This document summarises the results of several research campaigns over the past seven years. The main connecting theme is the physical layer of widely deployed sensors in Pervasive Computing domains. In particular, we have focused on the RF-channel or on ambient audio. The initial problem from which we started this work was that of distributed adaptive transmit beamforming. We have been looking for a simple method to align the phases of jointly transmitting nodes (e.g. sensor or IoT nodes). The algorithmic solution to this problem was to implement a distributed random optimisation method on the participating nodes in which the transmitters and the receiver follow an iterative question-and-answer scheme. We have been able to derive sharp asymptotic bounds on the expected optimisation time of an evolutionary random optimiser and presented an asymptotically optimal approach. One thing that we have learned from the work on these physical layer algorithms was that the signals we work on are fragile and perceptive to physical environmental changes. These could be obstacles such as furniture, opened or closed windows or doors as well as movement of individuals. This observation motivated us to view the wireless interface as a sensor for environmental changes in Pervasive Computing environments. Another use of physical layer RF-signals is for security applications. We are currently working to further push these mentioned directions and novel fields of physical prototyping. In particular, the calculation of mathematical operations on the wireless channel at the time of transmission appears to contain good potential for gains in efficiency for communication and computation in Pervasive Computing domains.
@cite_58: Among the various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is apt to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile in run-time. In practical dynamic environments, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and reform it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is 1 meter. Compared with the traditional RSS-based approaches in dynamic environment, the accuracy improves up to 10 times.
@cite_121: Systems and methods are disclosed for the use of sensor links in a network to estimate the breathing rate of a breathing subject within a structure, estimate the location of the subject within the structure, and detect if the subject is breathing. The structure may be a bed, a building, or a room in the building. The received signal strength of the sensor links is obtained and is then used in various breathing models to determine the breathing rate estimation, the location estimation, and the breathing detection.
The simultaneous localisation of multiple individuals at the same time was first mentioned and studied by Patwari and Wilson in @cite_121 . The authors derive a statistical model to approximate the position of a person based on RSSI variance which can be extended to multiple persons. This aspect together with the previously untackled problem that environmental changes over time might necessitate frequent calibration of the location system was approached by Zhang and others in @cite_58 . The authors isolate the LoS path by extracting phase information from the differences in the RSS on various frequency spectrums at distributed nodes. Their experimental system is with this approach able to simultaneously and continuously localise up to 5 persons in a changing environment with an accuracy of 1 meter.