Location Service Component for IoT Device Positioning
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Solution Overview
Problem
Existing IoT systems face challenges in accurately estimating the location of mobile devices that have lost the ability to communicate with servers due to low or no power, leading to inefficiencies in battery replacement and waste management, particularly as the number of IoT devices is expected to surge.
Innovation Solution
A location service component that utilizes a predictive model to estimate the location of devices by obtaining and validating initial data items, removing inconsistent data, and providing a pruned set of locations, enabling accurate positioning even when devices are in low or no power mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a device uses positioning capabilities such as GPS to update its location, then the location information is accurate, but the battery power is consumed significantly
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical location data and movement patterns while the device is operational. This pre-collected information is then used to predict the device's location after battery depletion, eliminating the need for continuous power-consuming positioning updates.
Solution Approach 2:
The system dynamically adapts its approach based on device status. When the device has power, it collects actual location data and movement patterns. When the device runs out of power, the system transitions to using predictive models based on the previously collected dynamic information, optimizing energy usage while maintaining location tracking capability.
2Loss of energy
If the device enters low or no power mode to conserve battery, then energy consumption is reduced, but the ability to communicate location information is lost
Solution Approach 1:
Before the device enters low or no power mode, the system performs preliminary actions by collecting historical location data, movement patterns, and contextual information. This pre-collected data serves as the basis for predicting the device's location after power depletion, ensuring location information remains available without requiring the device to maintain active communication.
Solution Approach 2:
The system introduces an intermediary predictive model that acts as a mediator between the device and the location query server. When the device cannot communicate directly due to power constraints, the predictive model uses historical data to generate and provide location estimates, bridging the communication gap without requiring the device to consume additional power.
3Quantity of substance
If the server stores only the last known position of a device, then storage requirements are minimized, but the location information becomes obsolete when the device is mobile and powered down
Solution Approach 1:
The system performs preliminary actions by collecting and storing multiple historical location data points and movement patterns while the device is operational. This pre-collected historical information enables the predictive model to estimate the device's current location based on its movement trajectory, providing accurate location information without requiring the device to continuously update its position or the server to store extensive real-time data.
Solution Approach 2:
The system applies partial action by storing only the essential historical location data and movement patterns needed for prediction, rather than storing complete continuous location histories. This selective storage approach provides sufficient information for accurate prediction while minimizing storage requirements, balancing data retention with storage efficiency.
Data Source
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AI summary
A method and a location service component (110) for providing at least one estimate of a location related to a device (131) are disclosed. The device (131) is currently unable to report the location. The location service component (110) receives, from a client component (120), a request for said at least one estimate of the location related to the device (131). The location service component (110) estimates the location based on a predictive model to obtain an initial set of data items. Each data item includes a respective estimate of the location related to the device (131) and a set of identities representing one or more further devices in a neighbourhood of the device (131). The location service component (110) obtains a respective location for each of said one or more further devices (132) based on the set of identities. The location service component (110) removes (2090) any data item from the initial set of data items that is inconsistent with the respective location for said one or more further devices (132) to obtain a pruned set of data items. The location service component (110) sends, to the client component (120), one or more respective estimates of the pruned set of data items. A corresponding computer program and a computer program carrier are also disclosed.