An Adaptive LoRaWAN MAC Protocol for Event Detection Applications

Tsakmakis, Athanasios; Valkanis, Anastasios; Beletsioti, Georgia A.; Kantelis, Konstantinos; Nicopolitidis, Petros; Papadimitriou, Georgios I.

doi:10.3390/s22093538

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…To face such challenges, random access techniques based on adaptive ALOHA are presented and discussed by different authors. These works cover techniques such as density-aware slotted ALOHA with access probability, Adaptive-opportunistic ALOHA, (FEC-ALOHA), which is ALOHA with forward error correction, and a time division multiple access (TDMA) switching with slotted ALOHA technique [45][46][47][48][49][50], where the reported throughput value of 55% is higher than the connected vehicles throughput reported in this work (50%), and lower than the autonomous and connected vehicles reported number of 70% in this work. The discussed work by the researchers did not take into account driver response time.…”

Section: Related Workmentioning

confidence: 83%

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

2023

IJIES

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The objective of this work is to mathematically model vehicular connectivity using ALOHA base function with modified logistic function as an adaptive control function. The focus of this work is to utilize the ALOHA protocol as a carrier sensing multiple access technique and add to it an intelligent part, which is the modified logistic function (MLF) to enable adaptive communication channel. The work correlates three time based parameters related to vehicle, communication, and human driver, which are associated with connected vehicles (driver response time, machine processing time, and channel communication time). The mathematical model showed that throughput for autonomous and connected vehicles is much higher than that for connected vehicles due to the elimination of human time factor, together with the evident low response time from sensors, electronics, and communication. In addition, a more stable connectivity is established if human element is not part of the model, as this eliminates both uncertainty and lengthy human response time. The ALOHA function role is to enable continuous tries by each node (vehicle) to establish communication with probability related to availability of communication channel and a back off functionality in case of collision. The proposed model provides dynamic and adaptive technique to adjust time slots using the continuous change in the vehicular connectivity environment, which affects the presented time parameters, thus changing frames delivery pattern and time slots utilization. The presented approach, supports better and more efficient use of communication channel, particularly in the case of autonomous and connected vehicles. It also, highlights through comparison the inhibitory effect driver actions can have on safe driving, through time delay, and low communication channel utilization. In addition, the work also focusses on the effect of processing time for sensors and electronics, which also have negative effect on channel usage and effectiveness, if there is a functional problem.

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Section: Related Workmentioning

confidence: 83%

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

2023

IJIES

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“…In this work, this strategy is adapted for the event response scenario. Tsakmakis et al (2022) [23] Hybrid random/scheduled access protocol…”

Section: Proposes Virtual Collision Detection Of Confirmed Transmissi...mentioning

confidence: 99%

“…Although the work in [23] focuses specifically on mixed regular and event-triggered traffic models, it does not provide details regarding the energy efficiency of the proposed method. Asteriou et al (2021) [24] Spatial Scheduling Proposes a scheduling algorithm for spatiotemporally correlated traffic.…”

Section: Proposes Virtual Collision Detection Of Confirmed Transmissi...mentioning

confidence: 99%

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LoRaWAN-Based Adaptive MACs for Event Response Applications

et al. 2022

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Low Power Wide Area Networks have emerged as a leading communications technology in the field of Internet of Things sensor and monitoring networks. In such networks, uplink traffic is characterized as a combination of periodic data reports and event-triggered alarm reports. When an many devices detect an event in a short timespan, a burst of concurrent transmissions can occur, leading to a surge of collisions, and thus severe data delivery performance degradation. In this paper, a hybrid random/scheduled access strategy is proposed for mitigating the impact of traffic-triggering events on network performance. Under periodic report traffic the LoRaWAN standard Class A protocol is in effect, but after an event a TDMA scheme is applied. Three implementations of this strategy are described. The first is a pair of novel MACs for LoRaWAN, allowing (a) synchronization of end devices with the network using the event detection as a crude synchronization point, and (b) the dynamic scheduling of groups of devices. The other two implementations build upon a single-hop and a two-hop previously proposed LoRaWAN-based wake-up architectures, respectively. The above approaches are validated and studied through extensive simulation. The results show improved packet delivery ratio over the Class A MAC. The effect is more prominent as the event propagation velocity increases. The proposed approach also surpasses LoRaWAN in energy per delivered bit for high event propagation velocities. Finally, the novel protocol has a lower hardware and deployment complexity than the wake up radio based alternatives, at the cost of higher energy consumption.

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“…The majority of previous research proposals for protocol improvements in LoRaWAN networks focus exclusively on improving network capacity for periodic sensor datacarrying traffic, to account for scalability issues that arise from the design of the network. For event-triggered traffic, two main types of approaches can be found in previously proposed solutions: either backoff-based (e.g., [5]) or TDMA-based (e.g., [6]). While the existing backoff-based approach is decentralized and focuses on managing the collision rate-latency tradeoff, it does not take into account the spatial aspect of traffic correlation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

Asteriou,

Kantelis,

Beletsioti

et al. 2024

Sensors

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Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs’ physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications.

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An Adaptive LoRaWAN MAC Protocol for Event Detection Applications

Cited by 8 publications

References 33 publications

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

LoRaWAN-Based Adaptive MACs for Event Response Applications

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

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