Medical assistance is crucial to disaster management. In particular, the situation of survivors as well as the environmental information after disasters should be collected and sent back to cloud/data centers immediately for further interpretation and analysis. Recently, unmanned aerial vehicle (UAV)-aided disaster management has been considered a promising approach to enhance the efficiency of searching and rescuing survivors after a disaster, in which a group of UAVs collaborates to accomplish the search and rescue task. However, the battery capacity of UAVs is a critical shortcoming that limits their usage. Worse still, the unstable network connectivity of disaster sites could lead to high latency of data transmission from UAV to remote data centers, which could make significant challenges on real-time data collecting and processing. To solve the above problems, in this paper, we investigate an energy-efficient multihop data routing algorithm with the guarantee of quality-of-service for UAV-aided medical assistance. Specifically, we first study the data routing problem to minimize the energy consumption considering transmission rate, time delay, and life cycle of the UAV swarms. Then, we formulate the issue as a mixed-integer nonlinear programming model. Because of the Non-deterministic Polynomial-hardness of this problem, we propose a polynomial time algorithm based on a genetic algorithm to solve the problem. To achieve high efficiency, we further enhance our algorithm based on DBSCAN and adaptive techniques. Extensive experiments show that our approach can outperform the state-of-the-art methods.

1.
J.
Sánchez-García
,
D. G.
Reina
, and
S. L.
Toral
, “
A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario
,”
Future Gener. Comput. Syst.
90
,
129
148
(
2019
).
2.
W.
Wu
,
W.
Lin
,
C.-H.
Hsu
, and
L.
He
, “
Energy-efficient hadoop for big data analytics and computing: A systematic review and research insights
,”
Future Gener. Comput. Syst.
86
,
1351
1367
(
2018
).
3.
S.
Li
,
Z.
Zheng
,
W.
Chen
,
Z.
Zheng
, and
J.
Wang
, “Latency-aware task assignment and scheduling in collaborative cloud robotic systems,” in 2018 IEEE 11th International Conference on Cloud Computing (IEEE, 2018), pp. 65–72.
4.
Y.
Pei
and
J.
Hao
, “Non-dominated sorting and crowding distance based multi-objective chaotic evolution,” in International Conference on Swarm Intelligence (Springer, Cham, 2017), pp. 15–22.
5.
M. M. E.
Mahmoud
,
J. J. P. C.
Rodrigues
,
S. H.
Ahmed
,
S. C.
Shah
,
J.
Al-Muhtadi
,
V.
Korotaev
, and
V. H. C.
de Albuquerque
, “
Enabling technologies on cloud of things for smart healthcare
,”
IEEE Access
6
,
31950
31967
(
2018
).
6.
J.
Lyu
,
Y.
Zeng
, and
R.
Zhang
, “
Cyclical multiple access in UAV-aided communications: A throughput-delay tradeoff
,”
IEEE Wirel. Commun. Lett.
5
(
6
),
600
603
(
2016
).
7.
S. N. A. M.
Ghazali
,
H. A.
Anuar
,
S. N. A. S
Zakaria
, and
Z.
Yusoff
, “Determining position of target subjects in Maritime Search And Rescue (MSAR) operations using rotary wing Unmanned Aerial Vehicles (UAVs),” in 2016 International Conference on Information and Communication Technology (IEEE, 2016), pp. 1–4.
8.
W.
Chen
,
Y.
Yaguchi
,
K.
Naruse
,
Y.
Watanobe
,
K.
Nakamura
, and
J.
Ogawa
, “
A study of robotic cooperation in cloud robotics: Architecture and challenges
,”
IEEE Access
6
,
36662
36682
(
2018
).
9.
W.
Chen
,
Y.
Yaguchi
,
K.
Naruse
,
Y.
Watanobe
, and
K.
Nakamura
, “
QoS-aware robotic streaming workflow allocation in cloud robotics systems
,”
IEEE Trans. Serv. Comput.
(unpublished).
10.
Q.
Wu
,
Y.
Zeng
, and
R.
Zhang
, “
Joint trajectory and communication design for multi-UAV enabled wireless networks
,”
IEEE Trans. Wirel. Commun.
17
(
3
),
2109
2121
(
2018
).
11.
K.
Li
,
W.
Ni
,
X.
Wang
,
R. P.
Liu
,
S. S.
Kanhere
, and
S.
