Low-power and cost-effective IoT sensing nodes enable scalable monitoring of extensive, inaccessible, remote, and dangerous environments. However, some of these environments impose rough and extreme operating conditions on the nodes and require continuous adaptation and reconfiguration of physical and link layer parameters. In this paper, we closely investigate the stability of wireless links established between low-power IoT nodes deployed on the surface of different water bodies and propose dynamic transmission power adaptation to deal with link quality fluctuation and frequent disconnections. Our model is based on Minimum Mean Square Estimation (MMSE) and relies on statistics pertaining to received power and the acceleration the movement of water induces on communicating nodes. The model achieves a prediction accuracy exceeding 97%. The cost of processing and communicating the 3D acceleration statistics can be forgone with a reduced accuracy of 90% with a Kalman Filter.

Wearable sensors enable to monitor patients in their everyday living and working environments. However, since they generate highly personal data, their protection from misuse or unauthorized use is crucial. Under certain conditions, it is important to examine whether the data they generate uniquely describe their deployment environment. This can be used as an additional measure to certify the authenticity of the data. In this paper, we uniquely identify persons based on the measurements of wireless electrocardiograms and motion sensors. We let 34 subjects perform 7 different activities while synchronously sampling a 5-lead wireless electrocardiogram, two 3D accelerometers, and a 3D gyroscope. Using both time-and frequency-domain features, we train a multi-layer CNN to achieve a classification accuracy exceeding 98%. Our model is able to uniquely classify 88% of the subjects with 100% accuracy, even though more than 82% of them have similar physical constitution (they are young people between the ages of 21 and 24, with a mean age of 22 years and standard deviation of 1.9 years). In order to demonstrate the variability of the data, we perform activity recognition using the same datasets; the model is able to classify the activities with an average accuracy of 92%.

Self-organizing protocols and algorithms require knowledge of the underlying topology of the network. The topology can be represented by a graph or an adjacency matrix. In most practical cases, establishing the topology prior to a deployment is not possible because the exact placement of nodes and the existence of a reliable link between any two individual nodes cannot guaranteed. Therefore, this task has to be carried out after deployment. If the network is stand-alone and certain aspects are fixed (such as the identity of the base station, the size of the network, etc.), the task is achievable. If, however, the network has to interact with other systems – such as Unmanned Aerial Vehicles (UAVs) or mobile robots – whose operation is affected by environmental factors, the task can be difficult to achieve. In this paper we propose a dynamic topology construction algorithm, assuming that the network is a part of a joint deployment and does not have a fixed based station.

A wireless sensor network (WSN) assisted by Unmanned Aerial Vehicles (UAVs) can be used to monitor various phenomena in remote, extensive, inaccessible, or dangerous places. The WSN on the ground can provide close-to-the-scene sensing, in-network data processing, and multi-hop communication. The UAVs can interface the ground network with a remote control station or facilitate fast and flexible data collection. To this end, the aerial links established between the UAVs and the WSN are critical. The reliability of the links depends on many factors, including Cross Technology Interference (CTI), the relative distance of the UAVs from the ground nodes, the drive quality of the UAVs, and noise. In this paper, we present experimental results addressing some of these issues. Our experiments consisted of eleven IEEE 802.15.4 compliant transceivers, nine of which were deployed on the ground in a grid topology with a further two being attached to a UAV. From careful examination of traces extracted from received packets we concluded that CTI is the most significant factor affecting the quality of aerial links. Our observations have application for deployment related decision-making and for the design of UAV-assisted data collection protocols.