WO2022073101A1

WO2022073101A1 - System and method for iterative location rippling in wireless networks

Info

Publication number: WO2022073101A1
Application number: PCT/CA2021/051375
Authority: WO
Inventors: Mohamed Maher Mohamed ATIA
Original assignee: Atia Mohamed Maher Mohamed
Priority date: 2020-10-05
Filing date: 2021-10-01
Publication date: 2022-04-14

Abstract

This disclosure relates to a method, system, and node for particularizing the location of a node in a wireless network. The method includes, while there are two or more non-localized nodes, iteratively transmitting a signal with one or more localized wireless nodes. A variance associated with each of the two or more wireless nodes which have not been localized is determined based on observations of the signal by the respective node of the two or more wireless nodes, if any. At least one of the two non-localized nodes is assigned as a further localized node, where the at least one of the two non-localized nodes a node having the lowest variance of the non-localized nodes. A fixed location is assigned to the further localized node.

Description

SYSTEM AND METHOD FOR ITERATIVE LOCATION RIPPLING IN WIRELESS NETWORKS

CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to U.S. Provisional Patent Application No. 63/087,833 filed on October 5, 2020, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

[0002] The following generally relates to particularizing a location of a wireless network, and more particularly to iterative location particularization of nodes in a wireless network.

BACKGROUND

[0003] Various technologies are implemented in locations that include or provide access to wireless networks including a plurality of nodes. The location of at least some of the plurality of nodes may be unknown, at least initially. Various systems exist to localize these nodes. Node localization can (or does) benefit the respective technology, for example by increasing accuracy in location-based services (LBS), allowing the health industry to implement patient/senior tracking, enabling the energy industry further means of underground/mining tracking or exploration. Various other technologies can benefit from networks having localized nodes, including disaster management services, security services, underground transit systems, technologies reliant upon the Internet of things (loT), such as smart building systems (e.g., they can increase energy efficiency) or smart city systems (e.g., interactive museums and libraries).

[0004] Existing systems to localize nodes include systems based on the Global Navigation Satellite System (GNSS) and protocols such as the Global Positioning System (GPS). While the GNSSs allow for node localization on a world-wide basis, GNSSs have limited availability in non-line-of-sight (NLOS) environments such as dense urban areas, subways, tunnels, office areas, and indoor environments. Limited NLOS availability impedes connected platform performance, and technologies such as self-driving cars, robotics, smartphones and wearable devices require or rely upon accurate localization in NLOS environments.

Other existing systems, such as high-speed networks (e.g., 5G wireless networks) can facilitate providing localization for NLOS environments that do not have open-sky access to GNSS. IEEE 802.11 WLAN (Wi-Fi), Bluetooth, ZigBee, Ultra-wide band (UWB), and Radio frequency identification (RFID) to Cellular-based wireless sensor network (WSN) are some examples of existing wireless networking technologies and infrastructures available to facilitate node localization.

[0005] Existing wireless localization techniques can include monitoring or evaluating a signal within the existing systems. Signal characteristics or measurements associated with the signal, such as Time of arrival (TOA), Time difference of Arrival (TDOA), Angle of arrival (AOA), Phase of arrival (POA), and Received signal strength indicator (RSSI) are used to localize nodes.

[0006] TOA, TDOA AOA, and POA based techniques can achieve positioning accuracies below 1 m in obstacle-free environments if reference nodes (known-location anchors) are available and time synchronization errors are compensated as described in US 10,469,982 B2, US 9,075,123 and US 2013/0310073 A1 .

[0007] RSSI based technologies use RSSI as a measure of range (RSSI-range methods) or location (RSSI-fingerprint methods). RSSI-range methods require the knowledge of reference node (anchors or access points) locations as described in US 10,349,286, US 8,849,926, and WO 2014/074837 Al. While RSSI-fingerprint methods may not require knowledge of reference node locations, they depend on site surveying and predata collection, pre-calibration at reference known locations as described in US 9,369,982 and US 10,349,286. These limitations inhibit these methods from being widely commercially adopted. Although the site surveying and pre-calibration can be automated and dynamically implemented online as described in US 10,349,286, UIUCDCS-R-2005-2629 (http://hdl.handle.net/2142/11091) and US 10,132,915 B2, these methods require prediction models to predict RSSI measurements which is time-consuming and adds significant computation complexities to the localization system. Furthermore, they still require the preknowledge about access point locations. Furthermore, the dynamic modeling of RSSI discussed in this paragraph requires all nodes to broadcast RSSI of all neighboring other nodes in a mech-network scheme which leads to significant power consumption.

[0008] Therefore, existing systems suffer from several limitations including, but not limited to, 1) requiring site surveying to achieve requisite accuracy, 2) requiring preknowledge about access point locations, 3) requiring excessive computational resources owing to the complexity of, for example, RSSI modeling and predicting, and 4) a lack of interoperability between mesh-networking and non-mesh networking schemes.

