  Industrial Internet of Things (IIoT) applications involve real-time
monitoring, detection, and data analysis. This is challenged by the
intermittent activity of IIoT devices (IIoTDs) and their limited battery
capacity. Indeed, the former issue makes resource scheduling and random access
difficult, while the latter constrains IIoTDs' lifetime and efficient
operation. In this paper, we address interconnected aspects of these issues.
Specifically, we focus on extending the battery life of IIoTDs sensing
events/alarms by minimizing the number of unnecessary transmissions. Note that
when multiple devices access the channel simultaneously, there are collisions,
potentially leading to retransmissions, thus reducing energy efficiency. We
propose a threshold-based transmission-decision policy based on the sensing
quality and the network spatial deployment. We optimize the transmission
thresholds using several approaches such as successive convex approximation,
block coordinate descent methods, Voronoi diagrams, explainable machine
learning, and algorithms based on natural selection and social behavior.
Besides, we propose a new approach that reformulates the optimization problem
as a $Q$-learning solution to promote adaptability to system dynamics. Through
numerical evaluation, we demonstrate significant performance enhancements in
complex IIoT environments, thus validating the practicality and effectiveness
of the proposed solutions. We show that Q-learning performs the best, while the
block coordinate descending method incurs the worst performance. Additionally,
we compare the proposed methods with a benchmark assigning the same threshold
to all the devices for transmission decision. Compared to the benchmark, up to
94\% and 60\% reduction in power consumption are achieved in low-density and
high-density scenarios, respectively.
