Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different user data sets have significant similarities on the feature spaces, the final goal is to build a high-performance global model. However, not all features are great contributors when training the global HFL model, some features even impair the HFL. Besides, the curse of dimension will delay the training time and cause more energy consumption (EC). In this case, it is critical to remove irrelevant features from the local and select the useful overlapping features from a federated global perspective. In addition, the uncertainty of data being labeled and the nonindependent and identically distributed (non-IID) client data should also consider. This article introduces an unsupervised federated feature selection approach (named FSHFL) for HFL in IoT networks. First, a feature relevance outlier detection method is applied to the HFL participants to remove the useless features, which combines with the improved one-class support vector machine. Besides, a feature relevance hierarchical clustering (FRHC) algorithm is proposed for HFL overlapping feature selection. Experiment results on four IoT data sets show that the proposed methods can select better-federated feature sets among HFL participants, thus improving the performance of the HFL system. Specifically, the global model accuracy improves up to 1.68% since fewer irrelevant features. Moreover, FSHFL can lower the average training time as high as 6.9%. Finally, when the global model gets the same test accuracy, FSHFL can decrease the average EC of training the model by approximately 2.85% compared to federated average and roughly 68.39% compared to Fed-SGD.

Voice user interfaces (VUIs) are increasingly popular, particularly in homes. However, little research has investigated their potential in other settings, such as schools. We investigated how VUIs could support inclusive education, particularly for pupils with visual impairments (VIs). We organised focused discussions with educators at a school, with support staff from local authorities and, through bodystorming, with a class of 27 pupils. We then ran a series of co-design workshops with participants with mixed-visual abilities to design an educational VUI application. This provided insights into challenges faced by pupils with VIs in mainstream schools, and opened a space for educators, sighted and visually impaired pupils to reflect on and design for their shared learning experiences through VUIs. We present scenarios, a design space and an example application that show novel ways of using VUIs for inclusive education. We also reflect on co-designing with mixed-visual-ability groups in this space.

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.