[0009] Improvements which address at least some of the limitations of the existing systems are desirable. SUMMARY

[0010] In one aspect, a method of particularizing the location of two or more wireless nodes in a network is disclosed. The method includes, while there are nodes of two or more wireless nodes that have not been localized, iteratively: i) transmitting a signal with one or more localized wireless nodes, ii) determining a variance associated with each of the two or more wireless nodes which have not been localized based on observations of the signal by the respective node of the two or more wireless nodes, if any, and iii) assigning, as a further localized node, a node of the two or more wireless nodes having a lowest variance and assigning a fixed location to the further localized node.

[0011] In another aspect a non-transitory computer readable medium comprising instructions is disclosed. The instructions, when implemented by a processor, cause the processor to, while there are nodes of the two or more wireless nodes that have not been localized, iteratively i) transmit a signal with one or more localized wireless nodes, ii) determine a variance associated with each of the two or more wireless nodes which have not been localized based on observations of the signal by the respective node of the two or more wireless nodes, if any, and iii) assign, as a further localized node, a node of the two or more wireless nodes having a lowest variance and assign a fixed location to the further localized node.

[0012] In yet another aspect, a system for localizing nodes is disclosed. The system includes a processor, a transceiver in communication with the processor, and a memory coupled to the processor. The memory includes instructions which when executed by the processor cause the processor to, while there are nodes of the two or more wireless nodes that have not been localized, iteratively i) transmit a signal with one or more localized wireless nodes, ii) determine a variance associated with each of the two or more wireless nodes which have not been localized based on observations of the signal by the respective node of the two or more wireless nodes, if any, and iii) assign, as a further localized node, a node of the two or more wireless nodes having a lowest variance and assign a fixed location to the further localized node.

[0013] In yet another aspect, a non-localized node is disclosed. The non-localized node includes a processor, a transceiver in communication with the processor, and a memory coupled to the processor. The memory includes instructions which when executed by the processor cause the processor to transmit to one or more localized wireless nodes or to a central server, via the transceiver, a request signal for a location. The instructions cause the processor to observe, via the transceiver, a characteristic associated with a signal received from the one or more localized wireless nodes. The instructions cause the processor to receive, from the one or more localized wireless nodes or the central server, a fixed location. The instructions cause the processor to observe in response to receiving, via the transceiver, a further request signal, transmit, via the transceiver, the signal.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Embodiments will now be described with reference to the appended drawings wherein:

[0015] FIG. 1 is a schematic diagram of an example network including multiple nodes.

[0016] FIG. 2 is an example illustrative flow diagram of a method of localizing nodes.

[0017] FIG. 3 is layout diagram of a location including nodes.

[0018] FIG. 4 is a graph of example initialized locations assigned to the nodes of

FIG. 3.

[0019] FIG. 5 is chart of model for associating a received characteristic of a signal with a variance.

[0020] FIG. 6 is block diagram of different example iterations of localizing the nodes of FIG. 3.

[0021] FIG. 7 is a table with illustrative values of signal characteristics observed by the nodes of FIG. 3.

[0022] FIG. 8 is schematic diagram of an example node.

[0023] FIG. 9 is a diagram of equipment used in experimental testing.

[0024] FIG. 10 is an example layout used during experimental testing.

[0025] FIGS. 11 to 13 are each a graph comparing determined variance based on observations by nodes within a wireless network.

DETAILED DESCRIPTION

[0026] It will be understood that in the description below, if suffix “A” or “B” or the like is not included, then the statements can be applicable to any one of the elements sharing the same reference number.

[0027] This disclosure is related to localizing at least some unlocalized nodes in a location covered by a network. The disclosed system and method propagate signals from at least one known-location network node(s) “localized nodes” to unknown-location network nodes “non-localized nodes”. The unknown-location network nodes are then “localized” (i.e., assigned or fixed with a location) in accordance with this disclosure to reduce or minimize location estimation errors. In at least one embodiment, the propagation of error is reduced or minimized by iteratively localizing non-localized nodes by sequentially localizing nonlocalized nodes based on their observed variance.

[0028] Alternatively stated, this disclosure describes an iterative system and method for location rippling in a wireless network. In example embodiments, starting from at least one known-location network node(s) “localized nodes”, the system uses a dilution of precision (DOP) technique to ripple location information to unknown-location network nodes “not-localized nodes” with reduced error propagation. The system further provides a reduced power and computation data-driven model/prediction-free localization method. It is understood that the term DOP, as used herein, is different from a geometric delusion of precision (GDOP) commonly used in describing technologies in the satellite navigation domain. The DOP of this disclosure is an indication of uncertainty of an estimation. If this uncertainty of estimation is based on the geometric arrangement of anchors (as in satellite navigation systems), the DOP is called GDOP.

[0029] The provided system and method can be used in any wireless network and can work with any localization technique such as techniques based on signal time-of-flight, signal angle-of-arrival, or signal amplitude.

