Mobile Crowd Computing (MCdC) leverages the idle computational capacity of consumer smartphones to enable distributed task processing at scale; however, widespread real-world adoption remains constrained by the absence of developer-oriented frameworks capable of transparently managing device heterogeneity, fault tolerance, and connectivity volatility. This paper introduces CROWDio, a centralized MCdC platform comprising three tightly integrated subsystems: (i) a declarative SDK that abstracts distributed execution to a single function annotation, eliminating the need for explicit parallelism management; (ii) a tiered checkpointing mechanism that enables fault-tolerant task resumption under the memory and execution constraints inherent to mobile runtimes; and (iii) a pluggable multi-criteria scheduling framework driven by continuous live device telemetry, supporting interchangeable decision strategies without modification to the dispatch core. Empirical evaluation across six heterogeneous Android devices spanning CPU-bound, AI/NLP inference, and data-parallel workloads demonstrates that capability-aware adaptive scheduling reduces total execution time by up to 56.9% relative to naive round-robin dispatch, while the checkpointing subsystem incurs a bounded overhead of only 2-3 s per task regardless of checkpoint frequency. A system-wide Jain's Fairness Index of 0.889 confirms equitable and stable workload distribution across heterogeneous worker devices.

翻译：移动群智计算（MCdC）利用消费级智能手机的闲置计算能力实现大规模分布式任务处理；然而，由于缺乏能够透明管理设备异构性、容错机制及连接波动性的面向开发者框架，其在实际环境中的广泛部署仍受制约。本文提出CROWDio——一种集中式MCdC平台，由三个紧密集成的子系统构成：（i）声明式SDK，将分布式执行抽象为单一函数注解，消除了显式并行管理的需求；（ii）分级检查点机制，在移动运行时固有的内存与执行约束下实现容错任务恢复；（iii）基于连续实时设备遥测的可插拔多准则调度框架，支持在不修改调度核心的前提下替换决策策略。在六种不同异构Android设备上进行的实证评估覆盖了CPU密集型、AI/NLP推理及数据并行工作负载。结果表明，相对于朴素轮询调度策略，具备能力感知的自适应调度可将总执行时间最高降低56.9%，而检查点子系统在任意检查点频率下均仅引入每任务2-3秒的有界开销。系统全局Jain公平指数达到0.889，证实了异构工作节点间公平且稳定的工作负载分布。