This study explores the use of the Fledge platform, an open-source solution based on Fog Computing, to improve real-time health data management in the context of urban home care. Faced with the growing challenges of urbanization and the increase in chronic diseases, our approach aims to optimize the collection, processing, and securing of health data. The proposed architecture combines Fog and Cloud Computing, allowing for data pre-processing at the source and advanced analysis in the cloud. The results demonstrate Fledge’s exceptional performance in terms of reduced latency, data security, and integration with existing systems. The study involved setting up a test environment simulating a mobile health laboratory, using real sensors to collect physiological data. Although promising, the study has limitations in terms of sample size and experiment duration. Future research should focus on integrating advanced AI algorithms and expanding Fledge’s interoperability, paving the way for more efficient and patient-centered urban health systems.
Software-Defined Networking (SDN) represents the next generation of network infrastructure, offering flexibility and the ability to quickly integrate services tailored to specific business applications and use cases. Motivated by the rapid rise of SDN and the increasing focus on Quality of Service (QoS) within SDN networks, this project explores how SDN can replace traditional network architectures and improve QoS management. Specifically, we investigate the role of protocols such as OpenFlow, iPerf, and Open vSwitch Database Protocol, which are pivotal in configuring and managing virtual bridges, ports, tunnels, and queues in SDN environments. The project emphasizes the QoSFlow solution for effectively managing QoS in SDN networks, demonstrating its potential for enhancing flow prioritization and overall network performance.
To address the growing demand of high-speed and low-latency communications in access network segments, IEEE 802.3ca task force developed Next Generation Ethernet Passive Optical Network (NG-EPON). In this network, an Optical Line Terminal (OLT) and an Optical Network Unit (ONU) can have multiple transceivers which allow them to communicate through multiple channels simultaneously (channel bonding) to achieve aggregated high transmission rate. Due to the use of a large number of transceivers in this network, the energy consumption should be supposedly high. In this paper, we propose an Adaptive Cyclic Transmission Operation (ACTO) scheme for NG-EPON with the objective of reducing energy consumption using sleep mode without compromising performance requirements. In particular, ACTO sets transmission cycle duration of each channel taking into consideration the traffic delay performance requirements. It also presents mechanisms how energy consumption for the idle channel(s) can be minimized and how signaling associated energy consumption in OLT/ONU transceivers can be saved. Performance evaluated based on simulation shows that the proposed solution conserves 15% of energy in ONUs even during high traffic load conditions. Furthermore, the OLT side energy consumption is also reduced by approximately 13.5% in ACTO compared to a conventional solution.
Wireless Rechargeable Sensor Network (WRSN) is a new paradigm that prolongs the lifetime of Wireless Sensor Network (WSN). To improve the survival rate of sensor nodes and energy usage efficiency, this paper studied how to schedule charging paths of multiple mobile chargers and allocate charging time simultaneously. We propose an on-demand partial charging scheme for multiple mobile chargers. First, to balance the charging workload of multiple mobile chargers, we proposed a new clustering algorithm to divide the sensor nodes into several clusters. Then, we proposed an algorithm of determining the target charging sensor node. Finally, based on deep reinforcement learning technique, the charging duration calculation strategy is designed to automatically allocate charging duration for the sensors. Extensive simulations show that, compared with baseline algorithms, our scheme can increase the survival rates of sensor nodes by 3.62%~11.95% and increase the energy usage efficiency by 1.57%.~6.15% in different network scale.
This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved.
