D2D-enabled Failure-tolerance in Cellular IoT

Malarski, Krzysztof Mateusz; Ballal, Kalpit Dilip; Ruepp, Sarah

doi:10.1109/nof52522.2021.9609924

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Finally, in terms of standardization, the 3rd Generation Partnership Project (3GPP) started to address D2D, or Proximity Service (ProSe), since Release 12 with relaying functionality being added in later releases. In [36] application of D2D to NB-IoT and LTE-M was further studied, however, it was not developed into a standard [37]. Nonetheless, non-3GPP radio access technologies are one of the main enablers of D2D.…”

Section: F Practical Aspectsmentioning

confidence: 99%

D2D Assisted Q-Learning Random Access for NOMA-Based MTC Networks

et al. 2022

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Machine-type communications (MTC) should account for half the connections to the internet by 2030. The use case massive MTC (mMTC) allows for applications to connect a massive number of low-power and low-complexity devices, leading to challenges in resource allocation. Not only that, mMTC networks suffer under rigid random access schemes due to mMTC ultra-dense nature resulting in poor performance. In this sense, this paper proposes a Q-Learning-based random access method for massive machine-type communications, with device clustering and non-orthogonal multiple access (NOMA). The traditional NOMA implementation increases spectral efficiency, but at the same time, demands a larger Q-Table, thus slowing down convergence, which is known to be a highly detrimental effect on massive networks. We use pre-clustering through short-range device-to-device technology to mitigate this drawback, allowing devices to operate with a smaller Q-Table . Furthermore, the previous selection of partner devices allows us to implement a full-feedback-based reward mechanism so that clusters avoid time slots already successfully allocated. Additionally, to cope with the negative impact of system overload, we propose an adaptive frame size algorithm to run in the base station (BS). It allows adjusting the frame size to the network load, preventing idle slots in an underloaded scenario, and providing extra slots when the network is overloaded. The results show the great benefits in terms of throughput of the proposed method. In addition, the impact of the use of clustering and the size of the clusters, as well as the frame size adaptation, are analyzed.

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Section: F Practical Aspectsmentioning

confidence: 99%

D2D Assisted Q-Learning Random Access for NOMA-Based MTC Networks

et al. 2022

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“…Small devices tend to fail, inducing different faults, sometimes rendering the entire IoT system non-operational and producing unpredictable behaviour. The faults that are generally induced in the IoT system include Cascading faults [14,15], Pattern faults [16], and device-to-device communication faults [17]. Various faults occurring within the devices have been reviewed by Norris et al, [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…It will be dangerous when the fault tolerance of a mission-critical system becomes zero. K. M. Malarski et al [17] presented a D2D-enabled Fault-tolerance in Cellular IoT. They have yet to consider the issue of the remoteness of the second base station and also based their discussion on a single relay system which generally cannot support a huge number of cluster heads.…”

Section: Introductionmentioning

confidence: 99%

Implementing Dual Base Stations within an IoT Network for Sustaining the Fault Tolerance of an IoT Network through an Efficient Path Finding Algorithm

Sastry

Budaraju

2023

Sensors

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The IoT networks for implementing mission-critical applications need a layer to effect remote communication between the cluster heads and the microcontrollers. Remote communication is affected through base stations using cellular technologies. Using a single base station in this layer is risky as the fault tolerance level of the network will be zero when the base stations break down. Generally, the cluster heads are within the base station spectrum, making seamless integration possible. Implementing a dual base station to cater for a breakdown of the first base station creates huge remoteness as the cluster heads are not within the spectrum of the second base station. Furthermore, using the remote base station involves huge latency affecting the performance of the IoT network. In this paper, a relay-based network is presented with intelligence to fetch the shortest path for communicating to reduce latency and sustain the fault tolerance capability of the IoT network. The results demonstrate that the technique improved the fault tolerance of the IoT network by 14.23%.

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Networking Microcontrollers and Balancing the Load on the Service Servers to Enhance the Fault Tolerance of the IoT Networks

Jammalamadaka,

Chokra,

Jammalamadaka

et al. 2024

Mathematics

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Different types of faults occur in different layers of IoT networks, which affect their fault tolerance. The Controller and Services layers are the weakest links in an IoT network, and any failure in these layers drastically reduces its fault tolerance. The flow of transactions in these layers is also very heavy, requiring load balancing. The major challenge is to retain the maximum Fault tolerance of the IoT network in the presence of failures and varying load conditions in these Layers. This paper proposes a combination of Embedded system networking and a specialised networking of microcontrollers with Service Servers, and a Middleware within the Microcontrollers for load-balancing transactions in different communication paths. These implementations help retain the highest fault tolerance of the IoT network. The fault tolerance of an IoT is improved by 8% due to these proposed implementations.

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D2D-enabled Failure-tolerance in Cellular IoT

Cited by 3 publications

References 9 publications

D2D Assisted Q-Learning Random Access for NOMA-Based MTC Networks

D2D Assisted Q-Learning Random Access for NOMA-Based MTC Networks

Implementing Dual Base Stations within an IoT Network for Sustaining the Fault Tolerance of an IoT Network through an Efficient Path Finding Algorithm

Networking Microcontrollers and Balancing the Load on the Service Servers to Enhance the Fault Tolerance of the IoT Networks

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