IoT-Enabled Autonomous System Collaboration for Disaster-Area Management

Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical,but also very challenging and risky.Despite first responders putting their lives at risk in saving others,human-physical limits cause delays in response time,resulting in fatality and property damage.In this paper,we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster.The framework consists of unmanned aerial vehicles(UAVs),unmanned ground vehicles(UGVs),a cloud-based remote control station(RCS).A light-weight message queuing telemetry transport(MQTT)based communication is adopted for facilitating collaboration between autonomous systems.To effectively work under unfavorable disaster conditions,antenna tracker is developed as a tool to extend network coverage to distant areas,and mobile charging points for the UAVs are also implemented.The proposed framework’s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language(AADL).Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols,and the implemented UAV control mechanisms are functioning properly.Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.

AADL2TASM: a Verification and Analysis Tool for AADL Models

Architecture analysis and design language （AADL） is an architecture description language standard for embedded real-time systems and it is widely used in safety-critical applications. For facilitating verifcafion and analysis, model transformation is one of the methods. A synchronous subset of AADL and a general methodology for translating the AADL subset into timed abstract state machine （TASM） were studied. Based on the arias transformation language （ ATL ） framework, the associated translating tool AADL2TASM was implemented by defining the meta-model of both AADL and TASM, and the ATL transformation rules. A case study with property verification of the AADL model was also presented for validating the tool.

Integrating behavior analysis into architectural modeling

Architectural modeling and behavior analysis are two important concerns in the software development. They are often implemented separately, and specified by their own supporting notations. Architectural modeling helps to guarantee the system design to satisfy the requirement, and behavior analysis can ensure the interaction correctness. To improve the trustworthiness, methods trying to combine architectural modeling and behavior analysis notations together have been proposed, e.g., establishing a one-way mapping relation. However, the one-way relation cannot ensure updating one notation specifications in accordance with the other one, which results in inconsistency problems. In this paper, we present an approach to integrating behavior analysis into architectural modeling, which establishes the interoperability between architectural modeling notation and behavior analysis notation by a bidirectional mapping. The architecture is specified by the modeling language, architecture analysis and design language （AADL）, and then mapped to behavior analysis notation, Darwin/FSP （finite state process） through the bidirectional transformation. The bidirectional transformarion provides traceability, which makes behavior analysis result provided by a model checker can be traced and reflected back to the original AADL specifications. In this way, the behavior analysis is integrated into architectural modeling. The feasibility of our approach is shown by a control system example.

Architecture-level particular risk modeling and analysis for a cyber-physical system with AADL

Cyber-physical systems(CPSs)are becoming increasingly important in safety-critical systems.Particular risk analysis(PRA)is an essential step in the safety assessment process to guarantee the quality of a system in the early phase of system development.Human factors like the physical environment are the most important part of particular risk assessment.Therefore,it is necessary to analyze the safety of the system considering human factor and physical factor.In this paper,we propose a new particular risk model(PRM)to improve the modeling ability of the Architecture Analysis and Design Language(AADL).An architecture-based PRA method is presented to support safety assessment for the AADL model of a cyber-physical system.To simulate the PRM with the proposed PRA method,model transformation from PRM to a deterministic and stochastic Petri net model is implemented.Finally,a case study on the power grid system of CPS is modeled and analyzed using the proposed method.