ALBERT

Description: The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications.

Description: Underwater acoustic digital communications suffer from inter-symbol interference deriving from signal distortions caused by the channel propagation. Facing such kind of impairment becomes particularly challenging when dealing with shallow water scenarios characterized by short channel coherence time and large delay spread caused by time-varying multipath effects. Channel equalization operated on the received signal represents a crucial issue in order to mitigate the effect of inter-symbol interference and improve the link reliability. In this direction, this contribution presents a preliminary performance analysis of acoustic digital links adopting pulse position modulation in severe multipath scenarios. First, we show how the spectral redundancy offered by pulse position modulated signals can be fruitfully exploited when using fractional sampling at the receiver side, which is an interesting approach rarely addressed by the current literature. In this context, a novel blind equalization scheme is devised. Specifically, the equalizer is blindly designed according to a suitably modified Bussgang scheme in which the zero-memory nonlinearity is replaced by a M-memory nonlinearity, M being the pulse position modulation order. Numerical results not only confirm the feasibility of the technique described here, but also assess the quality of its performance. An extension to a very interesting complex case is also provided.

Description: In the next few years, fundamental technological transitions are expected both for wireless communications, soon resulting in the 5G era, and for the kind of pervasiveness that will be achieved thanks to the Internet of Things. The implementation of such new communication paradigms is expected to significantly revolutionize people’s lives, industry, commerce, and many daily activities. Healthcare applications are considered to be one of the most impacted industries. Sadly, in relation to the COVID-19 pandemic currently afflicting our society, health remote monitoring has become a fundamental and urgent application. The overcrowding of hospitals and medical facilities due to COVID-19, has unavoidably created delays and key issues in providing adequate medical assistance. In several cases, asymptomatic or light symptomatic COVID-19 patients have to be continuously monitored to prevent emergencies, and such an activity does not necessarily require hospitalization. Considering this research direction, this paper investigates the potentiality of cloud-based cellular networks to support remote healthcare monitoring applications implemented in accordance with the IoT paradigm, combined with future cellular systems. The idea is to conveniently replace the physical interaction between patients and doctors with a reliable virtual one, so that hospital services can be reserved for emergencies. Specifically, we investigate the feasibility and effectiveness of remote healthcare monitoring by evaluating its impact on the network performance. Furthermore, we discuss the potentiality of medical data compression and how it can be exploited to reduce the traffic load.