  Internet of Things (IoT) is a system of interrelated devices that can be used
to allow large-scale collection and analysis of data. However, as it grew, IoT
networks were not capable of managing the data from these services. As a
result, cloud computing was introduced to address the need for datacentres for
IoT networks. As the technology evolved, the demand for a proper means of
supporting and managing crowdsensing and real-time data increased, and cloud
servers could no longer keep up with the large volumes of incoming data. This
demand brought rise to fog computing. It became an extension to the cloud and
allowed resources to be allocated around the network effectively. Its
integration to IoT reduced the strain towards the cloud servers. However,
issues in high power consumption at the end device and data management
constraints surfaced. This paper proposes two approaches to alleviate these
issues to keep fog computing remain as a reliable option for IoT-related
applications. We created an IoT-based sensing framework that used an urban
sound classification model. Through active low and high power states and
resource reallocation, we created a network configuration. We tested this
configuration against IoT frameworks that use the default fog and cloud setups.
The results improved the framework's end device power consumption and server
latency. Overall, with the proposed framework, fog computing was proven to be
capable of supporting a scalable IoT framework for urban sound sensing.
