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arxiv: 2510.22522 · v1 · submitted 2025-10-26 · 💻 cs.CY · cs.ET

Barriers to Integrating Low-Power IoT in Engineering Education: A Survey of the Literature

V. Sanchez Padilla , Albert Espinal , Jose Cordova-Garcia , Lisa Schibelius This is my paper

Pith reviewed 2026-05-18 04:47 UTC · model grok-4.3

classification 💻 cs.CY cs.ET
keywords low-power IoTengineering educationbarriers to adoptionliterature reviewtechnical barriersorganizational barrierscurricular barriersIoT in labs
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The pith

A literature review organizes barriers to low-power IoT in engineering education into technical, organizational, and curricular groups.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines recent studies on why low-power Internet of Things technologies have been slow to enter engineering courses and labs. It sorts the reported obstacles into three categories that cover hardware and system challenges, institutional and resource issues, and teaching and curriculum design problems. A reader would care because the review supplies concrete examples from each category that can guide practical planning for course updates. The authors position the three-group structure as a tool for educators and leaders who want to move from awareness of barriers to workable adoption steps.

Core claim

The paper claims that barriers to integrating low-power IoT technologies in engineering education can be organized into three groups—technical barriers such as energy management, scalability, and integration issues; organizational barriers such as cost, planning, and the need for trained staff; and curricular and pedagogical barriers such as gaps in student readiness, limited lab time, and budget-dependent platform choices—drawn from a review of recent studies, with the aim of helping educators develop more effective adoption strategies.

What carries the argument

The three-group categorization of barriers (technical, organizational, curricular/pedagogical) that collects and structures findings from the reviewed literature into distinct sets with practical examples for each.

If this is right

  • Educators can target specific technical fixes like energy management solutions when designing IoT lab experiments.
  • Academic leaders can prioritize staff training and budget planning to reduce organizational obstacles before rollout.
  • Curriculum committees can adjust course pacing and prerequisites to address student readiness and lab-time constraints.
  • Program planners can compare platform options against budget limits when choosing hardware for student projects.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same three-category lens could be applied to other emerging classroom technologies such as small-scale robotics or sensor networks.
  • Over time, falling hardware costs may shift the relative weight of organizational barriers compared with technical ones.
  • Departments that systematically address the identified barriers could see faster student transition from theory to applied IoT projects.
  • Periodic re-surveys could test whether the same three groups still dominate as low-power IoT platforms mature.

Load-bearing premise

The recent studies selected for review are representative of the full range of barriers faced by institutions and that the three-group split captures the issues without major omissions or overlaps.

What would settle it

A new survey of engineering programs that identifies major barriers, such as regulatory compliance or ethical data-handling requirements, that cannot be placed in any of the three groups would show the categorization is incomplete.

Figures

Figures reproduced from arXiv: 2510.22522 by Albert Espinal, Jose Cordova-Garcia, Lisa Schibelius, V. Sanchez Padilla.

Figure 1
Figure 1. Figure 1: Short-range IoT technologies comparison [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Long-range IoT technologies comparison [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Low-power IoT: Data Rate vs. Range hinder deployment or expansion, especially during startup. For low-power telecommunication systems, various barriers can emerge before full adoption. These often originate in academia, complicating industry’s ability to adopt solutions based on these technologies. The challenges identified in the literature can be categorized into technical, organizational, curricular, an… view at source ↗
Figure 4
Figure 4. Figure 4: Barriers classified according to themes [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original abstract

Low-power Internet of Things (IoT) technologies are becoming increasingly important in engineering education as a tool to help students connect theory to real applications. However, many institutions face barriers that slow down their adoption in courses and labs. This paper reviews recent studies to understand these barriers and organizes them into three groups: technical, organizational, and curricular/pedagogical. Technical barriers include energy management, scalability, and integration issues. Organizational barriers are related to cost, planning, and the need for trained staff. Curricular and pedagogical barriers include gaps in student readiness, limited lab time, and platform choices that depend on budget. By detailing these barriers with practical examples, this paper aims to help educators and academic leaders develop more effective strategies to adopt low-power IoT in engineering programs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. This manuscript reviews recent studies on barriers to integrating low-power IoT technologies in engineering education. It organizes the barriers into three categories: technical (energy management, scalability, and integration issues), organizational (cost, planning, and need for trained staff), and curricular/pedagogical (gaps in student readiness, limited lab time, and budget-dependent platform choices). The paper provides practical examples and aims to help educators and academic leaders develop adoption strategies.

