CN113574415B

CN113574415B - Apparatus, system and method for determining a geographic location

Info

Publication number: CN113574415B
Application number: CN201980082796.0A
Authority: CN
Inventors: 齐尔菲拉兹·西迪基
Original assignee: Qi ErfeilaziXidiji
Current assignee: Qi ErfeilaziXidiji
Priority date: 2018-12-11
Filing date: 2019-12-10
Publication date: 2022-06-07
Anticipated expiration: 2039-12-10
Also published as: KR20210099130A; CN113574415A; WO2020123493A1; JP2023512608A; EP3894897A1; EP3894897A4; SG11202106004WA

Abstract

An apparatus, system, and method are provided for determining a geographic location. The apparatus includes a receiver, a sensor, a processor, and a transmitter. The receiver is configured to receive a first geographic location. The sensor is configured to determine a change in a pose of the device. The processor is operably coupled to a memory, the receiver and the sensor the processor configured to determine a second geographic location based on the first geographic location and the sensor utilizing a neural network. The first transmitter is configured to output the second geographic position of the apparatus.

Description

Apparatus, system and method for determining a geographic location

Cross Reference to Related Applications

The present application claims priority to U.S. provisional application 62/777782, U.S. provisional application No.62/798, 754 entitled "GPS-free" technology, filed on 30/1/2010, U.S. provisional application No.62/872, 262 entitled "Location-estimating apparatus", U.S. provisional application No.62/872, 262, 62/942, 218 entitled "network-on-ground positioning system (nogp) and sensor-actuated neural network for estimating geographic Location", filed on 2/12/2019, the entire contents of which are incorporated herein by reference in their entirety.

Background

A Global Positioning System (GPS) may be used to determine the geographic location of a GPS-enabled device. For example, a GPS-enabled device may receive GPS broadcasts from satellite orbits 12,000 miles above the surface of the earth. There are challenges to the power requirements and accuracy of GPS-enabled devices.

Disclosure of Invention

In an example, an apparatus for determining a geographic location is provided. The apparatus includes a receiver, a sensor, a processor, and a transmitter. The receiver is configured to receive a first geographic location. The sensor is configured to determine a change in a pose of the device. A processor is operatively coupled to the memory, the receiver and the sensor processor configured to determine a second geographic location based on the first geographic location and the sensor utilizing the neural network. The first transmitter is configured to output the second geographic position of the apparatus.

In another example, a network for determining a geographic location is provided. The network includes nodes and mobile devices. The node comprises a transmitter configured to output a current geographic location of the node via a wireless communication protocol. The wireless communication protocol comprises a near field communication protocol, a Bluetooth low energy protocol, a Wi-Fi protocol or a ZigBee protocol, or a combination thereof. The receiver is configured to receive a current geographic location of the node via a wireless communication protocol. The sensor is configured to determine a change in a pose of the mobile device. A processor is operatively coupled to the memory, the receiver and the sensor processor configured to determine a current geographic location of the mobile device based on the current geographic location of the node and the sensors utilizing the neural network.

Drawings

The novel features believed characteristic of the various aspects described herein are set forth with particularity in the appended claims. However, various aspects relating to organization and method of operation, may be better understood by reference to the following description taken in conjunction with the accompanying drawings, as follows:

FIG. 1 illustrates an example of a system diagram of a location-enabled apparatus for determining a geographic location according to the present disclosure;

FIG. 2 illustrates an example of a process diagram for determining a second geographic location according to the present disclosure;

FIG. 3 shows an example of a process diagram for training a neural network in accordance with the present disclosure;

FIG. 4 shows an example of a process diagram for fine tuning a neural network according to the present disclosure;

FIG. 5 illustrates an example of a system diagram of a location-enabled apparatus for determining a geographic location that may receive an observed geographic location from a device and transmit a second geographic location to the device, in accordance with the present disclosure;

FIG. 6 illustrates an example of a system diagram of a location-enabled apparatus for determining a geographic location that may transmit a second geographic location to a first device and receive an observed geographic location from a second device in accordance with the present disclosure;

FIG. 7 shows an example of a process diagram for a mobile device including the functionality of a location-enabled device according to the present disclosure integrated within a location-enabled battery and location enabler application;

FIG. 8 illustrates an example of a process diagram for a mobile device including functionality for a location-enabled device and a location-enabled application in accordance with this disclosure; and

fig. 9 illustrates an example of a system diagram of a network of nodes for determining a geographic location according to the present disclosure.

Detailed Description

Various examples are described and illustrated herein to provide a thorough understanding of the structure, function, and use of the disclosed articles and methods. The various examples described and illustrated herein are non-limiting and non-exhaustive. Accordingly, the invention is not limited by the description of the various non-limiting and non-exhaustive examples disclosed herein. Rather, the invention is limited only by the claims and features shown and/or described in connection with various examples may be combined with features and features of other examples. Such modifications and variations are intended to be included within the scope of this description. Thus, the claims may be amended to recite any features or characteristics expressly or inherently described in, or otherwise expressly or inherently supported by, the present specification-in addition, applicants reserve the right to amend the claims to affirmatively ignore features or characteristics that may be present in the prior art. Various examples disclosed and described in this specification may include, consist of, or consist essentially of the features and characteristics as variously described herein.

Any reference herein to "some examples," "an example," or similar phrases, means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example. Thus, the phrases "in various examples," "in some examples," "in one example," "in an example," or similar phrases in various instances do not necessarily refer to the same example in the specification-furthermore, the particular described features, structures, or characteristics may be combined in any suitable manner in one or more examples. Thus, a particular feature, structure, or characteristic described or illustrated in connection with one example may be combined, in whole or in part, with features, structures, or characteristics of one or more other examples, and no limitation is intended to such modifications or variations be included within the scope of this example.

In this specification, unless otherwise indicated, all numerical parameters should be understood as being modified and amended in all instances by the term "about", wherein the numerical parameters have the inherent variability characteristic of the underlying measurement technique used to determine the numerical value of the parameter, at least, and not intended to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter described herein should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

Moreover, any numerical range recited herein includes all sub-ranges subsumed within that range. For example, a range of "1 to 10" includes all subranges between the recited minimum value of 1 and the recited maximum value of 10, i.e., having a minimum value equal to or greater than 1 and a maximum value of equal to or less than 10, any maximum numerical limitation recited in this specification is intended to include all lower numerical limitations subsumed therein, and any minimum numerical limitation recited in this specification is intended to include all higher numerical limitations subsumed therein.

Accordingly, applicants reserve the right to modify this specification (including the claims) to specifically recite any sub-ranges subsumed within that specifically recited range. All such ranges are inherently described in this specification.

