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

Sensory semantic user interfaces (SenSUI)

Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology

Adaptive service discovery on service-oriented and spontaneous sensor systems

Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

The Programmable City

AbstractThe worldwide proliferation of mobile connected devices has brought about a revolution in the way we live, and will inevitably guide the way in which we design the cities of the future. However, designing city-wide systems poses a new set of challenges in terms of scale, manageability and citizen involvement. Solving these challenges is crucial to making sure that the vision of a programmable Internet of Things (IoT) becomes reality. In this article we will analyse these issues and present a novel programming approach to designing scalable systems for the Internet of Things, with an emphasis on smart city applications, that addresses these issues

Towards Improved Visualization and Optimization of Aquaculture Production Process

Aquaculture is one of the largest, and fastest growing industries in Norway. Recently, the industry has experienced significant development in the daily operations acquiring new technologies and systems that capture data and automate the different processes. These emerging technologies enable the generation of enormous amounts of data from sensors in the fish cages, cameras, boats, and feeding control rooms. Additional information relevant to the aquaculture industry is based on e-mails, manual notes, or intrinsic experiences and knowledge exchanges. One of the critical aspects of successful fish farming operation management, which is yet not achieved, is to allow domain experts to gain insight into the interconnection between the broad spectrum of heterogeneous data currently realized. This paper describes a graph-based database approach to storing and retrieving critical information connected to fish farming operations. The overall architecture is presented with detailed illustrations of how data is visualized and interpreted through a user-friendly interface. Accordingly, this work demonstrates how aquaculture users can benefit from the system to identify possible connections in the data and reveal previously undiscovered causalities and correlations that suggest optimal actions. Further, studies and evaluations of the querying system are conducted, evaluating the capability of the proposed design to process complex relationships. This work showcases that the system helps fish farmers and aquaculture users gain knowledge, reveal hidden links in the data, and improve aquaculture operations.publishedVersio

Multi-Network Fusion Technology for Online Health Care

This research presents a multifunctional remote medical care structure based on multi-network fusion. The structure facilitates various processes, including entity registration, authentication, electronic medical record establishment, data acquisition, remote diagnosis, and information feedback. The user terminal consists of a smart phone and a medical sensor, while the structure server includes modules such as server communication, information management, safety, disease diagnosis, information feedback, and authentication center. The structure offers rapid response, timely detection of pathological changes, convenience, and rapidness. Additionally, it allows for easy functional expansion and significant upgrading capabilities