Title: Optimizing Mission Critical Data Dissemination in Massive IoT Networks,
Abstract: Mission critical data dissemination in massive Internet of things (IoT)
networks imposes constraints on the message transfer delay between devices. Due
to low power and communication range of IoT devices, data is foreseen to be
relayed over multiple device-to-device (D2D) links before reaching the
destination. The coexistence of a massive number of IoT devices poses a
challenge in maximizing the successful transmission capacity of the overall
network alongside reducing the multi-hop transmission delay in order to support
mission critical applications. There is a delicate interplay between the
carrier sensing threshold of the contention based medium access protocol and
the choice of packet forwarding strategy selected at each hop by the devices.
The fundamental problem in optimizing the performance of such networks is to
balance the tradeoff between conflicting performance objectives such as the
spatial frequency reuse, transmission quality, and packet progress towards the
destination. In this paper, we use a stochastic geometry approach to quantify
the performance of multi-hop massive IoT networks in terms of the spatial
frequency reuse and the transmission quality under different packet forwarding
schemes. We also develop a comprehensive performance metric that can be used to
optimize the system to achieve the best performance. The results can be used to
select the best forwarding scheme and tune the carrier sensing threshold to
optimize the performance of the network according to the delay constraints and
transmission quality requirements.