[0030] The disclosed systems and method may require reduced power to localize nodes, reduce the computational complexity associated with computational data-driven models or predictions to localize a node, increase the accuracy, or speed of node localizations. Further benefits of the disclosed systems and method include 1) removing the need for site surveying, 2) removing the need for pre-existing knowledge about the locations of nodes, 3) reducing the complexity associated with RSSI modeling and predicting to determine a node location, and 4) providing a robust system and method that can be used in both mesh networking and non-mesh networking schemes.

[0031] While the embodiments described herein reference RSSI as a signal characteristic and apply the disclosed invention to a mesh-network, it is understood that this disclosure contemplates applying the same methods to non-mesh networks, or using signal measures such as TOA, TDOA, AOA, and POA.

[0032] Referring to FIG. 1 , a schematic diagram of an example location 8, hereinafter alternatively referred to generally as a “system” to describe all nodes within the location 8. The location 8 includes a plurality of nodes, e.g., nodes 12A, 12B, 12C, 12D, 12E, (alternatively referred to as simply nodes 12). The nodes 12 may be connected to a larger communication network 14 (e.g., communication networks 14A and 14B) facilitating access to nodes of separate systems. For example, the communication networks 14A and 14B may facilitate access to the Internet, an enterprise, etc. Although the nodes 12 are shown connected to two communication networks, 14A and 14B, it is understood that the nodes 12 may be connected to one or more communication networks that permit the nodes 12 to relay signals from one node 12 to another. In at least one embodiment, the nodes 12 are connected to one another and not a communication network, in a mesh configuration, or in a non-mesh configuration. Each node 12 can transmit signals to, or observe signals from one or more devices 16 (with the shown device 16 providing a non-limiting illustrative example) to facilitate access by the devices 16 to the communication network 14, to other nodes, or to other devices connected to nodes within the same system. Device 16 can be a mobile device such as a cellphone, laptop, or other device capable of communicating with the nodes 12 to be connected via the nodes to the communication network 14 or to connect to directly to one or more nodes 12.

[0033] Communication networks 14A and 14B may include a telephone network, cellular, and/or data communication network to connect nodes 12 or to connect devices 16 connected to the nodes 12 to other nodes within or connected to the respective communication network 14. For example, the communication network 14 may include a private or public switched telephone network (PSTN), mobile network (e.g., code division multiple access (CDMA) network, global system for mobile communications (GSM) network, and/or any 3G, 4G, or 5G wireless carrier network, etc.), Wi-Fi or other similar wireless network, and a private and/or public wide area network (e.g., the Internet). The communication networks 14 can include other nodes or communication infrastructure at least in part linked together through wired connections. For example, communication network 14 may connect nodes 12 to an enterprise system which includes geographic data stored in a wired network of servers.

[0034] Each node 12 includes or otherwise has access to a memory (e.g., memory 908 of FIG. 9) that stores instructions to instantiate, access or host an application 20. The node 12 can be configured to instantiate the application 20 upon boot up or wake up, or to continuously or periodically instantiate the application 20. The application 20 may be managed by a central server (not shown) which may update the application or control access to the application 20 (e.g., the central server may update models used by the application 20 or may be used to set or alter the update frequency of the application 20, etc.). [0035] The application 20 can be configured such that at least some nodes 12 within the same system or location are localized. For example, the application 20 of node 12A can be preprogrammed with arbitrary coordinates (0, 0, 0) indicating that node 12A is intended to be placed in the middle of the location 8. The application 20 may be configured subsequent to placement of the node 12 (e.g., node 12A may be provided with the GPS coordinates of its current location).

[0036] At least some of the nodes 12 within the example location 8 are not localized; that is the application 20 associated with the respective node 12 is unaware of the location of the node 12. For example, in example embodiments the nodes 12 may be scattered throughout an example location 8 without a precise knowledge of their final resting places (e.g., the nodes 12 are thrown into hard-to-reach areas, or the nodes 12 are installed without a specific knowledge of the location in which the node 12 is installed).

[0037] Referring now to FIG. 2, a block diagram of an example method for localizing nodes 12 within a network is shown. Throughout the discussion of FIG. 2, reference will be made to FIGS. 3 to 7, to provide illustrative context to the method disclosed in FIG. 2. It is understood that the method shown in FIG. 2 is not limited to the scenarios described in relation to FIGS. 3 to 7. In FIG. 3, nodes 12A, 12B, and 12C are assumed to be localized (where localized nodes in general are referred to as nodes 12L), whereas nodes 12D, and 12E are assumed to be non-localized nodes (where localized nodes in general are referred to as nodes 12NL).

[0038] Optionally, at block 202, a wireless node 12 is initialized as a localized node 12L (alternatively referred to as an anchor node) within a location. For example, referring now to the example layout of nodes shown in FIG. 3, node 12A may be configured with data representing the location of the node 12A subsequent to node 12A being placed within the location 8. The data can represent the geographical position of the node 12A, or the position of node 12A with reference to the location 8 (e.g., with coordinates (0,0) representing the middle of location 8), and so forth. In example embodiments, nodes 12L are added to an existing location 8 or system having an initialized anchor node 12L, and this block is not completed. In the example shown in FIG. 4, each of nodes 12A, 12B, and 12C is shown localized with X and Y coordinates.

