Comparative Study of ZigBee and 6LoWPAN Protocols: Review

Ismaili, Imane Alaoui; Azyat, Abdelilah; Raissouni, Naoufal; Achhab, Nizar Ben; Chahboun, Asaad; Lahraoua, Mohammed

doi:10.4108/eai.24-4-2019.2284215

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…In contrast, 6LoWPAN, due to its lower power requirements, represents an ideal choice for low-power con-sumption (IP) devices such as controllers and sensors. WSN nodes have comparable functionality to existing protocols but have operational performance and power headroom limitations [35]. The charging infrastructure in certain areas can pose difficulties due to the different nature of the WSNs, which are designed to operate under many conditions [36].…”

Section: Enhances User Interaction and Effectiveness Of Iot Applicationsmentioning

confidence: 99%

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Ghadi,

Mazhar,

Shloul

et al. 2024

IEEE Access

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Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. This article focuses on machine learning algorithms used to solve the security issues of wireless sensor networks. This article also focuses on different types of attacks on layers of the wireless sensor network. Moreover, this study addresses several unsolved issues, including adapting machine learning algorithms to accommodate the sensors' functionalities in this network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.

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Section: Enhances User Interaction and Effectiveness Of Iot Applicationsmentioning

confidence: 99%

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Ghadi,

Mazhar,

Shloul

et al. 2024

IEEE Access

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“…WSN consists of a limited number of sensor nodes (motes) that are managed by a multi-layered protocol organization. The IEEE 802.15.4 protocol is the most widely used communication system for WSNs due to its low power consumption, cost effectiveness, and the ability to defy physical and connecting layers for wireless short-range transmissions [36], [39]. It operates on 800/900 MHz and 2.4 GHz ISM frequency bands and is the foundation for other standards such as ZigBee, Wireless Hart, WIA-PA, and ISA.100.11a [36].…”

Section: Rpl Backgroundmentioning

confidence: 99%

“…As illustrated in Figure 1 (a), IETF places RPL at the network layer [40]. The success of RPL as an IoT standard is also witnessed by companies part of ZigBee Alliance [39], [41]. These companies utilize industry-standard protocols, including IPv6, 6LoWPAN, RPL, and TCP/UDP/IP, to deliver end-toend IPv6 packets without the requirement of intermediary gateways.…”

Section: Rpl Backgroundmentioning

confidence: 99%

Replay Attacks in RPL-Based Internet of Things: Survey and Empirical Comparative Study

Albinali,

Azzedin

2023

Preprint

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RPL was developed as a routing protocol in low-power and lossy network contexts to connect many applications using IP-based communication. However, RPL has been subjected to several attacks, including replay attacks. Replay attacks pose significant challenges as any node can initiate the attack by replaying control messages. These messages play a vital role in establishing and sustaining network topology. However, studies that discourse replay attacks are severely limited. To address this issue, we conducted a comprehensive study on replay attack forms and their impact on RPL networks. Our study includes the latest security countermeasures. According to the literature, most RPL replay attacks rely on replaying DIO messages leading to neighbor, DIO suppression, or copycat attacks. DAO messages are also utilized to launch a replay attack. Nevertheless, the literature lacks any study that analyzes DAO replay attacks. To the best of our knowledge, this is the first study that evaluates DAO replay attacks and includes the route table falsification attack.We conduct a comparative study of these attacks and empirically investigate their impact on networks through extensive evaluation experiments. Our findings verify the harm of replay attacks in terms of packet delivery and latency. The average local delivery ratio dropped to less than 60% under DIO suppression and copycat attacks, and the communication latency increased by 50% under neighbor attacks. These results confirm the threat of replay attacks on RPL networks and the need to design countermeasures to mitigate them.

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“…El número de nodos que soporta una red de sensores inalámbricas depende de varios factores, como el estándar y el protocolo utilizado [136], la cobertura de la antena del nodo, la densidad del área monitorizada, la topología y el tipo de aplicación de la red [137]. Por ejemplo, el estándar Zigbee/802.15.4 puede soportar alrededor de 65,000 nodos en una red mientras que el protocolo 6LoWPAN solo soporta 100 nodos [138]; para la monitorización de la actividad de un volcán se pueden utilizar 10 nodos [139], sin embargo, el despliegue de sensores para aplicaciones militares necesitan más de 100 nodos [140].…”

Section: Capítulo 4 Simulaciónunclassified

Diseño e implementación de un modelo individual para la simulación de la propagación de malware en redes de sensores inalámbricas

GUERRA¹

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Una red de sensores inalámbrica (WSN, wireless sensor network) está formada por un conjunto de microsensores que se encuentran espacialmente distribuidos en posiciones no determinadas, con una infraestructura de comunicaciones inalámbricas que ha sido diseñada para monitorizar, en la región donde ha sido desplegada, algún fenómeno físico de interés, en áreas como defensa, vigilancia, salud, o medio ambiente; es decir, una WSN reúne datos de variables como temperatura, humedad, sonido, vibración, presión o contaminantes. La información recogida por cada sensor es enviada desde la puerta de enlace hacia un servidor principal, llamado estación base, que es el que se encarga de procesarla. La tecnología de las WSN es uno de los fundamentos del Internet de las Cosas (IoT) y la Industria 4.0 [1].Usualmente, las WSN proporcionan soluciones de rastreo y monitorización a gran escala y bajo coste, debido al bajo consumo de energía de los sensores [2,3]. Sin embargo, estos dispositivos tienen un radio de alcance corto y un área de cobertura pequeña, por lo que es necesario un gran número de sensores [4].Las WSN pueden gestionar información sensible y operar en entornos hostiles y sin vigilancia; por ello, es necesario considerar medidas de seguridad sólidas en el diseño de la red, que impidan que pueda verse afectada por algún tipo de software malicioso. Sin embargo, algunas características de estas redes como las limitaciones computacionales de los sensores, el espacio limitado para el almacenamiento de datos, la fuente de energía que utilizan y el canal de comunicación poco fiable, son obstáculos importantes para la aplicación de técnicas de ciberseguridad [5]. Estas restricciones son la razón por la que el estudio de la propagación de malware en una WSN ha generado un interés creciente en los últimos años. Además, los dispositivos inteligentes, como los teléfonos móviles de última generación, desempeñan un papel importante en la gestión de las WSN, por lo que se han desarrollado nuevas técnicas para la detección de software malicioso [6].

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Comparative Study of ZigBee and 6LoWPAN Protocols: Review

Cited by 8 publications

References 7 publications

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Replay Attacks in RPL-Based Internet of Things: Survey and Empirical Comparative Study

Diseño e implementación de un modelo individual para la simulación de la propagación de malware en redes de sensores inalámbricas

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