Context-Aware Decision Systems Using Multimodal Intelligence and Edge Computing for Secure Data Exchange
DOI:
https://doi.org/10.15662/IJEETR.2025.0705020Keywords:
Context-aware computing, multimodal intelligence, edge computing, secure data exchange, data fusion, artificial intelligence, privacy preservation, distributed decision-making, edge intelligence, cybersecurityAbstract
Context-aware decision systems are increasingly important for environments in which large volumes of heterogeneous data must be processed rapidly, securely, and intelligently. The integration of multimodal intelligence, edge computing, and secure data-exchange mechanisms provides a promising framework for developing such systems. Multimodal intelligence enables decision systems to interpret information originating from diverse sources, including text, images, audio, sensor streams, and contextual metadata, while edge computing allows data processing to occur closer to its point of generation. This combination can reduce latency, improve responsiveness, limit unnecessary transmission of sensitive information, and support intelligent decision-making in distributed environments. However, integrating heterogeneous modalities at resource-constrained edge nodes introduces challenges related to data interoperability, computational limitations, privacy, authentication, model robustness, and secure communication. This study proposes a context-aware decision framework that combines multimodal data fusion, edge-based intelligence, adaptive context modelling, and secure data-exchange mechanisms. The proposed methodology considers contextual variables, dynamically selects relevant information, performs localized processing, and exchanges protected decision-relevant data with authorized entities. Security mechanisms are incorporated throughout the data lifecycle to strengthen confidentiality, integrity, authentication, and access control. The framework is evaluated using performance, security, accuracy, latency, resource utilization, and communication-efficiency measures. The study aims to establish an adaptable architecture for secure and intelligent decision-making across distributed applications
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