The rapid proliferation of the Internet of Things (IoT) poses significant challenges for utility optimization in sharding blockchain systems. In this paper, we propose a Committee Scheduling Algorithm (CSA), which employs an iterative optimization framework based on the Markov chain to balance transaction throughput, cumulative latency, and transaction fees. CSA dynamically adjusts the committee members to achieve near-optimal solutions while addressing operational constraints. Theoretical analysis demonstrates the convergence bounds of the algorithm and its robustness against Sybil and eclipse attacks, ensuring high entropy for committee selection. Experimental results show that CSA outperforms Stochastic-Exploration (SE), Simulated Annealing (SA), and Policy Gradient-Based Computing Task Scheduling (PG-CTS) in terms of utility, convergence speed, and adaptability to dynamic events, with the committee scheduling utility improving by about 30%. Furthermore, CSA demonstrates stable performance in large-scale IoT environments characterized by dynamic node additions and failures. This paper offers a robust and adaptive solution for utility optimization in sharding blockchains, thereby improving the scalability, security, and efficiency of IoT applications.
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