Opportunistic position location determination and reporting for asset tracking and monitoring

1 . An apparatus for wireless communication at a user equipment (UE), comprising:

at least one memory; and

at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to:

receive, from a server, first data that is indicative of a first likelihood of the UE successfully performing at least one of a position fix or a transmission of sensor data based on a first set of characteristics associated with the UE and at least one additional UE;

compute, based on the first data and a second set of characteristics associated with the UE, a second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data;

schedule, based on the second likelihood, a wake-up time instance or a sleep time instance; and

transition the UE (1) from a sleep state to an active state at the wake-up time instance or (2) from the active state to the sleep state at the sleep time instance.

2 . The apparatus of claim 1 , wherein the at least one processor, individually or in any combination, is further configured to:

transmit, to the server, an indication of the second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data.

3 . The apparatus of claim 2 , wherein the at least one processor, individually or in any combination, is further configured to:

receive, from the server based on the second likelihood, second data that is indicative of a re-computed first likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data.

4 . The apparatus of claim 1 , wherein the at least one processor, individually or in any combination, is further configured to:

perform, based on the second likelihood, at least one of the position fix or the transmission of the sensor data at or after the wake-up time instance.

5 . The apparatus of claim 4 , wherein to perform at least one of the position fix or the transmission of the sensor data, the at least one processor, individually or in any combination, is configured to transmit, to the server via a least one of a satellite link, a cellular link, or a wireless local area network (WLAN) link, an indication of at least one of the position fix or the sensor data.

6 . The apparatus of claim 1 , wherein the at least one processor, individually or in any combination, is further configured to:

receive, from the server, an indication of a pre-scheduled wake-up time instance or a pre-scheduled sleep time instance, wherein the wake-up time instance or the sleep time instance differ from the pre-scheduled wake-up time instance or the pre-scheduled sleep time instance, respectively.

7 . The apparatus of claim 1 , wherein the UE comprises an Internet of things (IoT) device that belongs to a fleet of IoT devices, and wherein the server comprises a fleet management server.

8 . The apparatus of claim 1 , wherein the first set of characteristics comprises at least one of a battery life of the UE and the at least one additional UE, a path of the UE and the at least one additional UE, a number of permitted retransmissions associated with the UE and the at least one additional UE, or a signal strength associated with the UE and the at least one additional UE.

9 . The apparatus of claim 1 , wherein the second set of characteristics comprises at least one of a battery life of the UE, a path of the UE, a number of permitted retransmissions associated with the UE, or a signal strength associated with the UE.

10 . The apparatus of claim 1 , wherein the sensor data comprises at least one of a camera image, a video stream, a temperature measurement, a speed of the UE, or an indication of a battery life of the UE.

11 . The apparatus of claim 1 , wherein the first data is associated with a first machine learning (ML) model of the server, and wherein to compute the second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data, the at least one processor, individually or in any combination, is configured to compute the second likelihood by way of a second ML model of the UE.

12 . The apparatus of claim 11 , wherein at least one of the first ML model or the second ML model is trained based on at least one of spatial factors between the UE and the server or temporal factors between the UE and the server.

13 . The apparatus of claim 12 , wherein the spatial factors comprise at least one of a distance of the UE to the server or one or more structures that exist between the UE and the server, and wherein the temporal factors comprise at least one of weather in an environment associated with the UE or temporary interference sources in the environment.

14 . The apparatus of claim 1 , further comprising at least one of a transceiver or an antenna coupled to the at least one processor, wherein to receive the first data, the at least one processor, individually or in any combination, is configured to receive the first data via at least one of the transceiver or the antenna.

15 . An apparatus for wireless communication at a server, comprising:

at least one memory; and

at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to:

compute a first likelihood of a user equipment (UE) successfully performing at least one of a position fix or a transmission of sensor data based on a first set of characteristics associated with the UE and at least one additional UE;

transmit, to the UE, first data that is indicative of the first likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data; and

receive, from the UE, an indication of a second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data.

