Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard Cloud-based approaches to reduce the interaction timing and the huge amount of data coming from IoT devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge, a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing Edge gateway supporting multi-radio and multi-technology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public Cloud platforms to guarantee a high flexibility, robustness and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

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We are currently witnessing the increasing diffusion of new location-aware smartphones applications that make intensive use of positioning information, however, the widespread and pervasive GPS technology is gradually giving way to new or revised less power-hungry communication technologies able to guarantee almost the same accuracy. Within this general context, mainly motivated by the current green communication paradigm, this paper proposes an energy-efficient and cost-effective localization architecture based on the improvement of classical cell-tower schemes coupled with a dynamic fingerprinting update phase in order to face the natural changes in the radio environments. The proposed system architecture has been implemented and tested in a real scenario to measure the performances in terms of accuracy and energy saving that will make it preferable to the traditional GPS-based systems in the next future. The obtained results show the effectiveness of the considered approach that makes possible to estimate the current position of a mobile user with a very small error (≈ 20m).