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Minor Revision, Major Hope
Welcome to Minor Revision, Major Hope! This podcast is about sharing and exploring academic articles. ⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.
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Episodes
EP9. Services management and distributed multihop requests routing in mobile edge networks 22.09.2024 8:03
Article Link | Author's Personal Website Abstract: Multi-access Edge Computing (MEC) is an emerging computing architecture to release the resource burden of the centralized cloud and reduce the mobile application latency. Services management and MEC requests routing is a major problem in MEC systems. Existing works mainly focus on the one-hop centralized request routing strategies. However, th...
EP8. Dynamic redeployment of UAV base stations in large-scale and unreliable environments 22.09.2024 9:45
Article Link | Author's Personal Website Abstract: The deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BSs) has emerged as a promising solution to enhance communication services provided to ground users. However, deploying UAV-BSs faces challenges including the cooperation of multiple UAVs, dynamic user distribution, low-reliability issues of UAVs, and efficient rede...
EP7. QoS maximization scheduling of multiple UAV base stations in 3D environment 22.09.2024 9:52
Article Link | Author's Personal Website Abstract: Using Unmanned aerial vehicles (UAV) as base stations for providing communication services to urban residents has emerged as a novel trend in the era of the Internet of Things (IoT). However, UAV base stations (UAV-BSs) applications have faced some challenges, such as the finite energy capacity, the limited coverage range, and the complex flyi...
EP6. An efficient processing scheme for concurrent applications in the iot edge 22.09.2024 8:28
Article Link | Author's Personal Website Abstract: Due to the large volume of IoT data, conventional sensor network-based and cloud-based IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud-based IoT system is far away from the...
EP5. Exploring Connected Dominating Sets in Energy Harvest Networks 22.09.2024 11:09
Article Link | Author's Personal Website Abstract: Duty-cycle scheduling is an effective way to balance energy consumption and prolong network lifetime of wireless sensor networks (WSNs), which usually requires a connected dominating set (CDS) to guarantee network connectivity and coverage. Therefore, the problem of finding the largest number of CDSs is important for WSNs. The previous works a...
EP4. A novel framework for the coverage problem in battery-free wireless sensor networks 21.09.2024 12:17
Article Link | Author's Personal Website Abstract: Battery-free wireless sensor network (BF-WSN) is a newly proposed network architecture to address the limitations of traditional wireless sensor networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem i...
EP3. Minimizing Latency for Multi-DNN Inference on Resource-Limited CPU-Only Edge Devices 21.09.2024 15:25
Article Link | Author's Personal Website Abstract: Despite considerable advancements in specialized hardware, the majority of IoT edge devices still rely on CPUs. The burgeoning number of IoT users amplifies the challenges associated with performing multiple Deep Neural Network inferences on these resource-limited, CPU-only edge devices. Existing strategies, including model compression, ha...
EP2. A Hybrid Human-in-the-Loop Deep Reinforcement Learning Method for UAV Motion Planning 21.09.2024 8:20
Article Link | Author's Personal Website Abstract: Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial in achieving this goal. However, existing UAV collision-avoidance methods face two challenges...
EP1. Autonomous navigation of UAV in multi-obstacle environments based on a Deep Reinforcement Learning approach 21.09.2024 8:30
Article Link | Author's Personal Website Abstract: Path planning is one of the most essential parts of autonomous navigation. Most existing works suppose that the environment is static and fixed. However, path planning is widely used in random and dynamic environments (such as search and rescue, surveillance, and other scenarios). In this paper, we propose a Deep Reinforcement Learning (DRL)-b...
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