publications.bib

@article{ZORBAS2024107981,
title = {Supporting critical downlink traffic in LoRaWAN},
journal = {Computer Communications},
volume = {228},
pages = {107981},
year = {2024},
issn = {0140-3664},
doi = {https://doi.org/10.1016/j.comcom.2024.107981},
url = {https://www.sciencedirect.com/science/article/pii/S0140366424003281},
author = {Dimitrios Zorbas and Aruzhan Sabyrbek},
keywords = {Internet of Things, LoRa, LoRaWAN, Actuators, Downlink, Capacity, Fairness, Simulations},
abstract = {LoRaWAN, a low-power wide-area network (LPWAN) technology, has been successfully used in the Internet of Things (IoT) industry over the last decade. It is an easy-to-use, long-distance communication protocol combined with minimal power consumption. Supporting critical downlink traffic in LoRaWAN networks is crucial for ensuring the reliable and efficient delivery of essential data in certain actuating applications. However, challenges arise when prioritizing critical downlink traffic, including commands, alerts, and emergency notifications that demand immediate attention from actuating devices. This paper explores strategies to improve downlink traffic delivery in LoRaWAN networks, focusing on enhancing reliability, fairness, and energy efficiency through prioritization techniques and network parameter configurations in the EU868 spectrum. Theoretical as well as simulation results provide insights into the effectiveness of the available solutions for supporting critical downlink traffic in LoRaWAN networks.}
}

@INPROCEEDINGS{10646280,
  author={Baimukhanov, Batyrkhan and Gilazh, Bibarys and Zorbas, Dimitrios},
  booktitle={2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)}, 
  title={Autonomous Lightweight Scheduling in LoRa-Based Networks Using Reinforcement Learning}, 
  year={2024},
  volume={},
  number={},
  pages={268-271},
  keywords={Schedules;Mesh networks;Processor scheduling;Simulation;Reinforcement learning;Spread spectrum communication;Logic gates;Internet of Things;LoRa;Reinforcement Learning;SARSA;scheduling},
  doi={10.1109/BlackSeaCom61746.2024.10646280}
  }

@INPROCEEDINGS{10646253,
  author={Nurbay, Temirlan and Kasenov, Sultan and Yeltay, Adi and Zorbas, Dimitrios},
  booktitle={2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)}, 
  title={Cooperative Transmission of Large Files Over LoRa in Multimedia loT Networks}, 
  year={2024},
  volume={},
  number={},
  pages={153-158},
  keywords={Wireless networks;LoRa;Multimedia databases;Streaming media;Data collection;Multimedia communication;Internet of Things},
  doi={10.1109/BlackSeaCom61746.2024.10646253}
  }

@INPROCEEDINGS{10621459,
  author={Khamitov, Rakhat and Orel, Daniil and Zorbas, Dimitrios},
  booktitle={2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)}, 
  title={Predicting Solar-Harvested Energy for Resource-Constrained IoT Devices Using Machine Learning}, 
  year={2024},
  volume={},
  number={},
  pages={661-668},
  keywords={Power measurement;Prototypes;Production;Predictive models;Prediction algorithms;Boosting;Time measurement},
  doi={10.1109/DCOSS-IoT61029.2024.00103}
  }

@INPROCEEDINGS {10621547,
author = { Assylbek, Damir and Nadirkhanova, Aizhuldyz and Zorbas, Dimitrios },
booktitle = { 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) },
title = {{ Energy-efficient Clock-Synchronization in IoT Using Reinforcement Learning }},
year = {2024},
volume = {},
ISSN = {},
pages = {244-248},
abstract = { Clock synchronization in the Internet of Things (IoT) is a critical aspect of ensuring reliable and energy-efficient communications among devices within a network. In this paper, we propose an entirely autonomous and lightweight Reinforcement Learning (RL) approach to learn the periodicity of synchronized beacon transmissions between a transmitter and several receivers, while maximizing the sleep time between successive beacons to conserve energy. To do so, the proposed approach exploits a set of states, actions, and rewards so that each device adapts the radio-on time accordingly. The approach runs on each individual receiver without any prior knowledge of the network status. It is implemented and tested on off-the-shelf ESP32 IoT devices which are known to exhibit high clock drift rates. The testbed results demonstrate the ability of the approach to autonomously synchronize the receivers while achieving a similar performance in terms of packet (beacon) reception ratio but 45% better energy efficiency compared to a traditional approach followed in the literature for one-to-many type of synchronization. Apart from the improved energy consumption, the power characterization of the system shows that the RL approach requires negligible CPU resources. },
keywords = {Performance evaluation;Knowledge engineering;Radio transmitters;Receivers;Reinforcement learning;Energy efficiency;Internet of Things},
doi = {10.1109/DCOSS-IoT61029.2024.00044},
url = {https://doi.ieeecomputersociety.org/10.1109/DCOSS-IoT61029.2024.00044},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =May}

