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]: We propose a framework for secure device-pairing based on contextual information. In contrast to related work, we utilise fuzzy cryptography schemes to establish a common secret among devices and to account for noise in the contextual features. The framework is implemented for ambient audio and its feasibility is demonstrated in a case study. Additionally, we discuss properties of fingerprints from ambient audio and estimate the entropy of fingerprints in statistical tests.

[2]: Forming secure associations between wireless devices that do not share a prior trust relationship is an important problem. This paper presents ProxiMate, a system that allows wireless devices in proximity to securely pair with one another autonomously by generating a common cryptographic key directly from their shared time-varying wireless environment. The shared key synthesized by ProxiMate can be used by the devices to authenticate each others' physical proximity and then to communicate confidentially. Unlike traditional pairing approaches such as Diffie-Hellman, ProxiMate is secure against a computationally unbounded adversary and its computational complexity is linear in the size of the key. We evaluate ProxiMate using an experimental prototype built using an open-source software-defined platform and demonstrate its effectiveness in generating common secret bits. We further show that it is possible to speed up secret key synthesis by monitoring multiple RF sources simultaneously or by shaking together the devices that need to be paired. Finally, we show that ProxiMate is resistant to even the most powerful attacker who controls the public RF source used by the legitimate devices for pairing.
By utilising a context source that provides a sufficient amount of unique, context-related information, such as audio or radio frequency (RF), it is possible to get the user out of the loop. introduced ProxiMate that enables wireless devices in proximity to pair automatically and securely using their shared ambient RF-signals [2] . They generate fingerprints from RF-channel fluctuations and map these onto a codespace of an error-correcting code. By correcting potential errors in the fingerprints, they are mapped onto the closest regular codeword in the codespace. When the similarity between fingerprints is high, codewords are identical. Sigg et. al proposed to use audio instead of RF in a similar implementation [1] . They study the entropy of audio fingerprints and identify time synchronisation as a main hindrance to practically apply the method for mobile devices. Their instrumentation requires idealized conditions regarding the synchronisation of devices and to account for this a high number of fingerprints must be created (201 in their experiments) in order to find one matching fingerprint. For extensive computational load, this is feasible only in an offline approach. The high number of fingerprints created, however, was necessary since the utilized NTP synchronisation is not sufficiently accurate.