Captain Buzz

This is the author accepted manuscript. The final version is available from ACM at https://dl.acm.org/citation.cfm?id=2750682.Fully autonomous hobbyist drones are typically controlled using bespoke microcontrollers, or general purpose low-level controllers such as the Arduino. However, these devices only have limited compute power and sensing capabilities, and do not easily provide cellular connectivity options. We present Captain Buzz, an Android smartphone app capable of piloting a delta-wing glider autonomously. Captain Buzz can control servos directly via pulse width modulation sig- nals transmitted over the smartphone audio port. Compared with traditional approaches to building an autopilot, Cap- tain Buzz allows users to leverage existing Android libraries for flight attitude determination, provides innovative use- cases, allows users to reprogram their autopilot mid-flight for rapid prototyping, and reduces the cost of building drones.This work was supported by Google Inc., the Engineering and Physical Sciences Research Council; and CSR, Cambridge

Contextual location in the home using Bluetooth Beacons

Location sensing is a key enabling technology for Ubicomp to support contextual interaction. However, the laboratories where calibrated testing of location technologies is done are very different to the domestic situations where “context” is a problematic social construct. This study reports measurements of Bluetooth beacons, informed by laboratory studies, but done in diverse domestic settings. The design of these surveys has been motivated by the natural environment implied in the Bluetooth beacon standards relating to the technical environment of the beacon to the function of spaces within the home. This research method can be considered as a situated, “ethnographic” technical response to the study of physical infrastructure that arises through social processes. The results offer insights for the future design of “seamful” approaches to indoor location sensing, and to the ways that context might be constructed and interpreted in a seamful manner

Gaussian Mixture Filter for Multipath Assisted Positioning

Navigation in global navigation satellite system denied areas such as urban canyons or indoors has aroused large interest due to the recent growth of location aware services. In these scenarios, multipath-assisted positioning schemes are promising due to a rich multipath propagation. Instead of trying to combat multipath, multipath-assisted positioning approaches make use of multipath components arriving at a receiver that is to be located. In more detail, multipath components arriving at the receiver via different paths are regarded as pure line-of-sight signals from virtual transmitters. In general, the number of transmitters might be large, and their location may be unknown. The underlying estimation problem, i.e., estimating the positions of the receiver and the physical and virtual transmitters, tends to be very costly in computational terms. Within this paper, we present a Rao-Blackwellization approach to tackle the computational burden. The receiver location is tracked using a particle filter, while the probability density functions of the transmitter states are represented by Gaussian mixture models, whose parameters are estimated using cubature Kalman filters

SwiftScan: Efficient Wi-Fi scanning for background location-based services

The provision of location-based services on consumer devices has moved from on-demand navigation capabilities to always-on ubiquitous location-aware tools such as weather updates, travel information, location-based reminders and many more. Background localisation is generally provided by Wi-Fi fingerprinting, since GPS does not provide service in indoor environments where we spend 80% of our time. However the power consumption of a Wi-Fi scan is proportional to the number of channels scanned, and so naive full-channel scans are inefficient. Here we describe and validate SwiftScan, an intelligent, self-training Wi-Fi fingerprinting scheme that reduces the energy consumption of periodic background Wi-Fi scanning for localisation. SwiftScan is tested with data from more than a thousand Android users over a six month time period and we show that energy savings of over 90% are possible, and that the majority of users benefit from more than a 70% reduction in the energy consumption associated with a Wi-Fi scan for localisation purposes

Research data supporting "Soroban: Attributing Latency in Virtualized Environments"

This is data required for reproducing the figures in the "Soroban: Attributing Latency in Virtualized Environments" paper. It consists of two archives: [rscfl-exp.zip] containing (i) raw data from kernel level measurements of lighttpd serving requests while running in a virtualized environment (in json format, the .dat and .mdat metadata files) (ii) post-processed measurements with the data filtered and added into python pandas.DataFrame objects for querying and plotting (in standard binary python pickle serialization format, .sdat files) (iii) attribution model files containing serializations of python sklearn.gaussian_process.GaussianProcess objects after training (in standard binary python pickle serialization format, .pickle files) (iv) python scripts for processing and plotting the data (.py files) AND [rscfl-repr.zip] containing (i) experiment definition files for figures in the paper, containing the information required to reproduce them (.json format) (ii) python script for reproducing experiments (rscfl_exp.py)Additional information: As an example, for reproducing Figure 3 from the paper, one needs to unpack the two archives (rscfl-repr and rscfl-exp) and run $ ./rscfl_exp.py -c fig3.config.json -s /path/to/rscfl_exp --fg_load_vm localhostEPSRC EP/K503009/