Characterizing proximity risks within a radio mesh

1. An apparatus comprising:

collection logic to receive risk data from one or more source devices; and

memory, coupled to the collection logic, to store the risk data,

wherein pattern logic is to analyze the stored risk data to determine existence of one or more risks, wherein the risk data includes results of two-way ping signals communicated between the one or more source devices and a target device, wherein the one or more source devices comprise one or more communication devices, wherein the one or more communication devices communicate in accordance with millimeter Wave (mmWave) signals, wherein tracking logic is to track movement of the target device or presence of an obstacle based at least in part on machine learning or deep learning applied to the mmWave signals.

2. The apparatus of claim 1 , wherein the pattern logic is to determine existence of the one or more risks based at least in part on a rate of occurrence of successful two-way ping signals communicated between the one or more source devices and the target device.

3. The apparatus of claim 1 , comprising logic to generate a risk map of the one or more risks in a geographic area.

4. The apparatus of claim 3 , wherein the target device is to receive information corresponding to the risk map before entering or during presence in the geographic area.

5. The apparatus of claim 3 , wherein the geographic area comprises one or more of a parking lot, one or more street blocks, a factory floor, a warehouse, or a grocery store.

6. The apparatus of claim 3 , wherein logic to generate the risk map is to generate the risk map based at least in part on Global Positing System (GPS) information.

7. The apparatus of claim 1 , wherein the risk data includes the results of two-way ping signals communicated between the one or more source devices and the target device during a time period.

8. The apparatus of claim 7 , wherein the time period is 100 millisecond.

9. The apparatus of claim 1 , wherein the one or more communication devices are proximate or coupled to at least one of: a vehicle, a stationary object, a pedestrian, a bicyclist, a skateboarder, or a pet, wherein the one or more communication devices are to communicate the risk data.

10. The apparatus of claim 1 , wherein the one or more communication devices communicate in accordance with Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), Fourth Generation (4G), or Fifth Generation (5G) cellular network signals.

11. The apparatus of claim 1 , comprising logic to track movement of the target device or presence of an obstacle based at least in part on reflections in the mmWave signals.

12. The apparatus of claim 1 , wherein the one or more communication devices are proximate or coupled to at least a stationary object, wherein the stationary object comprises one or more of: a camera and a proximity sensor.

13. The apparatus of claim 1 , wherein the one or more communication devices are proximate or coupled to at least a stationary object, wherein the stationary object is to communicate through wired or wireless communication.

14. The apparatus of claim 1 , wherein the one or more communication devices comprise a smartphone, a Radio Frequency (RF) tag, or an Internet of Things (IoT) device.

15. The apparatus of claim 1 , wherein a central computing device comprises one or more of: the collection logic, the pattern logic, and the memory.

16. The apparatus of claim 1 , wherein at least one of the target device or the one or more source devices comprises one or more of: a processor having one or more processor cores, a Graphics Processing Unit (GPU) having one or more cores, a Field Programmable Gate Array (FPGA), the collection logic, the memory, and the pattern logic.

17. One or more computer-readable medium comprising one or more instructions that when executed on at least one processor configure the at least one processor to perform one or more operations to cause:

collection logic to receive risk data from one or more source devices; and

memory to store the risk data,

wherein pattern logic is to analyze the stored risk data to determine existence of one or more risks, wherein the risk data includes results of two-way ping signals communicated between the one or more source devices and a target device, wherein the one or more source devices comprise one or more communication devices, wherein the one or more communication devices communicate in accordance with millimeter Wave (mmWave) signals, wherein tracking logic is to track movement of the target device or presence of an obstacle based at least in part on machine learning or deep learning applied to the mmWave signals.

18. The computer-readable medium of claim 17 , further comprising one or more instructions that when executed on the at least one processor configure the at least one processor to perform one or more operations to cause the pattern logic to determine existence of the one or more risks based at least in part on a rate of occurrence of successful two-way ping signals communicated between the one or more source devices and the target device.

19. The computer-readable medium of claim 17 , further comprising one or more instructions that when executed on the at least one processor configure the at least one processor to perform one or more operations to generate a risk map of the one or more risks in a geographic area.

20. The computer-readable medium of claim 17 , wherein the risk data includes the results of two-way ping signals communicated between the one or more source devices and the target device during a time period.

21. A computing system comprising:

a processor having one or more processor cores;

memory, coupled to the processor, to store one or more bits of data; and

collection logic to receive risk data from one or more source devices; wherein the memory is to store the risk data,

wherein pattern logic is to analyze the stored risk data to determine existence of one or more risks, wherein the risk data includes results of two-way ping signals communicated between the one or more source devices and a target device, wherein the one or more source devices comprise one or more communication devices, wherein the one or more communication devices communicate in accordance with millimeter Wave (mmWave) signals, wherein tracking logic is to track movement of the target device or presence of an obstacle based at least in part on machine learning or deep learning applied to the mmWave signals.

22. The system of claim 21 , wherein the pattern logic is to determine existence of the one or more risks based at least in part on a rate of occurrence of successful two-way ping signals communicated between the one or more source devices and the target device.

23. The system of claim 21 , comprising logic to generate a risk map of the one or more risks in a geographic area.

24. The system of claim 21 , wherein the risk data includes the results of two-way ping signals communicated between the one or more source devices and the target device during a time period.

25. The system of claim 21 , wherein the one or more communication devices are proximate or coupled to at least one of: a vehicle, a stationary object, a pedestrian, a bicyclist, a skateboarder, or a pet, wherein the one or more communication devices are to communicate the risk data.