Global surveillance means it’s ‘Time to ditch the smartphone!’

If you ever thought that your smartphone or your home automation device might be spying on you, well you were right.

The US Airforce Research Lab is testing new surveillance software from a company called SignalFrame. The software allows them (users) to access your smartphone and to access any wireless or bluetooth device in the vicinity. The Wall Street Journal said: “the smartphone is used as a window onto usage of hundreds of millions of computers, routers, fitness trackers, modern cars, and other networked devices collective known as “The Internet of Things”.


Wireless connectivity blankets our cities, homes, and workplaces. Beyond just smartphones, it extends to a wide array of businesses and devices – from cars to electronics, and fast-food restaurants to hotel chains


Each detectable WiFi, Bluetooth, or BLE signal provides clues about the thing or place that emits it. SignalFrame has built the world's largest database of signal identifiers to associate every signal-emitting device back to a business, brand, or product.


By capturing detectable signals from thousands of products, venues, and brands, SignalFrame determines when and where new things appear in the world. Our SignalGraph™ platform sees cars drive off the lot, early adoption of products in specific markets, and businesses opening and turning on their WiFi networks.


By analysing each signal name (SSID) and unique identifier (MAC), our SignalGraph™ platform classifies signals into 10,000 product types (such as Tesla, Fitbit, or Roku) and over 10,000 businesses (such Starbucks, Marriott, or Cheesecake Factory).


The SignalGraph grows out of crowdsourced observations of the ambient signal environment that reveal the presence and proximity of wifi, bluetooth and BLE devices in the environment.


SignalFrame maintains a geospatial temporal graph platform that relates human movements through space using detectable WiFi and Bluetooth signals emitted by devices like smartphones, wearables, electronics, cars, and network routers. The graph is built and refreshed by signal observations crowdsourced from approximately 30 million monthly enabled smartphones and tablets moving around the world.

Core applications are focused on surfacing physical-social networks, community-based analytics, and tracking network evolution over time. For example, SignalFrame reveals co-location between people (i.e., which devices share the same space at the same time), and the nature and weight of relationships between those individuals (or groups of individuals). Similarly, co-occupation of spaces (i.e., which locations tend to share visitors across time) highlights relationships between different places and surfaces locations of significance to a social network.


SignalFrame has developed a “geographical-graph” (GeoGraph) model for public health applications to assess risk levels based on historical and current information about the spread of the COVID-19 virus. GeoGraph links geographic locations based on sets of mobile devices that those locations share in common across time. Geographic linkages rely on underlying social networks (excluding people who are merely in “transit”), and are updated in real-time as behaviours change.

For example, individuals that regularly frequent a certain Community Center may live in a distinct and far-away Residential Neighborhood. As a result, the Community Center and Residential Neighborhood form part of a geo-network linked by the social network of visitors. Without GeoGraph, that linkage would not be obvious given the lack of physical proximity between the two locations.


GeoGraph can:


1: Provide a broad risk-assessment to all locations:

  • not limited to single tracked individuals,

  • not limited by the frequency of location tracking (location scans),

  • not limited to “co-observation” of different individuals to deliver actionable information.

2: Update risk profiles in real-time with new information:

  • each confirmed case-location (and relevant available movement trajectories) updates risk profiles for all other geographies,

  • highlights new potential hotspot geographies . Finally, GeoGraph’s linkages can be strengthened using different data sources merged on geographic keys, including:

  • additional data elements attached to geographies,

  • data from multiple sources, including government, telecommunications, and media organisations.

At a glance:

  • 10+ thousand device types (electronics, wearables, cars, appliances, etc.)

  • 6.5+ thousand venues and businesses (restaurants, hotels, office buildings, travel hubs, etc.)

  • 8+ billion total unique signals (WiFi, BLE, Bluetooth)

  • 2+ million signals per minute


Leverage the Signal Classifier to understand the world through signals at any location.


By capturing detectable signals from thousands of products, venues, and brands, SignalFrame determines when and where new things appear in the world. Our SignalGraph™ platform sees cars drive off the lot, early adoption of products in specific markets, and businesses opening and turning on their WiFi networks.


At a glance:

  • 10+ thousand device types (electronics, wearables, cars, appliances, etc.)

  • 6.5+ thousand venues and businesses (restaurants, hotels, office buildings, travel hubs, etc.)

  • 8+ billion total unique signals (WiFi, BLE, Bluetooth)

  • 2+ million signals per minute


Traditional GPS-based location services are inaccurate in dense urban areas, limited vertically (no “z” axis), vulnerable to spoofing, and dependant on high-quality cell service or WiFi connections.

Signal-based location adds precision and reliability in environments where GPS solutions struggle.


Dense (indoor and urban) environments contain webs of pre-deployed WiFi, Bluetooth, and BLE signals. Our SignalGraph™ platform clusters persistent co-located signals, and — leveraging a knowledge-base of thousands of hospitality, retail, commercial, and transit-hub brands — maps them to places of interest.


The SignalGraph grows out of crowdsourced observations of the ambient signal environment that reveal the presence and proximity of wifi, bluetooth and BLE devices in the environment.


SignalFrame maintains a geospatial temporal graph platform that relates human movements through space using detectable WiFi and Bluetooth signals emitted by devices like smartphones, wearables, electronics, cars, and network routers. The graph is built and refreshed by signal observations crowdsourced from approximately 30 million monthly enabled smartphones and tablets moving around the world.

Core applications are focused on surfacing physical-social networks, community-based analytics, and tracking network evolution over time. For example, SignalFrame reveals co-location between people (i.e., which devices share the same space at the same time), and the nature and weight of relationships between those individuals (or groups of individuals). Similarly, co-occupation of spaces (i.e., which locations tend to share visitors across time) highlights relationships between different places and surfaces locations of significance to a social network.


SignalFrame has developed a “geographical-graph” (GeoGraph) model for public health applications to assess risk levels based on historical and current information about the spread of the COVID-19 virus. GeoGraph links geographic locations based on sets of mobile devices that those locations share in common across time. Geographic linkages rely on underlying social networks (excluding people who are merely in “transit”), and are updated in real-time as behaviours change.

For example, individuals that regularly frequent a certain Community Center may live in a distinct and far-away Residential Neighborhood. As a result, the Community Center and Residential Neighborhood form part of a geo-network linked by the social network of visitors. Without GeoGraph, that linkage would not be obvious given the lack of physical proximity between the two locations.


GeoGraph can:


1: Provide a broad risk-assessment to all locations:

  • not limited to single tracked individuals,

  • not limited by the frequency of location tracking (location scans),

  • not limited to “co-observation” of different individuals to deliver actionable information.

2: Update risk profiles in real-time with new information:

  • each confirmed case-location (and relevant available movement trajectories) updates risk profiles for all other geographies,

  • highlights new potential hotspot geographies . Finally, GeoGraph’s linkages can be strengthened using different data sources merged on geographic keys, including:

  • additional data elements attached to geographies,

  • data from multiple sources, including government, telecommunications, and media organizations.

2018

A new generation of location-enabled services - Proximity Services - emerges that builds upon traditional location-enabled services by improving precision, providing additional security, and addressing privacy concerns of legacy lat/lon offerings.


2 views0 comments