You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 

[2]: 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.

[3]: 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.

[4]: 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 .
Recognition utilising signals on the wireless channel has been generalised in [3] to activities and we can further imagine also situations [2] , gestures [4] or attention [1] to be identified by RF-based device-free implementations. These systems can be grouped into active and passive approaches conditioned on the presence of an active transmitter. Most previous work in this direction uses SDR devices.