[0039] At block 204, one or more of the localized nodes 12L transmits a signal. In example embodiments the localized nodes 12L transmit the signal upon awakening (e.g., being powered up). The localized nodes 12L may transmit the signal in response to receiving an initial query signal from a non-localized node 12NL. For example, node 12D may be a non-localized node that transmits a query signal to the localized node 12A, which node 12A may in response transmit the aforementioned signal. The localized nodes 12L can also transmit the signal in response receiving an instruction to do so from a central server (not shown).

[0040] Some or all of the localized nodes 12L may transmit the signal for each iteration. In at least one example embodiment, each successive iteration of localizing nodes localizes a previously non-localized node 12NL, and therefore subsequent iterations can include an increasing number of localized nodes12L transmitting the signal. In at least one example embodiment, a subset (alternatively referred to as a combination) of the localized nodes'! 2L transmits the signal. For example, where an initial iteration of localization reveals that all non-localized nodes 12NL only observe signals from three of four localized nodes 12L, in subsequent iterations the localized node which is not observable by the non-localized nodes 12NL does not transmit the signal. The signal can be any transmission according to any of a variety of protocols. For example, application 20 may be configured such that the signal is a step function, or a continuous signal, a signal responsive to any networking standard such as Wi-Fi, Bluetooth, ZigBee, or Ultrawideband networks or any network signals such as radio-frequency, ultrasonic, laser waves, or infra-red signals, and so forth. In example embodiments, a “best combination” of nodes are configured to transmit the signal, where best combination describes the localized nodes which led to the least DOP in previous iterations or testing. The process can be somewhat similar to selecting a best combination of GPS satellites to estimate a GPS receiver location.

[0041] At block 206, two or more non-localized nodes 12NL (e.g., nodes 12D and 12E) observe or receive the signal transmitted by localized node(s). In at least one example embodiment the non-localized node 12NL can be configured to transmit data associated with its reception of the signal back to a localized node 12L or other system component for localizing nodes. For example, the non-localized nodes 12NL may transmit data to a central server indicating a time of arrival, an angle of arrival, a phase of arrival, a received signal strength indicator, and so forth.

[0042] At block 208, a variance for each of the non-localized nodes 12NL which have observed the signal is determined. The variance may be determined by the application 20 running on the non-localized node 12NL, or by any other instance of the application 20 (e.g., application 20 running on a central server or a localized node 12L).

[0043] The variance can be a dilution of precision (DOP). In example embodiments, the DOP is calculated by (1) determining an estimated position of the non-localized nodes 12NL via, for example, a least-squares trilateration algorithm, and (2) determining the DOP based on the estimated position of the non-localized nodes 12NL.

[0044] In at least some example embodiments, the DOP is calculated by, for each non-localized node 12NL, (1) observing a characteristic of the signal received from each transmitting localized node 12L, (2) determining an intermediate DOP associated with each localized node 12L transmitting the received signal based on the associated determined characteristic, and (3) determining a final DOP by averaging the DOP associated with each localized node 12L transmitting the received signal to assign the overall DOP. The signal characteristic may be an RSSI, a TOA, a TDOA, an AOA, a POA, and so forth.

[0045] An example determination of an intermediate DOP is discussed below in reference to FIG. 3 . In the illustrative example, in one embodiment, the characteristic of the signal is RSSI, and the following values are received by the node 12D: RSSI from node 12A: RSSI_A, RSSI from node 12B: RSSI_B, RSSI from node 12C: RSSI_C. In one embodiment, the interim DOP can be calculated by normalizing and averaging individual RSSI measurements. Thereafter, the interim DOP factor between node 12D and each of the transmitting localized nodes 12A, 12B, and 12C are given from the following relationships:

[0046] DopA a (RSSI_max - RSSI_min)/(RSSI_A-RSSI_min);

DopB a (RSSI_max - RSSI_min)/(RSSI_B-RSSI_min);

DopC a (RSSI_max - RSSI_min)/(RSSI_C-RSSI_min);

[0047] It is understood that RSSI_min is configured to avoid diving by zero or getting a negative DOP.

[0048] In another embodiment, the characteristic of the signal is the signal itself, and the DOP is determined at least in part by the correlation between the known transmitted signals from node 12A, 12B, and 12C and a local replica at the receiving node 12D. This correlation can be calculated as follows:

[0049] DopA a 1/[sum(Signal_A*Replica_of_A_at_D)/normalize_factor];

DopB a 1/[sum (Signal_B* Replica_of_B_at_D)/normalize_factor];

DopC a 1/[sum (Signal_C* Replica_of_C_at_D)/normalize_factor];

[0050] It is understood that correlation is configured to avoid diving by zero or getting a negative DOP.

