Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Towards Data-driven Simulation of End-to-end Network Performance Indicators

Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations

Health visiting - the end of a UK wide service?

In 1997 Health Visiting was deemed by New Labour to be an important player in reducing health inequalities. It was acknowledged that if Health Visiting was to fulfill this vision it would have to work out with its traditional child health role and also engage with groups, communities and populations to tackle the determinants of ill health. Twelve years on, external factors such as, NHS cut backs, recent changes to how Health Visitors are regulated throughout the UK and devolved Health Visiting policy making structures have led to the rapid demise in status and legitimacy of Health Visiting and its wider public health role. This article argues that the unintended consequences of devolved Health Visiting policy has resulted in 3 recent community nursing and health-visiting reviews in Scotland and England which have made divergent policy recommendations about the role of the Health Visitor in tackling health inequalities. The recommendations outlined in the Scottish review in particular threatened to jeopardise the very future provision of a UK wide Health Visiting service. If Health Visiting is to survive as a UK wide entity, a radical independent rethink as to its future direction and its public health role is urgently required