Evidence map›Paper›PMID 41755310›Full record

ArticleSensors (Basel, Switzerland)2026

EC-RPLIE: An Innovative Protocol for RPL in IIoT Networks.

Mario A Bonilla Brito, Daladier Jabba Molinares

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mario A Bonilla BritoSystem and Computer Engineering Department, Universidad del Norte, Barranquilla 080001, Colombia.
Daladier Jabba MolinaresSystem and Computer Engineering Department, Universidad del Norte, Barranquilla 080001, Colombia.ORCID 0000-0001-5876-2559

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of Wireless Sensor Networks (WSNs) in Industrial Internet of Things (IIoT) applications presents significant challenges in terms of energy efficiency and network reliability, especially in dynamic industrial environments. The Routing Protocol for Low-Power and High-Loss Networks for Indoor Environments (RPLIE), while designed for low-power lossy networks (LLNs), lacks mechanisms to adequately balance energy consumption, a critical requirement for industrial sustainability. This research introduces an enhancement called Energy-Conscious Routing Protocol for Industrial Environments (EC-RPLIE), which incorporates the Expected Breakage Cost (EBC) metric to optimize energy distribution and network stability by managing medium-term jitter. Through extensive simulations in the Cooja environment, the performance of EC-RPLIE was evaluated against the state-of-the-art RPLIE across topologies of 11, 21, and 31 nodes. Quantitative results demonstrate that EC-RPLIE significantly reduces unnecessary retransmissions by maintaining a superior Packet Delivery Ratio (PDR) and optimizing parent selection. The protocol achieved energy savings of 9.6% in 11-node networks, which increased to 36.8% in high-density 31-node scenarios, effectively doubling the network persistence compared to RPLIE. Additionally, EC-RPLIE improved average latency by 12.68% in dense configurations, confirming its robustness in handling industrial traffic. These findings confirm that EC-RPLIE is particularly effective in high-density networks, where the EBC metric successfully mitigates the 'retransmission storms' typical of standard protocols. This proposal provides a robust framework for enhancing the sustainability and resilience of WSNs in Industry 4.0, offering a scalable solution that addresses the energy-reliability trade-off. The results lay the groundwork for future large-scale implementations in real-world industrial environments.

Indexed as

EBCECIIoTIoTRPL

Identifiers

PMID41755310
PMCPMC12943966

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.