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%.

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Conventional mobility management schemes tend to hit the core network with increased signaling load when the cell size is shrinking and the user mobility speed increases. To mitigate this problem research community has proposed various intelligent mobility management schemes that take advantage of the predictability of the users mobility pattern. However, most of the proposed solutions are only focused on signaling of the active-state (i.e., handover signaling) and proposals on improvement of the idle-state signaling has been limited and were not well received from the industrial practitioners. This paper first surveys the major shortcomings of the existing proposals for the idle mode mobility management and then proposes a new architecture, namely predictive mobility management (PrMM) to mitigate the identified challenges. An analytical framework is developed and a closed form solution for the expected signaling overhead of the PrMM is presented. The results of numerical evaluations confirm that, depending on user mobility and network configuration, the PrMM efficiency can surpass the long term evolution (LTE) 4G signaling scheme by over 90%. Analysis of the results shows that the best performance is achieved at highly dense paging areas and lower cell crossing rates.

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ” user grouping ” and ” content classification .” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.