Source-linked AI summary
Mobile Internet of Things: Can UAVs Provide an Energy-Efficient Mobile Architecture?
Mohammad Mozaffari, Walid Saad, Mehdi Bennis, Merouane Debbah
TL;DR
The paper addresses energy-efficient uplink data collection from mobile, battery-limited IoT devices using multiple UAVs as aerial base stations. It clusters devices to place UAVs and uses optimal transport theory to determine energy-efficient trajectories. The proposed approach reduces total device transmit power by 56% versus fixed Voronoi deployment and yields minimum-energy UAV paths for serving mobile IoT devices.
Problem
Energy-efficient uplink collection requires jointly deploying and moving multiple UAVs as device locations and clusters change over time.
Method
The framework uses constrained clustering for UAV deployment and optimal transport theory to assign UAVs to evolving cluster centers.
Results
56% reduction in total IoT-device transmit power is reported versus fixed Voronoi deployment for reliable uplink communications.
Takeaways & Limitations
The resulting UAV trajectories serve mobile IoT devices with minimum energy consumption while maintaining energy-efficient communications.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
The paper develops multiple mobile UAVs as aerial base stations for energy-efficient uplink data collection from mobile ground IoT devices. It combines device clustering with optimal UAV mobility to reduce communication and movement energy.
- UAVs can dynamically move toward battery-limited IoT devices, collect their data, and relay it beyond the devices’ communication ranges.
- Optimal UAV deployment and energy-efficient operation remain key challenges for UAV-based IoT communications.
- The proposed approach clusters ground IoT devices, assigns one UAV to each cluster, and determines UAV paths using dynamic clustering and optimal transport theory.
- 56% reduction in total IoT-device transmit power is achieved relative to fixed Voronoi deployment for reliable uplink communications.
- The paper is organized around system modeling, constrained clustering, discrete transport-based UAV mobility, simulations, and conclusions.
II. SYSTEM MODEL AND PROBLEM FORMULATION
The system models uplink collection from ground IoT devices by capacity-limited UAVs under line-of-sight and reliability constraints. The optimization minimizes device transmit power while determining feasible device–UAV assignments and locations.
- The system contains L ground IoT devices and K UAVs, with device and UAV locations represented in two and three dimensions, respectively.
- Each UAV supports at most Mj devices, and uplink transmissions use orthogonal frequency division multiple access.
- 26LoS probability depends on the environment, device and UAV locations, and elevation angle; ψ and β depend on carrier frequency and environment.
- Devices can connect only when the LoS probability reaches threshold ε, restricting assignments by UAV altitude and horizontal distance.
- Transmit power is selected to satisfy the minimum SNR required for successful decoding under a QPSK bit-error-rate requirement.
- The optimization partitions devices into disjoint UAV-served sets subject to UAV capacities, with transmit power increasing with squared device–UAV distance.
- In time-varying networks, changing device locations alter clusters, requiring UAV reassignment and movement to updated cluster centers with minimum energy.
III. CLUSTERING IOT DEVICES
The clustering stage places UAVs at centers of constrained device clusters to minimize the total transmit power of ground IoT devices. It solves the resulting partitioning problem through iterative assignment and center updates.
- UAV placement at each cluster center minimizes the cluster’s total squared distance to its assigned devices.
- Constrained K-means partitions L points into K disjoint clusters while minimizing the sum of squared distances to cluster centers.
- The algorithm alternates device assignment and UAV-location updates until the locations stop changing.
A. Assignment Step
The assignment step determines which UAV serves each device given the current UAV locations. It minimizes total device-to-UAV squared distance while representing assignments as binary decisions.
- A. Assignment Step: Given UAV locations, devices are assigned to clusters so that total squared distance to their corresponding centers is minimized.
- A. Assignment Step: The binary variable Aij equals 1 when device i is assigned to UAV j and 0 otherwise.
- A. Assignment Step: The assignment problem is formulated as integer linear programming and solved using the cutting plane method.
B. Update Step
The update step places each UAV at the center of its assigned cluster by solving a convex optimization problem, and iterates assignment and location updates until convergence.
