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Optimal path planning for multiple UAVs in energy efficient charging of
sensors in a Wireless Sensor Networks
Shakila Rahman, Seokhoon Yoon*
Department of Electrical, Electronic and Computer Engineering, University of Ulsan
shakila.rahman1119@gmail.com, * seokhoonyoon@ulsan.ac.kr
Abstract
In this paper, we consider a wireless sensor network scenario to charge sensor nodes for multiple UAVs. We
have formulated an integer linear programming (ILP) optimization problem to find the optimal paths for UAVs where
the total energy consumption of UAVs is minimized. Simulation results shows that our proposed algorithm UGreedy,
outperforms the other comparing algorithms with lowest energy consumption.
Ⅰ. Introduction
Energy minimization of WSNs has received a great
amount of attention for long term operations in
charging scenario [1]. In case of sensor’s run out of
battery power and UAV’s battery power, the network
becomes disconnected. So, we have considered the
UAV battery constraint along with the sensor’s
deadline constraint. To solve this issue, in this paper
we have studied deadline-constrained path planning
problem with the objective of minimizing the total
energy consumption for multiple UAVs and a utility
function based greedy algorithm called UGreedy is
proposed.
Ⅱ. Problem definition
Assuming a wireless sensor network consists of a
set of stationary sensor nodes and a sink. To keep the
sensors from completely draining out their battery and
to keep the WSN alive a set of UAVs is appointed to
charge the sensor nodes periodically before they are
draining out their battery. For our proposed model, the
total energy used by UAVs is considered by the
equation as follows:
𝐸𝑢𝑎𝑣
𝑝= 𝐸𝑡
𝑝+ 𝐸𝑐
𝑝+ 𝐸ℎ
𝑝 (1)
Objective of this work is to minimize the total energy
used by UAVs. In this case, total energy for each UAV
is the energy used by the UAV while travelling,
charging, and hovering. An integer linear programming
optimization problem is formulated to minimize the
total energy used by multiple UAVs.
Ⅲ. Algorithm
Our proposed UGreedy makes the optimal choice
according to our own two utility functions (UF1, UF2)
at each step as it attempts to find the overall optimal
paths for multiple UAVs. For each optimal choice the
highest utility value is selected. In UF1, the utility
value is the reciprocal of sum of travelling time and a
given deadline of the sensor. And in UF2, the utility
value is the reciprocal of the sum of weighted scaled
travelling distance and weighted scaled deadline of the
sensor. In this case, the weighted value (range 0 to 1)
is chosen from our experimental results for both
travelling distance and deadline of the sensor.
IV. Performance Analysis
In this section, performance of the proposed method
is evaluated by showing the effect of different number
of sensors in figure 1. The evaluation results shows
that UGreedy/UF2 performs better in total energy
consumption compared to UGreedy/UF1, traditional
greedy and 2-opt TSP algorithm.
ACKNOWLEDGMENT
This research was supported by the Basic Science Research
Program through the National Research Foundation of Korea
(NRF) by the Ministry of Education under Grant
2021R1I1A3051364.
References
[1] Pengfei Wu, Fu Xiao, et al. " Trajectory Optimization for
UAVs
’
Efficient Charging in Wireless Rechargeable
Sensor Networks," MIT Press IEEE transactions on
vehicular technology, vol. 69, NO. 4, April 2020.
[2] Shathee Akter, Thi-Nga Dao and Seokhoon Yoon. "Time-
Constrained Task Allocation and Worker Routing in
Mobile Crowd-Sensing Using a Decomposition Technique
and Deep Q-Learning," IEEE Access, July13,2021.
Fig. 1. Effect of number of sensors in Total
energy consumed by UAVs.
