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Wearable technologies are valuable tools that can encourage people to monitor their own well-being and facilitate timely health interventions. In this paper, we present SPW-2; a low-profile versatile wearable sensor that employs two ultra low power accelerometers and an optional gyroscope. Designed for minimum maintenance and a long-term operation outside the laboratory, SPW-2 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-2 can fully cover a room and - in most cases - the adjacent room, as well.

One of the challenges of deploying IoT battery-powered sensing systems is managing the maintenance of batteries. To that end, practitioners often employ prediction techniques to approximate the battery lifetime of the deployed devices. Following a series of longterm residential deployments in the wild, this paper contrasts real-world battery lifetimes and discharge patterns against battery lifetime predictions that were conducted during the development of the deployed system. The comparison highlights the challenges of making battery lifetime predictions, in an attempt to motivate further research on the matter. Moreover, this paper summarises key lessons learned that could potentially accelerate future IoT deployments of similar scale and nature.

We are interested in hot and cold water flow detection in domestic kitchen and bathroom taps for smart home environments. Water flow monitoring is particularly valuable for long-term behavioural monitoring systems for health-related applications, as it enables the collection of long-term data on the hydration levels of the house residents, and it is associated with several activities of daily life, such as cooking and cleaning. This paper presents a water flow sensing device that is based on sensing the vibrations on the pipe when water is flowing through them. The proposed solution is noninvasive and energy efficient, as it does not require cutting the water pipes or altering the plumbing system, and consumes less then 2 uA in continuous operation. The proposed water flow sensor has been integrated to SPHERE, a sensing platform of non-medical sensors for healthcare monitoring and behavioural analytics in a home environment, and deployed to more than 15 residential properties.

The Internet of Things promises to enable numerous future applications spanning many domains, including health care, and is comprised of devices that are constrained in terms of computational and energy resources. A specific health care application is to ascertain patients' activity of daily living while at home using accelerometer data from non-invasive wearables. It is often necessary to store this data on the device to be retrieved later for analysis. However, the devices typically store far more data than can be transmitted with commonly used low power radios. To mitigate the problem, this paper proposes an energy efficient, lossless compression algorithm that uses an offline frequency distribution to create a symbol-code lookup table. Using an extensive set of data from a previous study, an analysis of the entropy of activities of daily living accelerometer data is presented. The compression algorithm is compared against this estimated entropy. Energy being critical for IoT devices, the trade-off between energy cost for compression versus energy saved during transmission is also analysed.

It can be tempting to think about smart homes like one thinks about smart cities. On the surface, smart homes and smart cities comprise coherent systems enabled by similar sensing and interactive technologies. It can also be argued that both are broadly underpinned by shared goals of sustainable development, inclusive user engagement and improved service delivery. However, the home possesses unique characteristics that must be considered in order to develop effective smart home systems that are adopted in the real world [37].

In hardware deployments, it is often necessary to test platforms for suitability for particular purposes. As lengthy data collection processes often outlive specific iterations of hardware and firmware, it is likely that migration between platforms may become necessary. In this short paper we describe a practical approach employed for acceptance testing, comparison and validation of two iterations of a wearable accelerometer and localisation platform, based on an annotated 15-task activity script. We present an analysis on the data generated for the different activities, and compare device performance using common machine learning algorithms for activity recognition.