IoT Device Localization via 2D Encounter Geometry
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Solution Overview
Problem
Existing localization systems face challenges in accurately determining the location of IoT devices, especially in heterogeneous IoT systems where devices have varying capabilities and resource constraints, and perform poorly in indoor and multi-story environments.
Innovation Solution
A collaborative localization system using 2D encounter geometry, where IoT devices share localization information through machine-to-machine interactions, leveraging RF signal strengths and geometry to determine relative distances and orientations, enabling reliable localization without relying on high-end localization technologies like GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or satellite-based localization systems are used, then location accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces intermediary devices (anchor devices) that act as mediators between the localization system and resource-constrained devices. These anchor devices run sophisticated localization algorithms and share results with resource-constrained devices, enabling accurate localization without requiring the constrained devices to have complex localization hardware or algorithms.
Solution Approach 2:
The system enables resource-constrained devices to localize themselves by leveraging encounters with other devices and shared location information. Instead of requiring each device to independently perform complex localization, the system allows devices to self-localize using simple algorithms and shared data from the network.
2Reliability
If traditional localization systems are used in indoor environments, then location information can be obtained, but reliability deteriorates due to signal obstruction
Solution Approach 1:
The patent combines multiple localization data sources including RF signal strength, accelerometer data, gyroscope data, and encounter information from multiple devices. By merging these diverse data sources, the system achieves reliable localization in indoor environments where single-method approaches fail due to signal obstruction.
Solution Approach 2:
The system transitions from relying solely on RF signal propagation (2D plane) to incorporating temporal dimension through sequential encounters and movement data. By adding the time dimension and analyzing movement patterns across multiple encounters, the system overcomes signal obstruction problems in indoor environments.
3Ease of operation
If resource-constrained IoT devices perform localization independently, then device autonomy is improved, but energy consumption increases
Solution Approach 1:
Resource-constrained devices perform only partial localization computations locally, using simple algorithms for processing encounter data and accelerometer/gyroscope information. The more computationally intensive tasks are offloaded to anchor devices in the network, allowing constrained devices to maintain autonomy while minimizing energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides reliable and efficient localization for resource-constrained IoT devices, reducing communication overhead and latency, and improving localization accuracy in diverse environments without the need for additional sensors or infrastructure.
Implementation Method 1
detecting 2D encounter geometry associated with machine-to-machine encounters with other devices in the environment... determining, from the 2D encounter geometry, a change in position of the device within the environment
Data Source
AI summary
Sensors provisioned on a first device detect a movement of the first device corresponding to the first device changing position within an environment. Location information for the first device is updated based on the position change. A plurality of signals are detected, at the first device, from a second device in the environment, determine, and a distance between the first and second devices is determined based on each of the signals. From the signals, another change in position of the first device within the environment is determined and the location information updated for the first device. The movement is detected at the first device at an instance between two of the plurality of signals, and location information for the first device based on the first position change is updated prior to detection of the later of the two signals in the plurality of signals.