Understanding Path Reconstruction Algorithms in Multihop Wireless Networks

Abstract

Low-power and multihop wireless networking is envisioned as a promising technology to achieve both energy efficiency and easy deployment for many Internet of Things (IoT) applications. Measuring packet-level path is crucial for managing large-scale multihop wireless networks. Packet-level path information encodes the routing path, a packet that takes through a network. The availability of packet-level path information can greatly facilitate many network management tasks. It is challenging to reconstruct packet-level paths using a small overhead, especially for large-scale networks. While there is a long list of existing path reconstruction algorithms, these algorithms focus on specific network scenarios, e.g., periodic monitoring networks or event detection networks. There lacks a unified model for systematically understanding and comparing the performance of these algorithms in different network scenarios. In this paper, we fill this gap by proposing an abstract model. Using this model, it is possible to derive a decision space for selecting the best algorithm for different networks. Furthermore, this model also guides us to devise better path reconstruction algorithms (cPath <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ,</sub> cPath <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s,</sub> and cPath <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sT</sub> ) with respect to path reconstruction ratio. Extensive experiments demonstrate the prediction power of our model as well as the advantages of our proposed algorithms. The results show that our algorithm (cPath <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sT</sub> ) improves a path reconstruction ratio from 94.4%, 34.3%, and 30.8% to 98.9%, 99.9%, and 60.1% on average in three network scenarios, respectively, compared with the best state-of-the-art algorithms.