Advances in Wireless Communication: Applications and Developments

Dear Colleagues,

Wireless communications have evolved from user-centered services, where the system design was primarily focused on providing an adequate quality of service (QoS) for voice services, to things being connected to provide an entire ecosystem in support of the everyday life of a user. Indeed, communication systems are no longer focused on connecting peers but rather allowing everything that surrounds the user to perform context-based automatic routines that improve quality of life. For instance, modern and future communication systems must allow massive packet transmissions between user wearables (ranging from smart watches and augmented reality glasses to medical devices like prosthetics and peacekeepers) and the smart phone, but also to the user’s vehicle and domestic devices (such as surveillance cameras, smart appliances, robot vacuums, among others). In this regard, a form of machine learning algorithm must provide nodes with enough intelligence to scan the environment and learn user’s patterns in such a way as to distinguish everyday life situations from emergency events and even unusual activities such as vacations and social/sportif/cultural events. This capacity will be necessary to address data packets inside the user ecosystem. Additionally, it will aid other parties (mainly when the user is unable to communicate) like vehicles to directly communicating with the police in case of a robbery or with a hospital in case of an accident. Indeed, up to date, most devices communicate and transmit all the available data to the user’s smart phone, leaving the user to discard, attend or ignore these notifications or address them by configuring the notification settings. However, this is no longer be sustainable due to the high energy consumption, high bandwidth usage, and highly cumbersome procedure of configuring each node, especially in the near future where more and more things become smart and indiscriminately transmit packets. This will be the next step in Internet of Things (IoT) design and development. In the same context, due to the impact of the batteries of each of these things on the planet, the search for clean and sustainable options to power up the nodes will exert a major impact on wireless communication devices. This is because alternative means to provide electricity (such as solar, organic and bio-based electricity among others) generate much less energy than common lithium-based batteries. As such, communication systems now must consider the small amount of energy and charging times in energy harvesting modules to decide when and what information to transmit, lowering impacts on the personal ecosystem of the user. 

Also, social media has become a predominant service for connecting users among its friends and family, with a strong bias for video messages leading to the replacement of the now-almost-gone phone call. To this end, new architectures such as peer-to-peer networks could provide an efficient solution to the high-bandwidth requirements in social networks.

Hence, the design of wireless communications will require the use of one or more of these aspects in the near future:

• Energy consumption (energy harvesting, clean energies);
• Security guarantees (robust to attacks, privacy guarantees);
• Machine learning;
• IoT and social media communications.

Prof. Dr. Mario E. Rivero-Angeles
Guest Editor

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• intelligent communications
• low energy consumption schemes
• machine learning algorithms for communications
• energy harvesting
• secured communications