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Special Issue "Advancements in CAD Techniques for IoT: Modeling, Optimization, Surrogate-Assisted Methods"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 September 2019.

Special Issue Editor

Guest Editor
Dr. Adrian Bekasiewicz

Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
Website | E-Mail
Interests: surrogate-assisted design; circuit miniaturization; compact antennas; multi-objective optimization, computer-aided design; surrogate modeling; automated design of RF circuits

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a part of the ongoing technological revolution oriented towards the seamless gathering and processing of data by ubiquitous interconnected electronic devices. The reliability of IoT-based services depends on the availability of cheap radio-frequency (RF) components characterized not only by high performance, but also small dimensions and a low power consumption. The development of computer-aided design (CAD) techniques, associated with the boost of computational power over the past two decades, significantly affected the design paradigms of such components. The availability of advanced tools integrated with accurate electromagnetic (EM) solvers has led to the replacement of theory-based design methods by more versatile simulation-driven approaches. The latter stimulate the development of modern RF components that exceed the capabilities of conventional structures. However, simulation-driven design heavily relies on numerical optimization. As a consequence, its computational cost—associated with a large number of EM evaluations required to find the desired solution—is often prohibitive when complex multi-parameter structures are considered. In this context, the availability of reliable methods for the rapid design of state-of-the-art RF components for IoT applications is an important problem that remains open.

Challenges related to the design of RF structures can be addressed using advanced modeling techniques, surrogate-assisted methods, as well specialized single- and multi-objective optimization algorithms. Despite being useful for providing high-quality solutions within limited computational budgets, these tools have not received broader attention in the design of IoT components. Their introduction is considered to be of great practical importance for lowering development costs and shortening the time-to-market design cycles of IoT-based services.

The objective of this Special Issue is to report innovative methodologies for the design of IoT components that reach beyond the frontiers of the current state of the art. Review articles focused on introducing the concepts of rapid simulation-driven design are also anticipated. Topics of interest cover the design, modeling, and optimization of IoT circuits and devices, including but not limited to:

  • computer-aided design and techniques for IoT;
  • microwave circuits for IoT;
  • antenna structures for IoT;
  • on-body IoT devices;
  • energy harvesting circuits for IoT;
  • miniaturization of IoT devices and circuits;
  • modeling of IoT devices and circuits;
  • inverse design problems for IoT circuits and devices;
  • parallel computing for IoT design;
  • surrogate-assisted methods for low-cost IoT design;
  • optimization techniques for IoT;
  • multi-objective design of IoT components;
  • combination of analytical and numerical modeling of IoT;
  • measurement techniques for IoT;
  • effects of wearable IoT devices on human body;
  • failure identification in IoT systems;
  • evolution of IoT components topologies.

Dr. Adrian Bekasiewicz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-objective design
  • surrogate-assisted design
  • passive microwave circuits
  • antennas
  • internet of things
  • energy harvesting
  • circuit miniaturization
  • computer-aided design
  • optimization algorithms
  • automated design of RF circuits

Published Papers (3 papers)

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Research

Open AccessArticle
A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design
Sensors 2019, 19(14), 3065; https://doi.org/10.3390/s19143065
Received: 12 May 2019 / Revised: 24 June 2019 / Accepted: 3 July 2019 / Published: 11 July 2019
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Abstract
In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The [...] Read more.
In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications. Full article
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Open AccessArticle
Multi-Fidelity Local Surrogate Model for Computationally Efficient Microwave Component Design Optimization
Sensors 2019, 19(13), 3023; https://doi.org/10.3390/s19133023
Received: 12 May 2019 / Revised: 25 June 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
PDF Full-text (3170 KB) | HTML Full-text | XML Full-text
Abstract
In order to minimize the number of evaluations of high-fidelity (“fine”) model in the optimization process, to increase the optimization speed, and to improve optimal solution accuracy, a robust and computational-efficient multi-fidelity local surrogate-model optimization method is proposed. Based on the principle of [...] Read more.
In order to minimize the number of evaluations of high-fidelity (“fine”) model in the optimization process, to increase the optimization speed, and to improve optimal solution accuracy, a robust and computational-efficient multi-fidelity local surrogate-model optimization method is proposed. Based on the principle of response surface approximation, the proposed method exploits the multi-fidelity coarse models and polynomial interpolation to construct a series of local surrogate models. In the optimization process, local region modeling and optimization are performed iteratively. A judgment factor is introduced to provide information for local region size update. The last local surrogate model is refined by space mapping techniques to obtain the optimal design with high accuracy. The operation and efficiency of the approach are demonstrated through design of a bandpass filter and a compact ultra-wide-band (UWB) multiple-in multiple-out (MIMO) antenna. The response of the optimized design of the fine model meet the design specification. The proposed method not only has better convergence compared to an existing local surrogate method, but also reduces the computational cost substantially. Full article
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Open AccessArticle
Variable-Fidelity Simulation Models and Sparse Gradient Updates for Cost-Efficient Optimization of Compact Antenna Input Characteristics
Sensors 2019, 19(8), 1806; https://doi.org/10.3390/s19081806
Received: 19 March 2019 / Revised: 9 April 2019 / Accepted: 12 April 2019 / Published: 15 April 2019
PDF Full-text (895 KB) | HTML Full-text | XML Full-text
Abstract
Design of antennas for the Internet of Things (IoT) applications requires taking into account several performance figures, both electrical (e.g., impedance matching) and field (gain, radiation pattern), but also physical constraints, primarily concerning size limitation. Fulfillment of stringent specifications necessitates the development of [...] Read more.
Design of antennas for the Internet of Things (IoT) applications requires taking into account several performance figures, both electrical (e.g., impedance matching) and field (gain, radiation pattern), but also physical constraints, primarily concerning size limitation. Fulfillment of stringent specifications necessitates the development of topologically complex structures described by a large number of geometry parameters that need tuning. Conventional optimization procedures are typically too expensive when the antenna is evaluated using high-fidelity electromagnetic (EM) analysis, otherwise required to ensure accuracy. This paper proposes a novel surrogate-assisted optimization algorithm for computationally efficient design optimization of antenna structures. In the paper, the optimization of antenna input characteristic is presented, specifically, minimization of the antenna reflection coefficient in a given bandwidth. Our methodology involves variable-fidelity EM simulations as well as a dedicated procedure to reduce the cost of estimating the antenna response gradients. The latter is based on monitoring the variations of the antenna response sensitivities along the optimization path. The procedure suppresses the finite-differentiation-based sensitivity updates for variables that exhibit stable gradient behavior. The proposed algorithm is validated using three compact wideband antennas and demonstrated to outperform both the conventional trust region algorithm and the pattern search procedure, as well as surrogate-based procedures while retaining acceptable design quality. Full article
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