Standard-based IoT platforms interworking: implementation, experiences, and lessons learned

Kim, Jae-Ho; Yun, Jaeseok; Choi, Sung-Chan; Seed, Dale; Lu, Guang; Bauer, Martin; Al-Hezmi, Adel; Campowsky, Konrad; Song, JaeSeung

doi:10.1109/mcom.2016.7514163

Cited by 87 publications

(35 citation statements)

References 8 publications

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“…From (18) and (21), we can see that if β 1 ≈ β 2 (which is the case that the average duration of Stage 2 in CRA-2 is 2 The Lambert function is the inverse function of f (x) = xe x , i.e., x = W(y), where y = xe x . equal to that in CRA-1 orD ≈ N ), η 2 might be larger than 2) is positive and increases.…”

Section: Analysis Of Cra-1 With Packet Lossmentioning

confidence: 99%

On Throughput of Compressive Random Access for One Short Message Delivery in IoT

Choi

2020

IEEE Internet Things J.

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In this paper, we study compressive random access (CRA) with two stages for machine-type communication (MTC) in cellular Internet-of-Things (IoT). In particular, we consider the case that each user (IoT device or sensor) has only one short message (of the same length) when it is activated to send data in IoT applications. Two different CRA-based random access schemes are discussed (one is conventional and the other is new based on a simplified handshaking process). Based on the throughput analysis, we show that the CRA-based random access scheme with simplified handshaking process can outperform as its length of payload is adaptively decided depending on the number of active users. Simulation results confirm that the derived throughput expressions agree with them and can be used to design a random access system for MTC with each active device or sensor that has one short message.

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Section: Analysis Of Cra-1 With Packet Lossmentioning

confidence: 99%

On Throughput of Compressive Random Access for One Short Message Delivery in IoT

Choi

2020

IEEE Internet Things J.

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“…where a k,t = |h k,t | 2 and σ 2 2 We also assume that h k,t is independent in each round t. It can be shown that…”

Section: A Sequential Pollingmentioning

confidence: 99%

“…The Internet of Things (IoT) has been an important issue as it has a number of applications in various areas including smart cities and factories in the future [1] [2]. To build IoT systems, layered approaches are usually considered, where the bottom layer is responsible for collecting and processing information or data from devices or sensors [3].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Data-Aided Sensing With Multichannel Random Access and Model Selection

Choi

2020

IEEE Internet Things J.

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In this paper, we study data-aided sensing (DAS) for a system consisting of a base station (BS) and a number of nodes, where the BS becomes a receiver that collects measurements or data sets from the nodes that are distributed over a cell. DAS is an iterative data collection scheme that allows the BS to efficiently estimate a target signal (i.e., all nodes' measurements) with a small number of measurements (compared to random polling). In DAS, a set of nodes are selected in each round based on the data sets that are already available at the BS from previous rounds for efficient data collection. We consider DAS for measurements that are correlated Gaussian in this paper. The resulting DAS is referred to as Gaussian DAS. Using the mean squared error (MSE) criterion, in each round, the BS is able to choose a node that has a data set to minimize the MSE of the next round. Furthermore, we generalize Gaussian DAS in two different ways: i) with multiple parallel channels to upload measurements from nodes using random access; ii) with a model selection, where a multi-armed bandit problem formulation is used to combine the model selection with DAS.

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“…Internet of Things (IoT) is envisioned as a means to connect billions of small computing devices embedded in different environments (e.g., walls and soil) and even implanted in human bodies [2]- [5]. Aiming to provide connectivity opportunities for massive numbers of devices, two possible networking approaches have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Clustered LoRa Networks

Qin

Liu

et al. 2019

IEEE Trans. Veh. Technol.

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In this paper, we investigate the uplink transmission performance of low-power wide-area (LPWA) networks with regards to coexisting radio modules. We adopt long range (LoRa) radio technique as an example of the network of focus even though our analysis can be easily extended to other situations. We exploit a new topology to model the network, where the node locations of LoRa follow a Poisson cluster process (PCP) while other coexisting radio modules follow a Poisson point process (PPP). Unlike most of the performance analysis based on stochastic geometry, we take noise into consideration. More specifically, two models, with a fixed and a random number of active LoRa nodes in each cluster, respectively, are considered. To obtain insights, both the exact and simple approximated expressions for coverage probability are derived. Based on them, area spectral efficiency and energy efficiency are obtained. From our analysis, we show how the performance of LPWA networks can be enhanced through adjusting the density of LoRa nodes around each LoRa receiver. Moreover, the simulation results unveil that the optimal number of active LoRa nodes in each cluster exists to maximize the area spectral efficiency.

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Standard-based IoT platforms interworking: implementation, experiences, and lessons learned

Cited by 87 publications

References 8 publications

On Throughput of Compressive Random Access for One Short Message Delivery in IoT

On Throughput of Compressive Random Access for One Short Message Delivery in IoT

Gaussian Data-Aided Sensing With Multichannel Random Access and Model Selection

Performance Analysis of Clustered LoRa Networks

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