Mobile Internet of Things: Can UAVs Provide an Energy-Efficient Mobile Architecture? (1607.02766v1)

Abstract: In this paper, the optimal trajectory and deployment of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum energy consumption, a new approach for optimal mobility of the UAVs is proposed. First, given a fixed ground IoT network, the total transmit power of the devices is minimized by properly clustering the IoT devices with each cluster being served by one UAV. Next, to maintain energy-efficient communications in time-varying mobile IoT networks, the optimal trajectories of the UAVs are determined by exploiting the framework of optimal transport theory. Simulation results show that by using the proposed approach, the total transmit power of IoT devices for reliable uplink communications can be reduced by 56% compared to the fixed Voronoi deployment method. Moreover, our results yield the optimal paths that will be used by UAVs to serve the mobile IoT devices with a minimum energy consumption.

• The paper proposes an optimized UAV deployment strategy using constrained K-means clustering to minimize IoT devices' transmit power.
• It applies optimal transport theory for precise UAV trajectory planning, significantly reducing energy consumption during mobility.
• Simulations indicate a 56% power reduction compared to fixed deployments, highlighting UAVs' potential in mobile IoT networks.

Energy-Efficient Mobile IoT Architecture Utilizing UAVs

The paper investigates a novel approach that leverages Unmanned Aerial Vehicles (UAVs) to enhance the energy efficiency of data collection in mobile Internet of Things (IoT) networks. UAVs, functioning as aerial base stations, provide a high degree of mobility and adaptable line-of-sight (LoS) links to ground-based IoT devices, which are typically constrained by their limited battery life and geographical spread.

Key Components and Methodologies

Clustering and UAV Deployment

A significant contribution of this paper is the optimized deployment strategy for UAVs, aimed at minimizing the total transmit power of IoT devices. The research employs a constrained K-means clustering technique to dynamically partition IoT devices into multiple clusters. Each cluster is serviced by a single UAV stationed at the cluster's geometric center, thereby minimizing the squared distance—and thus the power requirements—between the UAV and devices in its respective cluster. This clustering approach is especially pertinent when the UAV's capacity, in terms of the number of devices it can serve simultaneously, is limited.

Mobility and Optimal Trajectories

For mobile IoT networks characterized by device mobility and intermittent data availability, UAVs must frequently relocate to maintain optimal connectivity. This necessitates the continuous adjustment of UAV trajectories. The paper explores this through the lens of optimal transport theory, which provides a robust framework for determining the minimization of UAV energy consumption during mobility. The application of discrete optimal transport allows for precise determination of UAV movement that aligns with energy efficiency goals while ensuring persistent, reliable uplink communication.

Simulation Results

Simulation results substantiate the efficacy of the proposed methodologies. Notably, the paper highlights a 56% reduction in total transmit power of IoT devices when using the optimal clustering and UAV deployment compared to a fixed Voronoi deployment method. Furthermore, the paper details the energy consumption associated with UAV mobility, illustrating significant gains in operational time through efficient path planning.

Implications and Future Directions

The findings underscore the viability of UAV-based communication frameworks in enhancing the energy efficiency of IoT networks. Practically, this research can contribute to the deployment of IoT networks in areas with limited infrastructure, such as rural or disaster-hit regions, where rapid deployment and coverage flexibility are critical. Theoretically, the integration of optimal transport theory into UAV trajectory planning represents a promising avenue for further exploration. Future developments could focus on real-time adaptive control methodologies for UAVs, accommodating dynamic environmental conditions and heterogeneous IoT deployments.

Conclusion

In conclusion, this paper provides a comprehensive analysis of UAV deployment strategies that are poised to significantly advance energy-efficient IoT communications. By optimizing device clustering and UAV mobility through rigorous mathematical frameworks, this paper offers new insights and practical solutions to the challenges posed by the growing integration of IoT and UAV technologies.