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{{SemNote
{{SemNote
|time='''2023-02-06 9:30'''
|time='''2024-12-06 10:30-12:00'''
|addr=4th Research Building A527-B
|addr=4th Research Building A518
|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]].
}}
}}


===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 = Packet routing in virtual networks requires virtual-to-physical address translation. The address mappings are updated by a single party, i.e., the network administrator, but they are read by multiple devices across the network when routing tenant packets. Existing approaches face an inherent read-write performance tradeoff: they either store these mappings in dedicated gateways for fast updates at the cost of slower forwarding or replicate them at end-hosts and suffer from slow updates.SwitchV2P aims to escape this tradeoff by leveraging the network switches to transparently cache the address mappings while learning them from the traffic. SwitchV2P brings the mappings closer to the sender, thus reducing the first packet latency and translation overheads, while simultaneously enabling fast mapping updates, all without changing existing routing policies and deployed gateways. The topology-aware data-plane caching protocol allows the switches to transparently adapt to changing network conditions and varying in-switch memory capacity.Our evaluation shows the benefits of in-network address mapping, including an up to 7.8× and 4.3× reduction in FCT and first packet latency respectively, and a substantial reduction in translation gateway load. Additionally, SwitchV2P achieves up to a 1.9× reduction in bandwidth overheads and requires order-of-magnitude fewer gateways for equivalent performance.
|confname=TMC2022
|confname =SIGCOMM'24
|link=https://ieeexplore.ieee.org/document/9410408/
|link = https://dl.acm.org/doi/abs/10.1145/3651890.3672213
|title=FLORA: Fuzzy Based Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSNs
|title= In-Network Address Caching for Virtual Networks
|speaker=Luwei}}
|speaker=Dongting
{{Latest_seminar
|date=2024-12-06
|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.
}}{{Latest_seminar
|confname=MobiCom 2022
|abstract = Visible light communication (VLC) has become an important complementary means to electromagnetic communications due to its freedom from interference. However, existing Internet-of-Things (IoT) VLC links can reach only <10 meters, which has significantly limited the applications of VLC to the vast and diverse scenarios. In this paper, we propose ChirpVLC, a novel modulation method to prolong VLC distance from ≤10 meters to over 100 meters. The basic idea of ChirpVLC is to trade throughput for prolonged distance by exploiting Chirp Spread Spectrum (CSS) modulation. Specifically, 1) we modulate the luminous intensity as a sinusoidal waveform with a linearly varying frequency and design different spreading factors (SF) for different environmental conditions. 2) We design range adaptation scheme for luminance sensing range to help receivers achieve better signal-to-noise ratio (SNR). 3) ChirpVLC supports many-to-one and non-line-of-sight communications, breaking through the limitations of visible light communication. We implement ChirpVLC and conduct extensive real-world experiments. The results show that ChirpVLC can extend the transmission distance of 5W COTS LEDs to over 100 meters, and the distance/energy utility is increased by 532% compared to the existing work.
|link=https://dl.acm.org/doi/abs/10.1145/3495243.3560551
|confname = IDEA
|title=Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI
|link = https://uestc.feishu.cn/file/Pbq3bWgKJoTQObx79f3cf6gungb
|speaker=Crong}}
|title= ChirpVLC：Extending The Distance of Low-cost Visible Light Communication with CSS Modulation
{{Latest_seminar
|speaker=Mengyu
|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.
|date=2024-12-06
|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}}
 
 
=== History ===


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 11:28, 6 December 2024

Time: 2024-12-06 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [SIGCOMM'24] In-Network Address Caching for Virtual Networks, Dongting
    Abstract: Packet routing in virtual networks requires virtual-to-physical address translation. The address mappings are updated by a single party, i.e., the network administrator, but they are read by multiple devices across the network when routing tenant packets. Existing approaches face an inherent read-write performance tradeoff: they either store these mappings in dedicated gateways for fast updates at the cost of slower forwarding or replicate them at end-hosts and suffer from slow updates.SwitchV2P aims to escape this tradeoff by leveraging the network switches to transparently cache the address mappings while learning them from the traffic. SwitchV2P brings the mappings closer to the sender, thus reducing the first packet latency and translation overheads, while simultaneously enabling fast mapping updates, all without changing existing routing policies and deployed gateways. The topology-aware data-plane caching protocol allows the switches to transparently adapt to changing network conditions and varying in-switch memory capacity.Our evaluation shows the benefits of in-network address mapping, including an up to 7.8× and 4.3× reduction in FCT and first packet latency respectively, and a substantial reduction in translation gateway load. Additionally, SwitchV2P achieves up to a 1.9× reduction in bandwidth overheads and requires order-of-magnitude fewer gateways for equivalent performance.
  2. [IDEA] ChirpVLC：Extending The Distance of Low-cost Visible Light Communication with CSS Modulation, Mengyu
    Abstract: Visible light communication (VLC) has become an important complementary means to electromagnetic communications due to its freedom from interference. However, existing Internet-of-Things (IoT) VLC links can reach only <10 meters, which has significantly limited the applications of VLC to the vast and diverse scenarios. In this paper, we propose ChirpVLC, a novel modulation method to prolong VLC distance from ≤10 meters to over 100 meters. The basic idea of ChirpVLC is to trade throughput for prolonged distance by exploiting Chirp Spread Spectrum (CSS) modulation. Specifically, 1) we modulate the luminous intensity as a sinusoidal waveform with a linearly varying frequency and design different spreading factors (SF) for different environmental conditions. 2) We design range adaptation scheme for luminance sensing range to help receivers achieve better signal-to-noise ratio (SNR). 3) ChirpVLC supports many-to-one and non-line-of-sight communications, breaking through the limitations of visible light communication. We implement ChirpVLC and conduct extensive real-world experiments. The results show that ChirpVLC can extend the transmission distance of 5W COTS LEDs to over 100 meters, and the distance/energy utility is increased by 532% compared to the existing work.

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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