# Special Issue: Quality of Service in Wireless Sensor/Actuator Networks and Systems
## 2 Summary of Contributions
The authors implement, evaluate, and validate their analytical model through experiments over six different low-power low-memory COTS WSAN nodes (from the Atmel ATmega and SAM families). Most WSAN technologies operate in the license-free ISM band, so communications are prone to external and cross-technology interference, namely from other wireless networks (e.g., WiFi and Bluetooth) and a myriad of daily use devices sharing the same spectrum (e.g., microwave ovens, baby monitors, cordless phones, audio/video transmitters, and remote controls). While some WSAN applications may cope with lost packets and extra communication delays, when we look into industrial WSAN application contexts, things may radically change, as reliability and timeliness are usually at stake. In this context, the authors of [4] describe a novel method for classifying interference sources in IEEE 802.15.4-based IWSANs, which may then be complemented with interference mitigation techniques. This scheme builds on a machine learning technique (support vector machines) for classifying interference from IEEE 802.11 networks and microwave ovens, as well as the presence of interference-free channels. Extensive tests in three industrial scenarios show that a high classification accuracy (over 80%) can be obtained in a very short channel sensing time (below 300 ms). The computational effort and memory footprint are very small, enabling this classifier to be implemented in low-cost COTS WSAN hardware. Unmanned aerial vehicles (UAVs) are becoming a commodity. While up to a decade ago they were used for very specific applications such as search & rescue and homeland security), nowadays it is increasingly common to use such devices for environmental monitoring, forest surveillance, 3D-mapping, and companies/sites promotion. Some scenarios may involve several UAVs intercommunicating and interoperating among each other (in swarms) and with fixed and mobile infrastructures, devices, and people on the ground. In such multi-agent cyber-physical ecosystems, localization/tracking and wireless communications are paramount and extremely challenging, considering that QoS must be guaranteed in highly dynamic, uncontrolled, open-space, and large-scale settings. The authors of [5, 6] engineer solutions toward this end. The authors of [5] elaborate on a cooperative MAC protocol specifically designed for networks of UAVs, supporting traffic differentiation between best-effort (on-demand) and guaranteed bandwidth (periodic). The authors complement this data-link layer protocol with two mechanisms that improve the reliability and range of communications, namely an adaptive antenna array that enablesomnidirectional and directional transmissions and a cooperative relayer mechanism. The proposed mechanism is evaluated through a probabilistic model (Markov chain) and simulations (ns2 and MatLab), proving its efficiency against four reference protocols. RSSI-based object/target localization and tracking mechanisms usually feature a training phase for mitigating the particularities of each environment and optimizing their accuracy. The authors of [6] propose two run-time (on-line) model-learning mechanisms that overcome the cons of pre-run-time (off-line) alternatives, namely in terms of (wasted) time, flexibility, and adaptability to changes in highly dynamic scenarios. Both mechanisms have been implemented and experimentally evaluated in a 2D test-bed with a set of anchor nodes and mobile robots, showing significant improvements over traditional RSSI-range models. A priori determination of WSAN performance is quite challenging, particularly when we aim at achieving the best energy-bandwidth tradeoff (worst-case dimensioning) in large-scale networks, considering the particularities of the communication stack (e.g., MAC and routing protocols non-determinism) and the dynamics of the environment (topology, obstacles, mobility, and EMI).