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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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High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserving MAC protocol, with the aim to extend the network lifetime. Our QL-MAC protocol is derived from Q-learning, which iteratively tweaks the MAC parameters through a trial-and-error process to converge to a low energy state. This has a dual benefit of 1) solving this minimization problem without the need of predetermining the system model and 2) providing a self-adaptive protocol to topological and other external changes. QL-MAC self-adjusts the WSN node duty-cycle, reducing energy consumption without detrimental effects on the other network parameters. This is achieved by adjusting the radio sleeping and active periods based on traffic predictions and transmission state of neighboring nodes. Our findings are corroborated by an extensive set of experiments carried out on off-the-shelf devices, alongside large-scale simulations. INDEX TERMS Wireless sensor network, artificial intelligence, reinforcement learning, energy-efficient network, medium access control.

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The concept of smart city has emerged worldwide as a feasible answer to the challenges raised by the increasing urbanisation. From the technological point of view, guaranteeing ubiquitous connectivity, reliable communications and seamless integration of multiple network access technologies are mandatory in a smart city. This is in contrast with the current infrastructure deployment in several urban areas, which is characterised by lack of ubiquitous connectivity and coverage and by fragmentation of networks that are usually deployed by different operators and without any centralised control by the city authorities. In this paper, we look at the heterogeneity of devices and network technologies under a different perspective by not perceiving it as a limitation but as a potential to increase the connectivity in a smart city. We propose a new generation of network nodes, called stem nodes, based on the innovative idea of 'stemness', which pushes forward the well-known self-configuration and self-management concepts towards the idea of node mutation and evolution. We also deployed prototypes that demonstrate the stem-node architecture and basic operations in different hardware platforms of common communication devices (an Alix-based router, a laptop and a smartphone).