control Mechanisms for COAP
DGS Based MIMO Microstrip Antenna for Wireless Applications
A Review on Optimized FFT/IFFT Architectures for OFDM Systems
Balanced Unequal Clustering AlgorithmFor Wireless Sensor Network
HHT and DWT Based MIMO-OFDM for Various ModulationSchemes: A Comparative Approach
Study and Comparison of Distributed Energy Efficient Clustering Protocols in Wireless Sensor Network: A Review
Diagnosis of Anemia using Non-Invasive Anemia Detector through Parametrical Analysis
Bandwidth estimation in network probing techniques plays a crucial role in modern networks, particularly in ensuring superior Quality of Service (QoS). This is largely influenced by advancements in IEEE 802.11 standards, which aim to enhance service performance. Existing methodologies are designed to assess the available resources on a given channel, providing valuable support for bandwidth-constrained applications. However, these methods encounter limitations due to potential non-linearities within networks. The current approach leverages min-plus algebra from network calculus, which, although effective, falls short in certain dynamic scenarios. In this research, we propose a novel methodology for bandwidth estimation by integrating other state-of-the-art QoS protocols while maintaining the foundational principles of the min-plus algebra technique. This approach aims to develop a more versatile and adaptable system, better suited for complex network environments.
Anemia, characterized by low hemoglobin levels, affects a significant portion of the global population, particularly children and women in low- and lower-middle-income countries. Traditional hematological tests for diagnosing anemia can be invasive, costly, and resource-intensive. This study presents the development of a wearable, non-invasive anemia detector utilizing infrared (IR) technology. The device integrates a NodeMCU microcontroller, an OLED display, a buck converter, an IR transmitter, and a receiver in a compact, wearable package. The IR system measures hemoglobin levels by analyzing the absorption of infrared light emitted through the skin, typically on a fingertip or earlobe. The NodeMCU processes the light absorption data using sophisticated algorithms to determine hemoglobin concentration and facilitates wireless data transfer to smartphones or cloud servers for further analysis. The device is designed with energy efficiency in mind, utilizing a buck converter to ensure optimal power distribution to sensitive components. The OLED display provides users with clear, real-time feedback on their hemoglobin levels, offering a user-friendly interface. Initial testing and calibration with known hemoglobin concentrations demonstrated strong correlations between device readings and traditional lab tests. The device employs a hierarchical ensemble classifier to categorize individuals as healthy, mildly anemic, or severely anemic, aligning with clinical standards. The non-invasive nature of this device, combined with its portability and cost-effectiveness, makes it an ideal solution for point-of-care anemia detection, particularly in resource-limited settings. The integration of IR technology, microcontroller processing, and wireless communication presents a practical tool for healthcare providers, facilitating rapid anemia screening and ongoing monitoring. This innovation holds the potential to improve patient outcomes by enabling early detection and intervention in anemia management.
The Internet of Things (IoT) has significantly impacted human life, enhancing quality of life and transforming various commercial sectors. The sensor nodes in the IoT are interconnected to facilitate the passage of data to the sink node over the network. Due to the constraints of battery power, energy in the nodes is preserved through the utilization of clustering techniques. Choosing a Cluster Head (CH) is crucial for prolonging the network's lifespan and increasing its throughput during the clustering process. Numerous optimization techniques have been developed to select the best Cluster Head (CH) to enhance energy efficiency in network nodes. Therefore, using incorrect CH selection methods leads to longer convergence times and faster depletion of sensor batteries. This research proposes a method that incorporates a CH selection strategy using the Jaya optimization method. The proposed methodology is evaluated against existing algorithms in terms of network longevity and energy efficiency. The simulation results indicate that the Jaya optimization algorithm-based CH selection scheme (Jaya-EEC) is much more effective in terms of network longevity compared to LEACH, LEACH-E, and PSO-C. Specifically, Jaya-EEC outperforms LEACH by 72%, LEACH-E by 64%, and PSO-C by 60%.
Stress is an elevated psycho-physiological state of the human body, arising in response to a difficult event or stressful situation. Environmental factors that cause pressure are referred to as stressors. Prolonged exposure to multiple stressors simultaneously can adversely affect a person's mental and physical health, potentially leading to chronic health issues. The negative effects of mental stress on human health have been recognized for decades. High levels of stress should be detected early to prevent these adverse effects. While the stress response helps the body overcome challenges and prepare for threats, prolonged stress can harm health. This paper aims to design a basic, cost-effective, low-power smart band for healthcare that detects mental stress based on skin conductance. This band can continuously monitor the user's mental stress and wirelessly transmit stress-related data to the user's smartphone. It not only helps users better understand their stress levels but also provides physicians with reliable data for improved treatment. The inputs to this tool come from various alerts generated by different sensors.
This study investigates the pivotal role of sink configuration in enhancing the lifetime of Wireless Sensor Networks (WSNs). A comparative analysis between configurations employing 2 sinks and 3 sinks reveals that the latter significantly outperforms the former in terms of network longevity. The study delves into the integration of an intrusion-aware protocol, providing resilience during security breaches. This protocol stabilizes network lifetime amidst intrusion attacks, which is crucial for maintaining system efficiency. This work introduces a novel machine learning model tailored for WSNs, exhibiting superior accuracy on a WSN-specific dataset. Through these combined efforts, it presents a comprehensive approach to improving WSN lifetime, encompassing sink configuration optimization, intrusion tolerance, and innovative machine learning techniques.
In wireless mobile networks, the signal transmitted from the transmitter to the receiver is vulnerable to many performance-degrading factors, such as distortion, noise, fading, and nonlinear distortion, due to the physical nature of the channel. To address these factors, accurate prediction and estimation must be employed to achieve optimized performance in wireless mobile networks. Several channel estimation techniques have been developed. In this article, we provide a review of some of these techniques under various categories, highlighting their strengths and limitations in different operational environments.