As used herein, the grammatical articles "a," and "are intended to include" at least one "or" one or more "unless otherwise indicated, even if at least one claim or one or more claims are explicitly used in some instances. Thus, the foregoing grammatical articles are used herein to refer to one or more (i.e., at least one) of the particular identified elements and, further, the use of a singular noun includes the plural, and the use of a plural noun includes the singular, unless the context of use requires otherwise.

The location-enabled device may include a Global Positioning System (GPS) receiver to determine its geographic location. However, GPS receivers may require a significant amount of power (e.g., more than 100 milliamps (mA) of current) in order to process GPS broadcasts from satellites and determine the geographic location of a location-enabled device-in addition, GPS receivers must remain polled after periodic intervals in order to keep the geographic location updated, consuming additional power. Further, the GPS typically remains in an "on" state because the GPS receives for a long time (e.g., more than 1 minute) to become operational. Many location-enabled devices (e.g., cellular telephones) have other energy-intensive functions, such as screens, speakers, and indicators (e.g., LEDs). accordingly, it may be advantageous to reduce the energy consumption of the location-enabled equipment to achieve increased battery life.

The inventors of the present disclosure have determined that limiting the use of GPS receivers can reduce the energy consumption of location-enabled devices. For example, it may be advantageous to utilize a sensor configured to determine a change in the geographic location of a location-enabled device that requires less current than a GPS receiver-e.g., a low power sensor for determining the geographic location, such as a micro-electromechanical system (MEMS) accelerometer, may require a low current to operate (e.g., less than or equal to eleven or less micro-amperes (mA)). Thus, utilizing a low power sensor to determine the geographic location of a location-enabled device rather than a GPS receiver may reduce energy consumption of the location-enabled device-however, typical accelerometer geolocation-only methods may be inaccurate.

Accordingly, the present disclosure provides a location-enabled apparatus that includes a neural network and a sensor configured to determine a change in a pose of a location-enabled device while reducing power consumption. The location-enabled device may initialize its geographic location (e.g., via a GPS receiver, a node of the network, other device) after the initialization of the geographic location, the neural network may determine a change in the pose of the location-enabled apparatus from the initialized geographic location using the sensors. The change in pose may be used to determine the current position of the position-enabled device without utilizing a GPS receiver. Thus, a reduction in the energy required to determine the geographic location of the location-enabled device may be achieved.

Additionally, a location-enabled device according to the present invention may be trained by periodically providing an observed geographic location (e.g., correct, accurate, actual) using a GPS receiver, a node of a network, and/or other observed geographic location-providing device. Accuracy of geographic location determination for location-enabled devices according to the present disclosure may be provided to a neural network (e.g., to fine tune algorithms within the neural network) during training as more observed geographic locations.

Fig. 1 illustrates an example of a system diagram of a location-enabled apparatus 100 for determining a geographic location according to the present disclosure. The apparatus 100 may include a processor 102 (e.g., a microcontroller unit (MCU)), a memory 104, a receiver 106, a transmitter 108, a sensor 110, and a neural network 116. The processor 102 is operatively coupled to the memory 104, the receiver 106, the transmitter 108, and the sensor 110.

The memory 104 may be a non-transitory memory and may include machine executable instructions that, when executed by the processor 102, may cause the processor 102 to perform the neural network 116, various algorithms, and other innovative functions described herein. Memory 104 may include any machine-readable or computer-readable medium capable of storing data, including volatile and non-volatile memory, for example, memory 104 may include read-only memory (ROM), Dynamic RAM (DRAM), double data rate DRAM (DDR-RAM), synchronous DRAM (sdram), static RAM (sram), programmable ROM (prom), erasable programmable ROM (eprom), electrically erasable programmable ROM (eeprom), flash memory (e.g., NOR or NAND flash memory), Content Addressable Memory (CAM), polymer memory (e.g., ferroelectric polymer memory), phase change memory (e.g., ovonic memory), ferroelectric memory, silicon oxide-nitride-oxide-silicon (SONOS) memory, disk memory (e.g., floppy disk, hard drive, optical disk, magnetic disk) or card (e.g., magnetic card, optical cards) or any other type of media suitable for storing information. In various examples, the memory 104 may be a secure memory, such as, for example, a write-once (WORM) memory, a blockchain enabled memory, or other secure storage memory, or a combination thereof.

The receiver 106 may be configured to receive a first geographic location. As used herein, a "geographic location" is the location (e.g., longitude, latitude, altitude) and/or orientation of an object relative to the earth. The first geographic location may be an initial geographic location, a current geographic location, a recent geographic location, an observed geographic location, or a combination thereof. Thereafter, the first geographic location may be stored in the memory 104 where the receiver 106 may be configured to receive the first geographic location via a first wireless communication protocol or a wired communication protocol, or a combination thereof. The wireless communication protocol may include a near field communication protocol, a bluetooth low energy protocol (e.g., 2.4MHz), a Wi-Fi protocol (e.g., 800MHz), or a ZigBee protocol, or a combination thereof.

The sensor 110 may be configured to measure a change in a pose (e.g., position and/or orientation) of the device 100, which may be at least two degrees of freedom, such as at least three degrees of freedom, at least four degrees of freedom, or at least five degrees of freedom. In various examples, the change in attitude may be in six degrees of freedom the sensor 110 may include an accelerometer, an inertial measurement unit, a gyroscope or magnetometer, or a combination thereof. The sensor 110 may output the measured change in attitude as an attitude signal to the processor 102 for utilization with the neural network 1. In various examples, the sensor 110 may output a gesture signal to the processor 102 that the device 100 has not moved.

The processor 102 may process the attitude signals from the sensors 110 using the neural network 116 and may determine a second geographic location of the apparatus 100 based on the first geographic location. The second geographic location may be a current geographic location, a nearest geographic location or an estimated geographic location, or a combination thereof.

The neural network 116 may be stored in the memory 104, as shown in FIG. 1, or a remote device (not shown). The neural network 116 receives the pose data from the sensors 110 and, together with the processor 102, processes the pose data into change vectors (e.g., direction and magnitude of the pose change). For example, the neural network 116 may include artificial neurons that may be connected together using edges. The artificial neurons may be arranged in at least two layers-for example, one of the layers may be an input layer that may receive gesture data, and a different one of the layers may be an output layer that may provide an output. The propagation function at each artificial neuron may compute an output (e.g., pose data, predecessor artificial neuron outputs) based on predefined weights associated with each input to that artificial neuron, and the resulting output of the resulting output neural network 116 that may use a bias to adjust the artificial neuron may be a variation vector. Thereafter, the processor 102 may receive the change vector and determine a second geographic location based on the first geographic location (e.g., a previous location).

