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.
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 .
@cite_197 @cite_35 @cite_38 @cite_140 @cite_81 @cite_164
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Background: We investigate direct use of 802.15.4 radio signal strength indication (RSSI) for human activity recognition when 1) a user carries a wireless node (device-bound) and when 2) a user moves in the wireless sensor net (WSN) without a WSN node (device-free). We investigate recognition feasibility in respect to network topology, subject and room geometry (door open, half, closed). Methods: In a 2 person office room 8 wireless nodes are installed in a 3D topology. Two subjects are outfitted with a sensor node on the hip. Acceleration and RSSI are recorded while subject performs 6 different activities or room is empty. We apply machine learning for analysis and compare our results to acceleration data. Results: 10-fold cross-validation with all nodes gives accuracies of 0.896 (device-bound), 0.894 (device-free) and 0.88 (accelerometer). Topology investigation reveals that similar accuracies may be reached with only 5 (device-bound) or 4 (device-free) selected nodes. Applying trained data from one subject to the other and vice-versa shows higher recognition difference on RSSI than on acceleration. Changing of door state has smaller effect on both systems than subject change; with least impact when door is closed. Conclusion: 802.15.4 RSSI suited for activity recognition. 3D topology is helpful in respect to type of activities. Discrimination of subjects seems possible. Practical systems must adapt no only to long-term environmental dispersion but consider typical geometric changes. Adaptable, robust recognition models must be developed. 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 . We consider the recognition of activities from passive entities by analysing radio-frequency (RF)-channel fluctuation. In particular, we focus on the recognition of activities by active Software-defined-radio (SDR)-based Device-free Activity Recognition (DFAR) systems and investigate the localisation of activities performed, the generalisation of features for alternative environments and the distinction between walking speeds. Furthermore, we conduct case studies for Received Signal Strength (RSS)-based active and continuous signal-based passive systems to exploit the accuracy decrease in these related cases. All systems are compared to an accelerometer-based recognition system. 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.