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Article

Socially Grounded IoT Protocol for Reliable Computer Vision in Industrial Applications

by
Gokulnath Chidambaram
1,*,
Shreyanka Subbarayappa
1,* and
Sai Baba Magapu
2
1
Department of Electronics and Communication Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru 560058, India
2
School of Natural Sciences and Engineering, National Institute of Advanced Studies (NIAS), Bengaluru 560012, India
*
Authors to whom correspondence should be addressed.
Future Internet 2026, 18(2), 69; https://doi.org/10.3390/fi18020069
Submission received: 30 December 2025 / Revised: 21 January 2026 / Accepted: 22 January 2026 / Published: 27 January 2026
(This article belongs to the Special Issue Social Internet of Things (SIoT))

Abstract

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency.
Keywords: Social Internet of Things (SIoT); trust management; trust-aware service selection; industrial computer vision; edge computing; heterogeneous IoT; performance-aware trust; graph-based trust modeling Social Internet of Things (SIoT); trust management; trust-aware service selection; industrial computer vision; edge computing; heterogeneous IoT; performance-aware trust; graph-based trust modeling
Graphical Abstract

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MDPI and ACS Style

Chidambaram, G.; Subbarayappa, S.; Magapu, S.B. Socially Grounded IoT Protocol for Reliable Computer Vision in Industrial Applications. Future Internet 2026, 18, 69. https://doi.org/10.3390/fi18020069

AMA Style

Chidambaram G, Subbarayappa S, Magapu SB. Socially Grounded IoT Protocol for Reliable Computer Vision in Industrial Applications. Future Internet. 2026; 18(2):69. https://doi.org/10.3390/fi18020069

Chicago/Turabian Style

Chidambaram, Gokulnath, Shreyanka Subbarayappa, and Sai Baba Magapu. 2026. "Socially Grounded IoT Protocol for Reliable Computer Vision in Industrial Applications" Future Internet 18, no. 2: 69. https://doi.org/10.3390/fi18020069

APA Style

Chidambaram, G., Subbarayappa, S., & Magapu, S. B. (2026). Socially Grounded IoT Protocol for Reliable Computer Vision in Industrial Applications. Future Internet, 18(2), 69. https://doi.org/10.3390/fi18020069

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