  Massive Internet of Things (IoT) networks have a wide range of applications,
including but not limited to the rapid delivery of emergency and disaster
messages. Although various benchmark algorithms have been developed to date for
message delivery in such applications, they pose several practical challenges
such as insufficient network coverage and/or highly redundant transmissions to
expand the coverage area, resulting in considerable energy consumption for each
IoT device. To overcome this problem, we first characterize a new performance
metric, forwarding efficiency, which is defined as the ratio of the coverage
probability to the average number of transmissions per device, to evaluate the
data dissemination performance more appropriately. Then, we propose a novel and
effective forwarding method, fishbone forwarding (FiFo), which aims to improve
the forwarding efficiency with acceptable computational complexity. Our FiFo
method completes two tasks: 1) it clusters devices based on the unweighted pair
group method with the arithmetic average; and 2) it creates the main axis and
sub axes of each cluster using both the expectation-maximization algorithm for
the Gaussian mixture model and principal component analysis. We demonstrate the
superiority of FiFo by using a real-world dataset. Through intensive and
comprehensive simulations, we show that the proposed FiFo method outperforms
benchmark algorithms in terms of the forwarding efficiency.