Jha
, “
Energy-efficient cooperative relaying for unmanned aerial vehicles
,”
IEEE Trans. Mobile Comput.
15
(
6
),
1377
1386
(
2016
).
12.
W.
Chen
,
B.
Liu
,
H.
Huang
,
S.
Guo
, and
Z.
Zheng
, “
When UAV swarm meets edge-cloud computing: The QoS perspective
,”
IEEE Netw.
33
(
2
),
36
43
(
2019
).
13.
S.
Bhattacharjee
and
S.
Bandyopadhyay
, “
An energy efficient-delay aware routing algorithm in multihop wireless sensor networks
,”
Ad Hoc Sens. Wirel. Netw.
43
(
1–2
),
1
32
(
2019
).
14.
H.
Huang
,
S.
Guo
,
P.
Li
, and
T.
Miyazaki
, “
Stochastic analysis on the deactivation-controlled epidemic routing in DTNs with multiple sinks
,”
Ad Hoc Sens. Wirel. Netw.
38
(
1–4
),
143
167
(
2017
).
15.
H.
Huang
and
S.
Guo
, “
Adaptive service provisioning for mobile edge cloud
,”
ZTE Commun.
15
(
2
),
1
9
(
2017
).
16.
D.
Yang
,
Q.
Wu
,
Y.
Zeng
, and
R.
Zhang
, “
Energy tradeoff in ground-to-UAV communication via trajectory design
,”
IEEE Trans. Veh. Technol.
67
(
7
),
6721
6726
(
2018
).
17.
Y.
Hao
,
Q.
Ni
,
H.
Li
, and
S.
Hou
, “A general framework for spectral efficiency, energy efficiency and delay tradeoff in d2d networks,” in 2017 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data (IEEE, 2017), pp. 407–413.
18.
J.
Wang
,
S.
Guo
,
Z.
Cheng
,
P.
Li
, and
J.
Wu
, “
Optimization of deployable base stations with guaranteed QoE in disaster scenarios
,”
IEEE Trans. Veh. Technol.
66
(
7
),
6536
6552
(
2017
).
19.
M.
Mozaffari
,
W.
Saad
,
M.
Bennis
, and
M.
Debbah
, “Optimal transport theory for power-efficient deployment of unmanned aerial vehicles,” in 2016 IEEE International Conference on Communications (IEEE, 2016), pp. 1–6.
20.
X.
Wei
,
S.
Chen
,
X.
Wu
,
D.
Ning
, and
J.
Lu
, “
Cooperative spreading processes in multiplex networks
,”
Chaos
26
(
6
),
065311
(
2016
).
21.
J.
Shang
and
Y.
Tian
, “Parameters identification of a novel micro-positioning stage based on adaptive real-coded genetic algorithm,” in 2015 International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (IEEE, 2015), pp. 218–222.
22.
See http://www.gurobi.com for more information about Gurobi.
23.
H.
Tu
,
Y.
Xia
,
H. H.
Iu
, and
X.
Chen
, “
Optimal robustness in power grids from a network science perspective
,”
IEEE Trans. Circuits Syst. II
66
(
1
),
126
130
(
2019
).
24.
Z.
Chen
,
J.
Wu
,
Y.
Xia
, and
X.
Zhang
, “
Robustness of interdependent power grids and communication networks: A complex network perspective
,”
IEEE Trans. Circuits Syst. II
65
(
1
),
115
119
(
2018
).
25.
H.
Tu
,
Y.
Xia
,
J.
Wu
, and
X.
Zhou
, “
Robustness assessment of cyberphysical systems with weak interdependency
,”
Physica A
522
,
9
17
(
2019
).
26.
B. A.
Carreras
,
V. E.
Lynch
,
I.
Dobson
, and
D. E.
Newman
, “
Critical points and transitions in an electric power transmission model for cascading failure blackouts
,”
Chaos
12
(
4
),
985
994
(
2002
).
27.
M.
Bouet
and
V.
Conan
, “
Mobile edge computing resources optimization: A geo-clustering approach
,”
IEEE Trans. Netw. Serv. Manage.
15
(
2
),
787
796
(
2018
).
28.
L.
Huang
,
Y.
Lai
,
K.
Park
,
J.
Zhang
, and
Z.
Hu
, “
Critical behavior of blind spots in sensor networks
,”
Chaos
17
(
2
),
023132
(
2007
).
You do not currently have access to this content.