The processor 102 may be a Central Processing Unit (CPU). The processor 102 may be implemented as a general purpose processor, a Chip Multiprocessor (CMP), a special purpose processor, an embedded processor, a Digital Signal Processor (DSP), a network processor, a media processor, an input/output (IO) processor, a Media Access Control (MAC) processor, a radio baseband processor, a vector coprocessor, a microprocessor such as a Complex Instruction Set Computer (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, and/or a Very Long Instruction Word (VLIW) microprocessor or other processing device processor and may also be implemented by a controller, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or the like. The processor 102 may be configured to run an Operating System (OS) and various other applications.

The processor 102 may be arranged to receive information via the communication interface. The communication interface may include any suitable hardware, software, or combination of hardware and software that is capable of coupling the processor 102 to another component of the apparatus 100, a network or other device, or a combination thereof-for example, the processor 102 may receive information, such as the first geographic location via the receiver 106 and the attitude signals from the sensors 110. As described herein, the processor 102 may utilize the neural network 116 to determine the second geographic location of the apparatus 100 based on the first geographic location and the sensor 110 (e.g., a pose signal from the sensor 110). for example, the processor 102, in conjunction with the sensor 110, may measure a change in pose of the device 100 relative to the first geographic location, and thereby determine the second geographic location based on the change from the first geographic location.

The processor 102 may store the first geographic position, the second geographic position, or the gesture signal, or a combination thereof, in the memory 104. For example, the processor 102 may be configured to store a first geographic location in the memory 104, and the processor 102 may be configured to overlay the first geographic location with a second geographic location in the memory 104.

A process diagram 200 for the processor 102 for determining the second geographic location is shown in fig. 2, and as shown, the nearest geographic location may be retrieved 202 from memory. In various examples, the recent geographic location may be the first geographic location received by the receiver 106 or a recently determined second geographic location. As the device 100 is moved, the sensor 110 may output a gesture signal, which may be received 204 by the processor 102, after which the processor 102 may process 206 the gesture signal using the neural network 116. In various examples, the neural network 116 may be pre-trained and/or fine-tuned as shown in fig. 3 and as shown in fig. 4 and/or as described herein and/or combinations thereof.

The processor may determine 208 a change vector processing 206 pose signal of the apparatus 100 based on the most recent geographic location stored in the memory relative to the most recent geographic location stored in the memory. In order for processor 102 to accurately calculate the change vector, attitude signals 206 processed by processor 102 should take into account any movement between the nearest geographic location stored in memory and the current location of device 100. Thereafter, the processor 102 may determine 210 the second geographic location based on the latest geographic location retrieved from the memory 104 and the determined change vector, the processor 102 may output 212 the second geographic location at step 214 and update 214 the memory with the second geographic location-for example, the processor 102 may overlay the latest geographic location with the second geographic location, whereby the second geographic location becomes the latest geographic location for another iteration of the process diagram 200 as shown in fig. 2, which 2, as shown in fig. 2, may execute based on a desired frequency, a triggering event (e.g., activation of an energy harvesting device as described herein) or other parameter, or a combination thereof.

The neural network 116 of the device 100 may be trained as the process diagram 300 shown in fig. 3 and described herein such that the second geographic location output may be accurate (e.g., substantially similar to the observed geographic location of the device 100). In various examples, the receiver 106 may be configured to receive the observed geographic location and the processor 102 may be configured to train the neural network 116 using the observed geographic location-for example, the processor 102 may be configured to train the neural network 116 by adjusting weights and biases in the neural network 116.

For example, in fig. 3, a process diagram 300 of the processor 102 training the neural network 116 is shown, as shown, the nearest geographic location may be retrieved 302 from memory, and an observed geographic location (e.g., a correct observed geographic location) 304 may be received. The most recent geographic location and the retrieved geographic location are provided 306 to the processor 102 for processing to determine a difference, if any, between the current geographic location and the observed geographic location (e.g., (comparison). thereafter, the processor 102 may return any observed errors via the neural network 1. for example, the processor 102 may determine 308 a derivative of an error function (e.g., a loss function) of the determined difference.

Thereafter, the trained neural network 116 may process 314 the sensor signals from the sensors 110 (e.g., reprocess), from the received 312 sensor signals, and determine a change in geographic location from a previous geographic location stored in memory (e.g., prior to the most recent geographic location). The processor 102 may output 316 the second geographic location and update 318 the memory with the second geographic location in the memory. Another iteration of the process diagram 300, as shown in fig. 3, may be performed based on a desired number of iterations, a calculated error between outputting the second geographic location and the observed geographic location, or other parameters or combinations thereof. After the training in fig. 3, the neural network 116 may be considered trained, and the neural network 116 may be encoded into other devices.

After the neural network 116 is trained, the neural network 116 may be trimmed in the desired device. The fine-tuning can also take place in the respective device at a desired frequency. A process diagram 400 of the fine tuning of the neural network 116 in the device 100 by the processor 102 is shown in fig. 4, as shown, the nearest geographic location may be retrieved 404 from memory at step 402, and the observed geographic location may be received and provided 406 to the processor 102 for processing. For example, the processor 102 may determine a derivative of the error function over the observed geographic location and back-propagate the derivative of the error function through the neural network 116.

Thereafter, the trained layers of the neural network 116 may be retrieved 408, and the trained layers of the neural network 116 may be fine-tuned with the adjusted weights and biases. The processor 102 may process (e.g., reprocess) the attitude signals from the sensors 110. For example, the processor may determine a change vector with respect to a current geographic location stored in the memory using the fine-tune neural network, and the second geographic location processor 102 of the determination device 100 may output 412 a second geographic location and update the memory with the second geographic location. In various examples, the processor 102 may determine a level of error in the second geographic location relative to a previous nearest geographic location and output the observed geographic location if the level of error is greater than or equal to a threshold, in various other examples, the processor 102 may output the second geographic location if the level of error in the second geographic location relative to the current geographic location is less than the threshold.

Another iteration of the process diagram shown in fig. 4 may be performed based on a desired number of iterations, a calculated error between the most recent geographic location and the observed geographic location, a desired frequency or other parameter, or a combination thereof.

Referring again to fig. 1, in various examples, the transmitter 108 may be configured to output the second geographic location of the apparatus 100. The second geographic location may be the same first geographic location or a different geographic location. For example, if the device 100 moves without receiving the first geographic location from the apparatus 100, the second geographic location may be the same as the first geographic location and the transmitter 108 may be configured to output the second geographic location via the first wireless communication protocol or the wired communication protocol, or a combination thereof. The wireless communication protocol may include a near-end field communication (NFC) protocol, a Bluetooth Low Energy (BLE) protocol (e.g., 2.4MHz), a Wi-Fi protocol (e.g., 800MHz), or a ZigBee protocol, or a combination thereof. The transmission of the second geographic location may be fixed or unsecured depending on the application.

