The integration of cheap and powerful sensors in smartphones has enabled the emergence of several context-aware applications and frameworks. However, the available smartphone context-aware frameworks are static because of using relational data models having predefined usage of sensory data. Importantly, the frameworks lack the soft integration of new data types and relationships that appear with the emergence of new smartphone sensors. Furthermore, sensors generate huge data that intensifies the problem of too much data and not enough knowledge. Smarting of smartphone sensory data is essential for advanced analytical processing, integration, inferencing, and interpretation by context-aware applications. In order to achieve this goal, novel smartphone sensors ontology is required for semantic modeling of smartphones and sensory data, which is the main contribution of this paper. This paper presents SmartOntoSensor, a lightweight mid-level ontology that has been developed using NeOn methodology and Content Ontology Design pattern. The ontology describes smartphone and sensors from different aspects including platforms, deployments, measurement capabilities and properties, observations, data fusion, and context modeling. SmartOntoSensor has been developed using Protégé and evaluated using OntoQA, SPARQL, and experimental study. The ontology is also tested by integrating into ModeChanger application that leverages SmartOntoSensor for automatic changing of smartphone modes according to the varying contexts. We have obtained promising results that advocate for the improved ontological design and applications of SmartOntoSensor.

The user interface (UI) is a primary source of interaction with a device. Since the introduction of graphical user interface (GUI), software engineers and designers have been trying to make user-friendly UIs for various computing devices, including smartphones, tablets, and computers. The modern smart TV also comes with built-in operating systems. However, little attention has been given to this prominent entertainment device, i.e., smart TV. The technological advancement and proliferation of smart TV enabled the manufacturer to provide rich functionalities and features; however, this richness resulted in more clutter and attention-demanding interfaces. Besides, smart TV is a lean-back supporting device having a diverse range of users. Therefore, smart TV’s usability and user experience (UX) are questionable due to diverse user interests and limited features of traditional remote controls. This study aimed to discuss and critically analyze the features and functionalities of the existing well-known smart TV UIs of various operating systems in the context of usability, cognition, and UX. Moreover, this study highlights the issues and challenges in the current smart TV UIs and recommends some research opportunities to cope with the smart TV UIs. This study further reports and validates some overlooked factors affecting smart TV UIs and UX. A subjective study and usability tests from diverse users are presented to validate these factors. The study concludes that a one-size-fits-all UI design is unsuitable for shared devices, i.e., smart TV. This study further recommends a personalized adaptive UI, which may enhance the learnability and UXs of the smart TV viewers.

The usage of a smartphone while driving has been declared a global portent and has been admitted as a leading cause of crashes and accidents. Numerous solutions, such as Android Auto and CarPlay, are used to facilitate for the drivers by minimizing driver distractions. However, these solutions restrict smartphone usage, which is impractical in real driving scenarios. This research paper presents a comprehensive analysis of the available solutions to identify issues in smartphone activities. We have used empirical evaluation and dataset-based evaluation to investigate the issues in the existing smartphone user interfaces. The results show that using smartphones while driving can disrupt normal driving and may lead to change the steering wheel abruptly, focus off the road, and increases cognitive load, which could collectively result in a devastating situation. To justify the arguments, we have conducted an empirical study by collecting data using maxed mode survey, i.e., questionnaires and interviews from 98 drivers. The results show that existing smartphone-based solutions are least suitable due to numerous issues (e.g., complex and rich interfaces, redundant and time-consuming activities, requiring much visual and mental attention, and contextual constraints), making their effectiveness less viable for the drivers. Based on findings obtained from Ordinal Logistic Regression (OLR) models, it is recommended that the interactions between the drivers and smartphone could be minimized by developing context-aware adaptive user interfaces to overcome the chances of accidents.

Mobile Phone Sensing: A New Application Paradigm

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.