Created by London-based musical duo the Network Ensemble, Selected Network Studies is a series of audiovisual pieces created using network data collected from a number of locations across London, Berlin and Rome. It is released as limited edition UV-printed, vacuum-sealed mylar package containing a 2GB SD Card with one hour of video material and 45 minutes of sound material.
Selected Network Studies contains mastered recordings of two performances carried out by the Network Ensemble in London and Berlin, and nine audiovisual pieces created with network data collected from a number of locations across London and Rome. The locations are of network-infrastructural importance: from places in which communications intersect with structures of power, to spaces where one might not ordinarily consider the network to be present. Airports and airplanes, embassies, the Vatican, to name a few. Visual and written documentation of both the hardware and software built for network exploration is included, alongside details of the data collection and performance sites.
One of the studies takes place in Stansted Airport. A hub for budget carrier Ryanair, the airport plays host to some of the cheapest flights in the country. Although this price point is achieved via some questionable employment practices and dubious customer service techniques, it makes it the largest European airline. Network Study XI used NE.app to explore Stansted’s network space, detecting 183296 packets from 17688 separate devices. An airport terminal designed for simplicity, there is little in this network forest to suggest the state-sanctioned force that occurs elsewhere: the devices on the network are mostly those of tourists whose phones remain un-confiscated, and whose destinations are known to them. Another site, Docklands in London, Network Study I follows the path of the underground cables as they connect London, and the UK, to the global Internet. As one of the most important European financial hubs, housing the headquarters of banks such as HSBC, Barclays or CitiBank, this location is no coincidence: the financial services and fast-trading algorithms thrive on the speed, performance and scalability offered by proximity to the one of the most important node of the Internet, fed by the world’s fastest telecommunication cables. Over 60,000 devices and 2,000 network channels were monitored.
To create compositions, the Network Ensemble used NE.app, a command line tool Node application built for the capture, analysis and performance of network data. When launched, it activates the WiFi card in a laptop to capture network data. NE.app forces the WiFi card into “monitor mode”, thereby collecting all traffic received from the local wireless network. This traffic takes the form of network packets: small units of data.
NE.app uses a network protocol analyzer called TShark to inspect this traffic. Upon receipt of a given packet, it categorises it according to its intent and to the layer of the network in which it sits. Networks make use of different types of packet to achieve all necessary communications. NE.app categorises WiFi packets as follows: Infrastructure / Structure – the structural level of the wireless technology: packets that organise the network; Gatekeeping – management of network access: packets that aid exchanging data between devices; Oversight / Communication – packets responsible for the meet and greet of devices; Content / Data – carried data: messages, images, videos. Packets transmitting the user’s media; Broken – corrupted and null packets: packets that may not reach their destination and Unknown – everything else incl the ghosts in the network: unusual, very rare or non-standard packets. The resulting transcript of network activity can then be either saved as a score for later playback, or used live to control audiovisual output when performing.
For generating sounds, NE.app converts the raw bytes of a network packet into audio, using a sound processing utility called SoX (Sound eXchange), effectively obtaining “network white noise”. NE.app also transmits OSC and MIDI messages to Ableton, which uses a set of custom Max for Live devices to deal with the complexity of incoming network data.
For generating visuals, NE.app transmits OSC messages to both Processing and VDMX, a VJing software. Processing creates three live data visualisations of the network data: a textual overview, a visual taxonomy and an aggregate of all the captured WiFi packets. It then uses syphon to send the visualisation to VDMX, which compiles and cuts in-between them and overlays warped satellite imagery of the location of collection.
Using the WiFi card embedded in a laptop also allows for covert data collection in locations with a higher level of security and scrutiny, such as airports, planes or embassies.
The Network Ensemble also uses NE3 (above), a network instrument, for the performances released in Selected Network Studies. NE3 is a compact board which captures and dissects the network, turning it into a source of data-noise. The machine has a WiFi card built into it and uses a stripped down, headless version of the NE.app running on a Raspberry Pi 3.
A single knob on the top-side of the board allows control of the speed at which the network data is transformed, ranging from high speeds, staying true to the intense nature of the WiFi, to low speeds, making it possible to identify patterns in the noise or investigate the sonic character of a particular slice of the network. Two jack ports allow for the connection of audio equipment for sonic manipulation and performance. As a base, the NE3 has a surface transducer, which turns nearly any surface into a speaker. As a result, the material character of the machine’s physical location blends with the specifics of the network space it inhabits, creating a sound unique to its position in both the online and offline worlds.
Selected Network Studies is released through the Italian label Rizosfera and is available on networkensemble.bandcamp.com as an SD Card and digital download.