[0051] The local replica can be an internal copy (i.e., stored in a memory local to the non-localized node) of the signal that the non-localized node is “expecting” to receive/observe from localized nodes. The application 20 of each non-localized node can be pre-configured with a local replica or the local replica may be acquired. In an illustrative example, the application 20 can be configured with a default local replica(s) scheme having one or more local replicas and corresponding signals. When initialized in a new location 8, the application 20 can assign a signal of a local replica and signal pair to each localized nodes and cause a processor and transceiver to broadcast the signal to non-localized nodes. In an example embodiment, each localized node is randomly assigned a different signal from a predefined number of signals, such that a non-localized node knows to search for signals of the predefined signals, and each unique match indicates a different localized node. In at least one embodiment, the same signal and replica pair is used for all localized nodes.

[0052] As set out in the equation above, after receipt of or measurement of the signal, the unlocalized node can determine the correlation (e.g., determine a peek or a strong match), and determine that the observed signal is coming from a particularized localized node. The stronger the correlation between the internal replica and the received signal, the stronger the certainty and accuracy of this measurement (the smaller the DOP).

[0053] Subsequently, the finalized or overall DOP as observed by each non-localized node 12NL is determined by averaging the interim DOP values DopA, DopB and DopC.

[0054] The DOP may be determined using inter-measurements of characteristics of the signal between nodes 12. The inter-measurements may be between all localized nodes 12L, or only localized nodes 12L visible to the non-localized node 12NL that is being evaluated. In at least some example embodiments, the DOP factor is determined as follows:

[0055] A proximity between characteristics of the signal observed by the nonlocalized node 12NL to the plotted characteristics of the localized nodes 12L may be used to determine DOP. Various proximity measures can be used, wherein a greater proximity between the characteristics observed by the non-localized nodes 12NL and the characteristics observed by the localized nodes 12L is understood to indicate a lower DOP associated with the non-localized node 12NL. In an example embodiment, proximity can be calculated by a radial-basis artificial neural network (RBF ANN), or by using Bayesian rules to evaluate the covariance of the measurement (e.g., determining a covariance of node 12D(m(4,1)) given the measurements of nodes 12A, 12B, and 12C [m(1 ,1), m(2,1), m(3,1) ]), or generating a DOP factor plot based on any of the aforementioned techniques (e.g., the plot shown in FIG. 5) and determining the proximity between points on said plot. [0056] In example embodiments, a weighted averaging of the observed interlocalized node measurements and the non-localized node 12NL are used to estimate a DOP. For example, where an RSSI measurement is observed by a non-localized node 12NL (e.g., node 12D), it may similarly be fitted to the plot in FIG. 5 to determine a DOP factor. A similar process is followed with respect to each of the other localized nodes 12L. A final DOP factor may be determined based on an average or weighted average of the DOP factor calculated with respect to each of the localized nodes 12L. As is shown in FIG. 5, values of observed RSSI do not necessarily have unique values of an associated distance (e.g., for example, an RSSI observation of 60dBm can indicate at least either that node 12D is 4 meters, or 10 to 16 meters away from node 12A). Therefore, an averaging or weighted averaging of all observed values between the localized node 12L signals observed by node 12D may be determined, such that a more accurate reading of a finalized DOP is determined. Regardless of the signal characteristics used (RSSI, ToA, AoA, PoA, TDoA), the finalized DOP can be estimated as follows:

[0057] DopA a Proximity(Signal_observed_by_D_from_A, inter_localized_node_measurements)

DopB a Proximity(Signal_observed_by_D_from_B, inter_localized_node_measurements)

DopC a Proximity(Signal_observed_by_D_from_C, inter_localized_node_measurements)

[0058] At block 210, at least one of the non-localized nodes 12NL having the lowest variance is assigned as a further localized node. For more than one of the non-localized nodes 12NL have similar or identical variances, further selection criteria may be enforced, such as selecting the fastest responding of the non-localized nodes 12NL as the further localized node 12L, etc.

[0059] FIG. 6 illustrates the possible scenarios for localizing the non-localized nodes 12NL in FIG. 3. In the event that the process determines that node12D has the lowest variance in view of the localized nodes (shown in block 602), the node 12D would be added to the localized nodes (shown in block 604A) and a subsequent generation would localize node 12E. Alternatively, in the event that node 12E was found to have the lowest variance in view of the localized nodes, node 12E would be localized first (shown in block 604B), after which node 12D would be localized. Starting with estimating a location calculation to the node 12 with the lowest variance will likely lead to an overall less error propagation as compared to the other scenario. [0060] The further localized node is also assigned a fixed location for use in subsequent localization iterations. The fixed location may be the location estimated based on observed characteristics of the signal from the localized nodes 12L. Inter-measurements of the characteristics of the signal between all localized nodes 12L may be plotted or associated with a location of the localized node 12L transmitting the signal. For example, as shown in FIGS. 7, the characteristic of the signal transmitted by each localized node 12A, 12B, and 12C (“m”) observed by the other respective localized nodes 12A, 12B, and 12C is shown in a table (FIG. 7).

