Collision prediction for a low power wide area network using deep learning methods

Cui, Shengmin; Joe, Inwhee

doi:10.1109/jcn.2020.000017

Cited by 28 publications

(11 citation statements)

References 34 publications

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“…Our work is inspired by many studies that deal with the collision problems in IoT networks [17], [18], [19], [20], especially focusing on the collision problem that occurs when using IEEE 802.15.4 TSCH. Prior work on the collision problem in LLNs can be divided into two types: TSCH scheduling algorithm and IEEE 802.15.4e TSCH CSMA/CA algorithm.…”

Section: Related Workmentioning

confidence: 99%

DualBlock: Adaptive Intra-Slot CSMA/CA for TSCH

Park

Kim

et al. 2022

IEEE Access

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IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) has drawn significant attention as a low-power network solution for the Internet of Things (IoT). To make the TSCH scalable and robust, slot scheduling is an important issue that needs to be addressed. The research community has made significant strides with various autonomous or decentralized technologies in recent years. While these techniques try to provide non-overlapped slots for each link, they take collision for granted when multiple links utilize the same slot. In this paper, we challenge the perspective, investigate what happens within a TSCH slot, and find room for performance improvement even when multiple links unfortunately share a slot. To this end, we propose DualBlock that provides another chance for collision avoidance when multiple links try to utilize a slot at the same time by enabling clear channel assessment (CCA) and random backoff within the congested slot. In addition, given that the intra-slot backoff consumes more energy, a control mechanism is added that adjusts maximum backoff by monitoring network congestion level. DualBlock operates in a distributed manner for scalability. Extensive experiments demonstrate that TSCH networks achieve significant performance improvement in many aspects when DualBlock is combined with a scheduler (i.e., Orchestra), up to 3.6 times higher packet delivery ratio and 75% less radio duty cycle.INDEX TERMS IEEE 802.15.4, time slotted channel hopping (TSCH), collision mitigation

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Section: Related Workmentioning

confidence: 99%

DualBlock: Adaptive Intra-Slot CSMA/CA for TSCH

Park

Kim

et al. 2022

IEEE Access

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“…Specifically, the LoRaWAN MAC uses an ALOHA-like access control for the sake of simplicity [16]. This access protocol does not implement any form of collision detection or avoidance technique, hence uplinks from EDs are random and only limited by regional duty cycle restrictions [17]. This random nature LoRaWAN end devices uplink transmission presents a myriad of challenges with collision and poor packet delivery rate being the most important.…”

Section: Ii1 Background To Study and Related Workmentioning

confidence: 99%

Heterogeneous LoRaWan deployment for application dependent IOT networks

Agbolade

Dahunsi

Oyetunji

2022

ITEGAM

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“…This sharing of weights is important, as it allows for the generalization of unseen sequences and the sharing of statistical strength across time steps. Long short-term memory (LSTM) and gated recurrent unit (GRU), which are variants of RNN, are commonly used for handling sequential data such as language modeling [33,34] and time series prediction [35,36]. LSTM outperforms RNN and GRU for many sequence prediction tasks because of its gating systems, so we chose LSTM for this task.…”

Section: Deep Learning-based Soc Predictionmentioning

confidence: 99%

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

Hong¹,

Hwang²,

Kim³

et al. 2021

Applied Sciences

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An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

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Collision prediction for a low power wide area network using deep learning methods

Cited by 28 publications

References 34 publications

DualBlock: Adaptive Intra-Slot CSMA/CA for TSCH

DualBlock: Adaptive Intra-Slot CSMA/CA for TSCH

Heterogeneous LoRaWan deployment for application dependent IOT networks

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

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