An Adaptive MAC Protocol for SDCS System Based on LoRa Technology

Deng, Tian; Zhu, Jiang; Nie, Zhiqiang

doi:10.2991/amcce-17.2017.146

Cited by 12 publications

(13 citation statements)

References 3 publications

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“…Thus, a fixed-allocation multiple access scheme (e.g., TDMA) can no longer satisfy transmission demands. Therefore, some novel protocols suitable for satellite IoT are proposed recently, including contention-free direct access protocols [15] and contended direct access protocols [16], [17]. Although these algorithms can achieve a higher using efficiency of bandwidth compared with TDMAbased schemes or slotted ALOHA schemes, the energy constraint of the satellite is seldom considered in these algorithms.…”

Section: B Link Layermentioning

confidence: 99%

A Deep Reinforcement Learning Based Approach for Energy-Efficient Channel Allocation in Satellite Internet of Things

2020

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Recently, Satellite Internet of Things (SIoT), a space network that consists of numerous Low Earth Orbit (LEO) satellites, is regarded as a promising technique since it is the only solution to provide 100% global coverage for the whole earth, without any additional terrestrial infrastructure supports. However, compared with Geostationary Earth Orbit (GEO) satellites, the LEO satellites always move very fast to cover an area within only 5-12 minutes per pass, bringing high dynamics to the network access. Furthermore, to reduce the cost, the power and spectrum channel resources of each LEO satellite are very limited, i.e., less than 10% of GEO. Therefore, to take fully advantage of the limited resource, it is very challenging to have an efficient resource allocation scheme for SIoT. Current resource allocation schemes for satellites are mostly designed for GEO, and these schemes do not consider many LEO specific concerns, including the constrained energy, the mobility characteristic, the dynamics of connections and transmissions etc. Towards this end, we proposed DeepCA, a novel reinforcement learning based approach for energy-efficient channel allocation in SIoT. In DeepCA, we firstly introduce a new sliding block scheme to facilitate the modeling of dynamic feature of the LEO satellite, and formulate the dynamic channel allocation problem in SIoT as a Markov decision process (MDP). We then propose a deep reinforcement learning algorithm for optimal channel allocation. To accelerate the learning process of DeepCA, we utilize the image form to represent the requests of users to reduce the input size, and carefully divide an action into multiple mini-actions to reduce the size of the action set. Extensive simulations show that our proposed DeepCA approach can save at least 67.86% energy consumption compared with traditional algorithms. INDEX TERMS Energy efficient, channel allocation, artificial intelligence, reinforcement learning, Internet of Things.

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Section: B Link Layermentioning

confidence: 99%

A Deep Reinforcement Learning Based Approach for Energy-Efficient Channel Allocation in Satellite Internet of Things

2020

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“…The analysis shows that the frequency drift in DtS-IoT narrowband increases the probability of collision, drastically limiting MAC performance beyond a network load of 0.2. Other MAC protocol for sensor data collection satellite systems using LoRa technology over the satellite link is proposed in [20]. The authors propose to condense both the LoRa gateway and the network server on the satellite.…”

Section: Link Layermentioning

confidence: 99%

Direct-To-Satellite IoT - A Survey of the State of the Art and Future Research Perspectives

Fraire

Céspedes

Accettura

2019

Lecture Notes in Computer Science

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The Internet of Things (IoT) has drawn an enormous attention into the scientific community thanks to unimaginable before applications newly available in everyday life. The technological landscape behind the implied surge of automated interactions among humans and machines has been shaped by plugging into the Internet very low power devices that can perform monitoring and actuation operations through very cheap circuitry. The most challenging IoT scenarios include deployments of low power devices dispersed over wide geographical areas. In such scenarios, satellites will play a key role in bridging the gap towards a pervasive IoT able to easily handle disaster recovery scenarios (earthquakes, tsunamis, and flash floods, etc.), where the presence of a resilient backhauling communications infrastructure is crucial. In these scenarios, Direct-to-Satellite IoT (DtS-IoT) connectivity is preferred as no intermediate ground gateway is required, facilitating and speeding up the deployment of wide coverage IoT infrastructure. In this work, an in-depth yet thorough survey on the state-of-the-art of DtS-IoT is presented. The available physical layer techniques specifically designed for the IoT satellite link are described, and the suitability of both the current Medium Access Control protocol and the upper layer protocols to communicate over space links will be argued. We also discuss the design of the overall satellite LEO constellation and topology to be considered in DtS-IoT networks.

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“…Adaptive Duty Cycle Medium Access Protocol (ADC MAC) is a MAC protocol that uses LoRa as the physical layer [26]. This asynchronous protocol enables the nodes to maintain three indicators: node load, network congestion rate, and residual energy.…”

Section: Adc Mac Protocolmentioning

confidence: 99%

LoRaWAN: State of the Art, Challenges, Protocols and Research Issues

Rahman

Ahmad

2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Low Power Wide Area Network (LPWAN) gaining much attention in the industry as well as in academia due to its ability to communicate over the long-range. Due to this reason, LPWAN is considered one of the favorable technologies for the Internet of Things (IoT). Long Range Wide Area Network (LoRaWAN) is one of the promising technologies of the LPWAN that provide communication with low power, low cost, long-range, and low data rate. This paper covers the state of the art of LoRaWAN technology. Further, this paper discusses the design goal of Long Range (LoRa) technology. Medium Access Control (MAC) layer recent advancements are also part of the paper. Finally, the future research challenges of Physical and MAC layer implementation of LoRaWAN are also highlighted that help researchers and industrialist to implement the concept of LoRaWAN in IoT.

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An Adaptive MAC Protocol for SDCS System Based on LoRa Technology

Cited by 12 publications

References 3 publications

A Deep Reinforcement Learning Based Approach for Energy-Efficient Channel Allocation in Satellite Internet of Things

A Deep Reinforcement Learning Based Approach for Energy-Efficient Channel Allocation in Satellite Internet of Things

Direct-To-Satellite IoT - A Survey of the State of the Art and Future Research Perspectives

LoRaWAN: State of the Art, Challenges, Protocols and Research Issues

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