Energy-Optimal Refactoring of Multiple Smart Sensors With Edge Computing Capability

This paper studies the refactoring problem of multiple smart sensors (MSSs) with edge computing capability, where the program consisting of codes and driving MSSs distributes into all the smart sensors, the available energy for refactorings of MSSs is limited, and each smart sensor adopts binary refactoring mode, i.e., either performs refactoring locally by itself or fully offloads its codes to the edge server (ES) for edge refactoring. More energy strengthens MSSs to wipe out more code bugs (CBs), while corresponding to more energy consumption. Meanwhile. the refactoring mode (i.e., local or edge refactoring) also influences the CB ratio (CBR) and energy efficiency. Therefore, how to make the optimal tradeoff between CBR and energy consumption for such MSSs arises as an interesting issue. To address this issue, this paper first reveals a necessary and sufficient condition to judge whether the minimum CBR can be reached as well as its analytical expression, and then discloses the optimal refactoring time, refactoring computation rate, and refactoring mode, in terms of guaranteeing the minimum CBR under the energy constraint. An algorithm based on our discovered foundations is proposed for such MSSs to minimize the CBR within the acceptable level of energy consumption. Theoretical analysis, simulation and field experiments verify its performance. To our best knowledge, this is the initial work towards the optimal refactoring of MSSs. Note to Practitioners—For MSSs in practice, CBs lurking in their driving program often endanger their normal function. As a kind of behavior-preserving code transformation, the refactoring built in MSSs can help them to remove CBs. Such refactoring must consume energy and often suffers energy setback because the electricity and computing power of MSSs are usually very limited. To overcome this setback, the edge computing is introduced for MSSs to offload their codes to the ES for refactoring. However, the combination of MSSs and edge computing heavily challenges energy-saving refactoring, involving the invisible and unknown CBs and the curse of dimensionality regarding code-offloading. Accordingly, this work allows MSSs with edge computing capability to operate in a program-healthy and energy-efficient manner by making the optimal tradeoff between CBR and energy consumption, covering both theoretical results and algorithm. Specifically, a necessary and sufficient condition to judge whether the minimum CBR can be reached is disclosed, the analytical expression of that minimum CBR is derived out, the optimal refactoring time, refactoring computation rate, and refactoring mode for each smart sensor are discovered, and an effective algorithm for the practitioners to enjoy the minimum CBR while maintaining the energy consumption within a given range is further proposed. By controlling the refactoring time, refactoring computation rate, and refactoring mode given that the necessary and sufficient condition is satisfied, this work can be applicable for MSSs with edge computing capability to suffer the minimum CBR when they are employed to sense the physical world in the scenarios where their available energy is limited.