Receiver 106 and/or transmitter 108 may include wireless communication circuitry, which may be mobile chipset Radio Frequency (RF) wireless circuitry, or simply a cellular radio. The wireless communication circuit may be a low power chipset and may be configured to connect to a network and another device 120 (e.g., a mobile device such as a cell phone, smartphone, tablet, laptop, gateway device, etc.) the wireless communication circuit may include an antenna, transmitter circuit or receiver circuit, or a combination thereof, for receiving and transmitting wireless signals.

As shown in fig. 1, device 120 may communicate with apparatus 100 via link 122. In various examples, the device 120 may receive the second geographic location from the apparatus 100 and transmit the observed geographic location (e.g., training data) to the apparatus 100, as shown in fig. 5 at 6, a plurality of devices may be in communication with the apparatus, and the first device 620a may receive the second geographic location and the second device 620b may transmit the observed geographic location to the apparatus 100.

Device 100 may be configured as a NFC-BLE beacon. For example, the receiver 106 may communicate wirelessly via NFC and the transmitter 108 may communicate wirelessly via BLE.

The apparatus 100 may include an energy harvesting device 112, which may include a piezoelectric energy harvesting device, an electrostatic energy harvesting device, an electromagnetic energy harvesting device, a photovoltaic cell, or a Radio Frequency (RF) energy harvesting device, or a combination thereof, for example, if the apparatus 100 is positioned near a roadway, traffic on the roadway may cause vibrations, which may be converted by the energy harvesting device 112 into electrical power to power the apparatus 100. In various other examples, the apparatus may be attached to a car and vibrate (e.g., from moving, engine vibration), and the amount of power that may be converted by the energy harvesting apparatus into electricity generated by the energy harvesting device 112 may be suitable for powering the apparatus 100 to determine the second geographic location (e.g., calculating longitude and latitude).

In various examples, device 100 may transmit a second geographic location that it receives vibrations from the road whenever energy-harvesting device 112 provides power to device 100 (e.g., when energy-harvesting device 112 provides power to device 100). At other times, the device 100 may not output the second geographic location in order to conserve power.

In various examples where the device 100 is batteryless, the device 100 includes an RF acquisition device and is configured to transmit the second geographic location via BLE, the device 100 may be used in a fixed location, for example, as a house number, a door clock, a pole, a wall, a traffic signal, a sidewalk, a transit station, or a public place, or a combination thereof.

The device 100 may include a GPS receiver 118. The GPS receiver 118 may be configured to provide the observed geographic location to the device 100 in order to train the neural network 116, as shown in fig. 3 and described herein. For example, the GPS receiver 118 may provide the observed geographic location for processing by the processor 102. In various examples, it may be desirable to limit the operation of the GPS receiver 118 in order to reduce the power consumption of the apparatus 100.

The device 100 may include circuitry designed to interface with various sensors and combinations of components of the device 100. For example, the apparatus 100 may provide a combination of analog front end, vector/digital signal processing, microprocessor and memory in a low power ASIC chip, which may include a plurality of functions, such as, for example, geolocation determination, neural network training, neural network fine tuning, etc. the apparatus 100 may include various components and modules for supporting the functions of the apparatus 100, such as a printed circuit board assembly, a Universal Serial Bus (USB), connection ports to external devices and/or sensors, and hardware accelerators, data storage, serial interfaces, such as SPI, Universal Asynchronous Receiver Transmitter (UART), two-wire multi-master serial single-ended bus interface (I2C), general purpose input/output (GPIO), real-time clocks, control circuitry, analog-to-digital converters (ADC), gain and adjustment circuitry, drivers, and other components.

In various examples, the apparatus 100 may include a battery 114, and the neural network 116 may be embedded in the battery 114 (not shown). In various examples, the memory 104, the processor 102, and the sensor 110 may be embedded in the battery 114. Thus, the device 100 may remain in an on state, and thus may keep updating the geographic location of the device 100 each time the device 100 including the battery 114 is moved-in various examples, the initial location of the device 100 including the battery 114 may be pre-seeded at the time of manufacture (e.g., received by the receiver 106 and stored in the memory 104 during manufacture) or at a later time.

The neural network 116 may be embedded in a Battery Management System (BMS) of the battery 114. The sensors 110 may track the movement of the battery 114 and the processor 102 utilizing the neural network 116 may continuously update the geographic location based on the attitude signals from the sensors 110. Thus, any device including a location-enabled device that includes a battery may utilize the location-enabled device for power and current geographic location.

In various examples in which the neural network 116 is embedded in the battery 114, the receiver 106 may listen for nodes (e.g., location beacons) as described herein and receive observed geographic locations from the nodes to train the neural network 116 (e.g., back propagation). Thus, the battery 114 may include circuitry for training the neural network 116, such that the battery 114 may be a self-training, standalone, location estimation unit, and may not require communication with a mobile device to train the neural network 116.

The location enabling apparatus according to the present disclosure may be integrated into a mobile device. For example, as shown in application diagram 700 shown in fig. 7, a mobile device including the functionality of a location-enabled device according to the present disclosure may be integrated with a location-enabled battery and a location-enabler application for execution by the mobile device as shown in application diagram 700, the location-enabled application 702 may receive an observed geographic location 704 for fine-tuning (when needed) from a location-enabled device 706 or from a GPS satellite 708 (up in the sky). The location enabler application 702 may also receive the estimated location 710 from the location-enabled battery 7122. Further, the location enabler application 702 may provide a back-propagation 714 to the location-enabled battery 712. furthermore, the location enabler application 702 may receive motion data 716 from the smartphone 718 and provide the estimated location 720 to the smartphone 7186. The location enabler application 702 provides the estimated location 720 using the neural network within the location enabler application 702 and the processor of the smartphone 7184. In various examples, the location enabler application 702 may be located on the smartphone 7184.

In some examples, when the smartphone 718 utilizes the Android OS, the location enabler application 702 may run as a "background service" that may not be visible to the end user. In various examples, when smartphone 718 utilizes an OS other than Android, multitasking may be enabled and location enabler application 702 may be configured as a local server. In some examples, a phone manufacturer may provide a place enabler application 702 bundled with its corresponding OS.

In various examples, as shown in application diagram 800 shown in fig. 8, a mobile device including the functionality of a location-enabled apparatus according to the present disclosure is provided, as well as a location-enabled application 816 for execution by the mobile device. The location-enabled apparatus according to the present disclosure may execute the mobile device by a service running in the context of the mobile device may include an Android operating system, iOS or other operating system, or a combination thereof. The location-based application 816 may receive the current geographic location 802 to form a smartphone 718, and provide a second geographic location 804 to the smartphone 718 if needed and back-propagate 806 the smartphone 718 including a location-aware application 808 and an in-memory neural network 810 that receives information from a motion sensor 810 on the smartphone 718 and an in-memory fine-tuning' layer (n +1) 814.

