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_197: 
@cite_35: 
@cite_38: We are surrounded by a sensor-enriched environment which is able to provide a broad spectrum of features from various sensor classes that can be utilised for situation and activity awareness. However, the probably most common sensor, shipped with nearly every electronic device nowadays such as mobile phones, notebooks, printers as well as keyboards, mouses, watches, shoes, media players – rumour has spread about even media cups – is mostly not utilised for activity recognition: The RF-sensor. Due to its high penetration, the additional cost for utilising this sensor in an application is considerably low. The application must simply utilise the information available but discarded unused in these devices. Although the wireless channel is frequently utilised for location detection of other active RF devices, it is seldom used to detect other contexts than location from entities that are not actively transmitting. We demonstrate a system for activity recognition based on features extracted from the RF channel. In particular, we show how static changes in the environment such as moved furniture, activity of a person and an ongoing phone call are detected based on RF channel measurements.
@cite_140: We introduce a passive system to recognise environmental situations. Differing from other RF-based approaches, our system has the advantage of neither installing a transmitter generating the signal nor equipping the monitored entities with any active component. When activities are performed, it consecutively samples ambient RF-signals, derived from a non-cooperating FM-radio source. Since changes in an environment impact the propagation of radio waves, this data implicitly contains information to distinguish environmental situations. We experimentally demonstrate the distinction of the situations 'empty room', 'opened door' and 'walking person' with an average accuracy of over 90 .
@cite_81: We introduce a novel activity recognition method based on the RF-signal originated from ambient FM radio source. For the purpose of classifying activities, we utilise a two stage approach which can initially distinguish between coarsegrained activities, then make further fine-grained recognition. Additionally, a study on features is conducted to investigate the most suitable combination to achieve the highest accuracy on the detection of activities. By comparing to a one stage classification process, the experimental results demonstrate the advantage of our designed approach.
@cite_164: We study the feasibility of utilising the RF transceiver of a mobile device to establish some kind of situation awareness. Our results show that the analysis of channel characteristics can provide additional and sufﬁciently accurate context information. This situation awareness is cheap in the sense that it can be obtained from already ongoing communication in a network of nodes. In this paper we present results from a case study with several USRP software radios. We show how the presence, position and activity of persons in a room can be obtained exclusively from channel measurements.
Recently, we also conducted preliminary studies regarding the use of features from a RF-transceiver to classify static environmental changes such as opened or closed doors, presence, location and count of persons with an accuracy of @math to @math @cite_164 @cite_35 @cite_38 @cite_197 . We utilised USRP Software defined radio devices (SDR) http: www.ettus.com from which one constantly transmits a signal that is read and analysed by other nodes. Devices were equipped with 900 MHz transceiver boards. With the software radios a higher sampling frequency than in previous studies is possible and we can also sample the actual channel instead of only tracking the RSSI. In these studies we concentrated on features related to the signal amplitude and derivation of the instantaneous amplitude from its mean. Furthermore, we conducted preliminary studies on passive device free situation awareness by utilising ambient signals from a FM radio station not under the control of the recognition system. In these studies, static environmental changes such as opened doors have been detected with an accuracy of about @math @cite_140 and a first study on suitable features to detect human activities could achieve an accuracy of about @math with a two stage recognition approach @cite_81 .