Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications compli...Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.展开更多
Research on blockchains addresses multiple issues,with one being the automated creation of smart contracts.Developing smart contract methods is more difficult than mainstream software development as the underlying blo...Research on blockchains addresses multiple issues,with one being the automated creation of smart contracts.Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity.We report on a new approach to developing smart contracts with the objective of automating the process to increase developer efficiency and reduce the risk of errors introduced by software developers.To support industry adoption,we use Business Process Model and Notation(BPMN)modeling to describe an application while targeting applications in the trade vertical.We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event(DE)modeling for concurrency with Hierarchical State Machines(HSMs)to represent application functionality.Then,further transformations are used to transform the DE-HSM model into methods in smart contracts.The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of(i)reducing processing costs and/or(ii)providing privacy so that other participants in the smart contract do not have visibility into the processing of the pattern.We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.展开更多
基金supported by the National Natural Science Foundation of China(62402415)in part by the Natural Science Foundation of Shandong Province of China(ZR2024MF129)in part by State Key Laboratory of Massive Personalized Customization System and Technology(No.H&C-MPC-2023-02-03).
文摘Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.
文摘Research on blockchains addresses multiple issues,with one being the automated creation of smart contracts.Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity.We report on a new approach to developing smart contracts with the objective of automating the process to increase developer efficiency and reduce the risk of errors introduced by software developers.To support industry adoption,we use Business Process Model and Notation(BPMN)modeling to describe an application while targeting applications in the trade vertical.We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event(DE)modeling for concurrency with Hierarchical State Machines(HSMs)to represent application functionality.Then,further transformations are used to transform the DE-HSM model into methods in smart contracts.The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of(i)reducing processing costs and/or(ii)providing privacy so that other participants in the smart contract do not have visibility into the processing of the pattern.We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.
