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All Journal Indonesian Journal of Applied Science and Engineering
Sigit Dani Perkasa
MECATA Foundation

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World Models Grounded by IoT Sensor Streams: A Survey of Architectures, Infrastructure, and Integration Pathways Sigit Dani Perkasa
Indonesian Journal of Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v3i2.160

Abstract

Physical AI and embodied AI systems operating in real-world environments require continuous sensor grounding from deployed infrastructure, yet the predictive world-model architectures that power these systems were developed under laboratory assumptions of clean, synchronous sensor data that rarely hold in practice. The world-model literature and the IoT sensor infrastructure literature have evolved in largely separate research communities with minimal cross-citation, leaving a critical gap in understanding how latent-dynamics models can be robustly grounded by noisy, asynchronous, and resource-constrained real-world sensor streams. This paper presents a systematic survey of 34 peer-reviewed publications organized into three streams—world-model architectures (n = 12), IoT sensor infrastructure (n = 11), and bridge literature at their intersection (n = 6), with five cross-listed papers—following PRISMA 2020 guidelines for systematic reviews. The survey identifies three novelty claim spaces where current world-model architectures remain structurally unprepared for real-world sensor deployment: robustness to missing, noisy, and irregular sensor data; clock-synchronization-aware temporal alignment between asynchronous sensor streams and world-model inference; and architectural compression strategies enabling world-model deployment on resource-constrained edge devices. This is the first systematic survey to establish a Stream A × Stream B taxonomy that maps the integration pathways between world-model architectures and IoT sensor infrastructure, providing a structured research agenda for physically grounded predictive AI.
Blockchain-Based Prediction Markets: A Survey of Architectures and Economic Mechanisms Sigit Dani Perkasa; Fachrizal Fikri
Indonesian Journal of Applied Science and Engineering Vol. 3 No. 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v3i1.161

Abstract

Prediction markets harness dispersed knowledge to forecast uncertain events. Blockchain technology has enabled a new generation of permissionless, oracle-dependent prediction platforms with diverse architectural and economic designs. To survey the engineering architectures, market microstructure, oracle designs, and economic mechanisms of operational blockchain-based prediction market platforms, using a structured comparative case-study approach. A documented platform-selection protocol (informed by Chong et al. [2019] and Treiblmaier [2019] for blockchain case-study research) was applied across DeFi literature, industry documentation, and technical whitepapers. Six blockchain-native platforms and one centralized regulated benchmark were selected based on market relevance, architectural diversity, and documentation availability. The six blockchain-native platforms—Polymarket, Augur, Gnosis Conditional Tokens/Omen, Azuro, SX Bet, and Overtime Markets—span three dominant architectural paradigms: the centralized limit order book with decentralized settlement, the fully on-chain automated market maker, and the modular conditional token infrastructure. Oracle design emerges as the critical security bottleneck across all platforms, with each adopting distinct trust assumptions and economic security models. Liquidity models range from hierarchical pooled AMMs to competitive order books to parimutuel systems, with no single architecture dominant across applications. Conclusions: The contrast between blockchain-native platforms and the CFTC-regulated Kalshi illustrates the fundamental trade-off between cryptographic trust minimization and regulatory compliance. These findings provide a structured framework for understanding the design space of blockchain-based prediction markets and identify open challenges including oracle security, cross-chain liquidity aggregation, and the integration of prediction market signals with autonomous decision-making systems.
Co-Authors Fachrizal Fikri