Adaptive Beam Scanning for IoT Sensor Data Collection
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Solution Overview
Problem
Current 5G network technologies face challenges in efficiently collecting sensory data from Internet of Things (IoT) devices deployed in remote and hard-to-reach areas due to power and computation limitations, leading to infrequent data collection and high power consumption.
Innovation Solution
The implementation of a software-defined radio access network intelligent controller (RIC) that uses adaptive beam scanning and beamforming to periodically wake up IoT sensors for data collection, leveraging mobile data collectors like drones or robots equipped with multi-radio access technologies to manage data collection and reduce operational expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data collection methods are used for IoT devices in remote areas, then data collection can be performed, but power consumption is high and data collection frequency is low
Solution Approach 1:
The system implements periodic wake-up signals sent from the base station to IoT devices at predetermined intervals. This allows sensors to remain in low-power sleep mode between collections while still enabling frequent data gathering. The periodic activation resolves the contradiction by structuring energy consumption into discrete intervals rather than continuous operation, thereby increasing data collection frequency without proportionally increasing average power consumption.
Solution Approach 2:
The IoT devices autonomously manage their own power states by entering sleep mode and waking up only when triggered by external signals or internal timers. This self-service mechanism allows devices to minimize their own power consumption while still participating in frequent data collection cycles, as they automatically transition between active and dormant states without requiring continuous external control.
2Area of stationary object
If sensors are deployed in remote and hard-to-reach areas, then coverage is extended, but access for data collection becomes difficult
Solution Approach 1:
The base station acts as an intermediary between remote IoT devices and the central network infrastructure. It receives data from sensors in hard-to-reach areas and relays it to the core network, eliminating the need for direct physical access to remote devices. This intermediary role resolves the contradiction by enabling data collection from extended coverage areas without requiring operators to physically access remote locations.
Solution Approach 2:
The system segments the data collection function into two parts: local data gathering by autonomous sensors in remote areas, and centralized data aggregation by the base station. This segmentation allows sensors to be deployed independently in hard-to-reach locations while the base station handles the complexity of data retrieval and network communication, thereby extending coverage without compromising operational ease.
3Productivity
If beam scanning and beamforming are implemented, then data collection efficiency is improved, but system complexity increases
Solution Approach 1:
The base station is designed with multi-functionality, combining beam scanning, beamforming, and data collection capabilities in a single platform. By making the base station universal and capable of performing multiple functions, the system improves data collection efficiency through advanced signal processing while avoiding the need for separate specialized devices, thereby limiting the increase in overall system complexity.
Solution Approach 2:
The system implements feedback mechanisms where the base station adjusts beam parameters based on received signals from IoT devices. This feedback loop enables adaptive beamforming that automatically optimizes data collection efficiency without requiring manual configuration or complex centralized control, thereby improving productivity while keeping system complexity manageable through self-adjusting algorithms.
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 enables more frequent and efficient data collection from IoT sensors, reducing power consumption and operational costs while maintaining coverage and range, making it suitable for low-budget industries with vast sensor deployments.
Implementation Method 1
sending a first beam to the network sensor device to wake up the network sensor device from a sleep mode
Implementation Method 2
receiving a second beam from the network sensor device, wherein the second beam comprises sensory data
Data Source
AI summary
Deployment of Internet-of-things devices can comprise sensors deployed in remote and hard to reach areas and locations. Due to lack of access to reliable power, these sensors cannot always be connected to a network and also have limited computation power. Consequently, a mechanism can be established to periodically access these sensors and collect data from them. The mechanism can utilize a mobile radio unit device to serve as data collectors. The mobile radio unit device can make use of an adaptive beam scanning to perform sensory data collection via the beam scanning operation. Additionally, the platform can also comprise a radio access network intelligent controller to manage the data collecting radio units by providing specific instructions and data collection methodologies.