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    DisruptaBLE: opportunistic BLE networking
    (IEEE, 2022-09)
    Rathje, Patrick
    ;
    Landsiedel, Olaf
    Bluetooth Low Energy (BLE) is the prevalent IoT radio technology and perfectly suited for mobile and battery-driven applications. However, it is not designed for intermittent connectivity and opportunistic networking. Hence, the vast infrastructure that BLE-equipped devices such as smartphones, wearables, and sensors provide, remains untapped, even with its potential for data collection, sharing, or emergency communication in disaster scenarios. This paper introduces DISRUPTABLE to unfold this potential: A universal BLE-based store-and-forward architecture for delay-tolerant and opportunistic networking. Tailored to the resource constraints of IoT nodes and the feature set of BLE, DISRUPTABLE enables opportunistic interactions between BLE-equipped devices, providing a resilient network even when established communication over cellular networks or Wi-Fi fails. In our evaluation, we show that in a highly dynamic pedestrian scenario in downtown Stockholm, broadcasts reliably inform pedestrians in 7.1 seconds, while unicast messages arrive within 20 minutes in 48.1% of cases.
    Publicationtype: Conference Paper
      12
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    MicroTL: transfer learning on low-power IoT devices
    (IEEE, 2022-09)
    Profentzas, Christos
    ;
    Almgren, Magnus
    ;
    Landsiedel, Olaf
    Deep Neural Networks (DNNs) on IoT devices are becoming readily available for classification tasks using sensor data like images and audio. However, DNNs are trained using extensive computational resources such as GPUs on cloud services, and once being quantized and deployed on the IoT device remain unchanged. We argue in this paper, that this approach leads to three disadvantages. First, IoT devices are deployed in real-world scenarios where the initial problem may shift over time (e.g., to new or similar classes), but without re-training, DNNs cannot adapt to such changes. Second, IoT devices need to use energy-preserving communication with limited reliability and network bandwidth, which can delay or restrict the transmission of essential training sensor data to the cloud. Third, collecting and storing training sensor data in the cloud poses privacy concerns. A promising technique to mitigate these concerns is to utilize on-device Transfer Learning (TL). However, bringing TL to resource-constrained devices faces challenges and trade-offs in computational, energy, and memory constraints, which this paper addresses. This paper introduces MicroTL, Transfer Learning (TL) on low-power IoT devices. MicroTL tailors TL to IoT devices without the communication requirement with the cloud. Notably, we found that the MicroTL takes 3x less energy and 2.8x less time than transmitting all data to train an entirely new model in the cloud, showing that it is more efficient to retrain parts of an existing neural network on the IoT device.
    Publicationtype: Conference Paper
      16