4.6 Article

Modeling and Verification of Symbolic Distributed Applications Through an Intelligent Monitoring Agent

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 94443-94457

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3204043

Keywords

Wireless sensor networks; Monitoring; Debugging; Codes; Embedded systems; Distributed processing; Fault diagnosis; Knowledge engineering; Software maintenance; Distributed applications; embedded systems; fault detection; Internet of Things; knowledge based systems; software monitoring; wireless sensor networks

Funding

  1. Project S6 Project (A Smart, Social and SDN-based Surveillance System for Smart-Cities),'' (PO FESR) Regione Siciliana, Italy

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This paper proposes a method for modeling and verifying distributed applications on wireless sensor networks, allowing for direct testing and reprogramming on deployed network devices. Experimental results demonstrate the feasibility and effectiveness of the proposed method.
Wireless Sensor Networks (WSNs) represent a key component in emerging distributed computing paradigms such as IoT, Ambient Intelligence, and Smart Cities. In these contexts, the difficulty of testing, verifying, and monitoring applications in their intended scenarios ranges from challenging to impractical. Current simulators can only be used to investigate correctness at source code level and with limited accuracy. This paper proposes a system and a methodology to model and verify symbolic distributed applications running on WSNs. The approach allows to complement the distributed application code at a high level of abstraction in order to test and reprogram it, directly, on deployed network devices. The proposed intelligent architecture enables the execution of distributed applications and the verification of the supplied correctness conditions. This paper shows the feasibility of the proposed approach and its effectiveness even when networks include resource-constrained nodes with some sample applications and quantitative experiments measuring the overhead introduced by the monitoring operations.

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