Article: STIRS: cyber-physical trust integration for sustainable resource sharing in IoT-enabled cold chains Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.35 pp.40 - 54 Abstract: To address resource fragmentation and energy inefficiency in cold chain logistics, we propose sensor-trust integrated resource synergy (STIRS) - a blockchain-secured framework integrating internet of things (IoT) sensing with thermodynamic optimisation. The architecture establishes a dynamic trust model that quantifies hardware reliability metrics (battery decay, signal strength, calibration cycles) into adaptive credit weights via Sensor Health Index (SHI). Combined with mixed-integer programming and lightweight Byzantine consensus (0.48s latency), it enables real-time co-scheduling with: 1) energy consumption modelling incorporating ambient temperature sensitivity (dT/dt) and door-opening penalties (0.8-1.2 kWh/event); 2) General Data Protection Regulation (GDPR)-compliant homomorphic encryption. Validation using public United States Department of Agriculture - Agricultural Research Service (USDA-ARS) and commercial Taobao datasets (38,700 orders) demonstrates statistically significant improvements: 15-18% energy reduction, 23.7% resource utilisation gain, and 40% decrease in temperature deviation versus four benchmarks. Inderscience Publishers - linking academia, business and industry through research

Title: STIRS: cyber-physical trust integration for sustainable resource sharing in IoT-enabled cold chains

Authors: Yuanzhao Zhao

Addresses: School of Economics and Management, Henan Vocational College of Water Conservancy and Environment, Zhengzhou, 450008, China

Abstract: To address resource fragmentation and energy inefficiency in cold chain logistics, we propose sensor-trust integrated resource synergy (STIRS) - a blockchain-secured framework integrating internet of things (IoT) sensing with thermodynamic optimisation. The architecture establishes a dynamic trust model that quantifies hardware reliability metrics (battery decay, signal strength, calibration cycles) into adaptive credit weights via Sensor Health Index (SHI). Combined with mixed-integer programming and lightweight Byzantine consensus (0.48s latency), it enables real-time co-scheduling with: 1) energy consumption modelling incorporating ambient temperature sensitivity (dT/dt) and door-opening penalties (0.8-1.2 kWh/event); 2) General Data Protection Regulation (GDPR)-compliant homomorphic encryption. Validation using public United States Department of Agriculture - Agricultural Research Service (USDA-ARS) and commercial Taobao datasets (38,700 orders) demonstrates statistically significant improvements: 15-18% energy reduction, 23.7% resource utilisation gain, and 40% decrease in temperature deviation versus four benchmarks.

Keywords: cold chain logistics; resource sharing model; IoT sensing technology; dynamic trust assessment; energy consumption optimisation.

DOI: 10.1504/IJICT.2025.149047

International Journal of Information and Communication Technology, 2025 Vol.26 No.35, pp.40 - 54

Received: 07 Jul 2025
Accepted: 16 Aug 2025
Published online: 10 Oct 2025 *

Title: STIRS: cyber-physical trust integration for sustainable resource sharing in IoT-enabled cold chains

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