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Register a new userA Lesson in Scaling 6LoWPAN -- Minimal Fragment Forwarding in Lossy Networks
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Added by
Martine Sophie Lenders
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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This paper evaluates two forwarding strategies for fragmented datagrams in the IoT: hop-wise reassembly and a minimal approach to directly forward fragments. Minimal fragment forwarding is challenged by the lack of forwarding information at subsequent fragments in 6LoWPAN and thus requires additional data at nodes. We compared the two approaches in extensive experiments evaluating reliability, end-to-end latency, and memory consumption. In contrast to previous work and due to our alternate setup, we obtained different results and conclusions. Our findings indicate that direct fragment forwarding should be deployed only with care, since higher packet transmission rates on the link-layer can significantly reduce its reliability, which in turn can even further reduce end-to-end latency because of highly increased link-layer retransmissions.
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LoRaWAN is one of the leading Low Power Wide Area Network (LPWAN) architectures. It was originally designed for systems consisting of static sensor or Internet of Things (IoT) devices and static gateways. It was recently updated to introduce new features such as nano-second timestamps which open up applications to enable LoRaWAN to be adopted for mobile device tracking and localisation. In such mobile scenarios, devices could temporarily lose communication with the gateways because of interference from obstacles or deep fading, causing throughput reduction and delays in data transmission. To overcome this problem, we propose a new data forwarding scheme. Instead of holding the data until the next contact with gateways, devices can forward their data to nearby devices that have a higher probability of being in contact with gateways. We propose a new network metric called Real-Time Contact-Aware Expected Transmission Count (RCA-ETX) to model this contact probability in real-time. Without making any assumption on mobility models, this metric exploits data transmission delays to model complex device mobility. We also extend RCA-ETX with a throughput-optimal stochastic backpressure routing scheme and propose Real-Time Opportunistic Backpressure Collection (ROBC), a protocol to counter the stochastic behaviours resulting from the dynamics associated with mobility. To apply our approaches seamlessly to LoRaWAN-enabled devices, we further propose two new LaRaWAN classes, namely Modified Class-C and Queue-based Class-A. Both of them are compatible with LoRaWAN Class-A devices. Our data-driven experiments, based on the London bus network, show that our approaches can reduce data transmission delays up to $25%$ and provide a $53%$ throughput improvement in data transfer performance.
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