2690-6791ISSN (Online)
2769-786XISSN (Print)
This research introduces a novel and comprehensive framework for digital forensics-based crime scene interpretation. The proposed framework comprises five algorithms, each serving a distinct purpose in enhancing image quality, extracting features, matching, and constructing a database, recognizing, and reconstructing objects in 3D, and conducting context-aware analysis. An ablation study validates the necessity of each algorithmic step. The framework consistently outperforms existing methods in terms of accuracy, precision, recall, and processing time. A detailed comparative analysis of parameters further highlights its cost-effectiveness, moderate complexity, superior data integration, and scalability. Visualizations underscore its dominance across multiple metrics and parameters, positioning it as an advanced solution for digital forensic-based object recognition in crime scene interpretation.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130201
Vol. 13 Issue. 2 PP. 08-24, (2024)
This research introduces an advanced network security methodology based on IoT, combining five innovative algorithms: Dynamic Threat Detection (DTD), Adaptive Intrusion Prevention System (AIPS), Anomaly-Based Security Metrics (ABSM), Context-Aware Firewall (CAF), and Cognitive Security Assessment (CSA). Each algorithm contributes specific functionalities, ranging from real-time threat detection and adaptive policy adjustments to anomaly quantification, contextual rule modifications, and holistic security risk assessments. The ablation study conducted on each algorithm reveals critical components driving their performance, ensuring a deep understanding of their inner workings. The proposed method demonstrates superior performance in accuracy, scalability, usability, and adaptability compared to existing network security methods. Visual representations and a comprehensive evaluation further validate the proposed method's effectiveness, positioning it as an advanced and efficient solution for addressing evolving network security challenges.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130203
Vol. 13 Issue. 2 PP. 35-51, (2024)
Key challenges in Wireless Sensor Networks (WSNs) include reduced dormancy, energy efficacy, reportage worries, and network lifetime. To solve the issues of energy efficiency and network longevity, more study of cluster-based WSNs is required. In order to address the challenges and constraints of WSNs, creative approaches are needed. WSNs use machine-learning techniques because of their unique characteristics. These characteristics include high communication costs, low energy reserves, high mobility, and frequent topological shifts. The current method picks cluster heads at random at the beginning of each cycle, not considering the remaining energy of these nodes. It is possible that the newly chosen CH nodes will have the lowest energy level in the network and will die off fast as a result. Energy is wasted while communicating over long distances between cluster heads and the BS, which occurs frequently in a big network due to Internet of things. This would mean that WSNs have a finite lifespan. Therefore, to increase the network's longevity and efficiency, we propose a machine-learning-based strategy called energy proficient crossbreed clustering methodology (ECCM). The experimental results reveal that the ECCM is superior to the LEACH approach, increasing residual energy by 35%, extending network lifetime by 37%, and increasing throughput by 15%.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130204
Vol. 13 Issue. 2 PP. 52-59, (2024)
New Adaptive Nano-Scale Sensor Network (ANSN) can quickly feel nanoscale surroundings. ANSN uses data in many scenarios to improve networks, consume less energy, and gain more accurate data. ANSI essentials are covered in detail here. This group has numerous parts. Making service better, collecting data with less energy, sending data with Q-learning, merging sensor data to increase accuracy, controlling power dynamically, and protecting data using AES are examples. Energy collection and sensor use are key to this effort. Academic research has proven that ANSN outperforms other techniques in several areas. Improvements include speed, security, latency, sensor accuracy, and network stability. With these changes, ANSN may be suitable for small wireless sensor networks.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130202
Vol. 13 Issue. 2 PP. 25-34, (2024)
The amalgamation of wearable sensor technologies and artificial intelligence (AI) presents a transformative paradigm for optimising athletic performance in real time. This paper explores the integration of cutting-edge sensors - including bioimpedance sensors, accelerometers, and gyroscopes - with advanced AI algorithms such as machine learning and decision support systems. By capturing diverse physiological, biomechanical, and environmental data, the proposed framework aims to offer personalized, actionable insights for athletes. This research synthesizes the current landscape of wearable sensor technology in sports and highlights the evolving role of AI in interpreting data for enhancing athletic performance. It delineates an innovative framework designed for real-time analysis, personalized feedback, and training optimization. The seamless interaction between sensors and AI models empowers athletes and coaches to make informed decisions, optimizing training regimens and minimizing injury risks. The paper discusses the practical implications, challenges, and ethical considerations associated with this integration, emphasizing its potential benefits in diverse sports disciplines. Results from real-world trials underscore the efficacy of the proposed framework in providing dynamic guidance to athletes, thereby augmenting their performance through tailored interventions.
Read MoreDoi: https://doi.org/10.54216/JISIoT.130205
Vol. 13 Issue. 2 PP. 60-77, (2024)
The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors