Asymptotic throughput capacity analysis of VANETs exploiting mobility diversity

Vehicular ad hoc networks (VANETs) rely on intervehicle relay to extend the communication range of individual vehicles for message transmissions to roadside units (RSUs). With the presence of a large number of quickly moving vehicles in the network, the end-to-end transmission performance from individual vehicles to RSUs through intervehicle relaying is, however, highly unreliable due to the violative intervehicle connectivity. As an effort toward this issue, this paper develops an efficient message routing scheme that can maximize the message delivery throughput from vehicles to RSUs. Specifically, we first develop a mathematical framework to analyze the asymptotic throughput scaling of VANETs. We demonstrate that in an urban-like layout, the achievable uplink throughput per vehicle from vehicle to RSUs scales as Θ(1/ log n) when the number of RSUs scales as Θ(n/log n) with n denoting vehicle population. By noting that the network throughput is bottlenecked by the unbalanced data traffic generated by hotspots of realistic urban areas, which may overload the RSUs nearby, a novel packet-forwarding scheme is proposed to approach the optimal network throughput by exploiting the mobility diversity of vehicles to balance the data traffic across the network. Using extensive simulations based on realistic traffic traces, we demonstrate that the proposed scheme can improve the network throughput approaching the asymptotic throughput capacity.

Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.