| 1. |
- Harms, Laura, 1991, et al.
(author)
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Opportunistic Routing and Synchronous Transmissions Meet TSCH
- 2021
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In: Proceedings - Conference on Local Computer Networks, LCN. ; 2021-October, s. 107-114
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Conference paper (peer-reviewed)abstract
- Low-power wireless networking commonly uses either Time-Slotted Channel Hopping (TSCH), synchronous transmissions, or opportunistic routing. All three of these different, orthogonal approaches strive for efficient and reliable communication but follow different trajectories. With this paper, we combine these concepts into one protocol: AUTOBAHN. AUTOBAHN merges TSCH scheduling with opportunistically routed, synchronous transmissions. This opens the possibility to create long-term stable schedules overcoming local interference. We prove the stability of schedules over several days in our experimental evaluation. Moreover, AUTOBAHN outperforms the autonomous scheduler Orchestra under interference in terms of reliability by 13.9 percentage points and in terms of latency by a factor of 9 under a minor duty cycle increase of 2.1 percentage points.
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| 2. |
- Poirot, Valentin, 1994, et al.
(author)
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Dimmer: Self-adaptive network-wide flooding with reinforcement learning
- 2021
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In: Proceedings - International Conference on Distributed Computing Systems. ; 2021-July, s. 293-303
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Conference paper (peer-reviewed)abstract
- The last decade saw an emergence of Synchronous Transmissions (ST) as an effective communication paradigm in low-power wireless networks. Numerous ST protocols provide high reliability and energy efficiency in normal wireless conditions, for a large variety of traffic requirements. Recently, with the EWSN dependability competitions, the community pushed ST to harsher and highly-interfered environments, improving upon classical ST protocols through the use of custom rules, hand-tailored parameters, and additional retransmissions. The results are sophisticated protocols, that require prior expert knowledge and extensive testing, often tuned for a specific deployment and envisioned scenario. In this paper, we explore how ST protocols can benefit from self-adaptivity; a self-adaptive ST protocol selects itself its best parameters to (1) tackle external environment dynamics and (2) adapt to its topology over time. We introduce Dimmer as a self-adaptive ST protocol. Dimmer builds on LWB and uses Reinforcement Learning to tune its parameters and match the current properties of the wireless medium. By learning how to behave from an unlabeled dataset, Dimmer adapts to different interference types and patterns, and is able to tackle previously unseen interference. With Dimmer, we explore how to efficiently design AI-based systems for constrained devices, and outline the benefits and downfalls of AI-based low-power networking. We evaluate our protocol on two deployments of resource-constrained nodes achieving 95.8 % reliability against strong, unknown WiFi interference. Our results outperform baselines such as non-adaptive ST protocols (27%) and PID controllers, and show a performance close to hand-crafted and more sophisticated solutions, such as Crystal (99 %).
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| 3. |
- Profentzas, Christos, 1989, et al.
(author)
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Performance of deep neural networks on low-power IoT devices
- 2021
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In: CPS-IoTBench 2021 - Proceedings of the 2021 Benchmarking Cyber-Physical Systems and Internet of Things. - New York, NY, USA : ACM.
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Conference paper (peer-reviewed)abstract
- Advances in deep learning have revolutionized machine learning by solving complex tasks such as image, speech, and text recognition. However, training and inference of deep neural networks are resource-intensive. Recently, researchers made efforts to bring inference to IoT edge and sensor devices which have become the prime data sources nowadays. However, running deep neural networks on low-power IoT devices is challenging due to their resource-constraints in memory, compute power, and energy. This paper presents a benchmark to grasp these trade-offs by evaluating three representative deep learning frameworks: uTensor, TF-Lite-Micro, and CMSIS-NN. Our benchmark reveals significant differences and trade-offs for each framework and its tool-chain: (1) We find that uTensor is the most straightforward framework to use, followed by TF-Micro, and then CMSIS-NN. (2) Our evaluation shows large differences in energy, RAM, and Flash footprints. For example, in terms of energy, CMSIS-NN is the most efficient, followed by TF-Micro and then uTensor, each with a significant gap.
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| 4. |
- Rathje, Patrick, et al.
(author)
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Poster: Exposure notification at hand
- 2021
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In: International Conference on Embedded Wireless Systems and Networks. - 2562-2331. ; 2021
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Conference paper (peer-reviewed)abstract
- Contact tracing is a tool for controlling infectious disease outbreaks. To foster widespread adoption, established tracing protocols focus on smartphone users. As a result, user groups who cannot afford a compatible smartphone cannot carry it continuously are left out. This work introduces the Contact Tracing Wristband (CWB) and its integration into Google and Apple’s Exposure Notification protocol. The wristband’s low-cost and versatility bring tracing to additional users and thus enhance the efficacy of tracing.
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| 5. |
- Rathje, Patrick, et al.
(author)
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Poster: Trace yourself-it could be easy
- 2021
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In: International Conference on Embedded Wireless Systems and Networks. - 2562-2331. ; 2021
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Conference paper (peer-reviewed)abstract
- Contact tracing helps to predict and prevent the spread of viruses. This work proposes Tracey for decentralized, privacy-preserving tracing. Unlike automated tracing solutions that operate in the background, such as the widespread governmental Corona Tracing Apps, our system builds on manual contact exchanges to ensure reliable contact tracing even for groups and venues. The devices share secrets that allow anonymous notifications using the health authorities’ trusted database. This work illustrates the concept, provides initial security analysis, first results, and gives an outlook on possible extensions.
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