Segmentized Clear Channel Assessment for IEEE 802.15.4 Networks

Son, Kyou Jung; Hong, Sung Hyeuck; Moon, Seong-Pil; Chang, Tae‐Gyu; Cho, Hanjin

doi:10.3390/s16060815

Cited by 16 publications

(12 citation statements)

References 12 publications

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“…To evaluate the performance of the classifiers, several metrics were used, namely probabilities of detection, false alarm, miss detection, and accuracy. Pd refers to the likelihood that the detection technique attributes signals coming from a jammer to the class of jamming signals meaning that it correctly detects that the link is under a jamming attack > = «¬-®¯° ±² ³°¬´µ ¶¯³¯·³¯ ¶ ¸³³¸·¹º »±³¸´ ¼¬-®¯° ±² ¸³³¸·¹º (23) Pmd is the percentage of attacks that the algorithm miss detected. It is given by: We conducted several experiments and examples of results are given from Fig.…”

Section: ) Neural Networkmentioning

confidence: 99%

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Arjoune

Salahdine

Islam

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

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Section: ) Neural Networkmentioning

confidence: 99%

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Arjoune

Salahdine

Islam

et al. 2020

2020 International Conference on Information Networking (ICOIN)

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“…However, it is wastage of energy if the second CCA is found busy due to data. In order to increase the chance of transmission by ignoring the channel busy condition due to end of ACK packet transmission during first CCA, authors in [ 25 ] propose one segmentized CCA. However, it has no improvements for the data transmission, if it is a data packet instead of ACK one.…”

Section: Related Workmentioning

confidence: 99%

A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks

Sahoo

Pattanaik

2017

Sensors

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IEEE 802.15.4e standard proposes Deterministic and Synchronous Multichannel Extension (DSME) mode for wireless sensor networks (WSNs) to support industrial, commercial and health care applications. In this paper, a new channel access scheme and beacon scheduling schemes are designed for the IEEE 802.15.4e enabled WSNs in star topology to reduce the network discovery time and energy consumption. In addition, a new dynamic guaranteed retransmission slot allocation scheme is designed for devices with the failure Guaranteed Time Slot (GTS) transmission to reduce the retransmission delay. To evaluate our schemes, analytical models are designed to analyze the performance of WSNs in terms of reliability, delay, throughput and energy consumption. Our schemes are validated with simulation and analytical results and are observed that simulation results well match with the analytical one. The evaluated results of our designed schemes can improve the reliability, throughput, delay, and energy consumptions significantly.

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“…The active period of the superframe consists of 16 time slots, each consisting of 20 symbol periods individually, called a unit backoff period. In 2.4-GHz operated IEEE 802.15.4, each symbol consists of 4 bits of data, with a symbol rate of 62.5 (ksymbols/s) [ 43 ].…”

Section: Tap-mac Performance Evaluationmentioning

confidence: 99%

A Fair Contention Access Scheme for Low-Priority Traffic in Wireless Body Area Networks

Henna

Sajeel

Bashir

et al. 2017

Sensors

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Recently, wireless body area networks (WBANs) have attracted significant consideration in ubiquitous healthcare. A number of medium access control (MAC) protocols, primarily derived from the superframe structure of the IEEE 802.15.4, have been proposed in literature. These MAC protocols aim to provide quality of service (QoS) by prioritizing different traffic types in WBANs. A contention access period (CAP)with high contention in priority-based MAC protocols can result in higher number of collisions and retransmissions. During CAP, traffic classes with higher priority are dominant over low-priority traffic; this has led to starvation of low-priority traffic, thus adversely affecting WBAN throughput, delay, and energy consumption. Hence, this paper proposes a traffic-adaptive priority-based superframe structure that is able to reduce contention in the CAP period, and provides a fair chance for low-priority traffic. Simulation results in ns-3 demonstrate that the proposed MAC protocol, called traffic- adaptive priority-based MAC (TAP-MAC), achieves low energy consumption, high throughput, and low latency compared to the IEEE 802.15.4 standard, and the most recent priority-based MAC protocol, called priority-based MAC protocol (PA-MAC).

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Segmentized Clear Channel Assessment for IEEE 802.15.4 Networks

Cited by 16 publications

References 12 publications

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks

A Fair Contention Access Scheme for Low-Priority Traffic in Wireless Body Area Networks

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