Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. Finally, we identify a number of open research challenges and outline several future research directions.

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment.

HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment.

Many Internet of Things (IoT) applications benefit greatly from low-power long-range connectivity. A promising technology to achieve the low-power and long-range requirements is seen in LoRaWAN, a media access control (MAC) protocol maintained by the LoRa Alliance and leveraging Semtech's patented LoRa radio modulation technology. LoRaWAN provides three different device classes (A, B and C), which provide a tradeoff between performance (i.e., throughput and latency) and energy consumption. This paper offers a theoretical and experimental comparison of these classes. The objective of the quantitative experiment was twofold: to verify the published current levels of different operating modes in a LoRa chip's datasheet and to compare the battery lifetime for the LoRa class A and C modes of operation. We used a high-end current sensing circuit to gather the voltage levels and temporal variation with increasing payload sizes and spreading factors. Using the Ohmic Law, the energy drain can be calculated and compared across the different spreading factors (SF) and classes.