What question did this study set out to answer?

The research aims to develop a more efficient clustering algorithm for energy management in wireless sensor networks.

January 21, 2026

A Dynamic‐Weight Multi‐Objective Sloth‐Inspired Clustering Algorithm for Wireless Sensor Networks

AAArdalan H. AwllaSulaimani Polytechnic University TRTarik A. RashidUniversity of Kurdistan Hewler RARonak M. AbdullahUniversity of Sulaimani

Key Points

The research aims to develop a more efficient clustering algorithm for energy management in wireless sensor networks.
Introduced MSloth, a lightweight clustering algorithm inspired by sloths.
Dynamic weighting based on node energy, density, and distance to the base station.
Compared MSloth with MPSO, MFDO, and MOANA using simulated and real dataset scenarios.
Implemented tests with networks ranging from 100 to 1000 nodes over 1000 and 10,000 rounds.
MSloth consumed 20%–27% less energy compared to other algorithms.
Significantly improved network lifetime and maintained low delays (0.9 s synthetic, 0.65 s real).
In the 300 nodes test, MSloth saved 20.26 J out of 50 J total energy and achieved an 83.4% packet delivery rate.
Statistical analysis confirmed the significance of improvements in performance metrics.

Abstract

ABSTRACT Clustered wireless sensor networks (WSNs) comprise many small, battery‐powered nodes that collect and transmit data to a central base station. Choosing the right cluster heads (CHs) is a crucial task in WSNs, as it involves a multi‐objective problem. It requires balancing conflicting goals such as minimizing energy consumption and delay while maximizing packet delivery and network lifetime. Current multi‐objective algorithms, such as MPSO and MFDO, try to balance these objectives. Nevertheless, they are computationally expensive to run, sensitive to parameter changes, and cannot easily adapt to large or dynamic WSNs. To address these limitations, this paper proposes a lightweight multi‐objective sloth (MSloth) algorithm that introduces a novelty in addressing the research gap of unstable and energy‐inefficient CH selection in WSNs. This bio‐inspired dynamic weighting selects CHs based on node energy, density, and distance to the base station, while a sloth‐inspired refractory mechanism and dynamic weighting schedule reduce CH rotation, ensuring adaptive and energy‐efficient network performance. We compared MSloth against three other multi‐objective optimization algorithms, MPSO, MFDO, and MOANA, using both simulated networks with 100–300 nodes and real WSN datasets with samples of 1000 nodes; the numerical results over 1000 and 10,000 rounds show that MSloth used 20%–27% less energy, demonstrated better network lifetime, and maintained low delays with 0.9 s for the synthetic dataset and 0.65 s for the real dataset. In tests involving 300 nodes, MSloth saved 20.26 J of energy out of a total of 50 J, kept 190 nodes active, achieved an 83.4% packet delivery rate, and recorded only 0.55 s of delay; statistical analysis confirmed that these improvements were significant across performance metrics. These findings suggest that MSloth is an effective and lightweight protocol suitable for real‐world applications such as environmental monitoring, warning systems, and smart farming.

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