Bluetooth Low Energy (BLE) and the iBeacons have recently gained large interest for enabling various proximity-based application services. Given the ubiquitously deployed nature of Bluetooth devices including mobile smartphones, using BLE and iBeacon technologies seemed to be a promising future to come. This work started off with the belief that this was true: iBeacons could provide us with the accuracy in proximity and distance estimation to enable and simplify the development of many previously difficult applications. However, our empirical studies with three different iBeacon devices from various vendors and two types of smartphone platforms prove that this is not the case. Signal strength readings vary significantly over different iBeacon vendors, mobile platforms, environmental or deployment factors, and usage scenarios. This variability in signal strength naturally complicates the process of extracting an accurate location/proximity estimation in real environments. Our lessons on the limitations of iBeacon technique lead us to design a simple class attendance checking application by performing a simple form of geometric adjustments to compensate for the natural variations in beacon signal strength readings. We believe that the negative observations made in this work can provide future researchers with a reference on how well of a performance to expect from iBeacon devices as they enter their system design phases.

/pdf/a-measurement-study-of-ble-ibeacon-and-geometric-adjustment-lwqb1tzb5v.pdf

A Measurement Study of BLE iBeacon and Geometric Adjustment Scheme for Indoor Location-Based Mobile Applications

An asset tag device including a housing, a battery disposed within the housing, a circuit board disposed within the housing and in electrical communication with the battery. The circuit board includes a local memory, a transceiver to detect local signals and transmit a response packet, and a processor. The processor to identify the detected local signals, determine whether the detected local signals include at least one beacon signal, store, to the local memory, the at least one beacon signal, sort, in the local memory, the stored at least one beacon signal by proximity to the asset tag device, parse the sorted at least one beacon signal, and append tag information to the parsed at least one beacon signal to generate the response packet.

Asset management using an asset tag device

Anonymous authentication and location privacy preserving schemes for LTE-A networks

Improving Indoor Positioning Accuracy with Dense, Cooperating Beacons

The embodiment of the invention discloses a wireless grid building method comprising the following steps: a first base station measures a sensing reference signal SRS sent by target user equipment UE so as to obtain a first uplink reference signal reception power RSRP, wherein the target UE refers to the UE in a service community to which the first base station is located; the first base station obtains the first emission power of the target UE sending the SRS; the first base station calculates the first uplink path loss of the target UE sending the SRS according to the first emission power and the first uplink RSRP; the first base station receives second uplink path losses sent by adjacent base stations; the first base station builds the wireless grid according to the first and second uplink path losses. The base station has much stronger receive and measuring ability when compared with the UE; normally the information sent by single UE can be received by a plurality of surrounding base stations, so the base station can obtain the UE uplink path losses so as to build the wireless grid with more accuracy, thus improving UE location positioning.

Wireless grid building method and base station

Indoor Positioning with an Enterprise Radio Access Network

Examples of determining the position of a mobile device within a structure are disclosed. In one example implementation according to aspects of the present disclosure, a computing device may include a processor and a memory. The computing device may further include an extraction module stored in the memory and executing on the processor to extract from a location-based service data stream a signal measurement of the path loss between a mobile device and a plurality of radio nodes distributed throughout a structure. The computing device may also include a positioning determining module stored in the memory and executing on the processor to determine the position of the mobile device within the structure using a known location for each of the plurality of radio nodes distributed throughout the structure by performing a linear optimization using the extracted signal measurement of the path loss.

Determining the position of a mobile device within a structure

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Indoor Positioning with an Enterprise Radio Access Network

Determining the position of a mobile device within a structure