Enrichment of raw sensor data to enable high-level queries

Sensor networks are increasingly used across various application domains. Their usage has the advantage of automated, often continuous, monitoring of activities and events. Ubiquitous sensor networks detect location of people and objects and their movement. In our research, we employ a ubiquitous sensor network to track the movement of players in a tennis match. By doing so, our goal is to create a detailed analysis of how the match progressed, recording points scored, games and sets, and in doing so, greatly reduce the eort of coaches and players who are required to study matches afterwards. The sensor network is highly efficient as it eliminates the need for manual recording of the match. However, it generates raw data that is unusable by domain experts as it contains no frame of reference or context and cannot be analyzed or queried. In this work, we present the UbiQuSE system of data transformers which bridges the gap between raw sensor data and the high-level requirements of domain specialists such as the tennis coach

Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

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

Integrating sensor streams in pHealth networks

Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Expanding sensor networks to automate knowledge acquisition

The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

A semantic sensor web framework for proactive environmental monitoring and control.

Doctor of Philosophy in Computer Science, University of KwaZulu-Natal, Westville, 2017.Observing and monitoring of the natural and built environments is crucial for main- taining and preserving human life. Environmental monitoring applications typically incorporate some sensor technology to continually observe specific features of inter- est in the physical environment and transmitting data emanating from these sensors to a computing system for analysis. Semantic Sensor Web technology supports se- mantic enrichment of sensor data and provides expressive analytic techniques for data fusion, situation detection and situation analysis. Despite the promising successes of the Semantic Sensor Web technology, current Semantic Sensor Web frameworks are typically focused at developing applications for detecting and reacting to situations detected from current or past observations. While these reactive applications provide a quick response to detected situations to minimize adverse effects, they are limited when it comes to anticipating future adverse situations and determining proactive control actions to prevent or mitigate these situations. Most current Semantic Sensor Web frameworks lack two essential mechanisms required to achieve proactive control, namely, mechanisms for antici- pating the future and coherent mechanisms for consistent decision processing and planning. Designing and developing proactive monitoring and control Semantic Sensor Web applications is challenging. It requires incorporating and integrating different tech- niques for supporting situation detection, situation prediction, decision making and planning in a coherent framework. This research proposes a coherent Semantic Sen- sor Web framework for proactive monitoring and control. It incorporates ontology to facilitate situation detection from streaming sensor observations, statistical ma- chine learning for situation prediction and Markov Decision Processes for decision making and planning. The efficacy and use of the framework is evaluated through the development of two different prototype applications. The first application is for proactive monitoring and control of indoor air quality to avoid poor air quality situations. The second is for proactive monitoring and control of electricity usage in blocks of residential houses to prevent strain on the national grid. These appli- cations show the effectiveness of the proposed framework for developing Semantic Sensor Web applications that proactively avert unwanted environmental situations before they occur