[0061] In example embodiments, illustrated in reference to FIG. 7, and regardless of the signal characteristic used (RSSI, ToA, AoA, PoA, TDoA), the estimated location is at least in part determined based on the proximity of measurements at the non-localized node 12D and the inter-localized node measurements 12L. In one embodiment, if the signal characteristic used is RSSI, the estimated location is based on the proximity of RSSI measurements at the non-localized node 12D and the RSSI inter-localized node measurements 12L without building any radiomaps or predicting RSSI anywhere in the location 8. A weighted average or an RBF ANN or any Machine Learning module (e.g., artificial neural network) used to describe the interrelation between a characteristic of the signal and the location from the inter-measurements table (Figure 7) without the need for building radiomaps or predicting RSSI anywhere in the network area.

[0062] Although shown as occurring in sequence, it is understood that blocks 208 and 210 may be performed simultaneously or in another order. For example, the RBF ANN may simultaneously determine a DOP and an estimated location of the non-localized node 12NL when provided with the characteristic observations. This may reduce the computation complexity. Where existing methods use the “inter-measurements” to “model” or “predict” the localized nodes measurements in any location to build a fingerprint database or a signal propagation model and then use these databases or models to calculate user location, the disclosure provides for a method where the non-localized node 12NL has the “intermeasurements”, a weighted average or an RBF ANN can be used to directly calculate the non-localized estimated or fixed location. The RBF ANN would as an input take the 1) “intermeasurements” 2) localized node locations (which are in part represented through the intermeasurements), and produces the estimated location directly in one step. There is therefore no need to produce the time and resource intensive fingerprint database of the location or predicting the signal in any location in the network area using the signal propagation model for all network localized nodes and then use these databases or predicted signals to calculate user location [0063] Optionally, at block 212, a location of a mobile node (e.g., device 16 of FIG. 1) which enters the location 8 can be localized in a manner similar to block 210. For example, a mobile node such as a car travelling through an intersection may be localized within the location 8, or a location of a user’s handheld mobile device (e.g., cellphone) can be localized,

[0064] In an example embodiment, the method of FIG. 2 may facilitate the following use case. One or more localized nodes 12L are provided by a user in a mine tunnel and initialized with a location (e.g., distribute them around or at the beginning of the tunnel). The method of FIG. 2 is then performed to discover any nodes 12 within the file, “infecting” a network of cells within the mine with location estimation as a virus would infect cells.

[0065] Referring now to FIG. 8, a schematic diagram of an example node 12 is shown. Node 12 includes a processor 802, which may be a special purpose processor, having the computational capacity to perform the method disclosed herein. Node 12 further includes a transceiver 804, and a storage 806, and a memory 808, wherein the memory 808 may be part of or separate from storage 806. Transceiver 804 propagates or receives the signal as described herein, and may include an antenna, or optical fibers system to implement an optical transceiver, etc. Storage 806 may store location data associated with node 12 where, for example, the node 12 is a localized node. Storage 806 can also store, for example, the RBF ANN used to associate characteristics of the observed signal with locations. In example embodiments, memory 808 stores the RBF ANN, or other model used to associate characteristics of the observed signal with locations in the location estimator 814.

Memory 808 includes the application 20.

[0066] There has been experimental work to date illustrating of operation of the disclosed method for localizing nodes. A simulation of the method was run, and a real environment was tested using low-power low-rate ZigBee communication network. The method has been verified by calculating the true geometry of the non-localized anchors using Cramer Rao Lower Bound (CRLB) and comparing it against the calculated DOP. Results showed strong correlation between the estimated DOP and the true geometry of the non-localized nodes. All non-localized anchors have been localized with a root mean square error (RMSE) of 3.2 m in a real inside-building NLOS environment.

[0067] A further description of one experiment with the concepts contained in this disclosure is described in reference to FIGS. 9 to 13. FIG. 9 shows example hardware used in the experiment, including nodes 902, each having a board 904 including a processor and a radio frequency model 906. Nodes 1004, 1006, 1008, 1010, 1012, 1014, 1016 (alternatively referred to as nodes 1 through 7 in FIG. 10) were placed in the location 1002, and nodes 1010, 1012, and 1014 were initialized as localized nodes. Using the example methods described herein, the determined DOP for the nodes (shown by bars 1104) is shown in FIG. 11 alongside the determined CRLB(shown by line 1102), showing a strong correlation. FIG. 12 shows the strong correlation between the determined DOP at each node and the CLRB and the RMSE (shown by line 1206) for each node. FIG. 13 shows the cumulative distribution function (CDF) of positional error for each of the non-localized nodes (i.e., nodes1004, 1006, 1008, 1010) when localized, with line 1302 showing the CDF for node 1006, line 1304 showing node 1004, line 1306 showing node 1008, and line 1308 showing node 1010.

