Jul 2026· Annual International Computer Software and Applications Conference· pp. 942-951· 0 citations· 31 references
Abstract
The ubiquitous deployment of Internet of Things (IoT) in smart building ecosystems generates massive volumes of multi-dimensional data, rendering secure storage and efficient retrieval paramount challenges. Although blockchain technology ensures data integrity and traceability, applying it to resource-constrained IoT networks exposes a fundamental “storage trilemma” among cost, latency, and scalability. Conventional approaches, relying on either static local retention or full cloud offloading, fail to reconcile these conflicting objectives. In this paper, we propose a Heat-Driven Hybrid Storage (HDHS) architecture that addresses limitations of existing hybrid storage systems-which rely on static parameters and reactive policiesthrough three key innovations: predictive heat modeling, dynamic redundancy adaptation, and multi-objective optimization. Specifically, HDHS incorporates a time-decay model with cost-aware uncertainty estimation to forecast block access “heat” under noisy conditions. Based on these predictions, the system dynamically tunes redundancy rates and utilizes rateless fountain codes to optimize the trade-off between storage footprint and data durability. Furthermore, we design a cloud-window optimizer that addresses a multi-objective trade-off to determine an effective boundary for local-cloud data migration. Extensive experiments on real-world datasets demonstrate that our scheme achieves a 40.7% reduction in storage costs, maintains sub-3ms query latency for 74.7% of queries, and ensures 99% + data reliability in permissioned blockchain environments.
The rapid proliferation of Internet of Things (IoT) devices has led to an exponential increase in data generation, enabling new data-driven insights while simultaneously introducing significant storage challenges for service providers. Conventional solutions address this growth by expanding cloud-based storage infrastructures, increasing dependence on cloud providers and associated operational costs. This paper proposes a decentralized IoT data storage architecture that shifts the storage of device-generated measurements from cloud infrastructure (CI) to users' mobile phones (MPs). By leveraging the computational and storage capabilities of modern edge devices and MPs, the proposed approach reduces reliance on centralized cloud storage while preserving low-latency data access. Contemporary MPs provide sufficient capacity to support long-term retention of user-generated IoT data at the user level. Key challenges related to security, trust, scalability, and edge-side storage optimization are discussed, along with potential solutions including lightweight blockchain-based verification, adaptive network optimization, and federated learning. The findings indicate that the proposed architecture offers an efficient, scalable, and cost-effective alternative to cloud-centric data storage, enabling more decentralized and user-centric IoT data management.
Filippos Serepas, Ioannis Papias, Dimitra Karoutsou et al.· 2026 6th International Confe...· 0 citations
A Cloud-Fog-IoT architecture integrated with a Directed Acyclic Graph (DAG)-based blockchain to enhance scalability and security for secure smart city deployments is proposed.
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
M. Harandi, Afshin Yaghoobi· Scientific Reports· 0 citations
Security in resource-constrained IoT deployments remains a persistent challenge: devices used in industrial control, smart healthcare, and transportation must authenticate quickly, consume minimal energy, and resist physical attacks — yet existing protocols rarely address all three requirements at once. To the best of current knowledge, no prior protocol jointly optimises security, energy, and latency within a single formally verified framework. This paper presents OPAQUE-IoT, an Optimization-driven PUF-Blockchain AKA Protocol for constrained IoT networks. The framework integrates PUF-based hardware identity verification, a permissioned blockchain for decentralized trust management, and the Adaptive Security-Energy Trade-off Optimizer (ASETO), which jointly minimizes authentication latency and energy consumption under formal security constraints. Convergence of ASETO is proven under Lipschitz-continuous objective functions. Formal security analysis under the Real-or-Random (RoR) model with explicit Random Oracle and ECDH hardness assumptions demonstrates resistance to replay, impersonation, man-in-the-middle, PUF modeling, insider, and side-channel attacks, with a security advantage bound of approximately 2^(-68). Simulation results across heterogeneous IoT topologies (N = 50 to 5000 devices) show 31.8% lower energy consumption, 30.2% reduced authentication latency, and 41.1% higher throughput compared to the best-performing blockchain-capable baseline, with O(log N) Merkle-indexed blockchain query complexity and O(T_max·N·P) per-epoch optimiser complexity.
Ibrahim Aqeel· Journal of King Saud Univers...· 0 citations
Preserving the quality and safety of perishable products requires continuous monitoring and reliable traceability. Although the Internet of Things (IoT) enables real‐time data collection, multi‐organizational supply chains lack a common mechanism for assigning data custody while maintaining transparency, integrity, and performance.
This study proposes and evaluates an architecture integrating IoT, edge/fog computing, and hybrid storage—an off‐chain traditional database combined with a permissioned blockchain—to monitor and trace perishable products.
A prototype was implemented using IoT devices and simulators, edge and fog components, and hybrid storage. High‐volume sensor data and critical records were stored off‐chain in MongoDB, while their corresponding hashes were stored on‐chain using Hyperledger Fabric. Four controlled experiments assessed insertion response time, the impact of increasing sensors and edge devices, blockchain queue performance under burst workloads, and blockchain storage consumption. The hybrid approach was compared with MongoDB‐only and Hyperledger Fabric‐only storage.
Hybrid storage achieved insertion up to six times faster than blockchain‐only storage. Response times increased with simultaneous requests and additional edge devices, while asynchronous ordered insertion prevented transaction conflicts during bursts. The prototype achieved 18.5 transactions per second, below the 65 estimated for an illustrative supply‐chain scenario. Blockchain storage grew approximately 8 MB per 100 records, reaching about 1 GB for 12,800 hashes.
The prototype demonstrates the feasibility of combining off‐chain storage, permissioned blockchain records, and edge/fog processing to provide verifiable traceability while reducing on‐chain load. Larger‐scale, real‐world evaluations and storage‐management strategies remain necessary.
R. Hoffmann, C. Moratelli, A. R. Pinto· Software, Practice & Experie...· 0 citations
Autonomous driving systems (ADSs) increasingly rely on LiDAR sensors for perception. However, the resulting high-volume data places a strain on storage systems and network bandwidth and raises data-privacy concerns. We propose an IoT data engineering framework for processing, transmitting, storing, and retrieving high-volume LiDAR sensor data in in-vehicle systems that combines error-bounded compression and blockchain-based storage over in-vehicle Time-Sensitive Networking (TSN). With IEEE 802.1Qbv-based TSN scheduling, our framework supports deterministic delivery within the evaluated setup. It combines AES-GCM encryption, blockchain smart contracts, and InterPlanetary File System (IPFS) storage to support confidential, tamper-evident archival under the stated trust and threat model. Experimental evaluation on the KITTI dataset demonstrates that our BEDM framework reduces LiDAR data volume by 75.4%, contributing to a total network bandwidth reduction of 53.7%. The results demonstrate the feasibility and effectiveness of the integrated framework within the evaluated KITTI-based setup and single-switch TSN abstraction, and cross-scene and TSN traffic-sensitivity analyses further characterize its robustness.
Ray-I Chang, Ting-Wei Hsu, Yu-Han Ke· Italian National Conference...· 0 citations