Random Access Protocols for Industrial Internet of Things: Enablers, Challenges, and Research Directions

Vilgelm, Mikhail; Gürsu, H. Murat; Kellerer, Wolfgang

doi:10.1007/978-3-030-51473-0_4

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…In contrast to scheduling approaches, IoT devices via RA algorithms can transmit their data packets without having to wait for a predetermined time slot, eliminating the signalling overhead required to register devices and schedule packets. In particular, by efficiently managing the transmission of short packets, RA algorithms can help reduce the overall latency of the IoT network, improving the user experience for applications such as real-time monitoring and control [2]. The main challenge for RA algorithms is to avoid collisions that occur when multiple devices try to transmit their data simultaneously, and the major families of RA algorithms differ in the way how the collisions are handled.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Tree Algorithm with Interference Cancellation in Uplink and Downlink

Vogel,

Deshpande,

Stefanovicl

et al. 2023

2023 57th Asilomar Conference on Signals, Systems, and Computers

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In this paper, we propose Advanced Tree-algorithm with Interference Cancellation (ATIC), a variant of binary tree-algorithm with successive interference cancellation (SICTA) introduced by Yu and Giannakis. ATIC assumes that Interference Cancellation (IC) can be performed both by the access point (AP), as in SICTA, but also by the users. Specifically, after every collision slot, the AP broadcasts the observed collision as feedback. Users who participated in the collision then attempt to perform IC by subtracting their transmissions from the collision signal. This way, the users can resolve collisions of degree 2 and, using a simple distributed arbitration algorithm based on user IDs, ensure that the next slot will contain just a single transmission. We show that ATIC reaches the asymptotic throughput of 0.924 as the number of initially collided users tends to infinity and reduces the number of collisions and packet delay. We also compare ATIC with other tree algorithms and indicate the extra feedback resources it requires.

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Section: Introductionmentioning

confidence: 99%

An Advanced Tree Algorithm with Interference Cancellation in Uplink and Downlink

Vogel,

Deshpande,

Stefanovicl

et al. 2023

2023 57th Asilomar Conference on Signals, Systems, and Computers

Self Cite

View full text Add to dashboard Cite

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“…They rely on knowing or estimating the active number of users in the network [6], [9]- [11]. This value is difficult for the users to evaluate in a decentralized setup [12]. One such scheme [10], relies on Poisson distributed packet arrivals at the users to estimate the number of active users in the network in order to optimize an AoI threshold.…”

Section: Introductionmentioning

confidence: 99%

Improving AoI via Learning-based Distributed MAC in Wireless Networks

Deshpande¹,

Ayan²,

Kellerer³

2022

Preprint

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In this work, we consider a remote monitoring scenario in which multiple sensors share a wireless channel to deliver their status updates to a process monitor via an access point (AP). Moreover, we consider that the sensors randomly arrive and depart from the network as they become active and inactive. The goal of the sensors is to devise a medium access strategy to collectively minimize the long-term mean network Age of Information (AoI) of their respective processes at the remote monitor. For this purpose, we propose specific modifications to ALOHA-QT algorithm, a distributed medium access algorithm that employs a Policy Tree (PT) and Reinforcement Learning (RL) to achieve high throughput. We provide the upper bound on the mean network AoI for the proposed algorithm along with pointers for selecting its key parameter. The results reveal that the proposed algorithm reduces mean network AoI by more than 50 percent for state of the art stationary randomized policies while successfully adjusting to a changing number of active users in the network. The algorithm needs less memory and computation than ALOHA-QT while performing better in terms of AoI.

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“…The promise of 5G and beyond networks is to support massive numbers of sensors and machine-type devices, along with sporadic low-latency communications, without affecting human communication traffic [2]. However, there are still many open problems in coordinating medium access for sporadically active sensors [3], particularly in remote deployments with very limited resources. In these cases, the lack of significant signaling requirements makes random access schemes, such as slotted ALOHA, the best choice for IIoT [4], but the drawbacks of these schemes are well-known, particularly for scenarios with a large number of nodes [5], in which collisions can become frequent.…”

Section: Introductionmentioning

confidence: 99%

Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message

Haque¹,

Chandak²,

Chiariotti³

et al. 2022

Preprint

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The rapid development of Industrial Internet of Things (IIoT) technologies has not only enabled new applications, but also presented new challenges for reliable communication with limited resources. In this work, we define a deceptively simple novel problem that can arise in these scenarios, in which a set of sensors need to communicate a joint observation. This observation is shared by a random subset of the nodes, which need to propagate it to the rest of the network, but coordination is complex: as signaling constraints require the use of random access schemes over shared channels, each sensor needs to implicitly coordinate with others with the same observation, so that at least one of the transmissions gets through without collisions. Unlike the existing medium access control schemes, the goal here is not to maximize total goodput, but rather to make sure that the shared message gets through, regardless of the sender. The lack of any signaling, aside from an acknowledgment or lack thereof from the rest of the network, makes determining the optimal collective transmission strategy a significant challenge. We analyze this coordination problem theoretically, prove its hardness, and provide low-complexity solutions. While a lowcomplexity clustering-based approach is shown to provide nearoptimal performance in certain special cases, for the general scenarios, we model each sensor as a multi-armed bandit (MAB), and provide a learning-based solution. Numerical results show the effectiveness of this approach in a variety of cases.

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Random Access Protocols for Industrial Internet of Things: Enablers, Challenges, and Research Directions

Cited by 5 publications

References 38 publications

An Advanced Tree Algorithm with Interference Cancellation in Uplink and Downlink

An Advanced Tree Algorithm with Interference Cancellation in Uplink and Downlink

Improving AoI via Learning-based Distributed MAC in Wireless Networks

Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message

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