[0068] For simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the examples described herein. However, it will be understood by those of ordinary skill in the art that the examples described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the examples described herein. Also, the description is not to be considered as limiting the scope of the examples described herein.

[0069] It will be appreciated that the examples and corresponding diagrams used herein are for illustrative purposes only. Different configurations and terminology can be used without departing from the principles expressed herein. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from these principles.

[0070] It will also be appreciated that any module or component exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and non-volatile, removable and nonremovable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information, and which can be accessed by an application, module, or both. Any such computer storage media may be part of the node 12, application 20 environment or other environment, any component of or related thereto, etc., or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media.

[0071] The steps or operations in the flow charts and diagrams described herein are just for example. There may be many variations to these steps or operations without departing from the principles discussed above. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified.

[0072] Although the above principles have been described with reference to certain specific examples, various modifications thereof will be apparent to those skilled in the art as outlined in the appended claims.

Claims

Claims:

1 . A method of particularizing the location of two or more wireless nodes in a network, the method comprising: while there are nodes of the two or more wireless nodes that have not been localized, iteratively: transmitting a signal with one or more localized wireless nodes; determining a variance associated with each of the two or more wireless nodes which have not been localized based on observations of the signal by the respective node of the two or more wireless nodes, if any; assigning, as a further localized node, a node of the two or more wireless nodes having a lowest variance and assigning a fixed location to the further localized node.

2. The method of claim 1 , wherein further localized nodes transmit the signal in iterations subsequent to being assigned the fixed location.

3. The method of claim 1 , further comprising storing the fixed locations of further localized nodes.

4. The method of claim 1 , wherein either all or a best combination of the one or more localized wireless nodes transmit the signal.

5. The method of any one of claims 1 to 4, further comprising: receiving a request to localize a mobile device; determining an estimated location of the mobile device in reference to the localized nodes.

6. The method of any one of claims 1 to 5, wherein the variance is an overall dilution of precision (DOP) determined by, for each of the two or more wireless nodes: determining a characteristic of the signal observed from each respective localized node; determining a DOP associated with each localized node transmitting the received signal based on the associated determined characteristic; and averaging the DOP associated with each localized node transmitting the received signal to assign the overall DOP.

7. The method of claim 6, further comprising: determining a proximity of each of the two or more wireless nodes to each of the localized nodes; and assigning the DOP by weighing each DOP associated with each localized node transmitting the received signal with the determined proximity.

8. The method of claim 7, wherein the proximity is determined by: for each localized wireless node, associating locations of the other of the one or more localized wireless nodes to measures of the signal received from the respective other of the one or more localized networks; determining the proximity by comparing the characteristics of the signal transmitted from localized nodes, and observed by the non-localized nodes, to characteristics of the signal transmitted from localized nodes, and observed by other localized nodes.

9. The method of any one of claims 6 to 8, wherein the characteristic is any combination of at least one of a time of arrival, an angle of arrival, a time difference of arrival, a phase of arrival, or a received signal strength indicator.

10. The method of claims 7 to 9, wherein the proximity is determined at least in part by a radial-basis artificial neural network, a Bayesian rule to evaluate the covariance of the characteristic observed from each localized node, an averaging.

11 . The method of any one of claims 1 to 10, further comprising: in response to receiving a geographical location associated with the one or more localized nodes, determining a geographical location associated with each localized node based on the respective fixed location and the geographical location.

12. The method of any one of claims 1 to 5, wherein the variance is a dilution of precision determined based on an estimated location, the estimated location determined at least in part by a least squares trilateration scheme.

13. A computer readable medium comprising instructions, which when implemented by a processor, cause the processor to: 18 while there are nodes of the two or more wireless nodes that have not been localized, iteratively: transmit a signal with one or more localized wireless nodes; determine a variance associated with each of the two or more wireless nodes which have not been localized based on observations of the signal by the respective node of the two or more wireless nodes, if any; assign, as a further localized node, a node of the two or more wireless nodes having a lowest variance and assign a fixed location to the further localized node.

14. The computer readable medium of claim 13, wherein further localized nodes transmit the signal in iterations subsequent to being assigned the fixed location.

15. The computer readable medium of claim 13, the instructions further comprising instructions to store the fixed locations of further localized nodes.

16. The computer readable medium of claim 13, wherein either all or a best combination of the one or more localized wireless nodes transmit the signal.

17. The computer readable medium of any one of claims 13 to 16, the instructions further comprising instructions to: receive a request to localize a mobile device; determine an estimated location of the mobile device in reference to the localized nodes.

18. The computer readable medium of any one of claims 13 to 17, wherein the variance is a dilution of precision determined based on an estimated location, the estimated location determined at least in part by a least squares trilateration scheme.

