Millimeter-wave communications (mmWave) are gaining significant attention for their diverse applications across various domains, being key for the development of ultra-fast, low-latency wireless systems. This paper surveys the main application use cases where mmWave technologies can be adopted, focusing not only on scenarios where they are used for communication purposes, but also on other applications such as imaging and sensing. Each use case is described and characterized in order to provide a general overview of the foreseen mmWave application scenarios. The document also surveys existing standardization activities regarding mmWave frequencies across several application areas and discusses recent works regarding mmWave propagation, including both channel modeling as well as electromagnetic field exposure assessment and dosimetry, in different application domains. The document serves as a reference for a quick overview of all use cases where mmWave will have a practical impact.

Standard LoRaWANs leverage pure ALOHA at the medium access control layer which is proved to be a performance bottleneck as the network size scales up. Stimulated by this fact, this work studies the applicability and the performance of Listen Before Talk (LBT) medium access schemes in the context of LoRaWANs; we consider two different implementations of LBT: physical layer LBT based on energy detection only, and MAC layer LBT based on layer-2 frame decoding, and we propose a Markovian framework to evaluate the performance of LoRaWANs under such setting in terms of Data Extraction Rate and average delay experienced by transmitted uplink messages. The proposed framework is also leveraged to assess the performance of “mixed” LoRaWAN scenarios where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to LBT.

MQTT, one of the most popular application layer protocols for the IoT, works according to a publish/subscribe paradigm where clients connect to a centralized broker. Sometimes (e.g., in high scalability and low latency applications), it is required to depart from such a centralized approach and move to a distributed one, where multiple MQTT brokers cooperate together. Many MQTT brokers (both open-source or commercially available) allow to create such a distributed environment: however, it is challenging to select the right solution due to the many available choices. This paper proposes therefore BORDER (Benchmarking framewORk for DistributEd MQTT bRokers), a framework for creating and evaluating distributed architectures of MQTT brokers with realistic and customizable network topologies. Based on isolated Docker containers and emulated network components, the framework provides quantitative metrics about the overall system performance, such as end-to-end latency as well as network and physical resources consumed. We use BORDER to compare five of the most popular MQTT brokers that allow the creation of distributed architectures and we release it as an open-source project to allow for reproducible researches.

Interpolating a radio map is a problem of great relevance in many scenarios such as network planning, network optimization and localization. In this work such a problem is tackled by leveraging recent results from the emerging field of signal processing on graphs. A technique for interpolating graph structured data is adapted to the problem at hand by using different graph creation strategies, including ones that explicitly consider NLOS propagation conditions. Extensive experiments in a realistic large-scale urban scenario demonstrate that the proposed technique outperforms other traditional methods such as IDW, RBF and model-based interpolation.

We focus on the problem of managing a shared physical wireless sensor network where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of Application Admission Control (AAC), that is, whether to admit the application/service to the physical network, and wireless Sensor Network Slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic wireless sensor networks infrastructures.