FiFo: Fishbone Forwarding in Massive IoT Networks

Abstract

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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiFo</monospace> method completes two tasks: 1) it clusters devices based on the unweighed pair group method with the arithmetic average and 2) it creates the main axis and subaxes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiFo</monospace> by using a real-world data set. Through intensive and comprehensive simulations, we show that the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiFo</monospace> method outperforms benchmark algorithms in terms of the forwarding efficiency.