19. The computer readable medium of any one of claims 13 to 17, wherein the variance is an overall dilution of precision (DOP) determined by, for each of the two or more wireless nodes: determining a characteristic of the signal observed from each respective localized node; determining a DOP associated with each localized node transmitting the received signal based on the associated determined characteristic; and 19 averaging the DOP associated with each localized node transmitting the received signal to assign the overall DOP.

20. The computer readable medium of claim 19, the instructions further comprising instructions to: determine a proximity of each of the two or more wireless nodes to each of the localized nodes; and assign the DOP by weighing each DOP associated with each localized node transmitting the received signal with the determined proximity.

21 . The computer readable medium of claim 19, wherein the proximity is determined by: for each localized wireless node, associating locations of the other of the one or more localized wireless nodes to measures of the signal received from the respective other of the one or more localized networks; determining the proximity by comparing the characteristics of the signal transmitted from localized nodes, and observed by the non-localized nodes, to characteristics of the signal transmitted from localized nodes, and observed by other localized nodes.

22. The computer readable medium of any one of claims 19 to 21 , wherein the characteristic is any combination of at least one of a time of arrival, an angle of arrival, a time difference of arrival, a phase of arrival, or a received signal strength indicator.

23. The computer readable medium of claims 20 to 22, wherein the proximity is determined at least in part by a radial-basis artificial neural network, a Bayesian rule to evaluate the covariance of the characteristic observed from each localized node, an averaging.

24. The computer readable medium of any one of claims 13 to 23, the instructions further comprising instructions to: in response to receiving a geographical location associated with the one or more localized nodes, determine a geographical location associated with each localized node based on the respective fixed location and the geographical location.

25. A system for localizing nodes, the system comprising: a processor; a transceiver in communication with the processor; 20 a memory coupled to the processor, the memory comprising instructions which when executed by the processor cause the processor to: while there are nodes of the two or more wireless nodes that have not been localized, iteratively: transmit a signal with one or more localized wireless nodes; determine a variance associated with each of the two or more wireless nodes which have not been localized based on observations of the signal by the respective node of the two or more wireless nodes, if any; assign, as a further localized node, a node of the two or more wireless nodes having a lowest variance and assign a fixed location to the further localized node.

26. The system of claim 25, wherein further localized nodes transmit the signal in iterations subsequent to being assigned the fixed location.

27. The system of claim 25, wherein the instructions further comprise instructions to store the fixed locations of further localized nodes.

28. The system of claim 25, wherein either all or a best combination of the one or more localized wireless nodes transmit the signal.

29. The system of claim 25 to 28, the instructions further comprising instructions to: receive a request to localize a mobile device; determine an estimated location of the mobile device in reference to the localized nodes

30. The system of any one of claims 25 to 28, wherein the variance is a dilution of precision determined based on an estimated location, the estimated location determined at least in part by a least squares trilateration scheme.

31 . The system of any one of claims 25 to 29, wherein the variance is an overall dilution of precision (DOP) determined by, for each of the two or more wireless nodes: determining a characteristic of the signal observed from each respective localized node; determining a DOP associated with each localized node transmitting the received signal based on the associated determined characteristic; and 21 averaging the DOP associated with each localized node transmitting the received signal to assign the overall DOP.

32. The system of claim 31 , the instructions further comprising instructions to: determine a proximity of each of the two or more wireless nodes to each of the localized nodes; and assign the DOP by weighing each DOP associated with each localized node transmitting the received signal with the determined proximity.

33. The system of claim 31 , wherein the proximity is determined by: for each localized wireless node, associating locations of the other of the one or more localized wireless nodes to measures of the signal received from the respective other of the one or more localized networks; determining the proximity by comparing the characteristics of the signal transmitted from localized nodes, and observed by the non-localized nodes, to characteristics of the signal transmitted from localized nodes, and observed by other localized nodes.

34. The system of any one of claims 31 to 33, wherein the characteristic is any combination of at least one of a time of arrival, an angle of arrival, a time difference of arrival, a phase of arrival, or a received signal strength indicator.

35. The system of claims 32 to 34, wherein the proximity is determined at least in part by a radial-basis artificial neural network, a Bayesian rule to evaluate the covariance of the characteristic observed from each localized node, an averaging.

36. The system of any one of claims 25 to 35, the instructions further comprising instructions to: in response to receiving a geographical location associated with the one or more localized nodes, determine a geographical location associated with each localized node based on the respective fixed location and the geographical location.

37. A non-localized node, the non-localized node comprising: a processor; a transceiver in communication with the processor; a memory coupled to the processor, the memory comprising instructions which when executed by the processor cause the processor to: 22 transmit to one or more localized wireless nodes or to a central server, via the transceiver, a request signal for a location; observe, via the transceiver, a characteristic associated with a signal received from the one or more localized wireless nodes; receive, from the one or more localized wireless nodes or the central server, a fixed location; and in response to receiving, via the transceiver, a further request signal, transmit, via the transceiver, the signal.

38. The non-localized node of claim 37, wherein the memory further comprises instructions which when executed by the processor cause the processor to store the fixed location.