Energetic SmartData: A data-driven power management approach for cyber–physical systems

Power management is a cornerstone for many Cyber-Physical Systems (CPSs), which relies on low-power circuits, dynamic power management algorithms and energy-aware software to match their requirements in terms of energy. As CPSs evolve towards data-centric designs to more promptly accommodate AI models and integration, traditional power management techniques must also be improved. In this paper, we build on SmartData to introduce a data-centric Power Manager (PM) framework that allows CPSs to model energy in terms of data. SmartData defines a high-level interface for sensing, actuation, and control in data-centric CPSs. It abstracts the myriad of features of modern embedded platforms related to processing, scheduling, synchronization, and communication. These Energetic SmartData encapsulate the components of a CPS, which interact in a publish–subscribe fashion, declaring interest on other SmartData and responding to other SmartData interests. We introduce an algorithm to extract a Directed Acyclic Graph (DAG) from these Interest relationships, with vertices representing the involved components and edges representing the associated cost in terms of energy. We also introduce a Power Manager that uses such DAGs to monitor the state of the system, eventually overriding low-priority Interests to reach the specified lifetime. We evaluated the proposed framework through a case study with Ocean-Bottom Nodes (OBNs) under realistic, dynamic energy conditions. Results show that without any power management, the system fails 12 days before its target operational lifetime. The proposed data-driven PM was then benchmarked against a fixed-schedule Static PM and a reactive Threshold PM. Our approach was the only strategy to guarantee a 365-day lifetime in all scenarios. With an ideal initial battery capacity of 260 Ah, it achieved a high utility of 23.1%. It also proved its adaptability in an energy-deficit scenario with an initial capacity of 257 Ah, where it reduced utility to 2.8% to survive, a condition in which the other strategies failed. • Data-driven Cyber-Physical-Systems (CPS) design allows for Power-State Estimation from the data dependencies, which dictate the operation mode for CPS components. • Realistic energy supply models impose relevant challenges on Power Management approaches. • Non-adaptive Power Management solutions tend to fail to meet requirements under complex energy supply models. • Machine-Learning-based solutions are often Component-based and fail to model the multi-component nature of modern data-driven CPS. • Data-driven Power Management approaches lead to a direct relationship between QoS and Power Configuration.