16 . The apparatus of claim 15 , wherein the at least one processor, individually or in any combination, is further configured to:

re-compute, based on the second likelihood, the first likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data.

17 . The apparatus of claim 16 , wherein the at least one processor, individually or in any combination, is further configured to:

transmit, to the UE, second data that is indicative of the re-computed first likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data.

18 . The apparatus of claim 15 , wherein the at least one processor, individually or in any combination, is further configured to:

receive, from the UE, an indication of at least one of the position fix or the transmission of the sensor data.

19 . The apparatus of claim 18 , wherein to receive the indication of at least one of the position fix or the transmission of the sensor data, the at least one processor, individually or in any combination, is configured to receive, via at least one of a satellite link, a cellular link, or a wireless local area network (WLAN) link, the indication of at least one of the position fix or the transmission of the sensor data.

20 . The apparatus of claim 18 , wherein the at least one processor, individually or in any combination, is further configured to:

transmit, to the UE, an indication of a pre-scheduled wake-up time instance or a pre-scheduled sleep time instance, wherein the indication of at least one of the position fix or the transmission of the sensor data is associated with a time instance that is different from the pre-scheduled wake-up time instance or the pre-scheduled sleep time instance.

21 . The apparatus of claim 15 , wherein the UE comprises an Internet of things (IoT) device that belongs to a fleet of IoT devices, and wherein the server comprises a fleet management server.

22 . The apparatus of claim 15 , wherein the first set of characteristics comprises at least one of a battery life of the UE and the at least one additional UE, a path of the UE and the at least one additional UE, a number of permitted retransmissions associated with the UE and the at least one additional UE, or a signal strength associated with the UE and the at least one additional UE.

23 . The apparatus of claim 15 , wherein the second likelihood is based on a second set of characteristics associated with the UE, and wherein the second set of characteristics comprises at least one of a battery life of the UE, a path of the UE, a number of permitted retransmissions associated with the UE, or a signal strength associated with the UE.

24 . The apparatus of claim 15 , wherein the sensor data comprises at least one of a camera image, a video stream, a temperature measurement, a speed of the UE, or an indication of a battery life of the UE.

25 . The apparatus of claim 15 , wherein to compute the first likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data, the at least one processor, individually or in any combination, is configured to compute the first likelihood by way of a first machine learning (ML) model of the server, and wherein the second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data is associated with a second ML model of the UE.

26 . The apparatus of claim 25 , wherein at least one of the first ML model or the second ML model is trained based on at least one of spatial factors between the UE and the server or temporal factors between the UE and the server.

27 . The apparatus of claim 26 , wherein the spatial factors comprise at least one of a distance of the UE to the server or one or more structures that exist between the UE and the server, and wherein the temporal factors comprise at least one of weather in an environment associated with the UE or temporary interference sources in the environment.

28 . The apparatus of claim 15 , further comprising at least one of a transceiver or an antenna coupled to the at least one processor, wherein to transmit the first data, the at least one processor, individually or in any combination, is configured to transmit the first data via at least one of the transceiver or the antenna.

29 . A method of wireless communication at a user equipment (UE), comprising:

receiving, from a server, first data that is indicative of a first likelihood of the UE successfully performing at least one of a position fix or a transmission of sensor data based on a first set of characteristics associated with the UE and at least one additional UE;

computing, based on the first data and a second set of characteristics associated with the UE, a second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data;

scheduling, based on the second likelihood, a wake-up time instance or a sleep time instance; and

transitioning the UE (1) from a sleep state to an active state at the wake-up time instance or (2) from the active state to the sleep state at the sleep time instance.

30 . A method of wireless communication at a server, comprising:

computing a first likelihood of a user equipment (UE) successfully performing at least one of a position fix or a transmission of sensor data based on a first set of characteristics associated with the UE and at least one additional UE;

transmitting, to the UE, first data that is indicative of the first likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data; and

receiving, from the UE, an indication of a second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data.