  The proliferation of novel industrial applications at the wireless edge, such
as smart grids and vehicle networks, demands the advancement of cyber-physical
systems. The performance of CPSs is closely linked to the last-mile wireless
communication networks, which often become bottlenecks due to their inherent
limited resources. Current CPS operations often treat wireless communication
networks as unpredictable and uncontrollable variables, ignoring the potential
adaptability of wireless networks, which results in inefficient and overly
conservative CPS operations. Meanwhile, current wireless communications often
focus more on throughput and other transmission-related metrics instead of CPS
goals. In this study, we introduce the framework of goal-oriented wireless
communication resource allocations, accounting for the semantics and
significance of data for CPS operation goals. This guarantees optimal CPS
performance from a cybernetic standpoint. We formulate a bandwidth allocation
problem aimed at maximizing the information utility gain of transmitted data
brought to CPS operation goals. Since the goal-oriented bandwidth allocation
problem is a large-scale combinational problem, we propose a divide-and-conquer
and greedy solution algorithm. The information utility gain is first
approximately decomposed into marginal utility information gains and computed
in a parallel manner. Subsequently, the bandwidth allocation problem is
reformulated as a knapsack problem, which can be further solved greedily with a
guaranteed sub-optimality gap. We further demonstrate how our proposed
goal-oriented bandwidth allocation algorithm can be applied in four potential
CPS applications, including data-driven decision-making, edge learning,
federated learning, and distributed optimization.
