Industrial internet of things over IEEE 802.15.4 TSCH networks: design and challenges

Mohamadi, Mohamed; Djamaa, Badis; Senouci, Mustapha Réda

doi:10.1504/ijitst.2020.104575

Cited by 4 publications

(1 citation statement)

References 44 publications

(46 reference statements)

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“…Figure 2 depicts the five-layer architecture of the IoT network and the protocols that operate on each layer. The physical layer provides functionalities, such as transferring data, detecting channel energy, and indicating link quality [11], whereas the MAC layer associates network clients with the access points, acknowledges frame arrival, and validates frames.…”

Section: Iot Overviewmentioning

confidence: 99%

Application Layer-Based Denial-of-Service Attacks Detection against IoT-CoAP

et al. 2023

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Internet of Things (IoT) is a massive network based on tiny devices connected internally and to the internet. Each connected device is uniquely identified in this network through a dedicated IP address and can share the information with other devices. In contrast to its alternatives, IoT consumes less power and resources; however, this makes its devices more vulnerable to different types of attacks as they cannot execute heavy security protocols. Moreover, traditionally used heavy protocols for web-based communication, such as the Hyper Text Transport Protocol (HTTP) are quite costly to be executed on IoT devices, and thus specially designed lightweight protocols, such as the Constrained Application Protocol (CoAP) are employed for this purpose. However, while the CoAP remains widely-used, it is also susceptible to attacks, such as the Distributed Denial-of-Service (DDoS) attack, which aims to overwhelm the resources of the target and make them unavailable to legitimate users. While protocols, such as the Datagram Transport Layer Security (DTLS) and Lightweight and the Secure Protocol for Wireless Sensor Network (LSPWSN) can help in securing CoAP against DDoS attacks, they also have their limitations. DTLS is not designed for constrained devices and is considered as a heavy protocol. LSPWSN, on the other hand, operates on the network layer, in contrast to CoAP which operates on the application layer. This paper presents a machine learning model, using the CIDAD dataset (created on 11 July 2022), that can detect the DDoS attacks against CoAP with an accuracy of 98%.

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Section: Iot Overviewmentioning

confidence: 99%

Application Layer-Based Denial-of-Service Attacks Detection against IoT-CoAP

et al. 2023

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Comparison of Contention-Based vs Timeslotted Channel Hopping Medium Access in Wireless Sensor Networks under Noisy Environment

Shudrenko

Kuladinithi

Timm‐Giel

2022

2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)

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Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach

2021

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In IEEE 802.15.4 standard for low-power lowrange wireless communications, only one channel is employed for transmission which can result in increased energy consumption, high network delay and poor packet delivery ratio (PDR). In the subsequent IEEE 802.15.4-2015 standard, a Time-slotted Channel Hopping (TSCH) mechanism has been developed which allows for a periodic yet fixed frequency hopping pattern over 16 different channels.Unfortunately, however, most of these channels are susceptible to high-power coexisting Wi-Fi signal interference and to possibly some other ISM-band transmissions. This interference manifests itself in the form of the presence/absence of other devices with either or both static and dynamic channel selection policies. In order to isolate channels with undesirable conditions, blacklisting mechanisms are defined to adapt the channel hopping process. However, the existing solutions which form blacklists unrealistically assume that the statistical model of the external interference remains fixed, and do not vary over time. In this paper, we realistically assume that the impact of external interferes on 802.15.4 may generally follow a non-stationary pattern, and accordingly formulate the adaptive channel hopping problem as a Dynamic Multi-Armed Bernoulli Bandit (Dynamic MABB) process from the machine learning theory. We then propose an online learning algorithm with track-ability properties for computing an adaptive hopping policy. Simulations confirm that when the statistics of the external interference has a switching regime, the proposed solution outperforms the previous schemes in terms of both energy efficiency as well as two important KPIs for TSCH-based networks, i.e., PDR and latency.

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Industrial internet of things over IEEE 802.15.4 TSCH networks: design and challenges

Cited by 4 publications

References 44 publications

Application Layer-Based Denial-of-Service Attacks Detection against IoT-CoAP

Application Layer-Based Denial-of-Service Attacks Detection against IoT-CoAP

Comparison of Contention-Based vs Timeslotted Channel Hopping Medium Access in Wireless Sensor Networks under Noisy Environment

Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach

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