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_37: We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.
@cite_9: This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94 .
@cite_155: Wi-Fi signals are typically information carriers between a transmitter and a receiver. In this paper, we show that Wi-Fi can also extend our senses, enabling us to see moving objects through walls and behind closed doors. In particular, we can use such signals to identify the number of people in a closed room and their relative locations. We can also identify simple gestures made behind a wall, and combine a sequence of gestures to communicate messages to a wireless receiver without carrying any transmitting device. The paper introduces two main innovations. First, it shows how one can use MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. Second, it shows how one can track a human by treating the motion of a human body as an antenna array and tracking the resulting RF beam. We demonstrate the validity of our design by building it into USRP software radios and testing it in office buildings.
@cite_57: We investigate the use of received RF-signals for activity recognition in scenarios with multiple receive nodes and multiple simultaneously active individuals. Our system features a short 0.5 second window over which features are calculated and we report on experiences in the choice of the neighbourhood size of the k-nearest neighbour (k-NN) classifier utilised. In a case study with software defined radio nodes utilised in an active, device-free activity recognition (DFAR) system, we observe a good recognition accuracy for the recognition of multiple simultaneously conducted activities with two and more receive devices. This is the first study to distinguish this particular set of activities from users conducting them simultaneously. For a single individual, we repeat the experiment and report the recognition accuracy in scenarios where the recognition area per receive node is larger than 8sqm
@cite_157: Developing a robust and reliable vehicle detection and speed estimation system that alerts drivers about driving conditions and helps them avoid joining traffic jams is an important problem that has attracted lots of attention recently. In this paper, we introduce a novel RF-based vehicle motion detection and speed estimation system (ReVISE). Our system leverages the fact that the presence of objects in an RF environment affects the received signal strength and hence, can be used to detect and identify different characteristics of the objects in an area of interest. Our long-term vision for ReVISE is to leverage common wireless networks, such as WiFi or cellular, to detect the density of traffic and estimate the car speed based on the mobile devices carried by users. This gives us an edge over the current techniques for traffic estimation as we do not require any specialized hardware and the cellular signal strength information is available from all cell phones, providing large-scale ubiquitous traffic estimation. We present the design and analysis of ReVISE including its vehicle detection and speed estimation modules. The detection module can differentiate between an empty street, stationary cars, and moving cars based on a multi-class SVM approach that uses features from the RF signal strength. We also present two novel speed estimation techniques based on statistical and curve fitting approaches. Evaluation of ReVISE in a real testbed shows that the proposed techniques can detect vehicle motion with an accuracy of 100 and estimate the vehicle speed with an accuracy of 90 in typical streets. This highlights the feasibility and promise of using RF for vehicle detection and speed estimation.
@cite_158: 
sense traffic situations by tracking frequency and speed of passing cars that intercept the direct line of sight between a pair of nodes @cite_157 . The authors of @cite_37 classify simple activities in an SDR-based active device-free system by extracting and interpreting features from a continuous signal between two nodes. Their approach explores also the multipath effects induced by persons that are not intercepting the direct path between nodes. It was later demonstrated that also simultaneously conducted activities from multiple persons can be distinguished by leveraging purely signal-strength based features @cite_57 . Furthermore, it was shown by Pu and others that simultaneous detection of gestures from multiple individuals is possible utilising multi-antenna nodes and micro Doppler fluctuations @cite_9 @cite_158 . In a related system, Adib and Katabi employ MIMO interference nulling and combine samples taken over time to achieve the same result while compensating for the missing spatial diversity in a single-antenna system @cite_155 .