@article{EDUARD2024101062,
title = {Ad-hoc train-arrival notification system for railway safety in remote areas},
journal = {Internet of Things},
volume = {25},
pages = {101062},
year = {2024},
issn = {2542-6605},
doi = {https://doi.org/10.1016/j.iot.2024.101062},
url = {https://www.sciencedirect.com/science/article/pii/S2542660524000040},
author = {Aida Eduard and Dnislam Urazayev and Aruzhan Sabyrbek and Daniil Orel and Dimitrios Zorbas},
keywords = {Railway safety, LoRa, ESP-NOW, Internet of Things, IEEE802.11, Machine Learning},
abstract = {In the last few years, a particular interest in using wireless technologies in the industrial domain in order to automate processes and increase the level of safety has been noticed. This paper introduces an affordable mobile system to notify railway workers in rural areas about the approach of trains, and thus, to enhance their safety allowing for the early evacuation of repair sites located near the rails. The system comprises three key elements: a train device, a portable station device, and wearable devices for the workers. The communication methods and the underlying protocols between these components are discussed in detail. The system has been developed for freight trains of the national railway company of Kazakhstan and has undergone extensive testing for each of its components before its final trial. The preliminary results demonstrate that the system meets the requirements in terms of evacuation time, range, and portability, while exhibiting a very low cost of manufacturing. More specifically, the system can achieve a reliable communication range of several kilometers and a maximum response time of 2.3 s. The cost does not exceed $500 for a set of train, station, and 5 worker devices.}
}

@INPROCEEDINGS{10453118,
  author={Javed, Shahzeb and Zorbas, Dimitrios},
  booktitle={2023 IEEE Conference on Standards for Communications and Networking (CSCN)}, 
  title={A LoRaWAN Adaptive Retransmission Mechanism}, 
  year={2023},
  volume={},
  number={},
  pages={324-327},
  keywords={Energy consumption;Adaptive systems;Simulation;Logic gates;Internet of Things;Uplink;Standards},
  doi={10.1109/CSCN60443.2023.10453118}}

@INPROCEEDINGS{10424407,
  author={Zarymkanov, Temirlan and Kargar, Amin and Pinotti, Cristina M. and O’Flynn, Brendan and Zorbas, Dimitrios},
  booktitle={2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)}, 
  title={Enhancing Machine Learning Training Performance in Smart Agriculture Datasets Using a Mobile App}, 
  year={2023},
  volume={},
  number={},
  pages={455-460},
  keywords={Training;Smart agriculture;Insects;Machine learning;Production;Mobile applications;Convolutional neural networks;Machine Learning;Image Classification;Pest Detection;Model retraining;Mobile application},
  doi={10.1109/MetroAgriFor58484.2023.10424407}}

@INPROCEEDINGS{10424396,
  author={Zurek, Leonard J. and Kargar, Amin and O’Flynn, Brendan and Niederprüm, David and Wolf, Lars and Zorbas, Dimitrios},
  booktitle={2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)}, 
  title={Evaluation of Wireless Technologies for an Embedded Camera-based Pest Monitoring System}, 
  year={2023},
  volume={},
  number={},
  pages={449-454},
  keywords={Wireless communication;IEEE 802.15 Standard;Power demand;Reliability;Internet of Things;Monitoring;Testing;IoT trap;LoRa;BLE;IEEE 802.15.4;Halyomor-pha halys;Autonomous monitoring;Hardware Evaluation},
  doi={10.1109/MetroAgriFor58484.2023.10424396}}

@INPROCEEDINGS{10286942,
  author={Baimukhanov, Batyrkhan and Zorbas, Dimitrios},
  booktitle={2023 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)}, 
  title={Infrastructure-Less Long-Range Text-Messaging System}, 
  year={2023},
  volume={},
  number={},
  pages={1-4},
  keywords={Disaster management;User experience;Electronic messaging;Information and communication technology;Internet of Things;Reliability;Testing;Internet of Things;Text-messaging;LoRa;Connected Dominating Set},
  doi={10.1109/ICT-DM58371.2023.10286942}}