UAV-Enabled IoT Networks: A SWIPT Energy Harvesting Architecture with Relay Support for Disaster Response

1. Problem Statement

The paper addresses the critical challenge of energy limitations in Unmanned Aerial Vehicles (UAVs) acting as relays in Disaster Response Networks (DRNs). In disaster scenarios (e.g., earthquakes, floods), ground base stations often fail, necessitating UAVs to act as Flying Base Stations (FBSs) to restore communication between isolated user groups.

Key Challenges Identified:

• Limited Battery Life: UAVs have finite energy, which restricts their flight time and service duration.
• Energy Consumption vs. Data Collection: UAVs must collect data from ground users (uplink) and transmit it to a destination base station (downlink). The energy required for flight and communication often exceeds the battery capacity, limiting the number of users served.
• Inefficiency of Traditional Models: Existing research often models Energy Harvesting (EH) and data collection separately. There is a lack of integrated models where a UAV harvests energy while collecting data during its uplink phase to power its subsequent flight and downlink transmission.

2. Methodology

The authors propose a system model involving two distinct areas (NET1 and NET2) separated by a distance. A UAV acts as a relay, collecting data from cellular users in NET1 (where ground infrastructure is down) and delivering it to a Base Station (BS) in NET2.

The study compares two scenarios:

• Scenario A (Conventional): The UAV relies solely on its initial battery charge. It collects data from NET1, flies to NET2, and transmits data. If energy runs out, the mission fails.
• Scenario B (Proposed SWIPT Architecture): The UAV utilizes Simultaneous Wireless Information and Power Transfer (SWIPT) during the uplink phase with NET1 users.
  • Receiver Architecture: The paper adopts the Power Splitting (PS) receiver architecture. The received RF signal is split into two streams: a fraction ($\theta$) is used for Information Decoding (ID), and the remaining fraction ($1-\theta$) is used for Energy Harvesting (EH) to recharge the UAV's battery.
  • System Dynamics: The UAV hovers over NET1, harvesting energy from user signals while decoding data. It then flies to NET2 to transmit the data.

Mathematical Formulation:

• Channel Modeling: The system considers Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) probabilities for Ground-to-Air (G2A) and Air-to-Ground (A2G) channels.
• Energy Consumption: The paper models UAV power consumption based on flight velocity ($v$), including blade profile power, induced power, and parasitic drag.
• Optimization Problem: The core objective is to minimize the total flight time ($T_{total}$) while ensuring the UAV has sufficient energy to complete the mission (serve all users and reach the destination).
  • Variables: The optimization seeks the optimal Power Splitting ratio ($\eta_{PS}$) and Battery Efficiency ($\eta_{Bat}$).
  • Constraints: The harvested energy must cover the energy threshold required for flight and transmission ($E_{th}^{EH}$), and the splitting ratios must be between 0 and 1.
  • Solution: The non-linear optimization problem is transformed into a convex problem and solved using the Lagrange Multiplier method and Karush–Kuhn–Tucker (KKT) conditions to derive closed-form expressions for optimal parameters.

3. Key Contributions

1. Integrated SWIPT Model for DRNs: The paper proposes a novel framework where a UAV simultaneously harvests energy and decodes information during the uplink phase, specifically tailored for disaster response relay scenarios.
2. Comparative Analysis: It rigorously compares a traditional energy-constrained scenario (A) against the proposed SWIPT-enabled scenario (B), demonstrating the quantitative benefits of EH.
3. Optimization of Flight Time: The authors formulate and solve an optimization problem to determine the optimal power splitting ratio and battery efficiency to minimize the total mission time while maximizing the number of served users.
4. Theoretical and Simulation Validation: The study provides theoretical derivations for optimal parameters and validates them through extensive simulations, showing a strong agreement between theory and simulation results.

4. Results

The simulation results, based on parameters such as user density, transmit power, and UAV altitude, highlight the following:

• Time Efficiency: In Scenario B (SWIPT), the total time spent by the UAV decreases as the uplink transmit power increases. Higher power leads to higher data rates (shorter collection time) and more harvested energy.
• User Capacity: Scenario B significantly outperforms Scenario A in terms of the number of users served. While Scenario A is limited by initial battery capacity, Scenario B can serve more users because the UAV recharges its battery during the data collection phase.
• Impact of User Density:
  • Initially, increasing user density reduces flight time (due to higher aggregate harvested energy).
  • Beyond a certain density threshold, flight time begins to increase slightly due to the overhead of serving more users, but the harvested energy remains sufficient to sustain the mission.
• Altitude and Power Trade-offs: The simulations show that UAVs can operate at lower altitudes if user transmit power is high, optimizing energy consumption. Conversely, lower user power requires the UAV to hover lower to maintain link quality, which impacts energy dynamics.
• Validation: Figure 2 in the paper demonstrates a near-perfect match between the theoretical mathematical model and the simulation results, confirming the accuracy of the proposed optimization framework.

5. Significance

This research is significant for the deployment of resilient communication networks in disaster zones:

• Extended Mission Duration: By integrating SWIPT, UAVs can operate for longer periods without needing physical battery replacement or external charging stations, which are often unavailable in disaster zones.
• Scalability: The proposed architecture allows a single UAV to serve a larger number of IoT users in a disaster area compared to conventional methods, improving the reach of emergency communications.
• Resource Optimization: The derived optimal parameters (power splitting ratio) provide a practical guideline for network operators to configure UAVs for maximum efficiency in real-world emergency scenarios.
• Future Directions: The paper suggests future work involving MIMO systems, beamforming, and the integration of Machine Learning to further optimize the trade-off between delay, energy efficiency, and data security in multi-UAV networks.

In conclusion, the paper demonstrates that SWIPT-enabled UAV relays are a superior solution for Disaster Response Networks, effectively overcoming the energy bottleneck that limits traditional UAV deployments.