Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-02-06 9:30'''
|time='''2023-02-13 9:30'''
|addr=4th Research Building A527-B
|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 = Many opportunistic routing (OR) schemes treat network nodes equally, neglecting the fact that the nodes close to the sink undertake more duties than the rest of the network nodes. Therefore, the nodes located at different positions should play different roles during the routing process. Moreover, considering various Quality-of-Service (QoS) requirements, the routing decision in OR is affected by multiple network attributes. The majority of these OR schemes fail to contemplate multiple network attributes while making routing decisions. To address the aforesaid issues, this paper presents a novel protocol that runs in three steps. First, each node defines a Routing Zone (RZ) to route packets toward the sink. Second, the nodes within RZ are prioritized based on the competency value obtained through a novel model that employs Modified Analytic Hierarchy Process (MAHP) and Fuzzy Logic techniques. Finally, one of the forwarders is selected as the final relay node after forwarders coordination. Through extensive experimental simulations, it is confirmed that FLORA achieves better performance compared to its counterparts in terms of energy consumption, overhead packets, waiting times, packet delivery ratio, and network lifetime.
|abstract = This paper presents the design and implementation of HyLink which aims to fill the gap between limited link capacity of LoRa and the diverse bandwidth requirements of IoT systems. At the heart of HyLink is a novel technique named parallel Chirp Spread Spectrum modulation, which tunes the number of modulated symbols to adapt bitrates according to channel conditions. Over strong link connections, HyLink fully exploits the link capability to transmit more symbols and thus transforms good channel SNRs to high link throughput. While for weak links, it conservatively modulates one symbol and concentrates all transmit power onto the symbol to combat poor channels, which can achieve the same performance as legacy LoRa. HyLink addresses a series of technical challenges on encoding and decoding of multiple payloads in a single packet, aiming at amortizing communication overheads in terms of channel access, radio-on power, transmission air-time, etc. We perform extensive experiments to evaluate the effectiveness of HyLink. Evaluations show that HyLink produces up to 10× higher bit rates than LoRa when channel SNRs are higher than 5 dB. HyLink inter-operates with legacy LoRa devices and can support new emerging traffic-intensive IoT applications.
|confname=TMC2022
|confname=Sensys2022
|link=https://ieeexplore.ieee.org/document/9410408/
|link=https://www4.comp.polyu.edu.hk/~csyqzheng/papers/HyLink-SenSys22.pdf
|title=FLORA: Fuzzy Based Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSNs
|title=HyLink: Towards High Throughput LPWANs with LoRa Compatible Communication
|speaker=Luwei}}
|speaker=Mengyu}}
{{Latest_seminar
{{Latest_seminar
|abstract = With the wide adoption of AI applications, there is a pressing need of enabling real-time neural network (NN) inference on small embedded devices, but deploying NNs and achieving high performance of NN inference on these small devices is challenging due to their extremely weak capabilities. Although NN partitioning and offloading can contribute to such deployment, they are incapable of minimizing the local costs at embedded devices. Instead, we suggest to address this challenge via agile NN offloading, which migrates the required computations in NN offloading from online inference to offline learning. In this paper, we present AgileNN, a new NN offloading technique that achieves real-time NN inference on weak embedded devices by leveraging eXplainable AI techniques, so as to explicitly enforce feature sparsity during the training phase and minimize the online computation and communication costs. Experiment results show that AgileNN's inference latency is >6X lower than the existing schemes, ensuring that sensory data on embedded devices can be timely consumed. It also reduces the local device's resource consumption by >8X, without impairing the inference accuracy.
|abstract = Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker’s moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers’ historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers’ and tasks’ spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.
|confname=MobiCom 2022
|confname=TMC 2023
|link=https://dl.acm.org/doi/abs/10.1145/3495243.3560551
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9451627
|title=Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI
|title=Multi-Task Allocation in Mobile Crowd SensingWith Mobility Prediction
|speaker=Crong}}
|speaker=Zhenguo}}
{{Latest_seminar
 
|abstract = Interoperability among a vast number of heterogeneous IoT nodes is a key issue. However, the communication among IoT nodes does not fully interoperate to date. The underlying reason is the lack of a lightweight and unified network architecture for IoT nodes having different radio technologies. In this paper, we design and implement TinyNet, a lightweight, modular, and unified network architecture for representative low-power radio technologies including 802.15.4, BLE, and LoRa. The modular architecture of TinyNet allows us to simplify the creation of new protocols by selecting specific modules in TinyNet. We implement TinyNet on realistic IoT nodes including TI CC2650 and Heltec IoT LoRa nodes. We perform extensive evaluations. Results show that TinyNet (1) allows interoperability at or above the network layer; (2) allows code reuse for multi-protocol co-existence and simplifies new protocols design by module composition; (3) has a small code size and memory footprint.
|confname=MobiSys 2022
|link=https://dl.acm.org/doi/abs/10.1145/3498361.3538919
|title=TinyNET: a lightweight, modular, and unified network architecture for the internet of things
|speaker=Xinyu}}




Revision as of 21:14, 12 February 2023

Time: 2023-02-13 9:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [Sensys2022] HyLink: Towards High Throughput LPWANs with LoRa Compatible Communication, Mengyu
    Abstract: This paper presents the design and implementation of HyLink which aims to fill the gap between limited link capacity of LoRa and the diverse bandwidth requirements of IoT systems. At the heart of HyLink is a novel technique named parallel Chirp Spread Spectrum modulation, which tunes the number of modulated symbols to adapt bitrates according to channel conditions. Over strong link connections, HyLink fully exploits the link capability to transmit more symbols and thus transforms good channel SNRs to high link throughput. While for weak links, it conservatively modulates one symbol and concentrates all transmit power onto the symbol to combat poor channels, which can achieve the same performance as legacy LoRa. HyLink addresses a series of technical challenges on encoding and decoding of multiple payloads in a single packet, aiming at amortizing communication overheads in terms of channel access, radio-on power, transmission air-time, etc. We perform extensive experiments to evaluate the effectiveness of HyLink. Evaluations show that HyLink produces up to 10× higher bit rates than LoRa when channel SNRs are higher than 5 dB. HyLink inter-operates with legacy LoRa devices and can support new emerging traffic-intensive IoT applications.
  2. [TMC 2023] Multi-Task Allocation in Mobile Crowd SensingWith Mobility Prediction, Zhenguo
    Abstract: Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker’s moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers’ historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers’ and tasks’ spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.


History

History

2024

2023

2022

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2020

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

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