Mohammed Bahutair

Azadeh Ghari Neiat, Athman Bouguettaya, Mohammed Bahutair

We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.

Mohammed Bahutair, Athman Bouguettaya

We propose a novel distributed integrity-preserving framework for storing trust information in crowdsourced IoT environments. The integrity and availability of the trust information is paramount to ensure accurate trust assessment. Our proposed framework leverages the blockchain to build a distributed storage medium for trust-related information that ensures its integrity. We propose a geo-scoping approach, which ensures that trust-related information is only available where needed, thus, enabling fast access and storage space preservation. We conduct several experiments using real datasets to highlight the effectiveness of our framework.

Mohammed Bahutair, Athman Bouguettaya, Azadeh Ghari Neiat

We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.

Mohammed Bahutair, Athman Bouguettaya, Azadeh Ghari Neiat

We propose just-in-time memoryless trust for crowdsourced IoT services. We leverage the characteristics of the IoT service environment to evaluate their trustworthiness. A novel framework is devised to assess a service's trust without relying on previous knowledge, i.e., memoryless trust. The framework exploits service-session-related data to offer a trust value valid only during the current session, i.e., just-in-time trust. Several experiments are conducted to assess the efficiency of the proposed framework.

Ismail Shahin, Mohammed Nasser Ba-Hutair

This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses Speech Under Simulated and Actual Stress (SUSAS) database, while the emotional talking environment uses Emotional Prosody Speech and Transcripts (EPST) database. The achieved results of this work using Mel-Frequency Cepstral Coefficients (MFCCs) demonstrate that CSPHMM2s outperform each of Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s), and Suprasegmental Hidden Markov Models (SPHMMs) in enhancing talking condition recognition in the stressful and emotional talking environments. The results also show that the performance of talking condition recognition in stressful talking environments leads that in emotional talking environments by 3.67% based on CSPHMM2s. Our results obtained in subjective evaluation by human judges fall within 2.14% and 3.08% of those obtained, respectively, in stressful and emotional talking environments based on CSPHMM2s.