The PATH2iot open-source platform presents a new approach to stream processing for Internet of Things applications by automatically partitioning and deploying the computation over the available infrastructure (e.g. cloud, field gateways and sensors) in order to meet non-functional requirements including energy, performance and security. The user gives a high-level declarative description of computation in the form of Event Processing Language queries. These are compiled, optimised, and partitioned to meet the non-functional requirements using database system techniques and cost models extended to meet the needs of IoT analytics. The paper describes the PATH2iot system, illustrated by a real-world digital healthcare analytics example, with sensor battery life as the main non-functional requirement to be optimised. It shows that the tool can automatically partition and distribute the computation across a healthcare wearable, a mobile phone and the cloud -increasing the battery life of the smart watch by 453% when compared to other possible allocations. The PATH2iot system can therefore automatically bring the benefits of fog/edge computing to IoT applications.

This is a case report in which a patient with SLE had a brainstem variant of PRES, and MRI demonstrated atypical distribution of FLAIR hyperintensity in the thalami and the midbrain sparing the red nuclei bilaterally (Figure 1). This impressive lesion pattern may reveal the disease mechanisms of PRES in patients with SLE.

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There has been a dramatic growth in the number and range of Internet of Things (IoT) sensors that generate healthcare data. These sensors stream high-dimensional time series data that must be analysed in order to provide the insights into medical conditions that can improve patient healthcare. This raises both statistical and computational challenges, including where to deploy the streaming data analytics, given that a typical healthcare IoT system will combine a highly diverse set of components with very varied computational characteristics, e.g. sensors, mobile phones and clouds. Different partitionings of the analytics across these components can dramatically affect key factors such as the battery life of the sensors, and the overall performance. In this work we describe a method for automatically partitioning stream processing across a set of components in order to optimise for a range of factors including sensor battery life and communications bandwidth. We illustrate this using our implementation of a statistical model predicting the glucose levels of type II diabetes patients in order to reduce the risk of hyperglycaemia.