The location enabler application 702 (e.g., a NOGPS application) may be a link between a fixed location beacon and a neural network in accordance with the present disclosure. The NOGPS application may provide location information to other applications (e.g., location-enabled application 816) on the mobile device, such as a navigation application (e.g., map, Waze), location service or SOS service, a gaming application, or a combination thereof, which may require the geographic location of the mobile device. The noggps application may obtain a trained neural network according to the present disclosure from memory on the mobile device, a battery, and/or from an auxiliary device (e.g., the cloud). For example, a mobile device that may utilize cloud storage to maintain a replica of a trained neural network according to the present disclosure may be configured to receive an observed geographic location from a location-enabled battery, a fixed location beacon, or GPS, or a combination thereof.

In various examples, the NOGPS application may periodically determine whether further fine-tuning of the neural network according to the present disclosure should be performed by comparing the second geographic location to the observed geographic location. The function of the NOGPS application of FIG. 7 is substantially similar to that of the NOGPS application of FIG. 8, except that the geographic location of FIG. 7 may be continually updated even if the mobile device is turned off, as the location-enabled battery may remain in an "on" state.

A location-enabled device according to the present disclosure may be a component or part of various other devices. For example, a mobile device, fastener, tag, doorbell, or anti-theft device, or a combination thereof, may include a location-enabled apparatus according to the present disclosure. For example, an anti-theft device may include a location-enabled apparatus according to the present disclosure and a secure memory for storing a current geographic location, such as a cellular telephone, various devices may listen for BLE transmissions of the geographic location and may compare the transmitted geographic location of the anti-theft device to the current geographic location of the cellular telephone. Thus, if theft protection is outside the designated area (or within the restricted area), the cellular telephone may send an alarm or the theft protection device may utilize the neural network and sensors of the location-enabled device according to the present invention to determine and send an alarm when the theft protection device is outside the designated area or within the restricted area. The anti-theft device may be batteryless (e.g., include an energy harvesting device) and concealed inside an object that needs to be secured to prevent theft.

In various examples, a device according to the present disclosure may be securely affixed to a wall, a road, a pathway, a store, an office, a counter, a building, or a table, or a combination thereof. The securely attached device may transmit its current geographic location to other devices or equipment or a combination thereof. The indicia may include cat eyes, bumper stickers, sign posts, house numbers, name boards or street signs, or combinations thereof, etc. for example, a house number or name board or combination thereof may be issued by a designated authority (e.g., a city office) and preprogrammed to transmit a particular geographic location of the location where the house number or name board or combination thereof is to be installed.

The position enabling device according to the present disclosure can be installed on a road, similar to how a cat eye is installed on a road. For example, a position-enabling device according to the present disclosure may be mounted atop a fastener (e.g., nail, screw) that is driven into the roadway. The fastener may define a cavity in the head of the fastener such that a position enabling device according to the present disclosure may be securely fixed in the cavity and the position enabling device according to the present disclosure may be fixed in the cavity after the fastener is installed.

In examples where a position-enabling device according to the present disclosure may convert vibration into power, the device may be installed into a roadway by drilling a hole and introducing the device into the hole. The hole may then be sealed with a quick set durable epoxy. Thereafter, when the automobile approaches a location-enabled device according to the present disclosure installed in the road, the vibrating road may power the device, which in turn broadcasts a second geographic location.

In various examples, a toy may be configured with a position-enabled device according to the present disclosure, and the toy may operate differently depending on its geographic location. For example, the toy may be considered intelligent, and the toy may speak french when the current geographic location of the toy is in france, and may speak english when the current geographic location of the toy is in england.

Referring back to fig. 1, in various examples, the apparatus 100 may also send messages. For example, the receiver 106 may receive a message, which may be stored in the memory 104, and may be transmitted by the transmitter 108. For example, around a table, transmitter 108 may transmit a message that does not generate noise around such a table.

The message may be sent to the receiver 106 using a mobile device or other device. The device 100 may be used in various applications, such as navigation, proximity applications or gaming applications, or a combination thereof.

A "smart bumper sticker" including a location-enabled device according to the present disclosure may be initialized by a companion application on a smartphone, which may transmit (e.g., a seed) an initial geographic location to a memory of the smart bumper sticker. The processor of the smart bumper sticker can utilize the neural network to update the geographic location of the smart bumper sticker when the sensor inside the smart bumper sticker detects any movement can utilize newton's law for the geographic location of the update device.

After seeding the initial geographic position into the memory of the smart bumper sticker, periodic re-seeding of observed geographic positions different from the initial geographic position can be transmitted to the smart bumper sticker and used to train a neural network on the smart bumper sticker (e.g., to enable a neural network smart watch or more accurate geographic position estimation). For example, a person may initialize a smart bumper sticker (e.g., family) at the location of their home, and then at the place where they work, the location of a friend's house, or the location of another person's house (e.g., relatives) or other locations. Whenever the observed geographic location is provided to the smart bumper sticker, the smart bumper sticker may become more intelligent and more accurate in determining the second geographic location.

A location-enabled device according to the present disclosure may be configured as a fixed location beacon (e.g., the second geographic location is the same as the first geographic location and does not change because the beacon is in a fixed pose)) which may include a battery or may be a batteryless energy-harvesting device or a mobile location beacon (e.g., the second geographic location changes that move as a beacon) which may include a battery or may be a batteryless energy-harvesting device or a combination thereof.

The mobile device may use fixed location beacons to obtain observed geographic locations to fine tune the neural network of the location-enabled device embedded in the mobile device. The mobile device may use various wireless communication protocols to obtain training data and/or observed geographic locations.

The location-enabled device according to the present disclosure may be configured in a network of at least two nodes or at least three nodes. For example, in fig. 9, a network 900 of nodes is provided, which may be configured to form a mesh network, whereby each node is able to negotiate its current geographic location with respect to its neighboring nodes. Nodes may utilize the Duly Authenticated Mutually Negotiated (DAMN) protocol to negotiate their current geographical location once the node successfully negotiates its current geographical location, the node may be an Authenticated Location Server (ALS). A node may be located.

Based on the requirements (e.g., routing, messaging) for their particular functions. In various examples, the network may also include a server.

A location-enabled device according to the present disclosure may be configured as a node of network 900, such as a router node, a base node, a service provider node or a service requester node, or a combination thereof. Each node may include a receiver configured to receive the geographic location of the respective node and/or neighboring nodes. Each node may include a transmitter configured to output a geographic location of the respective node.