Significance. If supported by a representative and systematically selected corpus, the three-category taxonomy could offer a useful organizing framework for institutions seeking to incorporate low-power IoT into engineering curricula, highlighting actionable challenges in technical implementation, institutional support, and teaching practices. The work has potential practical value for curriculum designers, though its impact depends on the rigor and completeness of the underlying review.

major comments (2)
  1. Abstract: the claim that recent studies can be organized into three exhaustive, non-overlapping barrier categories is presented without any description of the literature search strategy, databases, keywords, date ranges, inclusion/exclusion criteria, or total number of studies reviewed. This directly undermines the central claim, as representativeness cannot be evaluated and omitted categories (e.g., regulatory or equity-related barriers) cannot be ruled out.
  2. Introduction/Methods (inferred from structure): no section details the screening process or corpus size, leaving the three-group taxonomy as an untested assertion rather than a derived result from a verifiable set of sources.
minor comments (2)
  1. Abstract: the phrase 'recent studies' is undefined; specifying a date range (e.g., 2018–2024) would improve clarity.
  2. Consider adding a methods subsection or PRISMA-style diagram to document the review process, which would strengthen the paper without altering its scope.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. The feedback correctly identifies a lack of methodological transparency in how the literature corpus was assembled and analyzed. We will revise the manuscript to address these points directly by adding the requested details on search strategy and screening process.

read point-by-point responses

  1. Referee: Abstract: the claim that recent studies can be organized into three exhaustive, non-overlapping barrier categories is presented without any description of the literature search strategy, databases, keywords, date ranges, inclusion/exclusion criteria, or total number of studies reviewed. This directly undermines the central claim, as representativeness cannot be evaluated and omitted categories (e.g., regulatory or equity-related barriers) cannot be ruled out.

    Authors: We agree that the abstract currently provides no information on the literature search process. In the revised manuscript we will expand the abstract to include a concise statement of the search strategy and add a new Methods section that specifies the databases (IEEE Xplore, ACM Digital Library, Google Scholar), keywords, date range (primarily 2015–2024), inclusion/exclusion criteria, and the final number of studies retained. We will also add a limitations paragraph noting that the three categories reflect the dominant themes in the reviewed sources and that regulatory or equity-related barriers, while important, did not emerge as primary concerns in the selected corpus; we will not claim the taxonomy is exhaustive. revision: yes

  2. Referee: Introduction/Methods (inferred from structure): no section details the screening process or corpus size, leaving the three-group taxonomy as an untested assertion rather than a derived result from a verifiable set of sources.

    Authors: We acknowledge that the current manuscript lacks an explicit Methods section describing the screening process and corpus size. We will insert a dedicated Methods section that outlines the screening steps (title/abstract screening followed by full-text review), reports the initial hit count and final corpus size, and explains how the three categories were derived through thematic coding. This will make clear that the taxonomy is grounded in the reviewed literature rather than asserted a priori. revision: yes

Circularity Check

0 steps flagged

No circularity in external literature synthesis

full rationale

The paper is a survey that reviews external studies on barriers to low-power IoT adoption and organizes them into three categories (technical, organizational, curricular/pedagogical). No equations, fitted parameters, predictions, or self-citations appear in the provided text or abstract. The categorization is presented as a synthesis of reviewed literature rather than a derivation that reduces to the paper's own inputs by construction. Absence of search methodology details raises representativeness questions but does not create a self-definitional loop, fitted-input prediction, or load-bearing self-citation chain per the enumerated patterns. The analysis remains self-contained against external sources.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on standard assumptions of literature reviews without introducing fitted parameters or new entities; the main unstated premise is that the reviewed body of work is sufficient to identify representative barriers.

axioms (1)
  • domain assumption A selection of recent studies provides a representative view of barriers across institutions.
    The abstract relies on this to justify the three-group organization without stating search scope or completeness checks.

pith-pipeline@v0.9.0 · 5669 in / 1008 out tokens · 35347 ms · 2026-05-18T04:47:17.158631+00:00 · methodology

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Reference graph

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