Publication

Abstract : The emergence of edge computing and IoT has allowed computational resources closer to the end users, hence improving applications that are latency-sensitive and demand significant resources. Distributed computing is a viable method for advancing computations through parallel tasking across multiple computation nodes. Research has been predominantly focused on the masters and worker paradigm, wherein resource sharing is confined to one-hop neighborhoods. This limitation can disrupt the efficiency of distributed computing, particularly when local resources are scarce or the connectivity is inconsistent. This study offers a unique distributed computing paradigm that expands resource-sharing capabilities beyond local one-hop neighbors by leveraging layered network topologies. We propose a hybrid centralized mixed-integer linear programming optimization method that incorporates a chameleon swarm optimization approach to derive the optimal solutions. The efficacy of the proposed scheme was assessed through theoretical analyses and extensive simulation experiments, which demonstrated its advantages over traditional distributed computing and computation offloading schemes.   Received: 20 July 2025 | Revised: 4 November 2025 | Accepted: 24 November 2025   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.   Author Contribution Statement Sathishkumar Samiappan: Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Anand R: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.