Data Harvesting from Hard-to-Reach IoT Deployments: Aerial Data Collection Approach

Overview

Problem Statement

The growing adoption of the Internet of Things (IoT) across diverse industries faces critical challenges in energy sustainability and operational efficiency. IoT devices, which are predominantly battery-powered, experience significant energy consumption due to continuous communication and sensing operations. This results in frequent battery replacements, which are impractical for large-scale deployments, especially in hard-to-reach areas. Additionally, current data collection methods, such as terrestrial LoRaWAN networks, are limited in scalability, cost-efficiency, and reliability for non-line-of-sight (NLoS) operations. These limitations underscore the need for innovative solutions to sustain IoT deployments in challenging environments and integrate them into future Metaverse-supported smart cities.

Proposed Solution

This project presents the UAV-enabled Wake-Up Radio IoT (U-WuRIoT) system, a comprehensive solution combining unmanned aerial vehicles (UAVs) and wake-up radio (WuR) technology. By leveraging UAVs for aerial data aggregation and WuR for on-demand wake-ups, U-WuRIoT overcomes the trade-offs associated with traditional duty-cycling (DCY) schemes. The proposed system features an energy-efficient IoT device with an extended wake-up range and ultra-low power consumption, capable of addressing the scalability, energy efficiency, and reliability challenges in IoT networks.

The solution also integrates opportunistic sensing, where IoT devices transmit data only when detecting significant or anomalous events. This reduces redundant data transmission and optimizes UAV hover times, ensuring efficient energy usage and enhanced anomaly detection capabilities. The system’s modular design further allows for seamless adaptation in applications ranging from air pollution monitoring to structural health assessment.

Analytical Modelling

To extend the experimental findings, a robust analytical framework was developed using stochastic geometry. This framework models large-scale U-WuRIoT deployments, evaluating key performance metrics such as wake-up probabilities, false alarms, and UAV trajectory planning. The analysis incorporates factors like UAV altitude, transmit power, and device density to optimize system performance. Results from Monte Carlo simulations validate the theoretical models, demonstrating U-WuRIoT’s effectiveness in maximizing wake-up success rates while minimizing energy consumption. The model also incorporates multiple WuR antenna models to asses the advantage of beamforming on reduced false wake-up probability.

Opportunistic Sensing Scheme

The proposed opportunistic sensing scheme in the U-WuRIoT system enhances energy efficiency by enabling IoT devices to transmit data only when detecting significant or anomalous events. This selective approach avoids redundant transmissions and ensures that the UAV collects only valuable, non-redundant data, optimizing energy usage and reducing UAV hover times.

Advantages:

1. Energy Efficiency: By transmitting data only during significant events, IoT devices minimize unnecessary energy consumption, significantly extending their operational lifespan.
2. Reduced Redundancy: The scheme avoids data duplication across dense IoT networks, ensuring efficient use of bandwidth and storage.
3. Enhanced Anomaly Detection: By focusing on event-driven transmissions, the system prioritizes critical data, improving response times for anomaly detection.
4. UAV Optimization: The reduced transmission load allows UAVs to spend less time hovering over a region, thereby saving UAV energy and enabling faster coverage of larger areas.

UAV field scanning duration (𝑇𝑠𝑐𝑎𝑛) as function of threshold of IoT deviation from DT (𝛽𝐷𝑇).

Experimental Results

A prototype IoT device was developed with innovative features such as a subcarrier modulation (SCM) input stage for extended wake-up range and an efficient WuR module. Key experimental findings include:

• Wake-up Range: The IoT device achieved an aerial wake-up range of 23 meters using the proposed SCM input stage, significantly surpassing traditional WuR ranges.
• Power Efficiency: The device’s average power consumption in sleep mode was measured at 12.05 µW, enabling a battery life of 28 years under realistic conditions.
• Comparative Analysis: Against DCY-based schemes, U-WuRIoT demonstrated superior reliability and energy efficiency. For instance, DCY schemes offered limited battery lifetimes and required high sleep-to-active duty cycles, while U-WuRIoT maintained robust performance without such trade-offs.

Field experiments included indoor and outdoor data collection using UAVs equipped with custom-designed wake-up transmitters. The results validated the system’s practical feasibility, showcasing its adaptability in dynamic and large-scale IoT scenarios.

