Thermal stimulation is a feedback channel that has the potential to influence the emotional response of people to media such as images. While previous work has demonstrated that thermal stimuli might have an effect on the emotional perception of images, little is understood about the exact emotional responses different thermal properties and presentation techniques can elicit towards images. This paper presents two user studies that investigate the effect thermal stimuli parameters (e.g. intensity) and timing of thermal stimuli presentation have on the emotional perception of images. We found that thermal stimulation increased valence and arousal in images with low valence and neutral to low arousal. Thermal augmentation of images also reduced valence and arousal in high valence and arousal images. We discovered that depending on when thermal augmentation is presented, it can either be used to create anticipation or enhance the inherent emotion an image is capable of evoking.

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The ability of images to evoke emotions in people has been well documented in previous research, as well as the differences in the emotional perception of images when viewed on different-sized screens and device types. The ability of thermal stimuli to evoke emotions in people when used for media augmentation has also been examined. However, little is known about how thermal stimuli can be used to enhance or reduce affect in images with varying emotional properties displayed in different sizes or on different devices. To the best of our knowledge, no work has been conducted to investigate whether there is any difference in the effect thermal augmentation has on images displayed in different sizes on different device types. This paper presents two user studies to address this research gap. Study 1 explored the effect thermal stimulation has on images displayed in different sizes. Images were displayed in sizes corresponding to the full screen display of a laptop, tablet and mobile phone. In study 2, we examined whether the actual presentation device (tablet and mobile) plays a role in the emotional perception of images displayed on mobile devices. Results showed that thermal augmentation was most effective in modulating emotions in small-sized pictures (427 9 240 pixels display size) and pictures displayed on a mobile phone. Thermal stimuli also reduced emotions in medium display sizes (corresponding to the full screen display of a tablet).

Limited work has been undertaken to show how the emotive ability of thermal stimuli can be used for interaction purposes. One potential application area is using thermal stimuli to influence emotions in images shared online such as social media platforms. This paper presents a two-part study, which examines how the documented emotive property of thermal stimuli can be applied to enhance social media images. Participants in part-one supplied images from their personal collection or social media profiles, and were asked to augment each image with thermal stimuli based on the emotions they wanted to enhance or reduce. Part-one participants were interviewed to understand the effects they wanted augmented images to have. In part-two, these augmented images were perceived by a different set of participants in a simulated social media interface. Results showed strong agreement between the emotions augmented images were designed to evoke and the emotions they actually evoked as perceived by part-two participants. Participants in part-one selected thermal stimuli augmentation intended to modulate valence and arousal in images as a way of enhancing the realism of the images augmented. Part-two results indicate this was achieved as participants perceived thermal stimuli augmentation reduced valence in negative images and modulated valence and arousal in positive images.

Wireless sensor networks (WSNs) have become ubiquitous, permeating every aspect of human life. In environmental monitoring applications (EMAs), WSNs are essential and provide a holistic view of the deployed environment. Physical sensor devices and actuators are connected across a network in environmental monitoring applications to sense vital environmental factors. EMAs bring together the intelligence and autonomy of autonomous systems to make intelligent decisions and communicate them using communication technologies. This paper discusses the various architectures developed for WSNs in environmental monitoring applications and the support for specific design goals, including machine learning in WSNs and its potential in environmental monitoring applications.