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ASTL: Accumulative STL With a Novel Robustness Metric for IoT Service Monitoring

Deng Zhao,Zhangbing Zhou,Zhipeng Cai,Sami Yangui,Xiao Xue

Published 2023 in IEEE Transactions on Mobile Computing

ABSTRACT

The Internet of Things (<italic>IoT</italic>) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiable <italic>IoT</italic> services. Considering the fact that the capacities of <italic>IoT</italic> devices may change dynamically, whether or not, and to what extent, certain constraints can be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to achieve <italic>IoT</italic> service monitoring at runtime. Specifically, we formulate the problem of <italic>IoT</italic> service monitoring as a constraint satisfaction problem, where multiple constraints, including spatial-temporal constraints, energy limitation, and capacity restrictions, are considered. Specification-based monitoring is developed based on <italic>S</italic>ignal <italic>T</italic>emporal <italic>L</italic>ogic (<italic>STL</italic>), where a novel accumulative <italic>robustness</italic> metric is proposed, denoted <italic>A</italic>ccumulative <italic>STL</italic> (<italic>ASTL</italic>), to emphasize the robust satisfaction over the entire time domain. Thereafter, <italic>IoT</italic> service monitoring is converted to <italic>ASTL</italic> formulae, and its constraint satisfaction is interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets, where various influential factors are considered. Experimental results show that our <italic>ASTL</italic> performs better than the state-of-the-art's techniques with more robust satisfaction.

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