Optimal Parameters of Nonstatistical Sensor Data Aggregation Minimizing Latency in IoT Gateway

Yoshino, Hideaki; Ota, Kaoru; Hiraguri, Takefumi

doi:10.1109/globecom38437.2019.9014120

Cited by 2 publications

(10 citation statements)

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“…That is, the modeling for both aggregation schemes was not uniform, and it was difficult to compare these schemes with each other under common conditions. This work is an extension of our previous studies [18], [20] as we propose a unified model for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the LST and average of the stationary distribution of the latency, and approximate the average latency.…”

Section: Introductionmentioning

confidence: 87%

“…By contrast, as we also reviewed in the previous studies [17], [18], nonstatistical aggregation has been modeled and analyzed in detail using queueing theory [40]- [44]. Hong et al [40] analyzed a model with a limit on the maximum number of packets that can be aggregated in a single frame.…”

Section: Data Aggregationmentioning

confidence: 99%

“…In the aforementioned related studies, however, analyses were limited to steady state conditions, and the optimal aggregation parameters that minimize latency were not derived. In addition to the above survey, readers can find related studies on latency analysis of data aggregation, including the relationship with the age of information (AoI) in [17] and the batch service queue in [18].…”

Section: Data Aggregationmentioning

confidence: 99%

“…Furthermore, we clarified the existence of the optimal aggregation interval and number, which minimize the latency, and derived simple and accurate estimation formulae for these parameters. In addition, we analyzed the nonstatistical CNA (nCNA) scheme in [18] and derived the LST of the latency distribution, an approximation of the average latency, and an estimation formula that enables us to determine the optimal aggregation number and an optimal latency that minimizes the average latency. By applying the above estimation formulae for the statistical and nCNA models, we proposed an adaptive aggregation number control when the arrival rate fluctuates over time [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous analysis of the statistical aggregation scheme [17], the transmission time of aggregated data was assumed to have an exponential distribution. However, in the analysis of the nonstatistical aggregation scheme [18], it is assumed that the transmission time is a unit distribution comprising a fixed-length header and the number of aggregations deployed by the fixed unit data length. That is, the modeling for both aggregation schemes was not uniform, and it was difficult to compare these schemes with each other under common conditions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Traffic Reduction Technologies and Data Aggregation Control to Minimize Latency in IoT Systems

Yoshino

Ota

Hiraguri

2021

IEICE Trans. Commun.

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The spread of the Internet of Things (IoT) has led to the generation of large amounts of data, requiring massive communication, computing, and storage resources. Cloud computing plays an important role in realizing most IoT applications classified as massive machine type communication and cyber-physical control applications in vertical domains. To handle the increasing amount of IoT data, it is important to reduce the traffic concentrated in the cloud by distributing the computing and storage resources to the network edge side and to suppress the latency of the IoT applications.In this paper, we first present a recent literature review on fog/edge computing and data aggregation as representative traffic reduction technologies for efficiently utilizing communication, computing, and storage resources in IoT systems, and then focus on data aggregation control minimizing the latency in an IoT gateway. We then present a unified modeling for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the Laplace-Stieltjes transform and average of the stationary distribution of the latency and approximate the average latency; we subsequently apply it to an adaptive aggregation number control for the time-variant data arrival. The transient traffic characteristics, that is, the absorption of traffic fluctuations realizing a stable optimal latency, were clarified through a simulation with a time-variant Poisson input and non-Poisson inputs, such as a Beta input, which is a typical IoT traffic model.

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Section: Introductionmentioning

confidence: 87%