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Analysis of methods for prioritizing critical data transmissions in agricultural vehicular networks
(2020)
Applying wireless communication technologies to agricultural vehicular networks often results in high end-to-end delays and loss of packets due to intermittent or broken connectivity. This paper analyses the methods for the successful delivery of the vehicular data within acceptable delay times. Different kinds of data that are generated and transmitted in agricultural networks are considered in this paper, followed by the data prioritization methods which allow critical data to be prioritized against other data. In this regard, Enhanced Distributed Channel Access, Differentiated Services, and application-based data rate variation are discussed in conjunction with the Simple Network Management Protocol. These techniques are simulated or tested separately and then together and the results show that even in poor network conditions, high-prioritized data is not lost or delayed.
Auf vielen Landmaschinen wird der CAN-Bus zur Übertragung von Daten zwischen Sensoren, Aktoren und Steuergeräten genutzt. Anwendungen wie Rückfahrkameras und Bird-ViewAnzeigen erfordern in der Regel zusätzliche, breitbandige Kommunikationskanäle. Dieser Beitrag untersucht, inwieweit ein gemeinschaftliches Kommunikationsmedium auf Basis von Ethernet zur Realisierung aktueller und zukünftiger Anwendungen auf Landmaschinen genutzt werden kann. Zusätzlich wird der Einsatz aktueller Technologien wie Audio/Video Bridging, Time-Sensitive Networking und Wifi auf einem Landmaschinengespann untersucht und bewertet.
Recent real-time networking developments have enabled ultra reliability, very low latency and high data rates in wired networks. Wireless networking developments have also shown that they can achieve very high data rates with consistency, but they still lack in providing ultra reliability and extremely low latency. Time Sensitive Networking (TSN) developments have brought these capabilities in Industry automation and Automotive industry too. Although TSN is standardized for wired networks for a long time, for wireless networks it will be standardized within the IEEE 802.11be standard for Wi-Fi and 3GPP Release 17 for 5G in the near future. This paper provides an overview of TSN in wired and wireless networks with the aim of comparing different simulators and presenting their offered functionality and shortcomings. These tools can be used to make oneself familiar with TSN algorithms, standards, and for the development and testing of time sensitive networks. Afterwards, the paper discusses open research questions for using TSN over wireless networks.
The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism, and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS-based synchronization model of INET so that it works over UDP/IP. With that, it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds of synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Furthermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.
This paper presents a framework for OMNeT++ which includes time synchronization model for WLANs. Synchronization is based on the Generalized Precision Time Protocol (gPTP) standard, which aims to achieve an accuracy of less than 100 nanoseconds. The presented model is developed and implemented in OMNeT++, a discrete event network simulator, using its INET library. A new type of WLAN node is modeled which supports time synchronization at the Link layer. A clock module for WLAN nodes is also modeled which implements variable clock drift to simulate noise interference in clock frequency oscillators. Simulations with our WLAN nodes are done and the results show that using gPTP based time synchronization in wireless networks, accuracy of ±3ns can be achieved.
Long Range Wide Area Network (LoRaWAN) operates in the ISM band with 868 MHz, where the Time on Air (ToA) is regulated in the EU to 1 %. LoRaWAN nodes use the Adaptive Data Rate (ADR) algorithm to adapt their data rates during operation. The standard ADR algorithm works well with stationary nodes, however is very slow in the adaptation for mobile nodes. This paper introduces a new ADR algorithm for LoRaWAN that is supported by higher level meta-data for sensor streams, namely Quality of Information (QoI). With the help of QoI it is possible to provide additional information to the new ADR algorithm, reducing the convergence time and thus improving the Packet Delivery Ratio (PDR) of data from mobile sensor nodes. The new algorithm requires only modifications on network server side and keeps backwards compatibility with LoRaWAN nodes. Results show a significant better PDR compared to the standard ADR in scenarios with a limited number of mobile nodes.