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

The Internet of Things (
IoT
) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiable
IoT
services. Considering the fact that the capacities of
IoT
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
IoT
service monitoring at runtime. Specifically, we formulate the problem of
IoT
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
S
ignal
T
emporal
L
ogic (
STL
), where a novel accumulative
robustness
metric is proposed, denoted
A
ccumulative
STL
(
ASTL
), to emphasize the robust satisfaction over the entire time domain. Thereafter,
IoT
service monitoring is converted to
ASTL
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
ASTL
performs better than the state-of-the-art's techniques with more robust satisfaction.