Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract = Object detection is a fundamental building block of video analytics applications. While Neural Networks (NNs)-based object detection models have shown excellent accuracy on benchmark datasets, they are not well positioned for high-resolution images inference on resource-constrained edge devices. Common approaches, including down-sampling inputs and scaling up neural networks, fall short of adapting to video content changes and various latency requirements. This paper presents Remix, a flexible framework for high-resolution object detection on edge devices. Remix takes as input a latency budget, and come up with an image partition and model execution plan which runs off-the-shelf neural networks on non-uniformly partitioned image blocks. As a result, it maximizes the overall detection accuracy by allocating various amount of compute power onto different areas of an image. We evaluate Remix on public dataset as well as real-world videos collected by ourselves. Experimental results show that Remix can either improve the detection accuracy by 18%-120% for a given latency budget, or achieve up to 8.1× inference speedup with accuracy on par with the state-of-the-art NNs.
|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.
|confname= MobiCom 2021
|confname= IoTJ 2022
|link=https://dl.acm.org/doi/abs/10.1145/3447993.3483274
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9490646
|title=Flexible high-resolution object detection on edge devices with tunable latency
|title=Optimizing Power Allocation in LoRaWAN IoT Applications
|speaker=Rong
|speaker=Luwei
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.
|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.
|confname= TPDS 2022
|confname= INFOCOM 2021
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9497712
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9488741
|title=Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments
|title=Edge-assisted Online On-device Object Detection for Real-time Video Analytics
|speaker=Wenjie
|speaker=Silence
}}
{{Latest_seminar
|abstract = Data collection with mobile elements can improve energy efficiency and balance load distribution in wireless sensor networks (WSNs). However, complex network environments bring about inconvenience of path design. This work addresses the network environment issue, by presenting an objective-variable tour planning (OVTP) strategy for mobile data gathering in partitioned WSNs. Unlike existing studies of connected networks, our work focuses on disjoint networks with connectivity requirement and serves delay-hash applications as well as energy-efficient scenarios respectively. We first design a converging-aware location selection mechanism, which macroscopically converges rendezvous points (RPs) to lay a foundation of a short tour. We then develop a delay-aware path formation mechanism, which constructs a short tour connecting all segments by a new convex hull algorithm and a new genetic operation. In addition, we devise an energy-aware path extension mechanism, which selects appropriate extra RPs according to specific metrics in order to reduce the energy depletion of data transmission. Extensive simulations demonstrate the effectiveness and advantages of the new strategy in terms of path length, energy depletion, and data collection ratio.
|confname= TMC 2022
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9119834
|title=Objective-Variable Tour Planning for Mobile Data Collection in Partitioned Sensor Networks
|speaker=Zhuoliu
}}
}}


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

Revision as of 20:11, 28 February 2022

Time: 2021-12-24 9:00
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [IoTJ 2022] Optimizing Power Allocation in LoRaWAN IoT Applications, Luwei
    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.
  2. [INFOCOM 2021] Edge-assisted Online On-device Object Detection for Real-time Video Analytics, Silence
    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.

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

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