Source-linked AI summary
Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network
Cheng Zhan, Yong Zeng, Rui Zhang
TL;DR
The paper addresses energy-efficient, reliable data collection from battery-powered sensors using a UAV under fading channels. It jointly optimizes sensor wake-up schedules and UAV trajectory through a successive-convex-optimization-based iterative approach. Numerical results report significant energy savings over fixed-position and straight-trajectory benchmarks.
Problem
Battery-powered sensors need energy-efficient communication, while fading channels make reliable transmission dependent on wake-up timing and UAV trajectory.
Method
The paper formulates joint wake-up-schedule and UAV-trajectory optimization as a mixed-integer non-convex problem and solves it iteratively using successive convex optimization.
Results
Significant energy savings are achieved compared with fixed-position and straight-trajectory benchmark schemes.
Takeaways & Limitations
Jointly adapting when sensors wake and where the UAV travels supports reliable data collection while reducing the maximum energy consumption among sensors.
Abstract
from arXiv · showhide
In wireless sensor networks (WSNs), utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the ground sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. Specifically, since the UAV can sequentially move close to each of the SNs when collecting data from them and thus reduce the link distance for saving the SNs' transmission energy. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs' wake-up schedule and UAV's trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.
I. INTRODUCTION
UAV-based mobile collection can reduce sensor transmission energy, but reliable operation requires jointly designing wake-up timing and UAV movement under fading channels. The paper formulates this joint design to minimize the maximum sensor energy while meeting per-sensor data requirements.
- Battery-powered sensor nodes are difficult to recharge, making energy-efficient sensing and communication essential for extending WSN lifetime.
- A mobile UAV reduces sensor transmission energy by visiting nodes sequentially and shortening their communication distances.
- Sleep and wake-up operation saves sensor energy by activating a node only when the UAV is sufficiently nearby for communication.
- Fading and packet loss require the UAV trajectory to support low-outage transmission during each sensor’s wake-up periods.
- The paper jointly optimizes wake-up schedules and UAV trajectory to minimize maximum sensor energy while reliably collecting target data under general fading.
- The resulting mixed-integer non-convex design is addressed with successive convex optimization, producing an iterative sub-optimal solution.
II. SYSTEM MODEL AND PROBLEM FORMULATION
The system models a fixed-altitude UAV collecting data from ground sensors through scheduled wake-ups over discretized trajectory slots and fading channels. The formulation minimizes the largest sensor energy consumption while enforcing reliable per-sensor data collection, mobility, and scheduling constraints.
- System model: The UAV collects data from K ground sensors over T seconds while flying at fixed altitude H and bounded speed Vmax.
- System model: The continuous UAV path is discretized into M slots, with q[m] representing the approximately constant UAV location in slot m.
- Wake-up scheduling: Binary variable xk[m] indicates whether sensor uk wakes in slot m, and at most one sensor may communicate per slot.
- Channel model: The channel uses quasi-static block fading, with large-scale attenuation determined by UAV-sensor distance and small-scale fading represented by ρk[m,l].
- Reliable transmission: Transmission rates adapt to the UAV location, while the outage constraint sets each rate so the maximum tolerable outage probability is ǫ.
- Problem formulation: The optimization minimizes slack variable θ, the maximum sensor energy, while requiring each sensor’s collected data to reach rk and enforcing UAV mobility constraints.
III. PROPOSED SOLUTION
The proposed solution alternates wake-up-schedule and UAV-trajectory optimization after relaxing binary schedule variables, using successive convex optimization for the non-convex trajectory subproblem.
- Relaxed problem: Binary wake-up constraints are relaxed to 0 ≤ x_k[m] ≤ 1, and the resulting problem is solved iteratively using block coordinate descent.The relaxed schedule and trajectory are optimized alternately through subproblems (P2) and (P3).
- Wake-up schedule optimization: For a fixed trajectory, the integer-relaxed wake-up schedule is obtained by solving a standard linear program.The schedule variables are constrained between zero and one while satisfying the problem’s remaining constraints.
- Trajectory optimization: For a fixed wake-up schedule, the UAV trajectory maximizes the weighted minimum communication throughput across SNs, with weights inversely proportional to r_k.This trajectory subproblem remains non-convex because of its communication constraints.
- Trajectory optimization: Successive convex optimization replaces the trajectory subproblem with iteratively solved convex QCQPs based on first-order lower bounds.Each iteration maximizes a lower bound, and the resulting QCQP can be solved efficiently with existing software tools.
- Convergence and reconstruction: The alternating relaxed algorithm converges because its objective values are non-increasing and bounded below, while the inner trajectory algorithm has non-decreasing objective values.After convergence, fractional schedule values can be reconstructed as integer fading-block allocations by rounding Lx_k[m].
IV. NUMERICAL RESULTS
Numerical results show that the optimized UAV trajectory and wake-up schedule reduce min-max sensor-node energy consumption relative to straight-flight and static-collection benchmarks under fading channels.
- Simulation setup: The evaluation uses Rician fading with four sensor nodes randomly placed in a 1.6 × 1.6 km^2 area and specified UAV, channel, and transmission parameters.The UAV starts at [-800, 0]^T m, ends at [800, 0]^T m, and flies at up to 50 m/s.
- Trajectory and scheduling: As the flight duration T increases, the optimized UAV trajectory moves closer to the sensor nodes.For Sk = 10Mbits and ǫ = 10^-2, the optimized trajectories are compared with straight flight.
- Trajectory and scheduling: For T = 50s, sensor nodes remain asleep most of the time and wake only when the UAV is sufficiently close.The schedule demonstrates coordinated wake-up during trajectory-based data collection.
- Energy comparison: The optimized trajectory significantly outperforms straight flight and static collection in min-max energy consumption.The comparison uses optimized wake-up schedules for all schemes and evaluates θ against sensing data size Sk or outage target ǫ.
- Energy comparison: The performance gain becomes more pronounced as Sk increases or ǫ decreases.Closer UAV positioning enables better channels, higher reliable data rates, shorter transmission times, and lower sensor-node energy consumption.
V. CONCLUSION
The conclusion presents a joint design that optimizes sensor-node wake-up schedules and UAV trajectories for reliable, energy-efficient data collection, with numerical evidence of energy savings over benchmark schemes.
- Conclusion: The proposed design jointly optimizes sensor-node wake-up schedules and UAV trajectory to minimize maximum sensor-node energy while ensuring reliable data collection in fading channels.The problem is addressed using successive convex optimization to obtain an efficient iterative sub-optimal solution.
- Conclusion: Numerical results show significant energy savings compared with benchmark schemes.