Advances in Wireless Communication. Applications and Developments - Mario E. Rivero-Angeles

Wireless communications have evolved from a user-centered service, where the system design was primarily focused on providing adequate Quality of Service for voice services, to a system that connects things to form an entire ecosystem supporting users' everyday lives. They are no longer focused on connecting peers but rather on enabling everything around the user to perform context-aware automated routines to improve Quality of Life. To this end, machine learning algorithms must equip nodes with sufficient intelligence to scan the environment and learn the user's patterns, distinguishing everyday life situations from emergency and unusual events. This intelligence will prioritize data packets within the user ecosystem and for other parties, such as vehicles. To date, most devices transmit all available data to the user's smartphone, leaving the user to either discard, attend to, or ignore these notifications or to configure notification settings. However, this is not sustainable due to high energy consumption, high bandwidth usage, and the cumbersome process of configuring each node. In the same context, due to the environmental impact of the batteries in these devices, the search for clean, sustainable options to power nodes will have a major impact on wireless communication devices. As such, communication systems will consider the limited energy and charging times of energy-harvesting modules to decide what information to transmit and when. Hence, the design of wireless communications will require the use of energy consumption, security guarantees, machine learning, and IoT and social media communications.