The router node may be configured to route communications from one of the nodes to a different one of the nodes. The base node may transmit a second geographic location (e.g., the current location of the base node). The base node may be a low power node and may not include a GPS receiver. The service requester node may be a mobile device service requester node comprising a receiver, a sensor and a processor may be configured to determine a current geographical location of the service requester node based on a second geographical location output from at least one of the respective nodes in the network and at least one of the sensors of the service requester node utilizing the neural network.

Each node may include class 1, class 2, class 3, or class 4 transmit powers as shown in table 1 below.

Table 1:

for example, each node may have a communication range of 1, 000 meters or less, such as 500 meters or less, 100 meters or less, 10 meters or less, 1 meter or less, or 0.5 meters or less. In various examples, each node may have a communication range of at least 0.1 meter, such as at least 0.5 meter, at least 1 meter, at least 10 meters, at least 100 meters, or at least 500 meters nodes may not communicate with global positioning satellites or other objects 100 miles from the earth's surface in order to determine their current geographic location.

The service provider node may send messages other than the second geographic location. The message may be a short message service or a very short message service. The message may contain an advertisement. For example, a service provider node may be used to advertise a desired service on network 900 in fig. 9. The nodes adjacent to the service provider node may be able to route the advertisement to the desired node.

As shown in FIG. 9, nodes Q, R and S are router nodes, nodes a, B, C, D, E, F, G, H, I, J, K, L and M are base nodes, nodes O and P are service provider nodes, and node N is a service requester node.

The physical object may not know its physical location. The present inventors provide a location-enabled apparatus that is capable of providing a geographical location to physical objects with minimal power consumption and low energy requirements so that they can perform location-aware functions. Such objects are "smart objects" in that they can perform smart functions based on their context (e.g., gestures).

Powered location enabled devices according to the present disclosure may store their current geographic location in available memory and update their geographic location as needed. In accordance with the present disclosure, an unpowered object may utilize an energy harvesting device to provide power to the device in the activated position. However, unpowered objects may not know their current geographic location because they do not have memory to receive and store geographic locations-however, a powered location-enabled device or other location-aware device according to the present disclosure may store the relative geographic location of unpowered objects with reference to their own current geographic location. Thus, the unpowered object may receive its current geographic location from the powered location-enabled device according to the present disclosure and perform intelligent functions based on its context.

While several forms have been illustrated and described, it is not the intention of the applicants to restrict or limit the scope of the appended claims to such detail. Numerous modifications, changes, variations, substitutions, combinations, and equivalents of these forms can be made without departing from the scope of the present disclosure, and it will be apparent to those skilled in the art that these modifications, changes, variations, substitutions, combinations, and equivalents further that the structure of each element associated with the described forms can optionally be described as a means for providing the function performed by the element. In addition, where materials for certain components are disclosed, other materials may be used. It should be understood, therefore, that the foregoing description and the appended claims are intended to cover all such modifications, changes, variations, substitutions, modifications and equivalents as fall within the scope of the disclosed forms.

The foregoing detailed description has set forth various forms of devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Those skilled in the art will recognize that some examples of the forms disclosed herein, a computer program running on a computer (e.g., as a program running on a computer system), as a program running on a computer (e.g., as a program running on a computer system), may be equivalently implemented in whole or in part in integrated circuits, as a program running on a microprocessor), as firmware, or as virtually any combination thereof, and designing circuitry and/or writing code for software and/or firmware will be well within the skill of those in the art in light of the present disclosure, and it will be appreciated by those skilled in the art that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that the illustrative forms of the subject matter described herein apply regardless of the particular type of signal bearing media used to actually carry out the distribution.

The instructions for programming logic to perform the various disclosed examples may be stored within a memory in the system, such as a Dynamic Random Access Memory (DRAM), cache, flash memory, or other storage device. Further, the instructions may be distributed via a network or by other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), Random Access Memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable storage device or other form of propagated signal (e.g., carrier waves, infrared signals, digital signals, etc.) for transmitting information over the internet via electrical, optical, acoustical or optical means. Thus, a non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

As used herein, the term "control circuitry" may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor, a processing unit, a processor, a microcontroller unit, a controller, a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), a Programmable Logic Array (PLA) or FPGA) that includes one or more separate instruction processing cores), state machine circuitry, firmware that stores instructions executed by programmable circuitry, and any combination thereof. Control circuitry may be embodied collectively or individually as circuitry forming part of a larger system, e.g., an IC, an ASIC, an SoC, a desktop computer, a laptop computer, a tablet computer, a server, a smartphone, etc. thus, as used herein, control circuitry includes, but is not limited to, circuitry having at least one discrete circuit, circuitry having at least one IC, circuitry having at least one application-specific IC, circuitry forming a general-purpose computing device configured by a computer program (e.g., a general-purpose computer configured by a computer program that at least partially executes processes and/or devices described herein or a microprocessor configured by a computer program that at least partially executes processes and/or devices described herein), circuitry forming a memory device (e.g., in the form of RAM), and/or circuitry forming a communication device (e.g., modem, communications switch, or opto-electrical device) will recognize that the subject matter described herein may be implemented in an analog or digital manner, or some combination thereof.

As used herein, the term "logic" may refer to apps, software, firmware, and/or circuitry configured to perform any of the foregoing operations. The software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on a non-transitory computer readable storage medium. The firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in a memory device.

As used herein, the terms "component," "hardware," "module," and the like can refer to a computer-related entity, either hardware, a combination of hardware and software, or software in execution.

As used herein, an "algorithm" is a self-consistent sequence of steps leading to a desired result, wherein "steps" refer to the manipulation of physical quantities and/or logical states, although they need not necessarily take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. Usually, though not necessarily, these quantities take the form of electrical or magnetic quantities, such as electrical or magnetic quantities, optical quantities, magnetic or electronic quantities, and electrical or electronic quantities.

The network may comprise a packet switched network. The communication devices may be capable of communicating with each other using the selected packet switched network communication protocol. One example communication protocol may include an ethernet communication protocol capable of allowing communication using the transmission control protocol/internet protocol (TCPIP). The ethernet protocol may comply or be compatible with an ethernet standard entitled "IEEE802.3 standard" promulgated by the Institute of Electrical and Electronics Engineers (IEEE), which is promulgated in month 12 2008 and/or later versions of the standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an x.25 communication protocol. The X25 communication protocol may comply or be compatible with standards promulgated by the international telecommunication union, telecommunication standardization sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communication protocol that may comply or be compatible with standards promulgated by the international telegraph and telephone (CCITT) and/or standards promulgated by the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers can use an Asynchronous Transfer Mode (ATM) communication protocol to communicate with each other the ATM communication protocol may conform or be compatible with the ATM standard published by the ATM forum under the heading "ATM-MPLS network interworking 2.0" and/or later versions of that standard. Of course, different and/or later-developed connection-oriented network communication protocols are also contemplated herein.