- B. Update Step: The update step finds UAV locations by updating each cluster center after device assignments are determined.The UAV location is obtained by solving the update optimization problem for the given clusters.
- B. Update Step: The update problem is a convex quadratically constrained quadratic program because its quadratic matrices are positive semidefinite.
- B. Update Step: Assignment and update steps repeat until UAV locations stop changing, while the total transmit power decreases monotonically at each iteration.The solution converges after several iterations.
- B. Update Step: For fixed ground-device locations, the resulting cluster-center UAV locations minimize transmit power for reliable uplink communications.In mobile networks, UAV locations must subsequently follow changing cluster centers.
IV. MOBILITY OF UAVS: OPTIMAL TRANSPORT THEORY
The paper models UAV relocation in time-varying IoT networks as a discrete optimal transport problem, where UAV mobility energy defines the transportation cost. The resulting transport plan assigns initial UAV locations to new cluster-center destinations while minimizing movement energy.
- IV. MOBILITY OF UAVS: OPTIMAL TRANSPORT THEORY: UAVs must repeatedly relocate as mobile IoT devices and their clusters change over time.The objective is reliable uplink transmission with minimum total mobility energy so UAVs remain operational longer.
- IV. MOBILITY OF UAVS: OPTIMAL TRANSPORT THEORY: The discrete Monge-Kantorovich model maps initial UAV locations to new destinations using mobility energy as the transportation cost.The transportation plan represents assignments between the two location sets.
- IV. MOBILITY OF UAVS: OPTIMAL TRANSPORT THEORY: UAV movement energy is linearly proportional to travel distance when speed is constant.The energy expression uses travel distance D, constant speed v, travel time t, and speed-dependent power p(v).
- IV. MOBILITY OF UAVS: OPTIMAL TRANSPORT THEORY: The optimal transport plan is obtained from the dual solution when the transport potentials satisfy ϕ(l) − ξ(k) = E_kl.The revised simplex method is used to find the optimal mapping between initial and destination locations.
V. SIMULATION RESULTS AND ANALYSIS
Simulations evaluate clustering, UAV placement, mobility, and energy use in static and time-varying IoT networks. The proposed deployment lowers device transmit power relative to fixed Voronoi placement, while optimal transport determines energy-efficient UAV movement.
- Static deployment: Five UAVs form five clusters for 100 IoT devices, with each cluster limited to 30 devices and centered on its UAV.The device distribution affects both cluster sizes and optimal UAV locations.
- Static deployment: 56% lower total transmit power is achieved by the proposed clustering and deployment method than by the fixed Voronoi method.The comparison averages total transmit power over multiple simulation runs.
- Static deployment: 77 mW to 38 mW is the proposed method’s transmit-power decrease when the number of UAVs rises from 4 to 8.For the same increase, the Voronoi case decreases from 115 mW to 95 mW.
- UAV mobility: Optimal transport updates UAV destinations as mobile IoT clusters change over time.In the illustrated scenario, 8 UAVs serve 400 devices whose locations are perturbed at each time step.
- UAV mobility: 106 kJ is the minimum total mobility energy reported for 8 UAVs serving 400 devices across 10 time instances.Different UAVs consume different amounts because their travel distances to evolving cluster centers differ.
- Battery depletion: 1520 J to 2510 J is the average per-UAV energy increase when battery-depleted UAVs rise from 2 to 4.The remaining UAVs must update their locations as the number of operational UAVs decreases.
VI. CONCLUSIONS
The paper proposes a framework for clustering IoT devices and deploying and moving multiple UAVs for energy-efficient uplink data collection. Its results show reduced device transmit power relative to Voronoi deployment and longer UAV operation through intelligent movement.
- VI. CONCLUSIONS: The framework jointly optimizes constrained device clustering, UAV deployment, and UAV mobility for uplink data collection.The objective is to minimize IoT-device transmit power while meeting a required bit error rate.
- VI. CONCLUSIONS: The results show that careful clustering and UAV deployment significantly decrease device transmit power compared with classical Voronoi-based deployment.
- VI. CONCLUSIONS: Intelligent UAV movement enables the UAVs to remain operational for a longer time while serving ground devices.