Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Capturing personal health data from wearable sensors

Recently, there has been a significant growth in pervasive computing and ubiquitous sensing which strives to develop and deploy sensing technology all around us. We are also seeing the emergence of applications such as environmental and personal health monitoring to leverage data from a physical world. Most of the developments in this area have been concerned with either developing the sensing technologies, or the infrastructure (middleware) to gather this data and the issues which have been addressed include power consumption on the devices, security of data transmission, networking challenges in gathering and storing the data and fault tolerance in the event of network and/or device failure. Research is focusing on harvesting and managing data and providing query capabilities

Security and Privacy Implications of Pervasive Memory Augmentation

Pervasive computing is beginning to offer the potential to rethink and redefine how technology can support human memory augmentation. For example, the emergence of widespread pervasive sensing, personal recording technologies, and systems for the quantified self are creating an environment in which it's possible to capture fine-grained traces of many aspects of human activity. Contemporary psychology theories suggest that these traces can then be used to manipulate our ability to recall - to both reinforce and attenuate human memories. Here, the authors consider the privacy and security implications of using pervasive computing to augment human memory. They describe a number of scenarios, outline the key architectural building blocks, and identify entirely new types of security and privacy threats-namely, those related to data security (experience provenance), data management (establishing new paradigms for digital memory ownership), data integrity (memory attenuation and recall-induced forgetting), and bystander privacy. Together, these threats present compelling research challenges for the pervasive computing research community. This article is part of a special issue on privacy and security

Cooperative Spectrum Sensing Using Random Matrix Theory

In this paper, using tools from asymptotic random matrix theory, a new cooperative scheme for frequency band sensing is introduced for both AWGN and fading channels. Unlike previous works in the field, the new scheme does not require the knowledge of the noise statistics or its variance and is related to the behavior of the largest and smallest eigenvalue of random matrices. Remarkably, simulations show that the asymptotic claims hold even for a small number of observations (which makes it convenient for time-varying topologies), outperforming classical energy detection techniques.Comment: Submitted to International Symposium on Wireless Pervasive Computing 200

The Ex Hoc Infrastructure Framework: Enhancing Traffic Safety through LIfe Warning Systems

New pervasive computing technologies for sensing and communication open up novel possibilities for enhancing traffic safety. We are currently designing and implementing the Ex Hoc infrastructure framework for communication among mobile and stationary units including vehicles. The infrastructure will connect sensing devices on vehicles with sensing devices on other vehicles and with stationary communication units placed alongside roads. The current application of Ex Hoc is to enable the collection and dissemination of information on road condition through LIfe Warning Systems (LIWAS) units

Proposal of a clean slate network architecture for ubiquitous services provisioning

The Pervasive Computing field is almost always addressed from application, middleware, sensing or Human Computer Interaction perspective. Thus, solutions are usually designed at application level or involve developing new hardware. Although current layered network architectures (mainly TCP/IP stack) have enabled internetworking of lots of different devices and services, they are neither well-suited nor optimized for pervasive computing applications. Hence, we firmly believe that we should have an underlying network architecture providing the flexible, context-aware and adaptable communication infrastructure required to ease the development of ubiquitous services and applications. Herein, we propose a clean slate network architecture to deploy ubiquitous services in a Pervasive and Ubiquitous Computing environment. The architecture is designed to avoid hierarchical layering, so we propose a serviceoriented approach for a flow-oriented context-aware network architecture where communications are composed on the fly (using reusable components) according to the needs and requirements of the consumed service.Postprint (published version

Building Smart Space Applications with PErvasive Computing in Embedded Systems (PECES) Middleware

The increasing number of devices that are invisibly embedded into our surrounding environment as well as the proliferation of wireless communication and sensing technologies are the basis for visions like ambient intelligence, ubiquitous and pervasive computing. PErvasive Computing in Embedded Systems (PECES) project develops the technological basis to enable the global cooperation of embedded devices residing in different smart spaces in a context-dependent, secure and trustworthy manner. This paper presents PECES middleware that consists of flexible context ontology, a middleware that is capable of dynamically forming execution environments that are secure and trustworthy. This paper also presents set of tools to facilitate application development using the PECES middleware

Opening pervasive computing to the masses using the SEAP middleware

Abstract — The increasing availability of sensing devices has made the possibility of context-aware pervasive computing ap-plications real. However, constructing this software requires extensive knowledge about the devices and specialized program-ming languages for interacting with them. While the nature of pervasive computing lends users to demand individualized ap-plications, complexities render programming embedded devices unapproachable. In this paper we introduce the SEAP (Sensor Enablement for the Average Programmer) middleware which applies existing technologies developed for web programming to the task of collecting and using sensor data. We show how this approach can be used to create new applications and to update existing web applications to accept sensor data. I

PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP-2018