IoT Personal Tracking Device Autonomous Sensor Interaction
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Solution Overview
Problem
Current personal tracking devices lack full interconnectedness with other devices and external sensors to determine actions and interact with identified devices based on sensor data, and there are no systems that utilize multiple interconnected personal trackers for monitoring a single user through machine-to-machine communications.
Innovation Solution
Implementing IoT personal tracking devices that receive sensor data from various sensors to identify external IoT-capable devices and autonomously send control instructions via machine-to-machine communication to perform determined tasks, enabling interaction with wearable or implantable devices for physiological and environmental monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personal tracking devices are made standalone without full interconnectedness, then device complexity is reduced, but functionality and monitoring capability are limited
Solution Approach 1:
The system divides functionality between multiple independent components: personal tracking devices, external sensors, and a central server. Each component performs specific functions independently, yet collectively they achieve comprehensive monitoring capabilities. The personal tracking device handles local sensor data collection, while the server manages data aggregation and analysis, resolving the contradiction between functionality and device complexity.
Solution Approach 2:
The personal tracking device is designed with multi-functionality, serving as both a standalone monitoring unit and a networked communication node. It can operate independently to collect local data while also connecting to external sensors and the server to provide comprehensive monitoring, thus achieving versatility without requiring excessive complexity in a single device.
2Reliability
If multiple interconnected personal trackers are implemented for monitoring a single user, then monitoring capability is improved, but device complexity and system complexity increase
Solution Approach 1:
The system segments monitoring functions across multiple personal tracking devices and external sensors, with each device performing specific sensing and data collection tasks. The central server aggregates and processes data from all sources, enabling comprehensive monitoring through coordinated simplicity rather than individual complexity.
Solution Approach 2:
The server acts as an intermediary that receives, processes, and integrates data from multiple personal tracking devices and external sensors. This mediator coordinates the networked devices, managing communication and data flow between them, thereby reducing the complexity burden on individual devices while maintaining reliable multi-device monitoring capability.
3Productivity
If autonomous machine-to-machine communication is implemented, then productivity and automated response are improved, but device complexity increases
Solution Approach 1:
The personal tracking device and external sensors are equipped with autonomous capabilities to automatically transmit data to the server and receive control instructions without human intervention. The system performs self-service operations including automatic data collection, transmission, processing, and execution of control commands, thereby achieving high productivity through distributed automation rather than centralized complexity.
Solution Approach 2:
The system implements automated feedback loops where sensors continuously monitor conditions, transmit data to the server, receive control instructions, and execute actions automatically. This closed-loop feedback mechanism enables rapid automated responses to changing conditions, improving productivity while distributing the computational complexity across the network rather than concentrating it in one device.
Data Source
AI summary
Novel tools and techniques are provided for implementing Internet of Things (“IoT”) functionality. In some embodiments, an IoT-capable personal tracking device might receive sensor data from each of a plurality of sensors, and might analyze the sensor data to identify one or more external IoT-capable devices with which to interact and to determine one or more tasks to be performed by the identified IoT-capable devices, each based at least in part on the sensor data. In some cases, the plurality of first sensors might comprise at least one of one or more sensors that monitor physical conditions of a user's body and/or one or more sensors that monitor environmental conditions external to the user's body. The personal tracking device might subsequently autonomously send, via machine-to-machine communication, control instructions to each of the identified external IoT-capable devices, based on the determined tasks. Multiple personal tracking devices may also be used.