Unless specifically stated otherwise as apparent from the foregoing disclosure, it is appreciated that throughout the foregoing disclosure, discussions utilizing terms such as "processing," "computing," "calculating," "determining," "displaying," or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Unless the context requires otherwise, the components may be referred to herein as "configured to," "configurable," "operable/operable as," "adapted/adaptable," "capable," "conforming/compliant," or the like. Those skilled in the art will recognize that a configuration may optionally include active state components, inactive state components, and/or standby state components.

Those skilled in the art will recognize that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as open-ended terms (e.g., the term "including" should be interpreted as "including but not limited to," and the term "including" should be interpreted as "includes but is not limited to") that those skilled in the art will further understand that if a specific number of a claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of an introductory phrase, at least one claim and one or more claims to introduce claim recitations-however, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim contains an introductory phrase.

Furthermore, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of two recitations, without other modifiers, typically means at least two recitations, or two or more recitations). Moreover, in those instances where a convention analogous to that of at least one of a, B, and C, and so on, is common, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., a system having at least one of a, B, and C would include but not be limited to systems having B alone, C alone, having a and B together, a and C together, B and C together, and/or a, B, and C together). one having skill in the art would further understand that unless context dictates otherwise, disjunctive words and/or phrases presenting two or more alternative terms (whether in the specification, claims, or figures) should be understood to contemplate the possibilities of including one of the terms, either one of the terms, or both of the terms. For example, the phrase "a or B" is generally understood to include the possibilities of "a", "B", "a and B".

Those skilled in the art will appreciate from the appended claims that the operations described therein may generally be performed in any order. Further, while the various operational flow diagrams are presented in a sequence, it should be understood that the various operations may be performed in an order different than illustrated, or that examples of such alternative orderings may be performed concurrently, including overlapping, interleaved, interrupted, reordered, incremental, preliminary, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, unless the context dictates otherwise, terms such as "responsive to," "involving," or other past tense adjectives are generally not intended to exclude such variations.

Any patent application, patent, non-patent publication, or other disclosure material referred to in this specification is incorporated herein by reference to the extent that the incorporated material does not conform to the teachings of this specification. Accordingly, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions set forth herein, statements, or other disclosure material will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

Various aspects of the invention according to the present disclosure include, but are not limited to, the aspects set forth in the following numbered claims.

1. An apparatus for determining a geographic location, the apparatus comprising:

the receiver is configured to receive a first geographic location;

the sensor is configured to determine a change in the attitude of the device;

a processor operatively coupled to the memory, the receiver, and the sensor, wherein the processor is configured to determine a second geographic location based on the first geographic location and the sensor utilizing the neural network; and

the first transmitter is configured to output a second geographic location of the apparatus.

2. The device of clause 1, further comprising an energy harvesting device comprising a piezoelectric energy harvesting device, an electrostatic energy harvesting device, an electromagnetic energy harvesting device, a photovoltaic cell, or a radio frequency energy harvesting device, or a combination thereof.

3. The apparatus of any of clauses 1-2, further comprising a battery, and wherein the neural network is embedded in the battery.

4. The apparatus of any of clauses 1-3, wherein the receiver is configured to receive the first geographic location via a first wireless communication protocol and the first transmitter is configured to output the second geographic location via a second wireless communication protocol.

5. The apparatus of clause 4, wherein each wireless communication protocol comprises a near field communication protocol, a bluetooth low energy protocol, a Wi-Fi protocol, or a ZigBee protocol, or a combination thereof.

6. The apparatus of clause 4, wherein the first wireless communication protocol comprises a near field communication protocol and the second wireless communication protocol comprises a bluetooth low energy protocol.

7. The device of any of clauses 1-6, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope or magnetometer, or a combination thereof.

8. The apparatus of any of clauses 1-7, wherein the memory is a secure memory.

9. A mobile device, fastener, tag, doorbell, or anti-theft device, or a combination thereof, comprising the apparatus of any of clauses 1-8.

10. The apparatus of any of clauses 1-9, wherein the processor is configured to store the second location in the memory.

11. The apparatus of any of clauses 1-10, wherein the first transmitter is configured to transmit a message.

12. The apparatus of any of clauses 1-11, wherein the first geographic location is stored in the memory and the processor is configured to overlay the first geographic location with the second geographic location.

13. The apparatus of any of clauses 1-12, wherein the receiver is further configured to receive an observed geographic location, and the processor is configured to train the neural network using the observed geographic location.

14. The apparatus of clause 13, wherein the processor configured to train the neural network comprises a processor configured to adjust weights and biases in the neural network.

15. The apparatus of any of clauses 13-14, wherein the receiver is configured to receive the observed geographic location from a node comprising:

the second transmitter is configured to output the current geographical location of the node as the observed geographical location via a wireless communication protocol, wherein the wireless communication protocol comprises a near field communication protocol, a bluetooth low energy protocol, a Wi-Fi protocol, or a ZigBee protocol, or a combination thereof.

16. The apparatus of any of clauses 1-15, further comprising a global positioning system configured to provide the observed geographic location to the processor to train the neural network.

17. A network for determining a geographic location, the network comprising:

a node, comprising:

the first transmitter is configured to output the current geographic location of the node via a wireless communication protocol, wherein the wireless communication protocol comprises a near field communication protocol, a bluetooth low energy protocol, a Wi-Fi protocol, or a ZigBee protocol, or a combination thereof; and

a mobile device, comprising:

the receiver is configured to receive a current geographic location of the node over a wireless communication protocol;

the sensor is configured to determine a change in a pose of the mobile device; and

a processor is operatively coupled to the memory, the receiver, and the sensor, wherein the processor is configured to determine a current geographic location of the mobile device based on the current geographic locations of the nodes and the sensor using the neural network.

18. The system of clause 17, further comprising at least two nodes.

19. The system of clause 18, wherein the at least two nodes form a mesh network.

20. The system of any of clauses 18-19, wherein at least one of the nodes is configured to transmit a message.

21. The system of any of clauses 18-20, wherein at least one of the nodes is configured to route communications from one of the nodes to a different one of the nodes or the mobile device, or a combination thereof.

22. The system of any of clauses 18-21, further comprising at least one of the nodes configured to determine a geographic location of the node using a suitably authenticated, mutually negotiated protocol.

In summary, a number of benefits have been described that result from employing the concepts described herein. The foregoing description has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the precise examples disclosed. Modifications and variations are possible in light of the above teachings, and examples may be selected and described to illustrate the principles and practice, thereby enabling one of ordinary skill in the art to utilize the various examples and with various modifications as are suited to the particular use contemplated. The claims filed herewith define the entire scope.