Videos:

WuR UAV experiments Indoor + Outdoor + Underwater (youtube.com)

ZAJEL: Autonomous UAV Sensing and Package Delivery (youtube.com)

UAV Enabled Smart Agriculture (youtube.com)

AgriDoctor (youtube.com)

UPMARINE (youtube.com)

Industry Collaborations

Given the practical advantages of the proposed solution, particularly in addressing localized needs, the project is being advanced to the product development stage with funding and collaboration from prominent industry leaders, including NEOM and ARAMCO. The solution is tailored to support the following applications:

• NEOM: Environmental monitoring, security surveillance in remote or hard-to-reach areas, and hazard and emergency detection.
• ARAMCO: IoT-based monitoring of composite pipelines to enable early detection of potential failures.

This collaboration underscores the system's versatility and potential for large-scale industrial deployment.

Team:

The project is lead by Omar Khalifa, a Ph.D. student at KAUST, and Dr. Nour Kouzayha, Research Scientist at KAUST, under the guidance of Prof. Tareq Y. Al-Naffouri from the Information Science Lab at KAUST, and Prof. Hesham ElSawy, an assistant professor at the School of Computing, Queen’s University.

Coauthors:

Mohammed Abdullah Hussaini, Anas S. Mohammed, Ali Alhejab, Abdelrahman S. Abdelrahman, Ahmed Al-Radhwan, Ruslan Zhagypar, Krishnendu S. Tharakan, Hayssam Dahrouj, Noha AlHarthi, Jaafar Elmirghani, Mansoor Hanif, Zekeriya Aksoy

Awards

• 2nd place winners of the global competition on Non-Terrestrial Networks for 6G (NTNYS’24), organised by the IEEE Future Networks Initiative, hosted by the International Telecommunication Union (ITU) and the Communications, Space & Technology Commission (CST), 2024.
• 1st rank winner of Advanced Sensing, IoT, and Robotics Challenge in Aramco Upstream National Hackathon, 2024.
• 1st rank winner of the senior projects expo, KFUPM, 2022.
• Winner of the AIoT (AI & IoT) National Hackathon organized by STC and Nokia, 2021.

Publications

1. Omar Khalifa, Nour Kouzayha, Mohammed Abdullah Hussaini, Hesham ElSawy, Noha Al-Harthi, Jaafar Elmirghani, Mansoor Hanif, and Tareq Y. Al-Naffouri., “Energy conservative data aggregation for IoT devices: An aerial wake-up radio approach.”, IEEE Internet of Things Journal, July. 2023.
2.  Mohammed, Anas S., Omar Khalifa, Ali Alhejab, Abdelrahman S. Abdelrahman, Ahmed Al-Radhwan, Hesham ElSawy, Nour Kouzayha, Noha Al-Harthi, Jaafar Elmirghani, Mansoor Hanif,Tareq Y. Al-Naffouri., “Demo Abstract: Energy-Efficient Aerial Data Aggregation in IoT Networks with WuR.”, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), November. 2023.
3.  Krishnendu S. Tharakan, Omar Khalifa, Hayssam Dahrouj, Nour Kouzayha, Hesham ElSawy, Noha Al-Harthi, Zekeriya Aksoy, Jaafar Elmirghani, and Tareq Y. AlNaffouri, “Efficient Wake-up Strategy: UAV-Enabled Opportunistic Sensing in IoT Networks”, IEEE 6th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2024.
4.  Omar Khalifa, Anas S. Mohammed, Ali Alhejab, Abdelrahman S. Abdelrahman, Ahmed Al-Radhwan, Ruslan Zhagypar, Hesham ElSawy, Nour Kouzayha, Noha AlHarthi, Jaafar Elmirghani, Zekeriya Aksoy, and Tareq Y. Al-Naffouri, “Energy Efficient Aerial Data Aggregation for IoT: From Prototyping to Large-Scale Implementation”, IEEE Transactions on Instrumentation and Measurement, March. 2024.
5.  Omar Khalifa, Hesham ElSawy, Nour Kouzayha, Noha Al-Harthi, Jaafar Elmirghani, and Tareq Y. Al-Naffouri, “Data Supply for Digital Twins in Smart Cities: ANon-invasive NTN-IoT Approach”, Submitted soon to IEEE Communications Magazine.
6.  Omar Khalifa, Hesham ElSawy, Nour Kouzayha, Noha Al-Harthi, Jaafar Elmirghani, and Tareq Y. Al-Naffouri, “Stochastic Geometry-based Performance Analysis of UAV-enabled WuR and Data Collection from IoT Devices Under Different Antenna Models.”, Under Preparation