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_152: This paper explores the viability of precise indoor localization using physical layer information in WiFi systems. We find evidence that channel responses from multiple OFDM subcarriers can be a promising location signature. While these signatures certainly vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We attempt to harness these opportunities through a functional system called PinLoc, implemented on off-the-shelf Intel 5300 cards. We evaluate the system in a busy engineering building, a crowded student center, a cafeteria, and at the Duke University museum, and demonstrate localization accuracies in the granularity of 1m x 1m boxes, called "spots". Results from 100 spots show that PinLoc is able to localize users to the correct spot with 89 mean accuracy, while incurring less than 6 false positives. We believe this is an important step forward, compared to the best indoor localization schemes of today, such as Horus.
While all previously mentioned results considered special installations of the wireless transmitters, presented a system that allows the localisation of a wireless device with an accuracy of about 1 meter from WiFi physical layer information even when the receiver is carried by a person that might induce additional noise to the captured features @cite_152 .