What We Do? 

We pioneer in harnessing the building facilities’ data for actionable indoor emergency location intelligence. 

Why We Do This? 

Emergency service agencies have struggled to locate distressed wireless callers at floor level inside the multi-story buildings for far too long. It is a growing issue as emergency calls are now predominantly based on IP (internet protocol) addresses. FCC has adopted comprehensive rules and deadlines to improve location accuracies, such as the handset z-axis information and the dispatchable location.

From April 3, 2021, with a six-year benchmark.

  • …….CMRS providers also must deliver z-axis information to meet accuracy metric of within 3 meters above or below (plus or minus 3 meters) the handset in Height Above Ellipsoid and,  floor level information where available  (47 CFR § 9.10(i)(2)(ii)(C)

From January 6, 2022,

  • All CMRS providers shall provide the dispatchable location with wireless E911 calls if it is technically feasible for them to do so (47 CFR § 9.10(i)(2)(ii)(G)))

To achieve floor-level accuracy at any time poses a significant challenge. While FCC’s wireless dispatchable location information is a forward-looking mandate applicable only when technically feasible, the current z-axis +/- 3 meters standard does not warrant the precise floor level information. The wireless 911 dispatchable locations, unfortunately, remain at large.

Moreover, emergencies inside buildings often involve power cuts, whether caused by faults in commercial power supply networks, natural disasters, or firefighters’ MPO-Mains Power Off practise before entering a building when the dispatchable location may become unavailable. There is a need for a reliable indoor position system that is always up for action to provide 911 call dispatchable locations of floor level and room numbers.
IndoorSOS® is committed to serving this purpose.

A Technological Paradigm. 

We live in an increasingly wireless world, especially inside buildings, where we spend 80% of our lifetime. The influx of Internet of Things (IoT) devices and systems has started a new wireless era in our living environment, typically indoors. This in-building wireless RF technological paradigm creates an omnipresent opportunity to apply indoor positioning technologies to help locate emergency callers on demand.
Smart building facilities/utilities, such as smart lighting, access control, smoke alarms &, etc., are embedded with sensors/microcontrollers permanently deployed in the buildings. They transmit data packets under specific radio frequency communication protocols, which also work with smartphones. These wireless sensors have unique electronic IDs and addresses to communicate with each other and connect to the internet under specific networking topologies. On calling 911, smartphones can interact with these facilities. Computing the groups of clustered and indexed unique names and RF communication addresses of these sensor nodes can identify the indoor position of the smartphone that commits communication with these nodes. Therefore, the communication triggered by the emergency call can reveal the caller’s location relevant to these nodes’ physical location address, including the floor level and room number. Proudly, IndoorSoS™ has developed a cloud computing and mapping engine assisted by a machine-learning algorithm to empower these sensor nodes to act as ever-ready ‘satellites’ to locate emergency calls inside the buildings.

IndoorSOS® is a cutting-edge solution for indoor locations with certainty.

IndoorSoS® runs a cloud and a local edge mapping engine responsible for computing the caller’s indoor location. The groups of clustered IDs of the permanent facilities, such as the smart emergency lights that are always on, are indexed, referenced & bound to the data source of the physical areas of room number and floor level, forming a digital twin of a critical database of the Emergency addresses. The digital twin cloud program collects and processes real-time synchronised data from the sensors/controllers that interact with the caller’s handset on demand during the emergency call. A machine learning algorithm manoeuvres the datasets and continually gains knowledge of the facilities’ dynamics data for physical emergency call location enhancement. The emergency caller’s real-time location data are compiled into an Emergency Incident Data Document (EIDD) compliant with Next Generation 9-1-1 standards. The emergency call centre dispatchers and the field responders can then share the EIDD over the Emergency Service Internet (Esinet) through the dedicated first responder communication network, such as the FirstNet™,

IndoorSoS ™ tech of wireless access power-backup edge computing pioneers to provide a solution for ‘floor level information with certainty that is always on’.

Ever-ready Dispatchable Location Provides Peace of Mind in Calling for Emergencies.™ 


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