Claims

a receiver configured to receive a first geographic location;

a sensor configured to determine a change in a pose of the apparatus;

a processor operatively coupled to a memory, the receiver, and the sensor, wherein the processor is configured to determine a second geographic location utilizing a neural network based on the first geographic location and the sensor;

a first transmitter configured to output the second geographic position of the device; and

a battery, wherein the neural network is embedded in a battery management system of the battery.

2. The device of claim 1, further comprising an energy harvesting device comprising a piezoelectric energy harvesting device, an electrostatic energy harvesting device, an electromagnetic energy harvesting device, a photovoltaic cell, or a radio frequency energy harvesting device, or a combination thereof.

3. The device of claim 1, wherein the receiver is configured to receive the first geographic location via a first wireless communication protocol, and the first transmitter is configured to output the second geographic location via a second wireless communication protocol.

4. The apparatus of claim 3, wherein each wireless communication protocol comprises a near field communication protocol, a bluetooth low energy protocol, a Wi-Fi protocol, or a ZigBee protocol, or a combination thereof.

5. The apparatus of claim 3, wherein the first wireless communication protocol comprises a near field communication protocol and the second wireless communication protocol comprises a Bluetooth Low energy protocol.

6. The device of claim 1, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope or a magnetometer, or a combination thereof.

7. The apparatus of claim 1, wherein the memory is a secure memory.

8. The apparatus of claim 1, wherein the processor is configured to store the second geographic location in the memory.

9. The apparatus of claim 1, wherein the first transmitter is configured to transmit a message.

10. The apparatus of claim 1, wherein the first geographic location is stored in the memory and the processor is configured to overlay the first geographic location with the second geographic location.

11. The apparatus of claim 1, wherein the receiver is further configured to receive an observed geographic location, and the processor is configured to train the neural network using the observed geographic location.

12. The apparatus of claim 11, wherein the processor is configured to train the neural network, the processor being specifically configured to adjust weights and biases in the neural network.

13. The apparatus of claim 11, wherein the receiver is configured to receive the observed geographic location from a node comprising:

a second transmitter configured to output a current geographic location of the node as an observed geographic location via a wireless communication protocol, wherein the wireless communication protocol comprises a near field communication protocol, a Bluetooth low energy protocol, a Wi-Fi protocol, or a ZigBee protocol, or a combination thereof.

14. The apparatus of claim 1, further comprising a global positioning system configured to provide observed geographic locations to the processor to train the neural network.

15. An apparatus for determining a geographic location, the apparatus comprising:

a receiver configured to receive a first geographic location via a first wireless communication protocol;

a sensor configured to determine a change in a pose of the apparatus;

a first transmitter configured to output the second geographic position of the device via a second wireless communication protocol, wherein each wireless communication protocol comprises a near field communication protocol, a Bluetooth low energy protocol, a Wi-Fi protocol, or a ZigBee protocol, or a combination thereof; and

16. The device of claim 15, further comprising an energy harvesting device comprising a piezoelectric energy harvesting device, an electrostatic energy harvesting device, an electromagnetic energy harvesting device, a photovoltaic cell, or a radio frequency energy harvesting device, or a combination thereof.

17. The apparatus of claim 15, wherein the first wireless communication protocol comprises a near field communication protocol and the second wireless communication protocol comprises a bluetooth low energy protocol.

18. The device of claim 15, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope or a magnetometer, or a combination thereof.

19. The apparatus of claim 15, wherein the memory is a secure memory.

20. The apparatus of claim 15, wherein the processor is configured to store the second geographic location in the memory.

21. The apparatus of claim 15, wherein the first transmitter is configured to transmit a message.

22. The apparatus of claim 15, wherein the first geographic location is stored in the memory and the processor is configured to overlay the first geographic location with the second geographic location.

23. The apparatus of claim 15, wherein the receiver is further configured to receive an observed geographic location, and the processor is configured to train the neural network using the observed geographic location.

24. The apparatus of claim 23, wherein the processor is configured to train the neural network, the processor being specifically configured to adjust weights and biases in the neural network.

25. The apparatus of claim 23, wherein the receiver is configured to receive the observed geographic location from a node comprising:

26. The apparatus of claim 15, further comprising a global positioning system configured to provide observed geographic locations to the processor to train the neural network.

27. An apparatus for determining a geographic location, the apparatus comprising:

a receiver configured to receive a first geographical location and an observed geographical location;

a sensor configured to determine a change in a pose of the apparatus;

a processor operatively coupled to a memory, the receiver, and the sensor, wherein the processor is configured to determine a second geographic location using a neural network based on the first geographic location and the sensor, and wherein the processor is configured to train the neural network using the observed geographic location;

a first transmitter configured to output the second geographic location of the device; and

a battery, wherein the neural network is embedded in a battery management system of the battery;

wherein the processor is specifically configured to adjust weights and biases in the neural network.

28. The device of claim 27, further comprising an energy harvesting device comprising a piezoelectric energy harvesting device, an electrostatic energy harvesting device, an electromagnetic energy harvesting device, a photovoltaic cell, or a radio frequency energy harvesting device, or a combination thereof.

29. The device of claim 27, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope or a magnetometer, or a combination thereof.

30. The apparatus of claim 27, wherein the memory is a secure memory.

31. The apparatus of claim 27, wherein the processor is configured to store the second geographic location in the memory.

32. The apparatus of claim 27, wherein the first transmitter is configured to transmit a message.

33. The apparatus of claim 27, wherein the first geographic location is stored in the memory and the processor is configured to overlay the first geographic location with the second geographic location.

34. The apparatus of claim 27, wherein the receiver is configured to receive the observed geographic location from a node comprising:

35. The apparatus of claim 27, further comprising a global positioning system configured to provide the observed geographic location to the processor to train the neural network.

36. A mobile device comprising the apparatus of claim 1.

37. A fastener comprising the apparatus of claim 1.

38. A tag comprising the apparatus of claim 1.

39. A doorbell comprising the apparatus of claim 1.

40. An anti-theft device comprising the apparatus of claim 1.

41. A mobile device comprising the apparatus of claim 15.

42. A fastener comprising the apparatus of claim 15.

43. A tag comprising the apparatus of claim 15.

44. A doorbell comprising the apparatus of claim 15.

45. An anti-theft device comprising the apparatus of claim 15.

46. A mobile device comprising the apparatus of claim 27.

47. A fastener comprising the apparatus of claim 27.

48. A tag comprising the apparatus of claim 27.

49. A doorbell comprising the apparatus of claim 27.

50. An anti-theft device comprising the apparatus of claim 27.