  In the Urban Internet of Things devices and systems are interconnected at the
city scale to provide innovative services to the citizens.However, the traffic
generated by the sensing and processing systems may overload local access
networks. A coexistence problem arises where concurrent applications mutually
interfere and compete for available resources. This effect is further
aggravated by the multiple scales involved and heterogeneity of the networks
supporting the urban IoT. One of the main contributions of this paper is the
introduction of the notion of content-oriented cognitive interference control
in heterogeneous local access networks supporting computing and data processing
in the urban IoT. A network scenario where multiple communication technologies,
such as Device-to-Device and Long Term Evolution (LTE), is considered. The
focus of the present paper is on city monitoring applications, where a video
data stream generated by a camera system is remotely processed to detect
objects. The cognitive network paradigm is extended to dynamically shape the
interference pattern generated by concurrent data streams and induce a packet
loss trajectory compatible with video processing algorithms. Numerical results
show that the proposed cognitive transmission strategy enables a significant
throughput increase of interfering applications for a target accuracy of the
monitoring application.
