After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Over the past few years the semantics community\ud has developed ontologies to describe concepts and relationships\ud between different entities in various application domains,\ud including Internet of Things (IoT) applications. A key problem\ud is that most of the IoT related semantic descriptions are not\ud as widely adopted as expected. One of the main concerns\ud of users and developers is that semantic techniques increase\ud the complexity and processing time and therefore they are\ud unsuitable for dynamic and responsive environments such as\ud the IoT. To address this concern, we propose IoT-Lite, an\ud instantiation of the semantic sensor network (SSN) ontology\ud to describe key IoT concepts allowing interoperability and\ud discovery of sensory data in heterogeneous IoT platforms by\ud a lightweight semantics. We propose 10 rules for good and\ud scalable semantic model design and follow them to create\ud IoT-Lite. We also demonstrate the scalability of IoT-Lite by\ud providing some experimental analysis, and assess IoT-Lite\ud against another solution in terms of round time trip (RTT)\ud performance for query-response times

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.

Over the past few years the semantics community has developed several ontologies to describe concepts and relationships for Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time and therefore they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis, and assess IoT-Lite against another solution in terms of round trip time (RTT) performance for query-response times. We have linked IoTLite with Stream Annotation Ontology (SAO), to allow queries over stream data annotations and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.