Data-Aided Sensing for Gaussian Process Regression in IoT Systems

Choi, Jinho

doi:10.1109/jiot.2020.3040676

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…These constituting JSAC entities not only generate heterogeneous sensory data but also correlated data. It is also an important research subject how to analyze and communicate the sensory data distributed in the network to obtain a targeted feature and desirable information in various applications, e.g., regression [114]. For data without any privacy issue, the sensory data can be collected at a base-station or a relay for the centralized analysis and learning through efficient communication protocols.…”

Section: E Distributed and Collaborative Jsacmentioning

confidence: 99%

Toward Joint Radar, Communication, Computation, Localization, and Sensing in IoT

Kim

Choi

2022

IEEE Access

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Since the 1960s, joint sensing and communication (JSAC) has been proposed as an attractive technique with advantages of enhanced spectral and hardware efficiency along with low latency. However, in those old days, complex transceiver designs hindered the massive adoption of JSAC in many applications. Nevertheless, thanks to advancing wireless technologies in recent years, JSAC has recently attracted substantial attention for a wide range of civil and military applications. In particular, JSAC enables simultaneous sensing and communication functionalities with the full cooperation of both operations in shared resources such as hardware as well as radio resources (i.e., frequency, time, space and so on). Note that sensing functionality is associated with other sub-functionalities of radio-detectionand-ranging (Radar), computation, and localization which are sporadically used terms in the literature. Thus, to generalize the concept and avoid any confusion, we use the term "sensing" in the acronym JSAC as a consistent and general term associated with the other aforementioned sub-functionalities. This paper elaborates on defining sensing and communication operations and then, provides an overview with preliminaries, key latest findings, and state-of-the-art of JSAC. It also explores both existing and emerging Internet of Things (IoT) applications of JSAC. Next, it provides a new classification of JSAC technologies by taking into account not only the existing JSAC technologies but also a diverse range of technologies that allow JSAC to be used for various types of IoT applications. Eventually, this study projects future research directions and challenges of enabling JSAC in IoT.

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Section: E Distributed and Collaborative Jsacmentioning

confidence: 99%

Toward Joint Radar, Communication, Computation, Localization, and Sensing in IoT

Kim

Choi

2022

IEEE Access

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“…To further compare with the multiple agent processing methods, these approaches are tested: (1) Independent GP (IGP): model trained independently for each input [ 6 ]; (2) Combined GP (CGP): one agent to train GP by combining data across inputs [ 34 ]; (3) Proposed distributed GP with BIC (Bayesian Information Criterion) criterion. The training results (interpolation and extrapolation manners) of NMSE (normalized mean square error) with regard to the number of training data points are demonstrated in Figure 7 .…”

Section: Simulationsmentioning

confidence: 99%

“…More recently, data-driven approaches are getting more and more attention in many fields, such as the control and machine learning communities [ 4 , 5 ]. Since data-driven methods can train the system model with high efficiency and precision, they have become the most popular choice for system modeling [ 6 , 7 ]. In particular, the Gaussian process (GP) is the most representative one, and it has been successfully applied to many fields.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Modeling by Distributed Gaussian Processes in Multiagent Systems

Xin

Shi

2022

Sensors

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This paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a complex environment. To address model uncertainties and noises disturbances, a distributed Gaussian process is proposed to characterize the system model by using local information exchange among neighboring agents, in which a number of agents cooperate without central coordination to estimate a common Gaussian process function based on local measurements and datum received from neighbors. In addition, both the continuous-time system model and the discrete-time system model are considered, in which we design a control Lyapunov function to learn the continuous-time model, and a distributed model predictive control-based approach is used to learn the discrete-time model. Furthermore, we apply a Kullback–Leibler average consensus fusion algorithm to fuse the local prediction results (mean and variance) of the desired Gaussian process. The performance of the proposed distributed Gaussian process is analyzed and is verified by two trajectory tracking examples.

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“…1, such a mechanism allows us to distill the data that are strictly relevant for conveying (semantic) information from sender to receiver and focus on to clearly establish the purpose of communication. For example, data-aided sensing (DAS) proposed in [8] learns from prior acts and can be a valuable technique to considerably minimize the quantity of data to be transmitted (by decreasing irrelevant data transfer and using historically acquired experience).…”

Section: Introductionmentioning

confidence: 99%

Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks

Pokhrel

Choi

2023

IEEE Trans. Veh. Technol.

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In Shannon theory, semantic aspects of communi-• Technical (Level A): How correctly can the bits/packets be transmitted from the sender to the receiver? • Semantics (Level B): How accurately do the bits/packets carry the required meaning from the sender to the receiver? • Effectiveness (Level C): How well does the meaning transmitted (conveyed) influence the intended course of action at the receiver? At level A, semantic aspects of communication are assumed to be irrelevant to the technical engineering problems. Shannon noted "Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem" [1]. This is one of the fundamental assumptions of Shannon's theory. The relaxation of this fundamental assumption for communication between a sender and a receiver can jointly consider all three levels of problem, which can be referred to

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Data-Aided Sensing for Gaussian Process Regression in IoT Systems

Cited by 10 publications

References 22 publications

Toward Joint Radar, Communication, Computation, Localization, and Sensing in IoT

Toward Joint Radar, Communication, Computation, Localization, and Sensing in IoT

Trajectory Modeling by Distributed Gaussian Processes in Multiagent Systems

Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks

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