Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-3-4 10:20'''
|time='''2022-3-11 10:20'''
|addr=4th Building A503
|addr=4th Research Building A527-B
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
}}
}}
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract = Long-range wide-area network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWANs). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: 1) successful delivery rate and 2) efficient energy consumption. This article proposes a novel game-theoretic framework for LoRaWAN named best equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of the LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each spreading factor (SF). The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, SFs, and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in the packet delivery ratio and energy efficiency as compared to the adaptive data rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared to LoRaWAN ADR.
|abstract = Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. Recent work proposes CTC from IEEE 802.11b to LoRa but has a low efficiency due to their extremely asymmetric data rates. In this paper, we propose WiRa that emulates LoRa waveforms with IEEE 802.11ax to achieve an efficient CTC from WiFi to LoRa. By taking advantage of the OFDMA in 802.11ax, WiRa can use only a small Resource Unit (RU) to emulate LoRa chirps and set other RUs free for highrate WiFi users. WiRa carefully selects the RU to avoid emulation failures and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable
|confname= IoTJ 2022
subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix errors, based on our finding that different CP modes have different and even opposite impacts on the emulation of a specific LoRa symbol. We implement a prototype of WiRa on the USRP platform and commodity LoRa device. The extensive experiments demonstrate WiRa can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the symbol error rate (SER) lower than 0.1.
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9490646
|confname= INFOCOM 2022
|title=Optimizing Power Allocation in LoRaWAN IoT Applications
|link=https://www.jianguoyun.com/p/DQi5a8sQ_LXjBxj127EE
|speaker=Luwei
|title=WiRa: Enabling Cross-Technology Communication from WiFi to LoRa with IEEE 802.11ax
|speaker=Kaiwen
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Real-time on-device object detection for video analytics fails to meet the accuracy requirement due to limited resources of mobile devices while offloading object detection inference to edges is time-consuming due to the transference of video data over edge networks. Based on the system with both ondevice object tracking and edge-assisted analysis, we formulate a non linear time-coupled program over time, maximizing the overall accuracy of object detection by deciding the frequency of edge-assisted inference, under the consideration of both dynamic edge networks and the constrained detection latency. We then design a learning-based online algorithm to adjust the threshold for triggering edge-assisted inference on the fly in terms of the object tracking results, which essentially controls the deviation of on-device tracking between two consecutive frames in the video, by only taking previously observable inputs. We rigorously prove that our approach only incurs sub-linear dynamic regret for the optimality objective. At last, we implement our proposed online schema, and extensive testbed results with real-world traces confirm the empirical superiority over alternative algorithms, in terms of up to 36% improvement on detection accuracy with ensured detection latency.
|abstract = Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while offloading small object detection to the edge. The key challenge is to reduce the latency of small object detection. Accordingly, we develop EdgeDuet, the first edge-device collaborative framework for enhancing small object detection with tile-level parallelism. It optimizes the offloaded detection pipeline in tiles rather than the entire frame for high accuracy and low latency. Evaluations on drone vision datasets under LTE, WiFi 2.4GHz, WiFi 5GHz show that EdgeDuet outperforms local object detection in small object detection accuracy by 233.0%. It also improves the detection accuracy by 44.7% and latency by 34.2% over the state-of-the-art offloading schemes.
|confname= INFOCOM 2021
|confname= INFOCOM 2021
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9488741
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9488843
|title=Edge-assisted Online On-device Object Detection for Real-time Video Analytics
|title=EdgeDuet: Tiling Small Object Detection for Edge Assisted Autonomous Mobile Vision
|speaker=Silence
|speaker=Xianyang
}}
}}


=== History ===
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 21:23, 9 March 2022

Time: 2022-3-11 10:20
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM 2022] WiRa: Enabling Cross-Technology Communication from WiFi to LoRa with IEEE 802.11ax, Kaiwen
    Abstract: Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. Recent work proposes CTC from IEEE 802.11b to LoRa but has a low efficiency due to their extremely asymmetric data rates. In this paper, we propose WiRa that emulates LoRa waveforms with IEEE 802.11ax to achieve an efficient CTC from WiFi to LoRa. By taking advantage of the OFDMA in 802.11ax, WiRa can use only a small Resource Unit (RU) to emulate LoRa chirps and set other RUs free for highrate WiFi users. WiRa carefully selects the RU to avoid emulation failures and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable

subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix errors, based on our finding that different CP modes have different and even opposite impacts on the emulation of a specific LoRa symbol. We implement a prototype of WiRa on the USRP platform and commodity LoRa device. The extensive experiments demonstrate WiRa can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the symbol error rate (SER) lower than 0.1.

  1. [INFOCOM 2021] EdgeDuet: Tiling Small Object Detection for Edge Assisted Autonomous Mobile Vision, Xianyang
    Abstract: Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while offloading small object detection to the edge. The key challenge is to reduce the latency of small object detection. Accordingly, we develop EdgeDuet, the first edge-device collaborative framework for enhancing small object detection with tile-level parallelism. It optimizes the offloaded detection pipeline in tiles rather than the entire frame for high accuracy and low latency. Evaluations on drone vision datasets under LTE, WiFi 2.4GHz, WiFi 5GHz show that EdgeDuet outperforms local object detection in small object detection accuracy by 233.0%. It also improves the detection accuracy by 44.7% and latency by 34.2% over the state-of-the-art offloading schemes.

History

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Instructions

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