Selected work

Representative Papers

Edge-Oriented Orchestration of Energy Services Using Graph-Driven Swarm Intelligence

Apr 06, 2026

This work addresses the challenges of decentralized, low-latency service orchestration in smart grids under the integration of IoT and distributed energy management. To this end, the authors propose a unified task orchestration framework tailored for edge–fog–cloud collaborative architectures. The framework combines graph-driven modeling with swarm intelligence–based optimization to enable resource-aware, low-latency task offloading. System interoperability is ensured through adherence to Energy Data Space standards, while blockchain technology guarantees traceability of workloads. Real-world deployment experiments based on KubeEdge demonstrate that the proposed approach achieves zero-downtime service migration and sustained service availability under dynamic workloads, significantly enhancing both system responsiveness and reliability.

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Fair sharing ratios of Profit and Loss sharing contracts

Jun 30, 2025

This paper addresses the absence of a principled fairness criterion for profit-sharing ratios in Islamic Profit-and-Loss (PL) sharing contracts. We propose a *c-fair allocation ratio* framework grounded in a multidimensional contribution vector **c**, which formally quantifies heterogeneous partner contributions—namely, capital, labor, and risk-bearing—as measurable, structured weights. The framework establishes an explicit theoretical mapping between **c** and the expected profit distribution. By integrating principal-agent theory with stochastic diffusion modeling and econometric estimation, it yields an interpretable, computationally tractable approximation of terminal payoffs. Our key innovation lies in endogenizing **c** as the fairness benchmark, thereby unifying economic input measurement with distributive justice. This provides a theoretically rigorous yet operationally feasible parameterization paradigm for designing PL contracts.

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Recent publications

Latest Papers

Edge-Oriented Orchestration of Energy Services Using Graph-Driven Swarm Intelligence

Apr 06, 2026

This work addresses the challenges of decentralized, low-latency service orchestration in smart grids under the integration of IoT and distributed energy management. To this end, the authors propose a unified task orchestration framework tailored for edge–fog–cloud collaborative architectures. The framework combines graph-driven modeling with swarm intelligence–based optimization to enable resource-aware, low-latency task offloading. System interoperability is ensured through adherence to Energy Data Space standards, while blockchain technology guarantees traceability of workloads. Real-world deployment experiments based on KubeEdge demonstrate that the proposed approach achieves zero-downtime service migration and sustained service availability under dynamic workloads, significantly enhancing both system responsiveness and reliability.

0 citationsRead paper

Fair sharing ratios of Profit and Loss sharing contracts

Jun 30, 2025

This paper addresses the absence of a principled fairness criterion for profit-sharing ratios in Islamic Profit-and-Loss (PL) sharing contracts. We propose a *c-fair allocation ratio* framework grounded in a multidimensional contribution vector **c**, which formally quantifies heterogeneous partner contributions—namely, capital, labor, and risk-bearing—as measurable, structured weights. The framework establishes an explicit theoretical mapping between **c** and the expected profit distribution. By integrating principal-agent theory with stochastic diffusion modeling and econometric estimation, it yields an interpretable, computationally tractable approximation of terminal payoffs. Our key innovation lies in endogenizing **c** as the fairness benchmark, thereby unifying economic input measurement with distributive justice. This provides a theoretically rigorous yet operationally feasible parameterization paradigm for designing PL contracts.

0 citationsRead paper