The dynamic nature of vehicular networks imposes a lot of challenges in multi-hop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. Thus, packets frequently find it difficult to get through to the destination due to the limited lifetimes of links. The conventional broadcasting based Vehicular Ad-hoc Network (VANET) routing protocols struggle to accurately analyze the link dynamicity due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged Software Defined Vehicular Network (SDVN) paradigm, link stability can be better scrutinized pertaining to the availability of global network information. Thus, in this paper, we introduce an optimization based novel packet routing scheme with a source routing based Flow Instantiation (FI) operation for SDVN. The routing framework closely analyzes the stability of links in selecting the routes and the problem is formulated as a minimum cost capacitated flow problem. Further, an incremental packet allocation scheme is proposed to solve the routing problem in a less time complexity. The objective is to find multiple shortest paths which are collectively stable enough to deliver a given number of packets. The FI scheme efficiently delivers and caches flow information in the required nodes with a reduced extent of communication with the control plane. With the help of realistic simulation, we show that the proposed routing framework excels in terms of performance over the existing routing schemes of both SDVN and conventional VANET.

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A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.

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Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

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